%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 10.1109/CEC.2011.5949582 %U http://dx.doi.org/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 10.1016/S0898-1221(97)00025-4 %U http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-D/2/23afe396341b39baf74fcd29db315b46 %U http://dx.doi.org/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 10.1016/S0898-1221(97)82933-1 %U http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-T/2/4d3bcc2dda31e9aca679eba60ff95a3a %U http://dx.doi.org/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 10.1016/S0898-1221(99)91267-1 %U http://www.sciencedirect.com/science/article/B6TYJ-48778B1-3H/2/1d6f4728f10e14a24f4f28189d15f818 %U http://dx.doi.org/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 10.1016/S0898-1221(99)90375-9 %U http://www.sciencedirect.com/science/article/B6TYJ-489YTT5-2T/2/13179f12104abafe66b36e402ef358d9 %U http://dx.doi.org/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 10.1016/0898-1221(95)90099-3 %U http://www.sciencedirect.com/science/article/B6TYJ-48F4PJH-H/2/bd467ac24453cb0b3f9dbbf15075bedb %U http://dx.doi.org/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 10.1016/S0898-1221(99)91189-6 %U http://www.sciencedirect.com/science/article/B6TYJ-48778B1-24/2/ee28594e33abf3bd7c4a9fc997b98492 %U http://dx.doi.org/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 10.1016/S0278-6125(02)80094-2 %U http://www.sciencedirect.com/science/article/B6VJD-4920DSC-1N/2/93bf79c7eb0d6ad94d169ed1b37ec77f %U http://dx.doi.org/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 %O MIT Technolgy Review %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 %O 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 10.1145/3264700.3264702 %U http://www.sigevolution.org/issues/SIGEVOlution1102.pdf %U http://dx.doi.org/10.1145/3264700.3264702 %P 7 %0 Generic %T Store Steel 165 years %I Internal information magazine %F glasilo_1_16_ang %O Store Steel %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 10.1007/s11633-016-1049-4 %U http://link.springer.com/article/10.1007/s11633-016-1049-4 %U http://dx.doi.org/10.1007/s11633-016-1049-4 %0 Conference Proceedings %T A SIMD Interpreter for Linear Genetic Programming %A Ababsa, Tarek %S 2022 International Symposium on iNnovative Informatics of Biskra (ISNIB) %D 2022 %8 dec %F Ababsa:2022:ISNIB %X Genetic programming (GP) has been applied as an automatic programming tool to solve various kinds of problems by genetically breeding a population of computer programs using biologically inspired operations. However, it is well known as a computationally demanding approach with a significant potential of parallelization. In this paper, we emphasize parallelizing the evaluation of genetic programs on Graphics Processing Unit (GPU). We used a compact representation for genotypes. This representation is a memory-efficient method that allows efficient evaluation of programs. Our implementation clearly distinguishes between an individual’s genotype and phenotype. Thus, the individuals are represented as linear entities (arrays of 32 bits integers) that are decoded and expressed just like nonlinear entities (trees). %K genetic algorithms, genetic programming, linear genetic programming, GPU, Graphics, Automatic programming, Sociology, Graphics processing units, Arrays, Statistics, Parallel Processing, GPGPU, symbolic regression %R 10.1109/ISNIB57382.2022.10075819 %U http://dx.doi.org/10.1109/ISNIB57382.2022.10075819 %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 10.1109/SoCPaR.2009.93 %U http://dx.doi.org/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 10.1016/j.jhydrol.2020.124974 %U http://www.sciencedirect.com/science/article/pii/S0022169420304340 %U http://dx.doi.org/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 %@ 1683-3198 %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 https://iajit.org/PDF/vol.11,no.6/6348.pdf %P 582-589 %0 Conference Proceedings %T Automated Behavior-based Malice Scoring of Ransomware Using Genetic Programming %A Abbasi, Muhammad Shabbir %A Al-Sahaf, Harith %A Welch, Ian %S IEEE Symposium Series on Computational Intelligence, SSCI 2021 %D 2021 %8 dec 5 7 %I IEEE %C Orlando, FL, USA %F DBLP:conf/ssci/AbbasiAW21 %X Malice or severity scoring models are a technique for detection of maliciousness. A few ransom-ware detection studies use malice scoring models for detection of ransomware-like behaviour. These models rely on the weighted sum of some manually chosen features and their weights by a domain expert. To automate the modelling of malice scoring for ransomware detection, we propose a method based on Genetic Programming (GP) that automatically evolves a behavior-based malice scoring model by selecting appropriate features and functions from the input feature and operator sets. The experimental results show that the best-evolved model correctly assigned a malice score, below the threshold value to over 85percent of the unseen goodware instances, and over the threshold value to more than 99percent of the unseen ransomware instances. %K genetic algorithms, genetic programming Symbolic regression, ransomware, malice scoring %R 10.1109/SSCI50451.2021.9660009 %U https://doi.org/10.1109/SSCI50451.2021.9660009 %U http://dx.doi.org/10.1109/SSCI50451.2021.9660009 %P 1-8 %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 10.3882/j.issn.1674-2370.2013.02.007 %U http://www.sciencedirect.com/science/article/pii/S1674237015302362 %U http://dx.doi.org/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 10.1109/CEC.2002.1004490 %U http://sc.snu.ac.kr/PAPERS/TAGACOcec02.pdf %U http://dx.doi.org/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 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/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 10.1109/CEC48606.2020.9185637 %U http://dx.doi.org/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 10.1016/j.livsci.2020.104205 %U https://iris.unito.it/retrieve/e27ce430-63b3-2581-e053-d805fe0acbaa/Abbona2020_LS_OA.pdf %U http://dx.doi.org/10.1016/j.livsci.2020.104205 %P 104205 %0 Journal Article %T Towards a Vectorial Approach to Predict Beef Farm Performance %A Abbona, Francesca %A Vanneschi, Leonardo %A Giacobini, Mario %J Applied Sciences %D 2022 %V 12 %N 3 %@ 2076-3417 %F abbona:2022:AS %X Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and temporal patterns lying in the data. In this article, we explore the use of Genetic Programming (GP) to build a predictive model for the performance of Piemontese beef cattle farms. In particular, we investigate the use of vectorial GP, a recently developed variant of GP, that is particularly suitable to manage data in a vectorial form. The experiments conducted on the data from 2014 to 2018 confirm that vectorial GP can outperform not only the standard version of GP but also a number of state-of-the-art Machine Learning methods, such as k-Nearest Neighbors, Generalized Linear Models, feed-forward Neural Networks, and long- and short-term memory Recurrent Neural Networks, both in terms of accuracy and generalizability. Moreover, the intrinsic ability of GP in performing an automatic feature selection, while generating interpretable predictive models, allows highlighting the main elements influencing the breeding performance. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/app12031137 %U https://www.mdpi.com/2076-3417/12/3/1137 %U http://dx.doi.org/10.3390/app12031137 %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 Journal Article %T (AI) in Infrastructure Projects-Gap Study %A Abdel-Kader, Mohamed Y. %A Ebid, Ahmed M. %A Onyelowe, Kennedy C. %A Mahdi, Ibrahim M. %A Abdel-Rasheed, Ibrahim %J Infrastructures %D 2022 %V 7 %N 10 %@ 2412-3811 %F abdel-kader:2022:Infrastructures %X Infrastructure projects are usually complicated, expensive, long-term mega projects; accordingly, they are the type of projects that most need optimisation in the design, construction and operation stages. A great deal of earlier research was carried out to optimise the performance of infrastructure projects using traditional management techniques. Recently, artificial intelligence (AI) techniques were implemented in infrastructure projects to improve their performance and efficiency due to their ability to deal with fuzzy, incomplete, inaccurate and distorted data. The aim of this research is to collect, classify, analyse and review all of the available previous research related to implementing AI techniques in infrastructure projects to figure out the gaps in the previous studies and the recent trends in this research area. A total of 159 studies were collected since the beginning of the 1990s until the end of 2021. This database was classified based on publishing date, infrastructure subject and the used AI technique. The results of this study show that implementing AI techniques in infrastructure projects is rapidly increasing. They also indicate that transportation is the first and the most AI-using project and that both artificial neural networks (ANN) and particle swarm optimisation (PSO) are the most implemented techniques in infrastructure projects. Finally, the study presented some opportunities for farther research, especially in natural gas projects. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/infrastructures7100137 %U https://www.mdpi.com/2412-3811/7/10/137 %U http://dx.doi.org/10.3390/infrastructures7100137 %P ArticleNo.137 %0 Journal Article %T Interpretable soft computing predictions of elastic shear buckling in tapered steel plate girders %A AbdelAleem, Basem H. %A Ismail, Mohamed K. %A Haggag, May %A El-Dakhakhni, Wael %A Hassan, Assem A. A. %J Thin-Walled Structures %D 2022 %V 176 %@ 0263-8231 %F ABDELALEEM:2022:tws %X The complexity of the shear buckling in tapered plate girders has motivated researchers to conduct experimental and numerical investigations to understand the underlying mechanisms controlling such phenomenon, and subsequently develop related design-oriented expressions. However, existing predictive models have been developed and validated using limited datasets and/or traditional regression techniques-restricting both the model utility, when considering a wider range of design parameters, and the model generalizability, due to associated uncertainties. To address these issues, the present study employed a powerful soft computing technique-multi-gene genetic programming (MGGP), to develop design expressions to predict the elastic shear buckling strength of tapered end plate girder web panels. A dataset of 427 experimental and experimentally validated numerical results was used in training, validating, and testing the developed MGGP models. Guided by mechanics and findings from previous studies, the key parameters controlling the strength were identified, and MGGP were employed to reveal the interdependence between such parameters and subsequently develop interpretable predictive models. The prediction accuracy of the developed models was evaluated against that of other existing models using various statistical measures. Several filter and embedded variable importance techniques were used to rank the model input parameters according to their significance in predicting the elastic shear buckling strength. These techniques include the variable importance random forest and the relative influence gradient boosting techniques. Moreover, partial dependence plots were employed to explore the effect of the input variables on the strength. The results obtained from this study demonstrated the robustness of the developed MGGP expression for predicting the elastic shear buckling strength of tapered plate girder end web panel. The developed model also exhibited a superior prediction accuracy and generalizability compared to currently existing ones. Furthermore, the developed partial dependence plots facilitated interpreting the influence of all input variables on the predicted elastic shear buckling strength %K genetic algorithms, genetic programming, Data-driven models, Elastic shear buckling strength, Multi-gene genetic programming, Variable importance, Partial dependence plots, Tapered end web panel %9 journal article %R 10.1016/j.tws.2022.109313 %U https://www.sciencedirect.com/science/article/pii/S026382312200235X %U http://dx.doi.org/10.1016/j.tws.2022.109313 %P 109313 %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 10.1007/978-3-642-35380-2_48 %U http://works.bepress.com/almoataz_abdelaziz/42 %U http://dx.doi.org/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 10.14569/IJACSA.2018.091132 %U http://thesai.org/Downloads/Volume9No11/Paper_32-Applying_Machine_Learning_Techniques.pdf %U http://dx.doi.org/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 10.1145/3036331.3036336 %U http://doi.acm.org/10.1145/3036331.3036336 %U http://dx.doi.org/10.1145/3036331.3036336 %P 47-51 %0 Thesis %T Supporting System Dynamics Modeling using Computational Intelligence Techniques %A Abdelbari, Hassan Abbas %D 2018 %8 aug %C Australia %C School of Engineering and Information Technology, The University of New South Wales %F abdelbari:thesis %X Complex systems are ubiquitous in both natural and man-made systems. Understanding their behaviours is not a trivial task and modelling techniques are often used to analyse and experiment with their behavioral changes. Most end-to-end approaches for modeling complex systems, such as system dynamics, involve several processes, many of which rely heavily on the expertise of human modelers. In this context, the key focus of this research is to improve the performance of system dynamics processes by developing computational methods. This thesis offers three main contributions to this field of research. Firstly, an echo-state network-based technique for learning the causal loop diagrams is proposed. Its central idea is to encode an ech.o-state network’s dynamic reservoir with a known number of nodes, equal to the number of key system variables identified, and then train the network using the system observations to match the observed behaviour. Secondly, a novel genetic programming-based symbolic regression ensemble method based on pre-defined causal relationships between system variables is applied to learn the system equations. Information about these relationships is used to decompose the problem space. The ensemble members independently learn the equations for different output variables, with these learned models then combined to generate the final model. Finally, an integrated system for supporting the modeling of system dynamics which facilitates data-driven learning of the different processes involved, including causal loop, and stock and flow diagrams, equations and the values of the model parameters using multiple computational intelligence techniques, is presented. A prototype for the support system is developed to consist of two main components: a graphical user interface that allows the modeler to interact with the tool; and the core part of the support system, a learning engine, which is the back-end of the system, comprises the data and model repositories, and implements different intelligence algorithms. Although the actual utility of these methodologies can only be known through their use by modelers of system dynamics, we conduct a number of experiments on several real case studies to demonstrate their performances. The empirical results verify their efficiency in terms of learning models similar to the target ones. %K genetic algorithms, genetic programming, Cartesian genetic programming, artificial neural networks, ANN, causal loop diagrams, complex systems, computational intelligence, differential equations, differential evolution, echo state networks, ensemble learning, evolutionary computation, model calibration, modeling and simulation, modeling support system, particle swarm optimization, PSO, recurrent neural networks, simulated annealing, stock and flow diagrams, system dynamics %9 Ph.D. thesis %R 10.26190/unsworks/20676 %U https://unsworks.unsw.edu.au/entities/publication/2eb97515-03b0-4660-b013-5c8139b0baf6/full %U http://dx.doi.org/10.26190/unsworks/20676 %0 Journal Article %T A System Dynamics Modeling Support System Based on Computational Intelligence %A Abdelbari, Hassan %A Shafi, Kamran %J Systems %D 2019 %V 7 %N 4 %@ 2079-8954 %F abdelbari:2019:Systems %X System dynamics (SD) is a complex systems modelling and simulation approach with wide ranging applications in various science and engineering disciplines. While subject matter experts lead most of the model building, recent advances have attempted to bring system dynamics closer to fast growing fields such as data sciences. This may prove promising for the development of novel support methods that augment human cognition and improve efficiencies in the model building process. A few different directions have been explored recently to support individual modelling stages, such as the generation of model structure, model calibration and policy optimisation. However, an integrated approach that supports across the board modelling process is still missing. In this paper, a prototype integrated modelling support system is presented for the purpose of supporting the modellers at each stage of the process. The proposed support system facilitates data-driven inferring of causal loop diagrams (CLDs), stock-flow diagrams (SFDs), model equations and the estimation of model parameters using computational intelligence (CI) techniques. The ultimate goal of the proposed system is to support the construction of complex models, where the human power is not enough. With this goal in mind, we demonstrate the working and utility of the proposed support system. We have used two well-known synthetic reality case studies with small models from the system dynamics literature, in order to verify the support system performance. The experimental results showed the effectiveness of the proposed support system to infer close model structures to target models directly from system time-series observations. Future work will focus on improving the support system so that it can generate complex models on a large scale. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/systems7040047 %U https://www.mdpi.com/2079-8954/7/4/47 %U http://dx.doi.org/10.3390/systems7040047 %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 10.1155/2009/179230 %U http://downloads.hindawi.com/journals/ads/2009/179230.pdf %U http://dx.doi.org/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 10.1016/j.phycom.2016.08.001 %U http://www.sciencedirect.com/science/article/pii/S1874490716301094 %U http://dx.doi.org/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 10.23919/SICE.2018.8492687 %U http://dx.doi.org/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 10.1109/FG.2018.00079 %U http://dx.doi.org/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 10.1109/CEC.2009.4983280 %U http://dx.doi.org/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 10.1007/s00521-016-2450-1 %U http://dx.doi.org/10.1007/s00521-016-2450-1 %P 363-375 %0 Journal Article %T Predicting of compressive strength of recycled aggregate concrete by genetic programming %A Abdollahzadeh, Gholamreza %A Jahani, Ehsan %A Kashir, Zahra %J Computers and Concrete %D 2016 %8 aug %V 18 %N 2 %@ 1598-8198 %F Abdollahzadeh:2016:CC %X This paper, proposes 20 models for predicting compressive strength of recycled aggregate concrete (RAC) containing silica fume by using gene expression programming (GEP). To construct the models, experimental data of 228 specimens produced from 61 different mixtures were collected from the literature. 80% of data sets were used in the training phase and the remained 20% in testing phase. Input variables were arranged in a format of seven input parameters including age of the specimen, cement content, water content, natural aggregates content, recycled aggregates content, silica fume content and amount of superplasticizer. The training and testing showed the models have good conformity with experimental results for predicting the compressive strength of recycled aggregate concrete containing silica fume. %K genetic algorithms, genetic programming, gene expression programming, recycled aggregate concrete, silica fume, compressive strength %9 journal article %R 10.12989/CAC.2016.18.2.155 %U http://dx.doi.org/10.12989/CAC.2016.18.2.155 %P 155-163 %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 10.1016/j.eswa.2009.01.076 %U http://www.sciencedirect.com/science/article/B6V03-4VJSRWK-1/2/a3b8516f289c76c474c6a1eb9d26d7ec %U http://dx.doi.org/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 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 10.1109/ICARA.2011.6144882 %U http://dx.doi.org/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 10.1109/CEC.2012.6252877 %U http://dx.doi.org/10.1109/CEC.2012.6252877 %P 2710-2717 %0 Journal Article %T The PARSEC machine: a non-Newtonian supra-linear super-computer %A Abdulkarimova, Ulviya %A Ouskova Leonteva, Anna %A Rolando, Christian %A Jeannin-Girardon, Anne %A Collet, Pierre %J Azerbaijan Journal of High Performance Computing %D 2019 %8 dec %V 2 %N 2 %F abdulkarimova:2019:ajhpc %X transfer-learning can turn a Beowulf cluster into a full super-computer with supra-linear qualitative acceleration. Harmonic Analysis is used as a real-world example to show the kind of result that can be achieved with the proposed super-computer architecture, that locally exploits absolute space-time parallelism on each machine (SIMD parallelism) and loosely-coupled relative space-time parallelisation between different machines (loosely coupled MIMD) %K genetic algorithms, genetic programming, beowulf cluster, HPC, parallel computing, relative space-time, supra-linear acceleration, qualitative acceleration, GPGPU, loosely coupled machines, artificial evolution, transfer learning, harmonic analysis, super-resolution, non-uniform sampling, Fourier transform %9 journal article %R 10.32010/26166127.2019.2.2.122.140 %U https://publis.icube.unistra.fr/docs/14472/easeaHPC.pdf %U http://dx.doi.org/10.32010/26166127.2019.2.2.122.140 %P 122-140 %0 Thesis %T SINUS-IT: an evolutionary approach to harmonic analysis %A Abdulkarimova, Ulviya %D 2021 %8 February %C France %C Universite de Strasbourg %F abdulkarimova:tel-03700035 %X This PhD project is about harmonic analysis of signals coming from Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometer. The analysis of these signals is usually done using Fourier Transform (FT) method. However, there are several limitations of this method, one of which is not being able to find the phase parameter. Mass spectrometers are used to determine the chemical composition of compounds. It is known that if the phase component is known, it would yield an improvement in mass accuracy and mass resolving power which would help to determine the composition of a given compound more accurately. In this PhD work we use evolutionary algorithm to overcome the limitations of the FT method. We explore different sampling, speed optimization and algorithm improvement methods. We show that our proposed method outperforms the FT method as it uses short transients to resolve the peaks and it automatically yields phase values. %K genetic algorithms, genetic programming, EASEA, NVIDA, CUDA, Artificial evolution, Evolution strategies, QAES, Fourier transform, FFT, Harmonic analysis, FT-ICR, Isotopic structure, GPU, GPGPU parallelisation, Island-based parallelization, Glutathione, binary radians, Brad2rad, Rad2brad, global random sampling, GRS %9 Ph.D. thesis %U https://theses.hal.science/tel-03700035/ %0 Journal Article %T Harnessing evolutionary algorithms for enhanced characterization of ENSO events %A Abdulkarimova, Ulviya %A Abarca-del-Rio, Rodrigo %A Collet, Pierre %J Genetic Programming and Evolvable Machines %D 2025 %V 26 %@ 1389-2576 %F Abdulkarimova:2025:GPEM %O Online first %K genetic algorithms, genetic programming, El Nino Southern Oscillation, ENSO, Evolutionary algorithm, Symbolic regression, Stochastic optimization %9 journal article %R 10.1007/s10710-024-09497-z %U http://dx.doi.org/10.1007/s10710-024-09497-z %P Articleno4 %0 Thesis %T Android Malware Detection System using Genetic Programming %A Abdullah, Norliza Binti %D 2019 %8 mar %C UK %C Computer Science, University of York %F Abdullah:thesis %X Nowadays, smartphones and other mobile devices are playing a significant role in the way people engage in entertainment, communicate, network, work, and bank and shop online. As the number of mobile phones sold has increased dramatically worldwide, so have the security risks faced by the users, to a degree most do not realise. One of the risks is the threat from mobile malware. In this research, we investigate how supervised learning with evolutionary computation can be used to synthesise a system to detect Android mobile phone attacks. The attacks include malware, ransomware and mobile botnets. The datasets used in this research are publicly downloadable, available for use with appropriate acknowledgement. The primary source is Drebin. We also used ransomware and mobile botnet datasets from other Android mobile phone researchers. The research in this thesis uses Genetic Programming (GP) to evolve programs to distinguish malicious and non-malicious applications in Android mobile datasets. It also demonstrates the use of GP and Multi-Objective Evolutionary Algorithms (MOEAs) together to explore functional (detection rate) and non-functional (execution time and power consumption) trade-offs. Our results show that malicious and non-malicious applications can be distinguished effectively using only the permissions held by applications recorded in the application’s Android Package (APK). Such a minimalist source of features can serve as the basis for highly efficient Android malware detection. Non-functional tradeoffs are also highlight. %K genetic algorithms, genetic programming, Supervised Learning, Multi-objective Genetic Algorithm, SPEA2, MOGP, Android Malware %9 Ph.D. thesis %U https://etheses.whiterose.ac.uk/29027/ %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 10.1109/ICCIS.2006.252308 %U http://dx.doi.org/10.1109/ICCIS.2006.252308 %P 1-6 %0 Journal Article %T Classification of Retina Diseases from OCT using Genetic Programming %A Abdulrahman, Hadeel %A Khatib, Mohamed %J International Journal of Computer Applications %D 2020 %8 mar %V 177 %N 45 %I Foundation of Computer Science (FCS), NY, USA %C New York, USA %@ 0975-8887 %F Abdulrahman:2020:IJCA %X a fully automated method for feature extraction and classification of retina diseases is implemented. The main idea is to find a method that can extract the important features from the Optical Coherence Tomography (OCT) image, and acquire a higher classification accuracy. The using of genetic programming (GP) can achieve that aim. Genetic programming is a good way to choose the best combination of feature extraction methods from a set of feature extraction methods and determine the proper parameters for each one of the selected extraction methods. 800 OCT images are used in the proposed method, of the most three popular retinal diseases: Choroidal neovascularization (CNV), Diabetic Macular Edema (DME) and Drusen, beside the normal OCT images. While the set of the feature extraction methods that is used in this paper contains: Gabor filter, Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), histogram of the image, and Speed Up Robust Filter (SURF). These methods are used for the both of global and local feature extraction. After that the classification process is achieved by the Support Vector Machine (SVM). The proposed method performed high accuracy as compared with the traditional methods. %K genetic algorithms, genetic programming, feature extraction, Optical Coherence Tomography, OCT image classification, OCT feature extraction %9 journal article %R 10.5120/ijca2020919973 %U https://www.ijcaonline.org/archives/volume177/number45/abdulrahman-2020-ijca-919973.pdf %U http://dx.doi.org/10.5120/ijca2020919973 %P 41-46 %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 10.1109/ICEEI.2011.6021768 %U http://dx.doi.org/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 10.1016/j.mspro.2014.07.069 %U http://www.sciencedirect.com/science/article/pii/S2211812814004349 %U http://dx.doi.org/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 10.5772/48148 %U http://dx.doi.org/10.5772/48148 %P 141-172 %0 Journal Article %T Estimating the subgrade reaction at deep braced excavation bed in dry granular soil using genetic programming (GP) %A Aboelela, Abdelrahman E. %A Ebid, Ahmed M. %A Fayed, Ayman L. %J Results in Engineering %D 2022 %V 13 %@ 2590-1230 %F ABOELELA:2022:RE %X Modulus of subgrade reaction (Ks) is a simplified and approximated approach to present the soil-structure interaction. It is widely used in designing combined and raft foundations due to its simplicity. (Ks) is not a soil propriety, its value depends on many factors including soil properties, shape, dimensions and stiffness of footing and even time (for saturated cohesive soils). Many earlier formulas were developed to estimate the (Ks) value. This research is concerned in studying the effect of de-stressing and shoring rigidity of deep excavation on the (Ks) value. A parametric study was carried out using 27 FEM models with different configurations to generate a database, then a well-known ’Genetic Programming’ technique was applied on the database to develop a formula to correlate the (Ks) value with the deep excavation configurations. The results indicated that (Ks) value increased with increasing the diaphragm wall stiffness and decreases with increasing the excavation depth %K genetic algorithms, genetic programming, Deep braced excavation, Modulus of subgrade reaction %9 journal article %R 10.1016/j.rineng.2021.100328 %U https://www.sciencedirect.com/science/article/pii/S2590123021001298 %U http://dx.doi.org/10.1016/j.rineng.2021.100328 %P 100328 %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 10.1016/j.jngse.2014.11.006 %U http://www.sciencedirect.com/science/article/pii/S1875510014003394 %U http://dx.doi.org/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 10.1016/j.petrol.2018.09.073 %U http://www.sciencedirect.com/science/article/pii/S0920410518308283 %U http://dx.doi.org/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 10.1016/j.fuel.2020.117075 %U http://www.sciencedirect.com/science/article/pii/S0016236120300703 %U http://dx.doi.org/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 10.1007/s00521-017-3208-0 %U https://doi.org/10.1007/s00521-017-3208-0 %U http://dx.doi.org/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 10.1109/CEC.2003.1299832 %U http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-CEC03b.pdf %U http://dx.doi.org/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 10.1142/S0219649203000565 %U http://www.softcomputing.net/jikm.pdf %U http://dx.doi.org/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. %K genetic algorithms, genetic programming %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 10.1007/3-540-32498-4_1 %U http://www.softcomputing.net/gpsystems.pdf %U http://dx.doi.org/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 10.1007/3-540-32498-4_3 %U http://falklands.globat.com/~softcomputing.net/ids-chapter.pdf %U http://dx.doi.org/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 10.1109/CEC.2005.1554875 %U http://www.softcomputing.net/cec05.pdf %U http://dx.doi.org/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 10.1142/S0219649206001566 %U http://dx.doi.org/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 10.1016/j.jnca.2005.06.001 %U http://dx.doi.org/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 10.1109/ISI.2008.4565018 %U http://dx.doi.org/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 10.1109/UKSIM.2009.75 %U http://dx.doi.org/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 10.1109/IAS.2009.348 %U http://dx.doi.org/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 10.1109/LCOMM.2018.2806489 %U http://dx.doi.org/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 10.1109/ICCOINS.2016.7783234 %U http://dx.doi.org/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 10.1007/s11128-017-1609-8 %U http://dx.doi.org/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 10.1109/ICCOINS.2018.8510602 %U http://dx.doi.org/10.1109/ICCOINS.2018.8510602 %0 Journal Article %T Novel electrochemical impedance simulation design via stochastic algorithms for fitting equivalent circuits %A Abud Kappel, Marco Andre %A Fabbri, Ricardo %A Domingos, Roberto P. %A Bastos, Ivan N. %J Measurement %D 2016 %V 94 %@ 0263-2241 %F Abud-Kappel:2016:Measurement %X Electrochemical impedance spectroscopy (EIS) is of great value to corrosion studies because it is sensitive to transient changes that occur in the metal-electrolyte interface. A useful way to link the results of electrochemical impedance spectroscopy to corrosion phenomena is by simulating equivalent circuits. Equivalent circuit models are very attractive because of their relative simplicity, enabling the monitoring of electrochemical systems that have a complex physical mechanism. In this paper, the stochastic algorithm Differential Evolution is proposed to fit an equivalent circuit to the EIS results for a wide potential range. EIS is often limited to the corrosion potential despite being widely used. This greatly hinders the analysis regarding the effect of the applied potential, which strongly affects the interface, as shown, for example, in polarization curves. Moreover, the data from both the EIS and the DC values were used in the proposed scheme, allowing the best fit of the model parameters. The approach was compared to the standard Simplex square residual minimization of EIS data. In order to manage the large amount of generated data, the EIS-Mapper software package, which also plots the 2D/3D diagrams with potential, was used to fit the equivalent circuit of multiple diagrams. Furthermore, EIS-Mapper also computed all simulations. The results of 67 impedance diagrams of stainless steel in a 3.5percent NaCl medium at 25C obtained in steps of 10mV, and the respective values of the fitted parameters of the equivalent circuit are reported. The present approach conveys new insight to the use of electrochemical impedance and bridges the gap between polarization curves and equivalent electrical circuits. %K genetic algorithms, genetic programming, Differential evolution, Electrochemical impedance, Impedance measurements, Corrosion, Optimization, Stochastic methods %9 journal article %R 10.1016/j.measurement.2016.08.008 %U https://www.sciencedirect.com/science/article/pii/S0263224116304699 %U http://dx.doi.org/10.1016/j.measurement.2016.08.008 %P 344-354 %0 Thesis %T Stochastic computational techniques applied to the simulation of electrochemical impedance spectroscopy diagrams %A Kappel, Marco Andre Abud %D 2016 %8 August %C Nova Friburgo, Brazil %C Centro de Tecnologia e Ciencias, Instituto Politecnico, Universidadedo Estado do Rio de Janeiro %F Tese_MarcoAndreAbudKappel %X Electrochemical impedance spectroscopy is a widely used technique in electrochemical systems characterization. With applications in several areas, the technique is very useful in the study of corrosion because it is sensitive to transient changes that occur in the metal interface. The results from the technique can be expressed and interpreted in different ways, allowing different modeling and analysis methods, such as the use of kinetic models or equivalent circuits. In corrosion, the technique is usually applied only in a few specific potentials, such as the corrosion potential, the most important. With the motivation of improving the impedance modeling and analysis process, taking into consideration that the electrochemical phenomena are strongly linked to the potential, this work introduces the possibility to express the impedance data in a wide potential range, and use them to equivalent circuits fitting. Thus, different phenomena can be modeled adequately by equivalent circuits corresponding to different potentials. For this purpose, the related inverse problem is solved for each potential through a complex nonlinear optimization process. In addition to the transient data obtained by the spectroscopy, stationary data are also used in the optimization as a regularisation factor, supporting a consistent solution to the physical phenomena involved, from the maximum experimental frequency to theoretical zero frequency. An analysis, modeling and simulation software was developed with the following features: 1) validation of experimental data, through the Kramers-Kronig relations; 2) simultaneous visualization of impedance results for a wide potential range; 3) fitting different equivalent circuits for different ranges using transient and stationary experimental data, in conjunction with deterministic or stochastic methods; 4) generation of confidence regions for the estimated parameters, making them statistically significant; 5) simulations using the fitted equivalent circuits in computer cluster; 6) parameter sensitivity analysis according to the applied potential, revealing important physical characteristics involved in the electrochemical processes. Finally, experimental fitting results and the corresponding simulations are shown and discussed. Results show that the use of a population-based stochastic optimization method not only increases the odds of finding the global optimum, but also enables the generation of confidence regions around the found values. Furthermore, only the circuit fitted with the new objective function has equivalence with both transient data and stationary data for the entire potential range involved. %K genetic algorithms, genetic programming, Electrochemical impedance spectroscopy, Corrosion, Complex nonlinear optimization, Equivalent electrical circuit, Stochastic methods %9 Ph.D. thesis %U http://www.bdtd.uerj.br/handle/1/13692 %0 Journal Article %T A study of equivalent electrical circuit fitting to electrochemical impedance using a stochastic method %A Abud Kappel, Marco Andre %A Peixoto, Fernando Cunha %A Platt, Gustavo Mendes %A Domingos, Roberto Pinheiro %A Bastos, Ivan Napoleao %J Applied Soft Computing %D 2017 %8 jan %V 50 %@ 1568-4946 %F Abud-Kappel:2017:ASC %X Modeling electrochemical impedance spectroscopy is usually done using equivalent electrical circuits. These circuits have parameters that need to be estimated properly in order to make possible the simulation of impedance data. Despite the fitting procedure is an optimization problem solved recurrently in the literature, rarely statistical significance of the estimated parameters is evaluated. In this work, the optimization process for the equivalent electrical circuit fitting to the impedance data is detailed. First, a mathematical development regarding the minimization of residual least squares is presented in order to obtain a statistically valid objective function of the complex nonlinear regression problem. Then, the optimization method used in this work is presented, the Differential Evolution, a global search stochastic method. Furthermore, it is shown how a population-based stochastic method like this can be used directly to obtain confidence regions to the estimated parameters. A sensitivity analysis was also conducted. Finally, the equivalent circuit fitting is done to model synthetic experimental data, in order to demonstrate the adopted procedure. %K genetic algorithms, genetic programming, Differential evolution, Electrochemical impedance, Optimization, Stochastic method, Statistical analysis %9 journal article %R 10.1016/j.asoc.2016.11.030 %U https://www.sciencedirect.com/science/article/pii/S1568494616305993 %U http://dx.doi.org/10.1016/j.asoc.2016.11.030 %P 183-193 %0 Conference Proceedings %T Cartesian Genetic Programing Applied to Equivalent Electric Circuit Identification %A Abud Kappel, Marco Andre %A Domingos, Roberto Pinheiro %A Bastos, Ivan Napoleao %Y Rodrigues, H. C. %Y Herskovits, J. %Y Mota Soares, C. M. %Y Araujo, A. L. %Y Guedes, J. M. %Y Folgado, J. O. %Y Moleiro, F. %Y Madeira, J. F. A. %S Proceedings of the 6th International Conference on Engineering Optimization. EngOpt 2018 %D 2018 %8 17 19 sep %I Springer %C Lisbon, Portugal %F Abud-Kappel:2018:EngOpt %X Equivalent electric circuits are widely used in electrochemical impedance spectroscopy (EIS) data modeling. EIS modeling involves the identification of an electrical circuit physically equivalent to the system under analysis. This equivalence is based on the assumption that each phenomenon of the electrode interface and the electrolyte is represented by electrical components such as resistors, capacitors and inductors. This analogy allows impedance data to be used in simulations and predictions related to corrosion and electrochemistry. However, when no prior knowledge of the inner workings of the process under analysis is available, the identification of the circuit model is not a trivial task. The main objective of this work is to improve both the equivalent circuit topology identification and the parameter estimation by using a different approach than the usual Genetic Programming. In order to accomplish this goal, a methodology was developed to unify the application of Cartesian Genetic Programming to tackle system topology identification and Differential Evolution for optimization of the circuit parameters. The performance and effectiveness of this methodology were tested by performing the circuit identification on four different known systems, using numerically simulated impedance data. Results showed that the applied methodology was able to identify with satisfactory precision both the circuits and the values of the components. Results also indicated the necessity of using a stochastic method in the optimization process, since more than one electric circuit can fit the same dataset. The use of evolutionary adaptive metaheuristics such as the Cartesian Genetic Programming allows not only the estimation of the model parameters, but also the identification of its optimal topology. However, due to the possibility of multiple solutions, its application must be done with caution. Whenever possible, restrictions on the search space should be added, bearing in mind the correspondence of the model to the studied physical phenomena. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, Differential Evolution, Complex nonlinear optimization, Equivalent electric circuit identification %R 10.1007/978-3-319-97773-7_79 %U http://dx.doi.org/10.1007/978-3-319-97773-7_79 %P 913-925 %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, Cartesian Genetic Programming %R 10.1109/BRACIS.2019.00059 %U http://dx.doi.org/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 Journal Article %T Predicting failure pressure of the corroded offshore pipelines using an efficient finite element based algorithm and machine learning techniques %A Abyani, Mohsen %A Bahaari, Mohammad Reza %A Zarrin, Mohamad %A Nasseri, Mohsen %J Ocean Engineering %D 2022 %8 15 jun %V 254 %@ 0029-8018 %F ABYANI:2022:oceaneng %X This paper aims to predict the failure pressure of corroded offshore pipelines, employing different machine learning techniques. To this end, an efficient finite element based algorithm is programmed to numerically estimate the failure pressure of offshore pipelines, subjected to internal corrosion. In this process, since the computational effort of such numerical assessment is very high, the application of reliable machine learning methods is used as an alternative solution. Thus, 1815 realizations of four variables are generated, and each one is keyed into the numerical model of a sample pipeline. Thereafter, the machine learning models are constructed based on the results of the numerical analyses, and their performance are compared with each other. The results indicate that Gaussian Process Regression (GPR) and MultiLayer Perceptron (MLP) have the best performance among all the chosen models. Considering the testing dataset, the squared correlation coefficient and Root Mean Squared Error (RMSE) values of GPR and MLP models are 0.535, 0.545 and 0.993 and 0.992, respectively. Moreover, the Maximum Von-Mises Stress (MVMS) of the pipeline increases as the water depth grows at low levels of Internal Pressure (IP). Inversely, increase in water depth leads to reduction in the MVMS values at high IP levels %K genetic algorithms, genetic programming, Offshore pipelines, Corrosion, Artificial neural network, ANN, Genetic programing, Support vector machine, SVM, Random forest, Gaussian process regression %9 journal article %R 10.1016/j.oceaneng.2022.111382 %U https://www.sciencedirect.com/science/article/pii/S0029801822007697 %U http://dx.doi.org/10.1016/j.oceaneng.2022.111382 %P 111382 %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 10.1007/11546924_36 %U http://dx.doi.org/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 10.1016/j.eswa.2019.112908 %U http://www.sciencedirect.com/science/article/pii/S0957417419306268 %U http://dx.doi.org/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 10.1016/j.patrec.2020.03.005 %U http://www.sciencedirect.com/science/article/pii/S0167865520300830 %U http://dx.doi.org/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 %8 jul %V 164 %@ 0003-682X %F ACHARYA:2020:AA %X In cognitive science, the real-time recognition of humans emotional state is pertinent for machine emotional intelligence and human-machine interaction. Conventional emotion recognition systems use subjective feedback questionnaires, analysis of facial features from videos, and online sentiment analysis. This research proposes a system for real-time detection of emotions in response to emotional movie clips. These movie clips elicitate emotions in humans, and during that time, we have recorded their brain signals using Electroencephalogram (EEG) device and analyze their emotional state. This research work considered four class of emotions (happy, calm, fear, and sadness). This method leverages Fast Fourier Transform (FFT) for feature extraction and Genetic Programming (GP) for classification of EEG data. Experiments were conducted on EEG data acquired with a single dry electrode device NeuroSky Mind Wave 2. To collect data, a standardized database of 23 emotional Hindi film clips were used. All clips individually induce different emotions, and data collection was done based on these emotions elicited as the clips contain emotionally inductive scenes. Twenty participants took part in this study and volunteered for data collection. This system classifies four discrete emotions which are: happy, calm, fear, and sadness with an average of 89.14percent accuracy. These results demonstrated improvements in state-of-the-art methods and affirmed the potential use of our method for recognising these emotions %K genetic algorithms, genetic programming, Electroencephalogram, Fast Fourier Transform, Emotion recognition, Movie clips, Cinema Films %9 journal article %R 10.1016/j.apacoust.2020.107260 %U http://lrcdrs.bennett.edu.in:80/handle/123456789/1183 %U http://dx.doi.org/10.1016/j.apacoust.2020.107260 %P 107260 %0 Journal Article %T An enhanced fitness function to recognize unbalanced human emotions data %A Acharya, Divya %A Varshney, Nandana %A Vedant, Anindiya %A Saxena, Yashraj %A Tomar, Pradeep %A Goel, Shivani %A Bhardwaj, Arpit %J Expert Systems with Applications %D 2021 %V 166 %@ 0957-4174 %F ACHARYA:2021:ESA %X In cognitive science and human-computer interaction, automatic human emotion recognition using physiological stimuli is a key technology. This research considers two-class (positive and negative) of emotions recognition using electroencephalogram (EEG) signals in response to an emotional clip from the genres happy, amusement, sad, and horror. This paper introduces an enhanced fitness function named as eD-score to recognize emotions using EEG signals. The primary goal of this research is to assess how genres affect human emotions. We also analyzed human behaviour based on age and gender responsiveness. We have compared the performance of Multilayer Perceptron (MLP), K-nearest neighbors (KNN), Support Vector Machine (SVM), D-score Genetic Programming (DGP), and enhanced D-score Genetic Programming (eDGP) for classification of emotions. The analysis shows that for two class of emotion eDGP provides classification accuracy as 83.33percent, 84.69percent, 85.88percent, and 87.61percent for 50-50, 60-40, 70-30, and 10-fold cross-validations. Generalizability and reliability of this approach is evaluated by applying the proposed approach to publicly available EEG datasets DEAP and SEED. When participants in this research are exposed to amusement genre, their reaction is positive emotion. In compliance with the self-reported feelings, brain signals of 26-35 years of age group provided the highest emotional identification. Among genders, females are more emotionally active as compared to males. These results affirmed the potential use of our method for recognizing emotions %K genetic algorithms, genetic programming, Emotion recognition, Fitness function, EEG, Fast Fourier Transformation, Unbalanced dataset %9 journal article %R 10.1016/j.eswa.2020.114011 %U https://www.sciencedirect.com/science/article/pii/S0957417420307843 %U http://dx.doi.org/10.1016/j.eswa.2020.114011 %P 114011 %0 Journal Article %T Application of MGGP in Predicting Bearing Capacity of a Strip Footing Resting on the Crest of a Marginal Soil Hillslope %A Acharyya, Rana %A Dey, Arindam %J KSCE Journal of Civil Engineering %D 2024 %V 28 %N 10 %I Korean Society of Civil Engineers %@ 1226-7988 %F Acharyya:2024:KJCE %X A set of finite element investigations are performed to examine the maximum bearing strength of strip footings positioned on the crest of a cohesive-frictional marginal soil hill slope. In this regard, the influence of contributing geometrical and geotechnical parameters on the maximum bearing strength of the footing are illustrated. It is revealed that the nearness of slope face has negligible influence on the bearing strength of footing if it is located at a setback distance beyond six times the footing width. Further, using multi-gene genetic programming technique, a predictive relationship between the maximum bearing strength and the contributory factors is established and validated through relevant experimental findings. The hyper-parameters of the MGGP model are suitably optimised, as indicated by the coefficient of correlation attaining high magnitudes. A sensitivity analysis based on local perturbation is conducted to recognise the importance ranking of the contributory parameters. It is revealed that the friction angle of slope material predominantly influences the evaluation of maximum bearing strength for strip footing on slopes, followed by other contributing factors %K genetic algorithms, genetic programming, GPTIPS, Strip footing on slope, Finite element analysis, FE model, Tree GP, Maximum bearing strength, MGGP, Sensitivity assessment %9 journal article %R 10.1007/s12205-024-1217-y %U https://www.sciencedirect.com/science/article/pii/S1226798825001114 %U http://dx.doi.org/10.1007/s12205-024-1217-y %P 4244-4257 %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, Debugging, fault-repair, Python %R 10.1145/2001576.2001768 %U https://hdl.handle.net/2440/70777 %U http://dx.doi.org/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 International Journal of Pattern Recognition and Artificial Intelligence %D 2014 %V 28 %N 7 %F journals/ijprai/Acosta-MendozaMEA14 %X This paper introduces a novel approach for building heterogeneous ensembles based on genetic programming (GP). Ensemble learning is a paradigm that aims at combining individual classifier’s outputs to improve their performance. Commonly, classifiers outputs are combined by a weighted sum or a voting strategy. However, linear fusion functions may not effectively exploit individual models redundancy and diversity. In this research, a GP-based approach to learn fusion functions that combine classifiers outputs is proposed. Heterogeneous ensembles are aimed in this study, these models use individual classifiers which are based on different principles (e.g. decision trees and similarity-based techniques). A detailed empirical assessment is carried out to validate the effectiveness of the proposed approach. Results show that the proposed method is successful at building very effective classification models, outperforming alternative ensemble methodologies. The proposed ensemble technique is also applied to fuse homogeneous models outputs with results also showing its effectiveness. Therefore, an in-depth analysis from different perspectives of the proposed strategy to build ensembles is presented with a strong experimental support. %K genetic algorithms, genetic programming, Pattern classification, heterogeneous ensembles %9 journal article %R 10.1142/S0218001414600052 %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 10.1007/978-3-319-67997-6_17 %U http://dx.doi.org/10.1007/978-3-319-67997-6_17 %P 357-387 %0 Conference Proceedings %T How evolution creates complexity: From viruses to brains %A Adami, Chris %Y Banzhaf, Wolfgang %Y Burlacu, Bogdan %Y Kelly, Stephen %Y Lalejini, Alexander %Y Olivetti de Franca, Fabricio %S Genetic Programming Theory and Practice XXII %D 2025 %8 jun 5 7 %C Michigan State University, USA %F Adami:2025:GPTP %O Keynote %K genetic algorithms, genetic programming %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 10.1007/978-3-319-23291-1_6 %U http://eprints.lse.ac.uk/66168/ %U http://dx.doi.org/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 27 nov 1 dec %C Honolulu, HI, USA %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 10.1109/SSCI.2017.8280833 %U http://dx.doi.org/10.1109/SSCI.2017.8280833 %0 Journal Article %T Machine learning classification and regression models for predicting directional changes trend reversal in FX markets %A Adegboye, Adesola %A Kampouridis, Michael %J Expert Systems with Applications %D 2021 %8 January %V 173 %@ 0957-4174 %F ADEGBOYE:2021:ESA %X Most forecasting algorithms in financial markets use physical time for studying price movements, making the flow of time discontinuous. The use of physical time scale can make traders oblivious to significant activities in the market, which poses a risk. Directional changes (DC) is an alternative approach that uses event-based time to sample data. we propose a novel DC-based framework, which uses machine learning algorithms to predict when a trend will reverse. This allows traders to be in a position to take an action before this happens and thus increase their profitability. We combine our approach with a novel DC-based trading strategy and perform an in-depth investigation, by applying it to 10-min data from 20 foreign exchange markets over a 10-month period. The total number of tested datasets is 1,000, which allows us to argue that our results can be generalised and are widely applicable. We compare our results to ten benchmarks (both DC and non-DC based, such as technical analysis and buy-and-hold). Our findings show that our proposed approach is able to return a significantly higher profit, as well as reduced risk, and statistically outperform the other trading strategies in a number of different performance metrics %K genetic algorithms, genetic programming, Directional changes, Regression, Classification, Forex/FX, Machine learning %9 journal article %R 10.1016/j.eswa.2021.114645 %U https://kar.kent.ac.uk/89886/1/Adegboye-INT2021_preprint.pdf %U http://dx.doi.org/10.1016/j.eswa.2021.114645 %P 114645 %0 Thesis %T Estimating Directional Changes Trend Reversal in Forex Using Machine Learning %A Adegboye, Adesola Tolulope Noah %D 2022 %8 mar %C UK %C University of Kent %F Adegboye:thesis %X Most forecasting algorithms use a physical time scale data to study price movement in financial markets by taking snapshots in fixed schedule, making the flow of time discontinuous. The use of a physical time scale can make traders oblivious to significant activities in the market, which poses risks. For example, currency risk, the risk that exchange rate will change. Directional changes is a different and newer approach of taking snapshot of the market, which uses an event-based time scale. This approach summarises data into alternating trends called upward directional change and downward directional change according to a change in price a trader considers to be significant, which is expressed as a threshold. The trends in the summary are split into directional change (DC) and overshoot (OS) events. In this work, we propose a novel DC-based framework, which uses machine learning algorithms to forecast when the next, alternate trend is expected to begin. First, we present a genetic programming (GP) algorithm that evolves equations that express linear and non-linear relationships between the length of DC and OS events in a given dataset. Awareness of DC event and OS event lengths provide traders with an idea of when DC trends are expected to reverse and thus take appropriate action to increase profit or mitigate risk. Second, DC trends can be categorised into two distinct types: (1) trends with OS events; and (2) trends without OS events(i.e. OS event length is 0). Trends with OS events are those that continue beyond a period when they were first observed and trends without OS event are others that ends as soon as they were observed. To further improve trend reversal estimation accuracy, we identified these two categorises using classification techniques and estimated OS event length for trends that belong in the first category. We appraised whether this new knowledge could lead to an even greater excess return. Third, our novel trend reversal estimation approach was then used as part of a novel genetic algorithm (GA) based trading strategy. The strategy embedded an optimised trend reversal forecasting algorithm that was based on trend reversal point forecasted by multiple thresholds. We assessed the efficiency of our framework (i.e., a novel trend reversal approach and an optimised trading strategy) by performing an in-depth investigation. To assess our approach and evaluate the extent to which it could be generalised in Forex markets, we used five tailored thresholds to create 1000 DC datasets from 10, monthly 10 minute physical time data of 20 major Forex markets (i.e 5 thresholds * 10 months * 20 currency pairs). We compared our results to six benchmarks techniques, both DC and non-DC based, such as technical analysis and buy-and-hold. Our findings showed that our proposed approach can return a significantly higher profit at reduced risk, and statistically outperformed the other trading strategies compareds in a number of different performance metrics. %K genetic algorithms, genetic programming %9 Ph.D. thesis %R 10.22024/UniKent/01.02.94107 %U https://kar.kent.ac.uk/94107/ %U http://dx.doi.org/10.22024/UniKent/01.02.94107 %0 Journal Article %T Effect of varied fiber alkali treatments on the tensile strength of Ampelocissus cavicaulis reinforced polyester composites: Prediction, optimization, uncertainty and sensitivity analysis %A Adeyi, Abiola John %A Adeyi, Oladayo %A Oke, Emmanuel Olusola %A Olalere, Olusegun Abayomi %A Oyelami, Seun %A Ogunsola, Akinola David %J Advanced Industrial and Engineering Polymer Research %D 2021 %V 4 %N 1 %@ 2542-5048 %F ADEYI:2021:AIEPR %X Studies on modeling and optimization of alkali treatment, investigation of experimental uncertainty and sensitivity analysis of alkali treatment factors of natural fibers are important to effective natural fiber reinforced polymer composite development. In this contribution, response surface methodology (RSM) was employed to investigate and optimize the effect of varied treatment factors (sodium hydroxide concentration (NaOH) and soaking time (ST)) of the alkali treatment of Ampelocissus cavicaulis natural fiber (ACNF) on the tensile strength (TS) of alkali treated ACNF reinforced polyester composite. RSM and multi gene genetic programming (MGGP) were comparatively employed to model the alkali treatment. The best model was applied in Oracle Crystal Ball (OCB) to investigate the uncertainty of the treatment results and sensitivity of the treatment factors. Results showed that increased NaOH and ST increased the TS of the alkali treated ACNF reinforced polyester composite up to 28.3500 MPa before TS decreased. The coefficient of determination (R2) and root mean square error (RMSE) of RSM model were 0.8920 and 0.6528 while that of MGGP were 0.9144 and 0.5812. The optimum alkali treatment established by RSM was 6.23percent of NaOH at 41.99 h of ST to give a TS of 28.1800 MPa with a desirability of 0.9700. The TS of the validated optimum alkali treatment condition was 28.2200 MPa. The certainty of the experimental results was 71.2580percent. TS was 13.8000percent sensitive to NaOH and 86.2000percent sensitive to ST. This work is useful for effective polymer composite materials production to reduce the enormous material and energy losses that usually accompany the process %K genetic algorithms, genetic programming, Response surface methodology, Multigene genetic programming, Oracle crystal ball, Uncertainty and sensitivity analysis %9 journal article %R 10.1016/j.aiepr.2020.12.002 %U https://www.sciencedirect.com/science/article/pii/S2542504820300580 %U http://dx.doi.org/10.1016/j.aiepr.2020.12.002 %P 29-40 %0 Journal Article %T Process integration for food colorant production from Hibiscus sabdariffa calyx: A case of multi-gene genetic programming (MGGP) model and techno-economics %A Adeyi, Oladayo %A Adeyi, Abiola J. %A Oke, Emmanuel O. %A Okolo, Bernard I. %A Olalere, Abayomi O. %A Otolorin, John A. %A Okhale, Samuel %A Taiwo, Abiola E. %A Oladunni, Sunday O. %A Akatobi, Kelechi N. %J Alexandria Engineering Journal %D 2022 %V 61 %N 7 %@ 1110-0168 %F ADEYI:2022:AEJ %X This work presents an integrated heat-assisted extraction process for the production of crude anthocyanins powder (CAnysP) from Hibiscus sabdariffa calyx using SuperPro Designer. The influence of process scale-up (0.04 -1000L) and variables (temperature, time and ethanol proportion in solvent) were investigated by adopting a circumscribed central composite design on techno-economic parameters such as annual production rate (APR) and unit production cost (UPC) CAnysP. The individual runs in the CCCD were taken as different process scenario and simulated independently. Multi-gene genetic programming (MGGP) was further used to develop robust predictive models. The robustness of the model and sensitivity analysis were ascertained using the Monte Carlo simulation. The process scenario at 30 min, 30 degreeC, 50percent and 1000 L possessed the highest CAnysP APR and lowest UPC. MGGP- models predicted R2 = 0.9984 for CAnysP APR and R2 = 0.9643 for UPC and certainty (99.98percent for CAnysP APR and 98.47percent for UPC) %K genetic algorithms, genetic programming, Heat assisted technology based process, Multi-gene genetic programming, Annual production rate, Unit production cost, Techno-economics, calyx %9 journal article %R 10.1016/j.aej.2021.10.049 %U https://www.sciencedirect.com/science/article/pii/S1110016821006931 %U http://dx.doi.org/10.1016/j.aej.2021.10.049 %P 5235-5252 %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 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/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 10.1007/s40030-019-00367-x %U http://link.springer.com/article/10.1007/s40030-019-00367-x %U http://dx.doi.org/10.1007/s40030-019-00367-x %P 395-405 %0 Journal Article %T Genetic programming-based ordinary Kriging for spatial interpolation of rainfall %A Adhikary, Sajal Kumar %A Muttil, Nitin %A Yilmaz, Abdullah %J Journal of Hydrologic Engineering %D 2016 %8 feb %V 21 %N 2 %I American Society of Civil Engineers %F vu29881 %X Rainfall data provide an essential input for most hydrologic analyses and designs for effective management of water resource systems. However, in practice, missing values often occur in rainfall data that can ultimately influence the results of hydrologic analysis and design. Conventionally, stochastic interpolation methods such as Kriging are the most frequently used approach to estimate the missing rainfall values where the variogram model that represents spatial correlations among data points plays a vital role and significantly impacts the performance of the methods. In the past, the standard variogram models in ordinary kriging were replaced with the universal function approximator-based variogram models, such as artificial neural networks (ANN). In the current study, applicability of genetic programming (GP) to derive the variogram model and use of this GP-derived variogram model within ordinary kriging for spatial interpolation was investigated. Developed genetic programming-based ordinary kriging (GPOK) was then applied for estimating the missing rainfall data at a rain gauge station using the historical rainfall data from 19 rain gauge stations in the Middle Yarra River catchment of Victoria, Australia. The results indicated that the proposed GPOK method outperformed the traditional ordinary kriging as well as the ANN-based ordinary kriging method for spatial interpolation of rainfall. Moreover, the GP-derived variogram model is shown to have advantages over the standard and ANN-derived variogram models. Therefore, the GP-derived variogram model seems to be a potential alternative to variogram models applied in the past and the proposed GPOK method is recommended as a viable option for spatial interpolation. %K genetic algorithms, genetic programming, rainfall data, management of water resource systems, missing values, programming %9 journal article %R 10.1061/(ASCE)HE.1943-5584.0001300 %U https://vuir.vu.edu.au/29881/ %U http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0001300 %0 Thesis %T Optimal Design of a Rain Gauge Network to Improve Streamflow Forecasting %A Adhikary, Sajal Kumar %D 2017 %8 20 mar %C Melbourne, Australia %C College of Engineering and Science, Victoria University %F vu35054 %O This thesis includes 1 published article for which access is restricted due to copyright (Chapters 3, 4 (first paper). Details of access to these papers have been inserted in the thesis, replacing the articles themselves. %X Enhanced streamflow forecasting has always been an important task for researchers and water resources managers. However, streamflow forecasting is often challenging owing to the complexity of hydrologic systems. The accuracy of streamflow forecasting mainly depends on the input data, especially rainfall as it constitutes the key input in transforming rainfall into runoff. This emphasizes the need for incorporating accurate rainfall input in streamflow forecasting models in order to achieve enhanced streamflow forecasting. Based on past research, it is well-known that an optimal rain gauge network is necessary to provide high quality rainfall estimates. Therefore, this study focused on the optimal design of a rain gauge network and integration of the optimal network-based rainfall input in artificial neural network (ANN) models to enhance the accuracy of streamflow forecasting. The Middle Yarra River catchment in Victoria, Australia was selected as the case study catchment, since the management of water resources in the catchment is of great importance to the majority of Victorians. The study had three components. First, an evaluation of existing Kriging methods and universal function approximation techniques such as genetic programming (GP) and ANN were performed in terms of their potentials and suitability for the enhanced spatial estimation of rainfall. The evaluation confirmed that the fusion of GP and ordinary kriging is highly effective for the improved estimation of rainfall and the ordinary cokriging using elevation can enhance the spatial estimation of rainfall. Second, the design of an optimal rain gauge network was undertaken for the case study catchment using the kriging-based geostatistical approach based on the variance reduction framework. It is likely that an existing rain gauge network may consist of redundant stations, which have no contribution to the network performance for providing quality rainfall estimates. Therefore, the optimal network was achieved through optimal placement of additional stations (network augmentation) as well as eliminating or optimally relocating of redundant stations (network rationalization). In order to take the rainfall variability caused by climatic factors like El Nino Southern Oscillation into account, the network was designed using rainfall records for both El Nino and La Nina periods. The rain gauge network that gives the improved estimates of areal average and point rainfalls for both the El Nino and La Nina periods was selected as the optimal network. It was found that the optimal network outperformed the existing one in estimating the spatiotemporal estimates of areal average and point rainfalls. Additionally, optimal positioning of redundant stations was found to be highly effective to achieve the optimal rain gauge network. Third, an ANN-based enhanced streamflow forecasting approach was demonstrated, which incorporated the optimal rain gauge network-based input instead of using input from an existing non-optimal network to achieve the enhanced streamflow forecasting. The approach was found to be highly effective in improving the accuracy of stream-flow forecasting, particularly when the current operational rain gauge network is not an optimal one. For example, it was found that use of the optimal rain gauge network-based input results in the improvement of streamflow forecasting accuracy by 7.1percent in terms of normalised root mean square error (NRMSE) compared to the current rain gauge network based-input. Further improvement in streamflow forecasting was achieved through augmentation of the optimal network by incorporating additional fictitious rain gauge stations. The fictitious stations were added in sub-catchments that were delineated based on the digital elevation model. It was evident from the results that 18.3percent improvement in streamflow forecasting accuracy was achieved in terms of NRMSE using the augmented optimal rain gauge network-based input compared to the current rain gauge network-based input. The ANN-based input selection technique that was employed in this study for streamflow forecasting offers a viable technique for significant input variables selection as this technique is capable of learning problems involving very non-linear and complex data. %K genetic algorithms, genetic programming, rivers, water basins, streams, stream-flow simulation, modeling, water supply, spatial interpolation, genetic programming-based ordinary kriging, thesis by publication %9 Ph.D. thesis %U https://vuir.vu.edu.au/35054/ %0 Journal Article %T A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from SSMIS Data and Evaluation of its Reliability by Uncertainty Parameters %A Adib, Arash %A Zaerpour, Arash %A Kisi, Ozgur %A Lotfirad, Morteza %J Water Resources Management %D 2021 %8 jul %V 35 %I springer %F Adib:2021:WRM %X This study demonstrates the application of wavelet transform comprising discrete wavelet transform, maximum overlap discrete wavelet transform (MODWT), and multiresolution-based MODWT (MODWT-MRA), as well as wavelet packet transform (WP), coupled with artificial intelligence (AI)-based models including multi-layer perceptron, radial basis function, adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming to retrieve snow depth (SD) from special sensor microwave imager sounder obtained from the national snow and ice data center. Different mother wavelets were applied to the passive microwave (PM) frequencies; afterward, the dominant resultant decomposed subseries comprising low frequencies (approximations) and high frequencies (details) were detected and inserted into the AI-based models. The results indicated that the WP coupled with ANFIS (WP-ANFIS) outperformed the other studied models with the determination coefficient of 0.988, root mean square error of 3.458 cm, mean absolute error of 2.682 cm, and Nash–Sutcliffe efficiency of 0.987 during testing period. The final verification also confirmed that the WP is a promising pre-processing technique to improve the accuracy of the AI-based models in SD evaluation from PM data. %K genetic algorithms, genetic programming, gene expression programming, passive microwave, special sensor microwave imager sounder, snow depth retrieval, discrete wavelet transform, wavelet-packet transform %9 journal article %R 10.1007/s11269-021-02863-x %U http://link.springer.com/10.1007/s11269-021-02863-x %U http://dx.doi.org/10.1007/s11269-021-02863-x %P 2723-2740 %0 Conference Proceedings %T LooperGP: A Loopable Sequence Model for Live Coding Performance using GuitarPro Tablature %A Adkins, Sara %A Sarmento, Pedro %A Barthet, Mathieu %Y Johnson, Colin %Y Rodriguez-Fernandez, Nereida %Y Rebelo, Sergio M. %S 12th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMusArt 2023 %S LNCS %D 2023 %8 apr 12 14 %V 13988 %I Springer Verlag %C Brno, Czech Republic %F Adkins:2023:evomusart %X Despite their impressive offline results, deep learning models for symbolic music generation are not widely used in live performances due to a deficit of musically meaningful control parameters and a lack of structured musical form in their outputs. To address these issues we introduce LooperGP, a method for steering a Transformer-XL model towards generating loopable musical phrases of a specified number of bars and time signature, enabling a tool for live coding performances. We show that by training LooperGP on a dataset of 93681 musical loops extracted from the DadaGP dataset [Data GuitarPro], we are able to steer its generative output towards generating three times as many loopable phrases as our baseline. In a subjective listening test conducted by 31 participants, LooperGP loops achieved positive median ratings in originality, musical coherence and loop smoothness, demonstrating its potential as a performance tool. %K genetic algorithms, genetic programming, Controllable Music Generation, Sequence Models, Live Coding, Transformers, AI Music, Loops, Guitar Tabs %R 10.1007/978-3-031-29956-8_1 %U http://dx.doi.org/10.1007/978-3-031-29956-8_1 %P 3-19 %0 Generic %T Improving Readability of Scratch Programs with Search-based Refactoring %A Adler, Felix %A Fraser, Gordon %A Gruendinger, Eva %A Koerber, Nina %A Labrenz, Simon %A Lerchenberger, Jonas %A Lukasczyk, Stephan %A Schweikl, Sebastian %D 2021 %8 16 aug %I arXiv %F adler2021improving %X Block-based programming languages like Scratch have become increasingly popular as introductory languages for novices. These languages are intended to be used with a tinkering approach which allows learners and teachers to quickly assemble working programs and games, but this often leads to low code quality. Such code can be hard to comprehend, changing it is error-prone, and learners may struggle and lose interest. The general solution to improve code quality is to refactor the code. However, Scratch lacks many of the common abstraction mechanisms used when refactoring programs written in higher programming languages. In order to improve Scratch code, we therefore propose a set of atomic code transformations to optimise readability by (1) rewriting control structures and (2) simplifying scripts using the inherently concurrent nature of Scratch programs. By automating these transformations it is possible to explore the space of possible variations of Scratch programs. In this paper, we describe a multi-objective search-based approach that determines sequences of code transformations which improve the readability of a given Scratch program and therefore form refactorings. Evaluation on a random sample of 1000 Scratch programs demonstrates that the generated refactorings reduce complexity and entropy in 70.4percent of the cases, and 354 projects are improved in at least one metric without making any other metric worse. The refactored programs can help both novices and their teachers to improve their code. %K genetic algorithms, genetic programming, genetic improvement, SBSE %U https://arxiv.org/abs/2108.07114 %0 Conference Proceedings %T Improving Readability of Scratch Programs with Search-based Refactoring %A Adler, Felix %A Fraser, Gordon %A Gruendinger, Eva %A Koerber, Nina %A Labrenz, Simon %A Lerchenberger, Jonas %A Lukasczyk, Stephan %A Schweikl, Sebastian %S 21st IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2021 %D 2021 %8 sep 27 28 %C Luxembourg %F DBLP:conf/scam/AdlerFGKLLLS21 %O 16000 GP entry %X Block-based programming languages like SCRATCH have become increasingly popular as introductory languages for novices. These languages are intended to be used with a tinkering approach which allows learners and teachers to quickly assemble working programs and games, but this often leads to low code quality. Such code can be hard to comprehend, changing it is error-prone, and learners may struggle and lose interest. The general solution to improve code quality is to refactor the code. However, SCRATCH lacks many of the common abstraction mechanisms used when refactoring programs written in higher programming languages. In order to improve SCRATCH code, we therefore propose a set of atomic code transformations to optimise readability by (1) rewriting control structures and (2) simplifying scripts using the inherently concurrent nature of SCRATCH programs. By automating these transformations it is possible to explore the space of possible variations of SCRATCH programs. In this paper, we describe a multi-objective search-based approach that determines sequences of code transformations which improve the readability of a given SCRATCH program and therefore form refactorings. Evaluation on a random sample of 1000 SCRATCH programs demonstrates that the generated refactorings reduce complexity and entropy in 70.4% of the cases, and 354 projects are improved in at least one metric without making any other metric worse. The refactored programs can help both novices and their teachers to improve their code. %K genetic algorithms, genetic programming, genetic improvement, grammatical evolution, SBSE, NSGA-II, LitterBox, JSON, refactoring, Java %R 10.1109/SCAM52516.2021.00023 %U https://arxiv.org/abs/2108.07114 %U http://dx.doi.org/10.1109/SCAM52516.2021.00023 %P 120-130 %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 10.1007/BFb0055934 %U http://dx.doi.org/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 10.1007/3-540-48885-5_9 %U http://dx.doi.org/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 %I Taylor & Francis %@ 1058-6458 %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 10.1080/10586458.2016.1175393 %U http://dx.doi.org/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 10.1145/3321707.3321828 %U http://dx.doi.org/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 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/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 International Journal 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 10.1504/IJSPM.2010.032655 %U https://pure.fh-ooe.at/en/publications/effective-allele-preservation-by-offspring-selection-an-empirical-2 %U http://dx.doi.org/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 10.1007/978-3-642-53856-8_40 %U http://dx.doi.org/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 10.1007/978-1-4939-0375-7_10 %U http://dx.doi.org/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 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/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 10.1145/3067695.3082530 %U http://doi.acm.org/10.1145/3067695.3082530 %U http://dx.doi.org/10.1145/3067695.3082530 %P 1553-1558 %0 Conference Proceedings %T White Box vs. Black Box Modeling: On the Performance of Deep Learning, Random Forests, and Symbolic Regression in Solving Regression Problems %A Affenzeller, Michael %A Burlacu, Bogdan %A Dorfer, Viktoria %A Dorl, Sebastian %A Halmerbauer, Gerhard %A Koenigswieser, Tilman %A Kommenda, Michael %A Vetter, Julia %A Winkler, Stephan M. %Y Moreno-Diaz, Roberto %Y Pichler, Franz %Y Quesada-Arencibia, Alexis %S 17th International Conference, Computer Aided Systems Theory - EUROCAST 2019 %S Lecture Notes in Computer Science %D 2019 %8 feb 17 22 %V 12013 %I Springer %C Las Palmas de Gran Canaria, Spain %F DBLP:conf/eurocast/AffenzellerBDDH19 %O Revised Selected Papers, Part I %X Black box machine learning techniques are methods that produce models which are functions of the inputs and produce outputs, where the internal functioning of the model is either hidden or too complicated to be analyzed. White box modeling, on the contrary, produces models whose structure is not hidden, but can be analyzed in detail. In this paper we analyze the performance of several modern black box as well as white box machine learning methods. We use them for solving several regression and classification problems, namely a set of benchmark problems of the PBML test suite, a medical data set, and a proteomics data set. Test results show that there is no method that is clearly better than the others on the benchmark data sets, on the medical data set symbolic regression is able to find the best classifiers, and on the proteomics data set the black box modeling methods clearly find better prediction models. %K genetic algorithms, genetic programming %R 10.1007/978-3-030-45093-9_35 %U https://pure.fh-ooe.at/en/publications/white-box-vs-black-box-modeling-on-the-performance-of-deep-learni %U http://dx.doi.org/10.1007/978-3-030-45093-9_35 %P 288-295 %0 Conference Proceedings %T Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data %A Burlacu, Bogdan %A Kommenda, Michael %A Kronberger, Gabriel %A Winkler, Stephan M. %A Affenzeller, Michael %Y Trujillo, Leonardo %Y Winkler, Stephan M. %Y Silva, Sara %Y Banzhaf, Wolfgang %S Genetic Programming Theory and Practice XIX %S Genetic and Evolutionary Computation %D 2022 %8 jun 2 4 %I Springer %C Ann Arbor, USA %F Affenzeller:2022:GPTP %X Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and the understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow calculating the potential energy of an atomic system as a function of atomic coordinates and potentially other properties. First-principles-based ab initio potentials can reach arbitrary levels of accuracy, however, their applicability is limited by their high computational cost. Machine learning (ML) has recently emerged as an effective way to offset the high computational costs of ab initio atomic potentials by replacing expensive models with highly efficient surrogates trained on electronic structure data. Among a plethora of current methods, symbolic regression (SR) is gaining traction as a powerful white-box approach for discovering functional forms of interatomic potentials. This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results. A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomic positions and associated potential energy) is presented and empirically validated on ab initio electronic structure data. %K genetic algorithms, genetic programming %R 10.1007/978-981-19-8460-0_1 %U https://arxiv.org/abs/2206.06422 %U http://dx.doi.org/10.1007/978-981-19-8460-0_1 %P 1-30 %0 Conference Proceedings %T GP in Prescriptive Analytics %A Affenzeller, Michael %Y Hu, Ting %Y Ofria, Charles %Y Trujillo, Leonardo %Y Winkler, Stephan %S Genetic Programming Theory and Practice XX %S Genetic and Evolutionary Computation %D 2023 %8 jun 1 3 %C Michigan State University, USA %F Affenzeller:2023:GPTP %K genetic algorithms, genetic programming %0 Conference Proceedings %T On the Effects of Continuous Pruning on Symbolic Regression for Different Variants of Evolutionary Search %A Affenzeller, Michael %Y Banzhaf, Wolfgang %Y Burlacu, Bogdan %Y Kelly, Stephen %Y Lalejini, Alexander %Y Olivetti de Franca, Fabricio %S Genetic Programming Theory and Practice XXII %D 2025 %8 jun 5 7 %C Michigan State University, USA %F Affenzeller:2025:GPTP %K genetic algorithms, genetic programming %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 10.1016/j.rse.2017.05.017 %U http://www.sciencedirect.com/science/article/pii/S003442571730216X %U http://dx.doi.org/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 10.1145/3194810.3194817 %U http://dx.doi.org/10.1145/3194810.3194817 %P 17-18 %0 Thesis %T Automated Testing of Robotic and Cyberphysical Systems %A Afzal, Afsoon %D 2021 %8 may 2021 %C Pittsburgh, PA 15213, USA %C Institute for Software Research, School of Computer Science, Carnegie Mellon University %F Afsoon_Afzal:thesis %X Robotics and cyberphysical systems are increasingly being deployed to settings where they are in frequent interaction with the public. Therefore, failures in these systems can be catastrophic by putting human lives in danger and causing extreme financial loss. Large-scale assessment of the quality of these systems before deployment can prevent these costly damages. Because of the complexity and other special features of these systems, testing,and more specifically automated testing, faces challenges. In this dissertation, I study the unique challenges of testing robotics and cyberphysical systems, and propose an end-to-end automated testing pipeline to provide tools and methods that can help roboticists in large-scale, automated testing of their systems. My key insight is that we can use (low-fidelity) simulation to automatically test robotic and cyber-physical systems, and identify many potentially catastrophic failures in advance at low cost. My core thesis is: Robotic and cyberphysical systems have unique features such as interacting with the physical world and integrating hardware and software components, which creates challenges for automated, large-scale testing approaches. An automated testing framework using software-in-the-loop (low-fidelity) simulation can facilitate automated testing for these systems. This framework can be offered using a clustering approach as an automated oracle, and an evolutionary-based automated test input generation with scenario coverage fitness functions. To support this thesis, I conduct a number of qualitative, quantitative, and mixed method studies that 1) identify main challenges of testing robotic and cyberphysical systems, 2) show that low-fidelity simulation can be an effective approach in detecting bugs and errors with low cost, and 3) identify challenges of using simulators in automated testing. Additionally, I propose automated techniques for creating oracles and generating test inputs to facilitate automated testing of robotic and cyberphysical systems. I present an approach to automatically generate oracles for cyberphysical systems using clustering, which can observe and identify common patterns of system behavior.These patterns can be used to distinguish erroneous behavior of the system and act as an oracle. I evaluate the quality of test inputs for robotic systems with respect to their reliability, and effectiveness in revealing faults in the system. I observe a high rate of non-determinism among test executions that complicates test input generation and evaluation, and show that coverage-based metrics are generally poor indicators of test input quality. Finally, I present an evolutionary-based automated test generation approach with a fitness function that is based on scenario coverage. The automated oracle and automated test input generation approaches contribute to a fully automated testing framework that can perform large-scale, automated testing on robotic and cyberphysical systems in simulation. %K SBSE, testing cyber-physical systems, robotics testing, automated quality assurance, simulation-based testing, challenges of testing, automated oracle inference, automated test generation %9 Ph.D. thesis %U https://afsafzal.github.io/materials/thesis.pdf %0 Journal Article %T SOSRepair: Expressive Semantic Search for Real-World Program Repair %A Afzal, Afsoon %A Motwani, Manish %A Stolee, Kathryn T. %A Brun, Yuriy %A Le Goues, Claire %J IEEE Transactions on Software Engineering %D 2021 %V 47 %N 10 %@ 0098-5589 %F Afzal:2021:TSE %X Automated program repair holds the potential to significantly reduce software maintenance effort and cost. However, recent studies have shown that it often produces low-quality patches that repair some but break other functionality. We hypothesize that producing patches by replacing likely faulty regions of code with semantically-similar code fragments, and doing so at a higher level of granularity than prior approaches can better capture abstraction and the intended specification, and can improve repair quality. We create SOSRepair, an automated program repair technique that uses semantic code search to replace candidate buggy code regions with behaviorally-similar (but not identical) code written by humans. SOSRepair is the first such technique to scale to real-world defects in real-world systems. On a subset of the ManyBugs benchmark of such defects, SOSRepair produces patches for 23 (35percent) of the 65 defects, including 3, 5, and 8 defects for which previous state-of-the-art techniques Angelix, Prophet, and GenProg do not, respectively. On these 23 defects, SOSRepair produces more patches (8, 35percent) that pass all independent tests than the prior techniques. We demonstrate a relationship between patch granularity and the ability to produce patches that pass all independent tests. We then show that fault localization precision is a key factor in SOSRepair’s success. Manually improving fault localisation allows SOSRepair to patch 24 (37percent) defects, of which 16 (67percent) pass all independent tests. We conclude that (1) higher-granularity, semantic-based patches can improve patch quality, (2) semantic search is promising for producing high-quality real-world defect repairs, (3) research in fault localization can significantly improve the quality of program repair techniques, and (4) semi-automated approaches in which developers suggest fix locations may produce high-quality patches. %K genetic algorithms, genetic programming, genetic improvement, APR %9 journal article %R 10.1109/TSE.2019.2944914 %U https://doi.org/10.1109/TSE.2019.2944914 %U http://dx.doi.org/10.1109/TSE.2019.2944914 %P 2162-2181 %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 10.1109/CSA.2008.13 %U http://dx.doi.org/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 10.1109/ICSEA.2008.9 %U http://dx.doi.org/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 10.1109/INMIC.2008.4777762 %U http://drfeldt.googlepages.com/afzal_submitted0805icsea_prediction_.pdf %U http://dx.doi.org/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 10.1109/SSBSE.2009.17 %U http://dx.doi.org/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 10.1016/j.infsof.2008.12.005 %U http://drfeldt.googlepages.com/afzal_submitted0805ist_sysrev_nfr_sb.pdf %U http://dx.doi.org/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 10.4018/978-1-61520-809-8.ch006 %U http://dx.doi.org/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 10.1109/SSBSE.2010.19 %U http://dx.doi.org/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 10.1109/APSEC.2010.54 %U http://dx.doi.org/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 10.1016/j.eswa.2011.03.041 %U http://www.sciencedirect.com/science/article/B6V03-52C8FT6-5/2/668361024e4b2bcf9a4a73195271591c %U http://dx.doi.org/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 10.1007/s11219-013-9205-3 %U http://www.bth.se/fou/forskinfo.nsf/all/3d40224f7cbf862dc1257b7800251e66?OpenDocument %U http://dx.doi.org/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 10.1007/978-3-319-25964-2_3 %U http://dx.doi.org/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 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/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 10.1007/978-3-030-16692-2_21 %U http://dx.doi.org/10.1007/978-3-030-16692-2_21 %P 308-324 %0 Thesis %T Evolutionary Computation for Feature Manipulation in Salient Object Detection %A Afzali Vahed Moghaddam, Shima %D 2020 %C New Zealand %C Computer Science, Victoria University of Wellington %F Afzali:thesis %X The human visual system can efficiently cope with complex natural scenes containing various objects at different scales using the visual attention mechanism. Salient object detection (SOD) aims to simulate the capability of the human visual system in prioritizing objects for high-level processing. SOD is a process of identifying and localizing the most attention grabbing object(s) of a scene and separating the whole extent of the object(s) from the scene. In SOD, significant research has been dedicated to design and introduce new features to the domain. The existing saliency feature space suffers from some difficulties such as having high dimensionality, features are not equally important, some features are irrelevant, and the original features are not informative enough. These difficulties can lead to various performance limitations. Feature manipulation is the process which improves the input feature space to enhance the learning quality and performance. Evolutionary computation (EC) techniques have been employed in a wide range of tasks due to their powerful search abilities. Genetic programming (GP) and particle swarm optimization (PSO) are well-known EC techniques which have been used for feature manipulation. The overall goal of this thesis is to develop feature manipulation methods including feature weighting, feature selection, and feature construction using EC techniques to improve the input feature set for SOD. This thesis proposes a feature weighting method using PSO to explore the relative contribution of each saliency feature in the feature combination process. Saliency features are referred to the features which are extracted from different levels (e.g., pixel, segmentation) of an image to compute the saliency values over the entire image. The experimental results show that different datasets favour different weights for the employed features. The results also reveal that by considering the importance of each feature in the combination process, the proposed method has achieved better performance than that of the competitive methods. This thesis proposes a new bottom-up SOD method to detect salient objects by constructing two new informative saliency features and designing a new feature combination framework. The proposed method aims at developing features which target to identify different regions of the image. The proposed method makes a good balance between computational time and performance. This thesis proposes a GP-based method to automatically construct foreground and background saliency features. The automatically constructed features do not require domain-knowledge and they are more informative compared to the manually constructed features. The results show that GP is robust towards the changes in the input feature set (e.g., adding more features to the input feature set) and improves the performance by introducing more informative features to the SOD domain. This thesis proposes a GP-based SOD method which automatically produces saliency maps (a 2-D map containing saliency values) for different types of images. This GP-based SOD method applies feature selection and feature combination during the learning process for SOD. GP with built-in feature selection process which selects informative features from the original set and combines the selected features to produce the final saliency map. The results show that GP can potentially explore a large search space and find a good way to combine different input features. This thesis introduces GP for the first time to construct high-level saliency features from the low-level features for SOD, which aims to improve the performance of SOD, particularly on challenging and complex SOD tasks. The proposed method constructs fewer features that achieve better saliency performance than the original full feature set. %K genetic algorithms, genetic programming, FM, SOD, PSO, GPFCSOD, wPSOSOD, SED1, ASD, ECSSD, SLIC, FBC, PASCAL %9 Ph.D. thesis %U http://hdl.handle.net/10063/8897 %0 Journal Article %T An automatic feature construction method for salient object detection: A genetic programming approach %A Afzali Vahed Moghaddam, Shima %A Al-Sahaf, Harith %A Xue, Bing %A Hollitt, Christopher %A Zhang, Mengjie %J Expert Systems with Applications %D 2021 %V 186 %@ 0957-4174 %F Afzali:2021:ESA %X Over the last two decades, salient object detection (SOD) has received increasingly more attention due to its ability to handle complex natural scenes and its various real-world applications. The performance of an SOD method mainly relies on saliency features that are extracted with different levels of information. Low-level saliency features are often effective in simple scenarios, but they are not always robust in challenging scenarios. With the recent prevalence of high-level saliency features such as deep convolutional neural networks (CNNs) features, a remarkable progress has been achieved in the SOD field. However, CNN-based constructed high-level features unavoidably drop the location information and low-level fine details (e.g., edges and corners) of salient object(s), leading to unclear/blurry boundary predictions. In addition, deep CNN methods have difficulties to generalize and accurately detect salient objects when they are trained with limited number of images (e.g. small datasets). This paper proposes a new automatic feature construction method using Genetic Programming (GP) to construct informative high-level saliency features for SOD. The proposed method takes low-level and hand-crafted saliency features as input to construct high-level features. The constructed GP-based high-level features not only detect the general objects, but they are also good at capturing details and edges/boundaries. The GP-based constructed features have better interpretability compared to CNN-based features. The proposed GP-based method can potentially cope with a small number of samples for training to obtain a good generalization as long as the given training data has enough information to represent the distribution of the data. The experiments on six datasets reveal that the new method achieves consistently high performance compared to twelve state-of-the-art SOD methods %K genetic algorithms, genetic programming, Salient object detection, Feature construction %9 journal article %R 10.1016/j.eswa.2021.115726 %U https://www.sciencedirect.com/science/article/pii/S0957417421011076 %U http://dx.doi.org/10.1016/j.eswa.2021.115726 %P 115726 %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 10.1007/11729976_15 %U http://dx.doi.org/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 16 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 10.1109/CEC.2006.1688643 %U http://privatewww.essex.ac.uk/~aagapi/papers/AgapitosLucasEvolvingSort.pdf %U http://dx.doi.org/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 10.1007/978-3-540-71605-1_27 %U http://dx.doi.org/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 10.1007/978-3-540-71605-1_28 %U http://dx.doi.org/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 10.1145/1276958.1277271 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1543.pdf %U http://dx.doi.org/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 10.1109/CEC.2007.4424659 %U 1977.pdf %U http://dx.doi.org/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 10.1145/1389095.1389326 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1155.pdf %U http://dx.doi.org/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 10.1145/1389095.1389327 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1163.pdf %U http://dx.doi.org/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 10.1109/CIG.2008.5035632 %U http://julian.togelius.com/Agapitos2008Generating.pdf %U http://dx.doi.org/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 10.1007/978-3-642-15844-5_30 %U http://dx.doi.org/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 10.1007/978-3-642-16239-8_50 %U http://dx.doi.org/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 10.1007/978-3-642-20407-4_6 %U http://dx.doi.org/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 10.1145/2001858.2001969 %U http://dx.doi.org/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 10.1109/CIG.2011.6032010 %U http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper54.pdf %U http://dx.doi.org/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 10.1007/978-3-642-23336-4_7 %U http://hdl.handle.net/10197/3552 %U http://dx.doi.org/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 10.1007/978-3-642-29178-4_14 %U http://dx.doi.org/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 10.1007/978-3-642-32937-1_44 %U http://dx.doi.org/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 10.1007/978-3-642-37207-0_1 %U http://dx.doi.org/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 10.1007/978-3-662-44303-3_1 %U http://dx.doi.org/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 10.1109/CEC.2014.6900567 %U http://dx.doi.org/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 10.1109/CEC.2015.7257189 %U http://dx.doi.org/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 10.1007/978-3-319-31204-0_2 %U http://dx.doi.org/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 10.1007/s10710-016-9277-5 %U http://dx.doi.org/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 10.1007/s10287-017-0280-y %U https://ideas.repec.org/a/spr/comgts/v14y2017i3d10.1007_s10287-017-0280-y.html %U http://dx.doi.org/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 10.1109/TEVC.2019.2900916 %U http://ncra.ucd.ie/papers/08648159.pdf %U http://dx.doi.org/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. %K genetic algorithms, genetic programming %R 10.1007/3-540-61723-X_964 %U http://dx.doi.org/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 Conference Proceedings %T Probabilistic Analysis of a Geosynthetic Reinforced Soil Retaining Wall Under Seismic Conditions Using Genetic Programming %A Agarwal, Ekansh %A Verma, Ajeet Kumar %A Pain, Anindya %A Sarkar, Shantanu %S Soil Dynamics, Earthquake and Computational Geotechnical Engineering %D 2023 %I Springer %F agarwal:2023:SDECGE %K genetic algorithms, genetic programming %R 10.1007/978-981-19-6998-0_20 %U http://link.springer.com/chapter/10.1007/978-981-19-6998-0_20 %U http://dx.doi.org/10.1007/978-981-19-6998-0_20 %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 10.1007/978-3-540-24650-3_20 %U http://web.mit.edu/varun_ag/www/aggarwal-eurogp2004.pdf %U http://dx.doi.org/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 10.1007/978-0-387-49650-4_14 %U http://people.csail.mit.edu/unamay/publications-dir/gptp06.pdf %U http://dx.doi.org/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 10.1016/j.energy.2015.11.008 %U http://www.sciencedirect.com/science/article/pii/S0360544215015327 %U http://dx.doi.org/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 %8 jan %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 10.1016/S0167-8655(01)00128-3 %U http://dx.doi.org/10.1016/S0167-8655(01)00128-3 %P 303-309 %0 Conference Proceedings %T Proofster: Automated Formal Verification %A Agrawal, Arpan %A First, Emily %A Kaufman, Zhanna %A Reichel, Tom %A Zhang, Shizhuo %A Zhou, Timothy %A Sanchez-Stern, Alex %A Ringer, Talia %A Brun, Yuriy %S Proceedings of the Demonstrations Track at the 45th International Conference on Software Engineering (ICSE) %D 2023 %8 14 20 may %C Melbourne %F Agrawal:2023:ICSE %X Formal verification is an effective but extremely work-intensive method of improving software quality. Verifying the correctness of software systems often requires significantly more effort than implementing them in the first place, despite the existence of proof assistants, such as Coq, aiding the process. Recent work has aimed to fully automate the synthesis of formal verification proofs, but little tool support exists for practitioners. This paper presents Proofster, a web-based tool aimed at assisting developers with the formal verification process via proof synthesis. Proofster inputs a Coq theorem specifying a property of a software system and attempts to automatically synthesize a formal proof of the correctness of that property. When it is unable to produce a proof, Proofster outputs the proof-space search tree its synthesis explored, which can guide the developer to provide a hint to enable Proofster to synthesize the proof. Proofster runs online at https://proofster.cs.umass.edu/ and a video demonstrating Proofster is available at https://youtu.be/xQAi66lRfwI/. %K genetic algorithms, genetic programming %R 10.1109/ICSE-Companion58688.2023.00018 %U http://dx.doi.org/10.1109/ICSE-Companion58688.2023.00018 %P 26-30 %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 10.1109/CEC.2006.1688718 %U http://ieeexplore.ieee.org/servlet/opac?punumber=11108 %U http://dx.doi.org/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 %8 30 nov %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 10.1016/j.eswa.2015.06.001 %U http://www.sciencedirect.com/science/article/pii/S0957417415003954 %U http://dx.doi.org/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 10.1109/EH.1999.785434 %U http://dx.doi.org/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 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/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 10.1109/CEC.2004.1331048 %U http://delta.cs.cinvestav.mx/~ccoello/conferences/cec04-muxmutual.pdf.gz %U http://dx.doi.org/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 10.1016/j.powtec.2018.08.064 %U http://www.sciencedirect.com/science/article/pii/S0032591018307022 %U http://dx.doi.org/10.1016/j.powtec.2018.08.064 %P 728-746 %0 Journal Article %T Modeling the downhole density of drilling muds using multigene genetic programming %A Agwu, Okorie Ekwe %A Akpabio, Julius Udoh %A Dosunmu, Adewale %J Upstream Oil and Gas Technology %D 2021 %V 6 %@ 2666-2604 %F AGWU:2021:UOGT %X The main objective of this paper is to use experimental measurements of downhole pressure, temperature and initial mud density to predict downhole density using multigene genetic programming. From the results, the mean square error for the WBM density model was 0.0012, with a mean absolute error of 0.0246 and the square of correlation coefficient (R2) was 0.9998; while for the OBM, the MSE was 0.000359 with MAE of 0.01436 and R2 of 0.99995. In assessing the OBM model’s generalization capability, the model had an MSE of 0.031, MAE of 0.138 and mean absolute percentage error (MAPE) of 0.95percent %K genetic algorithms, genetic programming, Multigene genetic programming, Downhole mud density, Drilling mud, HTHP %9 journal article %R 10.1016/j.upstre.2020.100030 %U https://www.sciencedirect.com/science/article/pii/S266626042030030X %U http://dx.doi.org/10.1016/j.upstre.2020.100030 %P 100030 %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 10.1142/S0129183108011942 %U http://dx.doi.org/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 10.1007/s12043-008-0125-x %U http://dx.doi.org/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 10.1007/978-3-642-01181-8_2 %U http://dx.doi.org/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 10.1145/1830483.1830658 %U http://dx.doi.org/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 10.1007/978-3-642-20407-4_1 %U http://dx.doi.org/10.1007/978-3-642-20407-4_1 %P 1-12 %0 Conference Proceedings %T Variations in Financial Time Series: Modelling Through Wavelets and Genetic Programming %A Ahalpara, Dilip P. %A Panigrahi, Prasanta K. %A Parikh, Jitendra C. %S Econophysics of Markets and Business Networks %D 2007 %I Springer %F ahalpara:2007:EMBN %K genetic algorithms, genetic programming %R 10.1007/978-88-470-0665-2_3 %U http://link.springer.com/chapter/10.1007/978-88-470-0665-2_3 %U http://dx.doi.org/10.1007/978-88-470-0665-2_3 %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 10.1108/02644401111131902 %U http://dx.doi.org/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 Journal Article %T An evolutionary-based polynomial regression modeling approach to predicting discharge flow rate under sheet piles %A Ahangar-Asr, Alireza %A Johari, A. %A Javadi, Akbar A. %J Engineering with Computers %D 2023 %V 39 %N 6 %F DBLP:journals/ewc/AhangarAsrJJ23 %K genetic algorithms, genetic programming, EPR, Sheet piles/cut-off walls, Seepage flow rate, Evolutionary computation, Data mining %9 journal article %R 10.1007/S00366-023-01872-1 %U https://rdcu.be/dPatP %U http://dx.doi.org/10.1007/S00366-023-01872-1 %P 4093-4101 %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 10.1109/ICoSP.2012.6491563 %U http://dx.doi.org/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 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/10.1145/3387940.3392089 %P 276-284 %0 Conference Proceedings %T Testing Web Enabled Simulation at Scale Using Metamorphic Testing %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 Lomeli, Maria %A Meijer, Erik %A Sapora, Silvia %A Spahr-Summers, Justin %Y van Deursen, Arie %Y Xie, Tao %Y Dieste, Natalia Juristo Oscar %S Proceedings of the International Conference on Software Engineering, ICSE 2021 %D 2021 %8 25 28 may %I IEEE %F Ahlgren:2021:ICSE %X We report on Facebook deployment of MIA (Metamorphic Interaction Automaton). MIA is used to test Facebook’s Web Enabled Simulation, built on a web infrastructure of hundreds of millions of lines of code. MIA tackles the twin problems of test flakiness and the unknowable oracle problem. It uses metamorphic testing to automate continuous integration and regression test execution. MIA also plays the role of a test bot, automatically commenting on all relevant changes submitted for code review. It currently uses a suite of over 40 metamorphic test cases. Even at this extreme scale, a non-trivial metamorphic test suite subset yields outcomes within 20 minutes (sufficient for continuous integration and review processes). Furthermore, our offline mode simulation reduces test flakiness from approximately 50percent (of all online tests) to 0percent (offline). Metamorphic testing has been widely-studied for 22 years. This paper is the first reported deployment into an industrial continuous integration system. %K genetic algorithms, genetic programming, Genetic Improvement, SBSE, Metamorphic Testing, Oracle Problem, Scalability, Testing, Test Flakiness, Web-Enabled Simulation %R 10.1109/ICSE-SEIP52600.2021.00023 %U https://research.fb.com/publications/testing-web-enabled-simulation-at-scale-using-metamorphic-testing/ %U http://dx.doi.org/10.1109/ICSE-SEIP52600.2021.00023 %P 140-149 %0 Conference Proceedings %T Facebook’s Cyber-Cyber and Cyber-Physical Digital Twins %A Ahlgren, John %A Bojarczuk, Kinga %A Drossopoulou, Sophia %A Dvortsova, Inna %A George, Johann %A Gucevska, Natalija %A Harman, Mark %A Lomeli, Maria %A Lucas, Simon M. %A Meijer, Erik %A Omohundro, Steve %A Rojas, Rubmary %A Sapora, Silvia %A Zhou, Norm %Y Chitchyan, Ruzanna %Y Li, Jingyue %Y Weber, Barbara %Y Yue, Tao %S EASE 2021: Evaluation and Assessment in Software Engineering %D 2021 %8 jun 21 24 %I ACM %C Trondheim, Norway %F DBLP:conf/ease/AhlgrenBDDGGHLL21 %X A cyber/cyber digital twin is a simulation of a software system. By contrast, a cyber-physical digital twin is a simulation of a non-software (physical) system. Although cyberphysical digital twins have received a lot of recent attention, their cyber–cyber counterparts have been comparatively overlooked. In this paper we show how the unique properties of cyber cyber digital twins open up exciting opportunities for research and development. Like all digital twins, the cyber–cyber digital twin is both informed by and informs the behaviour of the twin it simulates. It is therefore a software system that simulates another software system, making it conceptually truly a twin, blurring the distinction between the simulated and the simulator. Cyber-cyber digital twins can be twins of other cyber–cyber digital twins, leading to a hierarchy of twins. As we shall see, these apparently philosophical observations have practical ramifications for the design, implementation and deployment of digital twins at Meta. %K genetic algorithms, genetic programming, genetic improvement, SBSE, Web Enabled Simulation, Digital Twin, facebook, meta, social media, online, software engineering %R 10.1145/3463274.3463275 %U https://research.facebook.com/publications/facebooks-cyber-cyber-and-cyber-physical-digital-twins/ %U http://dx.doi.org/10.1145/3463274.3463275 %0 Unpublished Work %T Using Genetic Programming to Play Mancala %A Ahlschwede, John %D 2000 %F ahlschwede:2000:ugppm %X This paper will explain what genetic programming is, what mancala is, how I used genetic programming to evolve mancala-playing programs, and the results I got from doing so. %K genetic algorithms, genetic programming %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 10.1007/BFb0040753 %U http://dx.doi.org/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 %X john holland rules s-expressions wilson ZCS stumulus-response feature extraction KNN niche-based %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 Bristol, UK %C Faculty of Computer Studies and Mathematics, University of the West of England %F Ahluwalia:thesis %K genetic algorithms, genetic programming %9 Ph.D. thesis %U https://uwe.primo.exlibrisgroup.com/discovery/fulldisplay?docid=alma991000187179707511&context=L&vid=44UWE_INST:44UWE_INST&lang=en&search_scope=MyInst_and_CI&adaptor=Local%20Search%20Engine&tab=Everything&query=any,contains,ahluwalia%20phd&offset=0 %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 10.1016/S1383-7621(01)00016-9 %U https://uwe-repository.worktribe.com/output/1090581 %U http://dx.doi.org/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, Cartesian Genetic Programming, real world applications, Algorithms, Design, Performance, Breast Cancer, Fine Needle Aspiration, FNA, ANN, Artificial Neural Network, Neuro-evolution %R 10.1145/2330163.2330307 %U http://dx.doi.org/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 10.1109/FIT.2012.54 %U http://dx.doi.org/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 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/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 10.1007/978-3-662-44654-6_20 %U http://dx.doi.org/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 10.1145/3205651.3208218 %U http://dx.doi.org/10.1145/3205651.3208218 %P 1878-1881 %0 Conference Proceedings %T CirFix: automatically repairing defects in hardware design code %A Ahmad, Hammad %A Huang, Yu %A Weimer, Westley %Y Falsafi, Babak %Y Ferdman, Michael %Y Lu, Shan %Y Wenisch, Thomas F. %S ASPLOS 2022: 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems %D 2022 %8 28 feb 4 mar %I ACM %C Lausanne, Switzerland %F DBLP:conf/asplos/Ahmad0W22 %X CirFix, is a framework for automatically repairing defects in hardware designs implemented in languages like Verilog. We propose a novel fault localization approach based on assignments to wires and registers, and a fitness function tailored to the hardware domain to bridge the gap between software-level automated program repair and hardware descriptions. We also present a benchmark suite of 32 defect scenarios corresponding to a variety of hardware projects. Overall, CirFix produces plausible repairs for 21/32 and correct repairs for 16/32 of the defect scenarios. This repair rate is comparable to that of successful program repair approaches for software, indicating CirFix is effective at bringing over the benefits of automated program repair to the hardware domain for the first time. %K genetic algorithms, genetic programming, genetic improvement, automated program repair, APR, hardware designs, HDL benchmark %R 10.1145/3503222.3507763 %U https://doi.org/10.1145/3503222.3507763 %U http://dx.doi.org/10.1145/3503222.3507763 %P 990-1003 %0 Conference Proceedings %T Digging into Semantics: Where Do Search-Based Software Repair Methods Search? %A Ahmad, Hammad %A Cashin, Padraic %A Forrest, Stephanie %A Weimer, Westley %Y Rudolph, Guenter %Y Kononova, Anna V. %Y Aguirre, Hernan E. %Y Kerschke, Pascal %Y Ochoa, Gabriela %Y Tusar, Tea %S Parallel Problem Solving from Nature - PPSN XVII - 17th International Conference, PPSN 2022, Proceedings, Part II %S Lecture Notes in Computer Science %D 2022 %8 sep 10 14 %V 13399 %I Springer %C Dortmund, Germany %F DBLP:conf/ppsn/AhmadCFW22 %X Search-based methods are a popular approach for automatically repairing software bugs, a field known as automated program repair (APR). There is increasing interest in empirical evaluation and comparison of different APR methods, typically measured as the rate of successful repairs on benchmark sets of buggy programs. Such evaluations, however, fail to explain why some approaches succeed and others fail. Because these methods typically use syntactic representations, i.e., source code, we know little about how the different methods explore their semantic spaces, which is relevant for assessing repair quality and understanding search dynamics. We propose an automated method based on program semantics, which provides quantitative and qualitative information about different APR search-based techniques. Our approach requires no manual annotation and produces both mathematical and human-understandable insights. In an empirical evaluation of 4 APR tools and 34 defects, we investigate the relationship between search-space exploration, semantic diversity and repair success, examining both the overall picture and how the tools search unfolds. Our results suggest that population diversity alone is not sufficient for finding repairs, and that searching in the right place is more important than searching broadly, highlighting future directions for the research community. %K genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Semantic search spaces, Program repair, Patch diversity, Daikon, Defects4J %R 10.1007/978-3-031-14721-0_1 %U https://web.eecs.umich.edu/~weimerw/p/weimer-asplos2022.pdf %U http://dx.doi.org/10.1007/978-3-031-14721-0_1 %P 3-18 %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 10.1109/MCC.2000.10016 %U http://csdl.computer.org/comp/mags/pd/2000/03/p3toc.htm %U http://dx.doi.org/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 10.1145/3297280.3297408 %U http://dx.doi.org/10.1145/3297280.3297408 %P 1278-1285 %0 Journal Article %T Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation %A Ahmadi, Farshad %A Mehdizadeh, Saeid %A Mohammadi, Babak %A Pham, Quoc Bao %A Doan, Thi Ngoc Canh %A Vo, Ngoc Duong %J Agricultural Water Management %D 2021 %V 244 %@ 0378-3774 %F Ahmadi:2021:AWM %X Reference evapotranspiration (ET0) is one of the most important parameters, which is required in many fields such as hydrological, agricultural, and climatological studies. Therefore, its estimation via reliable and accurate techniques is a necessity. The present study aims to estimate the monthly ET0 time series of six stations located in Iran. To achieve this objective, gene expression programming (GEP) and support vector regression (SVR) were used as standalone models. A novel hybrid model was then introduced through coupling the classical SVR with an optimisation algorithm, namely intelligent water drops (IWD) (i.e., SVR$-$IWD). Two various types of scenarios were considered, including the climatic data- and antecedent ET0 data-based patterns. In the climatic data-based models, the effective climatic parameters were recognised by using two pre-processing techniques consisting of τ Kendall and entropy. It is worthy to mention that developing the hybrid SVR-IWD model as well as using the τ Kendall and entropy approaches to discern the most influential weather parameters on ET0 are the innovations of current research. The results illustrated that the applied pre-processing methods introduced different climatic inputs to feed the models. The overall results of present study revealed that the proposed hybrid SVR-IWD model outperformed the standalone SVR one under both the considered scenarios when estimating the monthly ET0. In addition to the mentioned models, two types of empirical equations were also used including the Hargreaves$-$Samani (H$-$S) and Priestley$-$Taylor (P$-$T) in their original and calibrated versions. It was concluded that the calibrated versions showed superior performances compared to their original ones. %K genetic algorithms, genetic programming, gene expression programming, empirical models, intelligent water drops, reference evapotranspiration, support vector regression %9 journal article %R 10.1016/j.agwat.2020.106622 %U https://www.sciencedirect.com/science/article/pii/S0378377420321697 %U http://dx.doi.org/10.1016/j.agwat.2020.106622 %P 106622 %0 Journal Article %T Bi-level game theoretic approach for robust design: A case study of path-generating four-bar %A Ahmadi, Bahman %A Jamali, Ali %A Mallipeddi, Rammohan %A Nariman-zadeh, Nader %A Ahmadi, Behzad %A Khayyam, Hamid %J Swarm and Evolutionary Computation %D 2024 %V 89 %@ 2210-6502 %F Ahmadi:2024:swevo %X This study addresses the bi-level multi-objective optimisation problems (MOP) that raise in robust design and optimisation of engineering systems through establishing a state-of-the-art game theoretic scenario. A novel leader-follower decentralized decision-making scenario is proposed, leveraging the synergy of game theory, Robust Design Optimisation (RDO), Monte Carlo Simulation (MCS), and Artificial Intelligence (AI). The proposed algorithm can be employed for optimum robust Pareto design of a wide range of dynamical systems. In order to achieve a robust design, both the mean and variance of each objective function are considered as players in a multi-agent game setting. In this approach, both Stackelberg and cooperative games are used to model the behaviours of the players. Genetic Programming (GP) meta-models are employed to capture the Stackelberg protocol between two levels specifically for constructing the follower’s rational reaction set (RRS). Additionally, the Nash bargaining function is -use to model the cooperative behaviours among players in each level. The proposed approach is applied and demonstrated through a case study involving multi-objective robust design of planar four-bar linkages. In this manner, four objective functions are assigned to four players within the system. Each player is responsible for optimising a specific objective criterion, namely the mean of tracking error (TE), variance of tracking error, mean of transmission angle and variance of transmission angle (TA) of the linkage. As a result, the four-objective optimisation problem of mechanism is transformed into a single-objective robust synthesis problem. The comparisons of the results show a significant enhancement in the robust behaviour of the linkage, while ensuring that deterministic criteria such as quality of motion and precision are preserved %K genetic algorithms, genetic programming, Bi-level optimization, Game theory, Linkage synthesis, Robust design %9 journal article %R 10.1016/j.swevo.2024.101636 %U https://www.sciencedirect.com/science/article/pii/S2210650224001743 %U http://dx.doi.org/10.1016/j.swevo.2024.101636 %P 101636 %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 10.1109/ICIP.2015.7350956 %U http://dx.doi.org/10.1109/ICIP.2015.7350956 %P 1031-1035 %0 Journal Article %T A novel genetic-programming based differential braking controller for an 8x8 combat vehicle %A Ahmed, Moataz %A El-Gindy, Moustafa %A Lang, Haoxiang %J International Journal of Dynamics and Control %D 2020 %V 8 %N 4 %F ahmed:2020:IJDC %X Lateral stability of multi-axle vehicle’s was not considered and studied widely despite its advantages and use in different fields such as transportation, commercial, and military applications. In this research, a novel adaptive Direct Yaw moment Control based on Genetic-Programming (GPDB) is developed and compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS). In addition, a phase-plane analysis of the vehicles nonlinear model is also discussed to introduce the activation criteria to the proposed controller in order to prevent excessive control effort. The controller is evaluated through a series of severe maneuvers in the simulator. The developed GPDB resulting in comparable performance to the ANFIS controller with better implementation facility and design procedure, where a single equation replaces multiple fuzzy rules. The results show fidelity and the ability of the developed controller to stabilize the vehicle near limit-handling driving conditions %K genetic algorithms, genetic programming, Stability control, Direct yaw control, Differential braking, Adaptive neuro-fuzzy, Fuzzy logic %9 journal article %R 10.1007/s40435-020-00693-0 %U http://link.springer.com/article/10.1007/s40435-020-00693-0 %U http://dx.doi.org/10.1007/s40435-020-00693-0 %0 Thesis %T Integrated Chassis Control Strategies For Multi-Wheel Combat Vehicle %A Ahmed, Moataz Aboelfadl %D 2021 %8 nov %C Oshawa, Ontario, Canada %C Department of Automotive and Mechatronics Engineering Faculty of Engineering and Applied Science, University of Ontario Institute of Technology %F Aboelfadl_Ahmed_Moataz %X Combat vehicles are exposed to high risks due to their high ground clearance and nature of operation in harsh environments. This requires robust stability controllers to cope with the rapid change and uncertainty of driving conditions on various terrains. Moreover, it is required to enhance vehicle stability and increase safety to reduce accidents fatality probability. This research focuses on investigating the effectiveness of different lateral stability controllers and their integration in enhancing the cornering performance of an 8x8 combat vehicle when driving at limited handling conditions. In this research, a new Active Rear Steering (ARS) stability controller for an 8x8 combat vehicle is introduced. This technique is extensively investigated to show its merits and effectiveness for human and autonomous operation. For human operation, the ARS is developed using Linear Quadratic Regulator (LQR) control method, which is compared with previous techniques. Furthermore, the controller is extended and tested for working in a rough and irregular road profile using a novel adaptive Integral Sliding Mode Controller (ISMC). In the case of autonomous operation, a frequency domain analysis is conducted to show the benefits of considering the steering of the rear axles in the path-following performance at different driving conditions. The study compared two different objectives for the controller; the first is including the steering of the rear axles in the path following controller, while the second is to integrate it as a stability controller with a front-steering path-following controller. In addition, this research introduces a novel Differential Braking (DB) controller. The proposed control prevents the excessive use of braking forces and consequently the longitudinal dynamics deterioration. Besides, it introduces an effective DB controller with less dependency and sensitivity to the reference yaw model. Eventually, two various Integrated Chassis Controllers (ICC) are developed and compared. The first is developed by integrating the ISMC-ARS with the DB controller using a fuzzy logic controller. The second ICC integrates the ISMC-ARS with a developed robust Torque Vectoring Controller (TVC). This integration is designed based on a performance map that shows the effective region of each controller using a new technique based on Machine Learning (ML). %K genetic algorithms, genetic programming, Chassis control, Lateral stability, Intelligent control, Multi-axle, Combat vehicles %9 Ph.D. thesis %U https://hdl.handle.net/10155/1380 %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 10.1007/978-3-642-35101-3_23 %U https://rdcu.be/ee4sM %U http://dx.doi.org/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 10.1007/978-3-642-37189-9_5 %U https://rdcu.be/ee4ry %U http://dx.doi.org/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 %X Biomarker detection in LC-MS data depends mainly on feature selection algorithms as the number of features is extremely high while the number of samples is very small. This makes classification of these data sets extremely challenging. In this paper we propose the use of genetic programming (GP) for subset feature selection in LC-MS data which works by maximizing the signal to noise ratio of the selected features by GP. The proposed method was applied to eight LC-MS data sets with different sample sizes and different levels of concentration of the spiked biomarkers. We evaluated the accuracy of selection from the list of biomarkers and also using the classification accuracy of the selected features via the support vector machines (SVMs) and Naive Bayes (NB) classifiers. Features selected by the proposed GP method managed to achieve perfect classification accuracy for most of the data sets. The results show that the proposed method strikes a reasonable compromise between the detection rate of the biomarkers and the classification accuracy for all data sets. The method was also compared to linear Support Vector Machine-Recursive Features Elimination (SVM-RFE) and t-test for feature selection and the results show that the biomarker detection rate of the proposed approach is higher. %K genetic algorithms, genetic programming %R 10.1109/CEC.2013.6557621 %U http://dx.doi.org/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 10.1007/978-3-662-45523-4_74 %U http://dx.doi.org/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 10.1109/CEC.2014.6900317 %U http://dx.doi.org/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 10.1145/2576768.2598292 %U https://homepages.ecs.vuw.ac.nz/~xuebing/Papers/Multiple%20Feature%20Construction%20for%20Effective%20Biomarker%20Identification%20and%20Classification%20using%20Genetic%20Programming.pdf %U http://dx.doi.org/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 10.1145/2598394.2598421 %U http://doi.acm.org/10.1145/2598394.2598421 %U http://dx.doi.org/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 %I Taylor & Francis %@ 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 10.1080/09540091.2014.906388 %U http://dx.doi.org/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 Thesis %T Genetic Programming for Biomarker Detection in Classification of Mass Spectrometry Data %A Ahmed, Soha %D 2015 %C New Zealand %C School of Engineering and Computer Science, Victoria University of Wellington %F ahmed:thesis %X Mass spectrometry (MS) is currently the most commonly used technology in biochemical research for proteomic analysis. The primary goal of proteomic profiling using mass spectrometry is the classification of samples from different experimental states. To classify the MS samples, the identification of protein or peptides (biomarker detection) that are expressed differently between the classes, is required. However, due to the high dimensionality of the data and the small number of samples, classification of MS data is extremely challenging. Another important aspect of biomarker detection is the verification of the detected biomarker that acts as an intermediate step before passing these biomarkers to the experimental validation stage. Biomarker detection aims at altering the input space of the learning algorithm for improving classification of proteomic or metabolomic data. This task is performed through feature manipulation. Feature manipulation consists of three aspects: feature ranking, feature selection, and feature construction. Genetic programming (GP) is an evolutionary computation algorithm that has the intrinsic capability for the three aspects of feature manipulation. The ability of GP for feature manipulation in proteomic biomarker discovery has not been fully investigated. This thesis, therefore, proposes an embedded methodology for these three aspects of feature manipulation in high dimensional MS data using GP. The thesis also presents a method for biomarker verification, using GP. The thesis investigates the use of GP for both single-objective and multi-objective feature selection and construction. In feature ranking, the thesis proposes a GP-based method for ranking subsets of features by using GP as an ensemble approach. The proposed algorithm uses GP capability to combine the advantages of different feature ranking metrics and evolve a new ranking scheme for the subset of the features selected from the top ranked features. The capability of GP as a classifier is also investigated by this method. The results show that GP can select a smaller number of features and provide a better ranking of the selected features, which can improve the classification performance of five classifiers. In feature construction, this thesis proposes a novel multiple feature construction method, which uses a single GP tree to generate a new set of high-level features from the original set of selected features. The results show that the proposed new algorithm outperforms two feature selection algorithms. In feature selection, the thesis introduces the first GP multi-objective method for biomarker detection, which simultaneously increase the classification accuracy and reduce the number of detected features. The proposed multi-objective method can obtain better subsets of features than the single-objective algorithm and two traditional multi-objective approaches for feature selection. This thesis also develops the first multi-objective multiple feature construction algorithm for MS data. The proposed method aims at both maximising the classification performance and minimizing the cardinality of the constructed new high-level features. The results show that GP can discover the complex relationships between the features and can significantly improve classification performance and reduce the cardinality. For biomarker verification, the thesis proposes the first GP biomarker verification method through measuring the peptide detectability. The method solves the imbalance problem in the data and shows improvement over the benchmark algorithms. Also, the algorithm outperforms a well-known peptide detection method. The thesis also introduces a new GP method for alignment of MS data as a preprocessing stage, which will further help in improving the biomarker detection process. %K genetic algorithms, genetic programming, Biomarker detection, Mass spectrometry, Pattern Recognition and Data Mining, Bioinformatics Software, Expanding Knowledge in the Information and Computing Sciences, NS-GPMOFS, SP-GPMOFS, SPEA2, NSGAII, NB, J48, SVM, PEP-GP, GPMS, Electrospray ionisation, ESI, ensemble %9 Ph.D. thesis %R 10.26686/wgtn.17013680.v1 %U https://openaccess.wgtn.ac.nz/articles/thesis/Genetic_Programming_for_Biomarker_Detection_in_Classification_of_Mass_Spectrometry_Data/17013680?file=31469105 %U http://dx.doi.org/10.26686/wgtn.17013680.v1 %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 10.1007/978-3-319-31204-0_8 %U https://homepages.ecs.vuw.ac.nz/~xuebing/Papers/Ahmed2016.pdf %U http://dx.doi.org/10.1007/978-3-319-31204-0_8 %P 106-122 %0 Journal Article %T Analysis and Optimization of Machining Hardened Steel AISI 4140 with Self-Propelled Rotary Tools %A Ahmed, Waleed %A Hegab, Hussien %A Mohany, Atef %A Kishawy, Hossam %J Materials %D 2021 %V 14 %N 20 %@ 1996-1944 %F ahmed:2021:Materials %X It is necessary to improve the machinability of difficult-to-cut materials such as hardened steel, nickel-based alloys, and titanium alloys as these materials offer superior properties such as chemical stability, corrosion resistance, and high strength to weight ratio, making them indispensable for many applications. Machining with self-propelled rotary tools (SPRT) is considered one of the promising techniques used to provide proper tool life even under dry conditions. In this work, an attempt has been performed to analyse, model, and optimise the machining process of AISI 4140 hardened steel using self-propelled rotary tools. Experimental analysis has been offered to (a) compare the fixed and rotary tools performance and (b) study the effect of the inclination angle on the surface quality and tool wear. Moreover, the current study implemented some artificial intelligence-based approaches (i.e., genetic programming and NSGA-II) to model and optimise the machining process of AISI 4140 hardened steel with self-propelled rotary tools. The feed rate, cutting velocity, and inclination angle were the selected design variables, while the tool wear, surface roughness, and material removal rate (MRR) were the studied outputs. The optimal surface roughness was obtained at a cutting speed of 240 m/min, an inclination angle of 20?, and a feed rate of 0.1 mm/rev. In addition, the minimum flank tool wear was observed at a cutting speed of 70 m/min, an inclination angle of 10?, and a feed rate of 0.15 mm/rev. Moreover, different weights have been assigned for the three studied outputs to offer different optimised solutions based on the designer’s interest (equal-weighted, finishing, and productivity scenarios). It should be stated that the findings of the current work offer valuable recommendations to select the optimised cutting conditions when machining hardened steel AISI 4140 within the selected ranges. %K genetic algorithms, genetic programming, modeling, machining, optimization, rotary tools %9 journal article %R 10.3390/ma14206106 %U https://www.mdpi.com/1996-1944/14/20/6106 %U http://dx.doi.org/10.3390/ma14206106 %0 Journal Article %T Acoustic monitoring of an aircraft auxiliary power unit %A Ahmed, Umair %A Ali, Fakhre %A Jennions, Ian %J ISA Transactions %D 2023 %@ 0019-0578 %F AHMED:2023:isatra %X In this paper, the development and implementation of a novel approach for fault detection of an aircraft auxiliary power unit (APU) has been demonstrated. The developed approach aims to target the proactive identification of faults, in order to streamline the required maintenance and maximize the aircraft’s operational availability. The existing techniques rely heavily on the installation of multiple types of intrusive sensors throughout the APU and therefore present a limited potential for deployment on an actual aircraft due to space constraints, accessibility issues as well as associated development and certification requirements. To overcome these challenges, an innovative approach based on non-intrusive sensors i.e., microphones in conjunction with appropriate feature extraction, classification, and regression techniques, has been successfully demonstrated for online fault detection of an APU. The overall approach has been implemented and validated based on the experimental test data acquired from Cranfield University’s Boeing 737-400 aircraft, including the quantification of sensor location sensitivities on the efficacy of the acquired models. The findings of the overall analysis suggest that the acoustic-based models can accurately enable near real-time detection of faulty conditions i.e., Inlet Guide Vane malfunction, reduced mass flows through the Load Compressor and Bleed Valve malfunction, using only two microphones installed in the periphery of the APU. This study constitutes an enabling technology for robust, cost-effective, and efficient in-situ monitoring of an aircraft APU and potentially other associated thermal systems i.e., environmental control system, fuel system, and engines %K genetic algorithms, genetic programming, Aircraft, Auxiliary power unit, Condition monitoring, Acoustics, Signal processing, Machine learning, Sensors, Feature extraction, Fault detection, Microphones %9 journal article %R 10.1016/j.isatra.2023.01.014 %U https://www.sciencedirect.com/science/article/pii/S0019057823000149 %U http://dx.doi.org/10.1016/j.isatra.2023.01.014 %0 Journal Article %T Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease %A Ahmed, Usman %A Lin, Jerry Chun-Wei %A Srivastava, Gautam %J IEEE Journal of Biomedical and Health Informatics %D 2023 %@ 2168-2208 %F Ahmed:JBHI %X Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients’ health status and notifies caregivers and medical professionals when necessary. Using real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients’ lives through early diagnosis of their health conditions. For this purpose, a Naive Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is used to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model’s flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. %K genetic algorithms, genetic programming, Diseases, Medical diagnostic imaging, Medical services, Heart, Predictive models, Machine learning, Decision trees, Artificial intelligence, Sensor readings, Heart disease, Long-term lung disease %9 journal article %R 10.1109/JBHI.2023.3270888 %U http://dx.doi.org/10.1109/JBHI.2023.3270888 %0 Journal Article %T Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases %A Ahmed, Usman %A Lin, Jerry Chun-Wei %A Srivastava, Gautam %J Sustainable Computing: Informatics and Systems %D 2023 %V 38 %@ 2210-5379 %F AHMED:2023:suscom %X Approximately 19 million people die each year from cardiovascular and chronic respiratory diseases. As a result of the recent Covid-19 epidemic, blood pressure, cholesterol, and blood sugar levels have risen. Not only do healthcare institutions benefit from studying physiological vital signs, but individuals also benefit from being alerted to health problems in a timely manner. This study uses machine learning to categorize and predict cardiovascular and chronic respiratory diseases. By predicting a patient’s health status, caregivers and medical professionals can be alerted when needed. We predicted vital signs for 180 seconds using real-world vital sign data. A person’s life can be saved if caregivers react quickly and anticipate emergencies. The tree-based pipeline optimization method (TPOT) is used instead of manually adjusting machine learning classifiers. This paper focuses on optimizing classification accuracy by combining feature pre-processors and machine learning models with TPOT genetic programming making use of linear and Prophet models to predict important indicators. The TPOT tuning parameter combines predicted values with classical classification models such as Naive Bayes, Support Vector Machines, and Random Forests. As a result of this study, we show the importance of categorizing and increasing the accuracy of predictions. The proposed model achieves its adaptive behavior by conceptually incorporating different machine learning classifiers. We compare the proposed model with several state-of-the-art algorithms using a large amount of training data. Test results at the University of Queensland using 32 patient’s data showed that the proposed model outperformed existing algorithms, improving the classification of cardiovascular disease from 0.58 to 0.71 and chronic respiratory disease from 0.49 to 0.70, respectively, while minimizing the mean percent error in vital signs. Our results suggest that the Facebook Prophet prediction model in conjunction with the TPOT classification model can correctly diagnose a patient’s health status based on abnormal vital signs and enables patients to receive prompt medical attention %K genetic algorithms, genetic programming, Machine learning, Sensor data, Cardiovascular disease, Chronic respiratory disease, TPOT, Linear regression, Facebook Prophet %9 journal article %R 10.1016/j.suscom.2023.100868 %U https://www.sciencedirect.com/science/article/pii/S2210537923000239 %U http://dx.doi.org/10.1016/j.suscom.2023.100868 %P 100868 %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 10.1007/978-3-642-27186-1_15 %U http://dx.doi.org/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 10.1109/IBCAST47879.2020.9044554 %U http://dx.doi.org/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 10.3837/tiis.2019.04.002 %U https://doi.org/10.3837/tiis.2019.04.002 %U http://dx.doi.org/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 10.1007/978-3-540-78987-1_16 %U http://dx.doi.org/10.1007/978-3-540-78987-1_16 %P 169-178 %0 Conference Proceedings %T A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification %A Ain, Qurrat Ul %A Xue, Bing %A Al-Sahaf, Harith %A Zhang, Mengjie %S 2022 IEEE International Conference on Data Mining Workshops (ICDMW) %D 2022 %8 nov %F Ain:2022:ICDMW %X Breast density is widely used as an initial indicator of developing breast cancer. At present, current classification methods for mammographic density usually require manual operations or expert knowledge that makes them expensive in real-time situations. Such methods achieve only moderate classification accuracy due to the limited model capacity and computational resources. In addition, most existing studies focus on improving classification accuracy using only raw images or the entire set of original attributes and remain unable to identify hidden patterns or causal information necessary to discriminate breast density classes. It is challenging to find high-quality knowledge when some attributes defining the data space are redundant or irrelevant. In this study, we present a novel attribute construction method using genetic programming (GP) for the task of breast density classification. To extract informative features from the raw mammographic images, wavelet decomposition, local binary patterns, and histogram of oriented gradients are used to include texture, local and global image properties. The study evaluates the goodness of the proposed method on two benchmark real-world mammographic image datasets and compares the results of the proposed GP method with eight conventional classification methods. The experimental results reveal that the proposed method significantly outperforms most of the commonly used classification methods in binary and multi-class classification tasks. Furthermore, the study shows the potential of G P for mammographic breast density classification by interpreting evolved attributes that highlight important breast density characteristics. %K genetic algorithms, genetic programming %R 10.1109/ICDMW58026.2022.00057 %U http://dx.doi.org/10.1109/ICDMW58026.2022.00057 %P 378-387 %0 Journal Article %T Automatically Diagnosing Skin Cancers From Multimodality Images Using Two-Stage Genetic Programming %A Ain, Qurrat Ul %A Al-Sahaf, Harith %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Cybernetics %D 2022 %@ 2168-2275 %F Ain:2022:ieeeTC %X Developing a computer-aided diagnostic system for detecting various skin malignancies from images has attracted many researchers. Unlike many machine-learning approaches, such as artificial neural networks, genetic programming (GP) automatically evolves models with flexible representation. GP successfully provides effective solutions using its intrinsic ability to select prominent features (i.e., feature selection) and build new features (i.e., feature construction). Existing approaches have used GP to construct new features from the complete set of original features and the set of operators. However, the complete set of features may contain redundant or irrelevant features that do not provide useful information for classification. This study aims to develop a two-stage GP method, where the first stage selects prominent features, and the second stage constructs new features from these selected features and operators, such as multiplication in a wrapper approach to improve the classification performance. To include local, global, texture, color, and multiscale image properties of skin images, GP selects and constructs features extracted from local binary patterns and pyramid-structured wavelet decomposition. The accuracy of this GP method is assessed using two real-world skin image datasets captured from the standard camera and specialized instruments, and compared with commonly used classification algorithms, three state of the art, and an existing embedded GP method. The results reveal that this new approach of feature selection and feature construction effectively helps improve the performance of the machine-learning classification algorithms. Unlike other black-box models, the evolved models by GP are interpretable; therefore, the proposed method can assist dermatologists to identify prominent features, which has been shown by further analysis on the evolved models. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TCYB.2022.3182474 %U http://dx.doi.org/10.1109/TCYB.2022.3182474 %0 Journal Article %T Genetic programming for automatic skin cancer image classification %A Ain, Qurrat Ul %A Al-Sahaf, Harith %A Xue, Bing %A Zhang, Mengjie %J Expert Systems with Applications %D 2022 %V 197 %@ 0957-4174 %F AIN:2022:eswa %X Developing a computer-aided diagnostic system for detecting various types of skin malignancies from images has attracted many researchers. However, analyzing the behaviors of algorithms is as important as developing new systems in order to establish the effectiveness of a system in real-time situations which impacts greatly how well it can assist the dermatologist in making a diagnosis. Unlike many machine learning approaches such as Artificial Neural Networks, Genetic Programming (GP) automatically evolves models with its dynamic representation and flexibility. This study aims at analyzing recently developed GP-based approaches to skin image classification. These approaches have used the intrinsic feature selection and feature construction ability of GP to effectively construct informative features from a variety of pre-extracted features. These features encompass local, global, texture, color and multi-scale image properties of skin images. The performance of these GP methods is assessed using two real-world skin image datasets captured from standard camera and specialized instruments, and compared with six commonly used classification algorithms as well as existing GP methods. The results reveal that these constructed features greatly help improve the performance of the machine learning classification algorithms. Unlike ’black-box’ algorithms like deep neural networks, GP models are interpretable, therefore, our analysis shows that these methods can help dermatologists identify prominent skin image features. Further, it can help researchers identify suitable feature extraction methods for images captured from a specific instrument. Being fast, these methods can be deployed for making a quick and effective diagnosis in actual clinic situations %K genetic algorithms, genetic programming, Image classification, Dimensionality reduction, Feature selection, Feature construction %9 journal article %R 10.1016/j.eswa.2022.116680 %U https://www.sciencedirect.com/science/article/pii/S0957417422001634 %U http://dx.doi.org/10.1016/j.eswa.2022.116680 %P 116680 %0 Conference Proceedings %T A New Genetic Programming Representation for Feature Learning in Skin Cancer Detection %A Ain, Qurrat Ul %A Al-Sahaf, Harith %A Xue, Bing %A Zhang, Mengjie %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F ain:2023:GECCOcomp %X The process of automatically extracting informative high-level features from skin cancer images is enhanced by integrating well-developed feature descriptors into learning algorithms. This paper develops a new genetic programming-based feature learning approach to automatically select and combine six well-developed descriptors to extract high-level features for skin cancer image classification. The new approach can automatically learn various global features for image classification. The experimental results show that the new approach achieves significantly better classification performance than the baseline approach and six commonly used feature descriptors on two real-world skin image datasets. %K genetic algorithms, genetic programming, feature learning, feature extraction, melanoma detection, image classification: Poster %R 10.1145/3583133.3590550 %U http://dx.doi.org/10.1145/3583133.3590550 %P 707-710 %0 Conference Proceedings %T Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming %A Ain, Qurrat Ul %A Xue, Bing %A Al-Sahaf, Harith %A Zhang, Mengjie %S Australasian Conference on Data Science and Machine Learning, AusDM 2023 %D 2023 %I Springer %F ain:2023:AusDM %K genetic algorithms, genetic programming %R 10.1007/978-981-99-8696-5_18 %U http://link.springer.com/chapter/10.1007/978-981-99-8696-5_18 %U http://dx.doi.org/10.1007/978-981-99-8696-5_18 %0 Conference Proceedings %T Exploring Genetic Programming Models in Computer-Aided Diagnosis of Skin Cancer Images %A Ain, Qurrat UI %A Al-Sahaf, Harith %A Xue, Bing %A Zhang, Mengjie %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F ain:2024:CEC %X Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-depth analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of using feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyses the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learnt during the evolutionary process. %K genetic algorithms, genetic programming, Representation learning, Visualization, Computational modeling, Feature extraction, Skin, Lesions, Image Classification, Skin Cancer Detection %R 10.1109/CEC60901.2024.10612105 %U http://dx.doi.org/10.1109/CEC60901.2024.10612105 %0 Journal Article %T Automatically Evolving Interpretable Feature Vectors Using Genetic Programming for an Ensemble Classifier in Skin Cancer Detection %A Ain, Qurrat Ul %A Al-Sahaf, Harith %A Xue, Bing %A Zhang, Mengjie %J IEEE Computational Intelligence Magazine %D 2024 %8 aug %V 19 %N 3 %@ 1556-6048 %F Ain:2024:CIM %X Early skin cancer diagnosis saves lives as the disease can be successfully treated through complete excision. Computer-aided diagnosis methods are developed using artificial intelligence techniques to help earlier detection and identify hidden causes leading to cancers in skin lesion images. In skin cancer image classification problems, an ensemble of classifiers has demonstrated better classification ability than a single classification algorithm. Traditionally, training an ensemble uses the complete set of original features, where some of these features can be redundant or irrelevant and hence, may not provide useful information in generating good models for ensemble classification. Moreover, newly created features may help improve classification performance. To address this issue, the existing methods have used feature construction for building an ensemble classifier, which usually creates a fixed number of features that may fit the training data too well, resulting in poor test performance. This study develops a novel classification approach that combines ensemble learning, feature selection, and feature construction using genetic programming (GP) to handle the above limitations. The proposed method automatically evolves variable-length feature vectors consisting of GP-selected and GP-constructed features suitable for training an ensemble classifier. This study evaluates the effectiveness of the proposed method on two benchmark real-world skin image datasets that include dermoscopy and standard camera images. The experimental results reveal that the proposed algorithm significantly outperforms four state-of-the-art convolutional neural network methods, the existing GP approaches, and 11 commonly used machine learning methods. Furthermore, this study also includes interpreting evolved individuals that highlight important skin cancer characteristics playing a vital role in discriminating images of different cancer classes. This study shows that high classification performance can be achieved at a low cost of computational resources and inference time, and accordingly, this method is potentially suitable to be implemented in mobile devices for the automated screening of skin lesions and many other malignancies in low-resource settings. %K genetic algorithms, genetic programming, Training data, Image colour analysis, Feature extraction, Vectors, Skin cancer, Lesions, Task analysis, Medical diagnosis, Classification algorithms, Detection algorithms, Image classification, Neural networks %9 journal article %R 10.1109/MCI.2024.3401342 %U http://dx.doi.org/10.1109/MCI.2024.3401342 %P 26-41 %0 Journal Article %T Genetic Programming for Malignancy Diagnosis From Breast Cancer Histopathological Images: A Feature Learning Approach %A Ain, Qurrat Ul %A Al-Sahaf, Harith %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Emerging Topics in Computational Intelligence %@ 2471-285X %F Ain:TETCI %X Identifying breast cancer using histopathological images is crucial for early detection and treatment of breast cancer. Histopathological images suffer from a high inter-class and intra-class variability, making breast cancer identification a challenging task. Integration of well-developed feature descriptors into learning algorithms can enhance the automatic extraction of high-level features from these images. With its flexible representation and global search abilities, Genetic Programming (GP) is a good learning algorithm to potentially accomplish this goal. This paper proposes a new GP-based feature learning method for automatically selecting and combining various image descriptors to detect breast cancer from histopathological images, which is an emerging topic in computational intelligence. In the new approach, various global features can be learnt for the task of breast cancer image classification and it is capable of automatically evolving solutions. A significant improvement in the classification performance comes from the new approach compared to the existing methods on real-world histopathological image dataset. Taking a closer look at the evolved solutions helps identify the most effective feature descriptors for breast cancer image classification. Unlike the black-box models evolved in existing methods, the proposed method evolves models/solutions that can assist dermatologists in making diagnoses by identifying breast cancer characteristics captured by the feature descriptors that are automatically selected during the evolutionary process. %K genetic algorithms, genetic programming, Feature extraction, Breast cancer, Cancer, Representation learning, Breast, Histograms, Accuracy, Image colour analysis, Vectors, Training, image classification, feature learning, histopathological images %9 journal article %R 10.1109/TETCI.2024.3523769 %U http://dx.doi.org/10.1109/TETCI.2024.3523769 %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 10.1049/cp:19971216 %U http://uk.geocities.com/markcsinclair/ps/galesia97_aiy.ps.gz %U http://dx.doi.org/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 10.1109/ISPA.2013.6703827 %U http://dx.doi.org/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 Journal Article %T Evolving Suspiciousness Metrics From Hybrid Data Set for Boosting a Spectrum Based Fault Localization %A Ajibode, Adekunle Akinjobi %A Shu, Ting %A Ding, Zuohua %J IEEE Access %D 2020 %V 8 %@ 2169-3536 %F Ajibode:2020:A %X Spectrum Based Fault Localization (SBFL) uses different metrics called risk evaluation formula to guide and pinpoint faults in debugging process. The accuracy of a specific SBFL method may be limited by the used formulae and program spectra. However, it has been demonstrated recently that Genetic Programming could be used to automatically design formulae directly from the program spectra. Therefore, this article presents Genetic Programming approach for proposing risk evaluation formula with the inclusion of radicals to evolve suspiciousness metric directly from the program spectra. 92 faults from Unix utilities of SIR repository and 357 real faults from Defect4J repository were used. The approach combines these data sets, used 2percent of the total faults (113) to evolve the formulae and the remaining 7percent (336) to validate the effectiveness of the metrics generated by our approach. The proposed approach then uses Genetic Programming to run 30 evolution to produce different 30 metrics. The GP-generated metrics consistently out-performed all the classic formulae in both single and multiple faults, especially OP2 on average of 2.2percent in single faults and 3.4percent in multiple faults. The experiment results conclude that the combination of Hybrid data set and radical is a good technique to evolve effective formulae for spectra-based fault localization. %K genetic algorithms, genetic programming, Measurement, Debugging, Boosting, Debugging, fault localization, SBFL %9 journal article %R 10.1109/ACCESS.2020.3035413 %U http://dx.doi.org/10.1109/ACCESS.2020.3035413 %P 198451-198467 %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 Journal Article %T Application of Fixed Length Gene Genetic Programming (FLGGP) in Hydropower Reservoir Operation %A Akbari-Alashti, Habib %A Haddad, Omid Bozorg %A Marino, Miguel A. %J Water Resources Management %D 2015 %V 29 %N 9 %F akbari-alashti:2015:WRM %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s11269-015-1003-1 %U http://link.springer.com/article/10.1007/s11269-015-1003-1 %U http://dx.doi.org/10.1007/s11269-015-1003-1 %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 10.1109/FUZZY.2008.4630598 %U FS0398.pdf %U http://dx.doi.org/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 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 10.1109/FUZZY.1998.686289 %U http://www-robotics.jpl.nasa.gov/people/Edward_Tunstel/fieee98.pdf %U http://dx.doi.org/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 10.1109/NAFIPS.2003.1226756 %U http://dx.doi.org/10.1109/NAFIPS.2003.1226756 %P 61-66 %0 Generic %T Evolutionary Optimization of Model Merging Recipes %A Akiba, Takuya %E Wilson, Dennis G. %E Kalkreuth, Roman %E Medvet, Eric %E Nadizar, Giorgia %E Squillero, Giovanni %E Tonda, Alberto %E Lavinas, Yuri %D 2024 %8 14 jul %I Association for Computing Machinery %C Melbourne %F Akiba:2024:GGP %O Invited talk %K genetic algorithms, genetic programming, ANN, LLM %U https://arxiv.org/abs/2403.13187 %0 Conference Proceedings %T Enhancing Fetal Health Monitoring through TPOT and Optuna in Machine Learning-Driven Prenatal Care %A Akilandeswari, A. %A G, Arasuraja %A Yamsani, Nagendar %A Radhika, S. %A Legapriyadharshini, N. %A Padmakala, S. %S 2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI) %D 2024 %8 jun %F Akilandeswari:2024:APCI %X This study delves into the application of advanced machine learning techniques for the classification of fetal health, a critical domain in prenatal care. Using a dataset based on cardiotocograms (CTGs), which record key fetal indicators like heart rate and uterine contractions, we compare two distinct machine learning approaches: a Random Forest Classifier optimised with the hyper parameter tuning tool Optuna, and a genetic programming-based model developed using TPOT (Tree-based Pipeline Optimisation Tool).The Random Forest Classifier, configured with specific hyper parameters, delivered an accuracy of 94.13percent and an impressive AUC of 0.9826. In contrast, the TPOT-optimised model, a Gradient Boosting Classifier with finely tuned parameters, achieved a higher accuracy of 96.01percent and an internal CV score of approximately 95.24percent. This comparison underscores the strengths and potential applications of these advanced methodologies in predicting and ensuring fetal health. %K genetic algorithms, genetic programming, Analytical models, Accuracy, Refining, Machine learning, Predictive models, Boosting, Medical diagnosis, Fetal Health Monitoring, Machine Learning in Prenatal Care, Cardiotocogram Analysis, Hyper parameter Optimisation, Automated Machine Learning (AutoML), Predictive Modelling in Healthcare %R 10.1109/APCI61480.2024.10617339 %U http://dx.doi.org/10.1109/APCI61480.2024.10617339 %0 Conference Proceedings %T Evaluation of Feature Selection Techniques for Predicting Parkinson’s Disease using Machine Learning Models %A Akilandeswari, A. %S 2025 International Conference on Electronics and Renewable Systems (ICEARS) %D 2025 %8 feb %F Akilandeswari:2025:ICEARS %X Parkinson’s disease is one of the major progressive neurodegenerative disorders that majorly affect both motor and non-motor functions. The significance of this study lies in spotlighting the importance of early, accurate detection of Parkinson’s disease in the betterment of patient outcomes as well as management. This paper sets out to make a case discussion about the use of machine learning models in predictive Parkinson’s disease through feature selection. We apply the following methods to machine learning models, including Random Forest and Logistic Regression, in order to compare approaches, namely Boruta, Recursive Feature Elimination with Cross-Validation (RFECV), TPOT-Tree-based Pipeline Optimisation Tool, and Variance Threshold. The Random Forest model with Variance Threshold feature selection showed a high accuracy of 95percent. Our experiments also present the fact that feature selection is relevant to improve the performance of the model: indeed, different techniques show different levels of prediction accuracy. ConclusionThis paper has proven it possible to improve the prediction of disease to a considerable extent by the right selection of the feature selection technique and machine learning model. %K genetic algorithms, genetic programming, Logistic regression, Renewable energy sources, Accuracy, Parkinson’s disease, Pipelines, Machine learning, Predictive models, Feature extraction, Random forests, Optimisation, Parkinson’s Disease Prediction, Random Forest, Boruta, Recursive Feature Elimination (RFE), TPOT %R 10.1109/ICEARS64219.2025.10940164 %U http://dx.doi.org/10.1109/ICEARS64219.2025.10940164 %P 1431-1435 %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 10.1007/978-3-540-46239-2_15 %U http://dx.doi.org/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 10.4018/IJOSSP.2017100102 %U http://dx.doi.org/10.4018/IJOSSP.2017100102 %P 32-51 %0 Journal Article %T Software Effort Estimation Using Multi Expression Programming %A Al-Saati, Najla Akram %A Alreffaee, Taghreed Riyadh %J AL-Rafidain Journal of Computer Sciences and Mathematics %D 2014 %V 11 %N 2 %I Mosul University %@ 1815-4816 %F Al-Saati:2014:mosul %X The process of finding a function that can estimate the effort of software systems is considered to be the most important and most complex process facing systems developers in the field of software engineering. The accuracy of estimating software effort forms an essential part of the software development phases. A lot of experts applied different ways to find solutions to this issue, such as the COCOMO and other methods. Recently, many questions have been put forward about the possibility of using Artificial Intelligence to solve such problems, different scientists made ​​several studies about the use of techniques such as Genetic Algorithms and Artificial Neural Networks to solve estimation problems. We use one of the Linear Genetic Programming methods (Multi Expression programming) which apply the principle of competition between equations encrypted within the chromosomes to find the best formula for resolving the issue of software effort estimation. As for to the test data, benchmark known datasets are employed taken from previous projects, the results are evaluated by comparing them with the results of Genetic Programming (GP) using different fitness functions. The gained results indicate the surpassing of the employed method in finding more efficient functions for estimating about 7 datasets each consisting of many projects. %K genetic algorithms, genetic programming, Effort Estimation, Multi Expression Programming %9 journal article %R 10.33899/csmj.2014.163756 %U https://csmj.mosuljournals.com/article_163756.html %U http://dx.doi.org/10.33899/csmj.2014.163756 %P 53-71 %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 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 10.1208/s12249-012-9836-x %U http://dx.doi.org/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 10.1007/978-3-540-75256-1_76 %U http://dx.doi.org/10.1007/978-3-540-75256-1_76 %P 878-888 %0 Journal Article %T Adaptive Gene Level Mutation %A Al-Afandi, Jalal %A Horvath, Andras %J Algorithms %D 2021 %V 14 %N 1 %@ 1999-4893 %F al-afandi:2021:Algorithms %X Genetic Algorithms are stochastic optimisation methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach. %K genetic algorithms, genetic programming, evolution strategies, adaptive mutation, evolutionary programming, 8-Queens Problem %9 journal article %R 10.3390/a14010016 %U https://www.mdpi.com/1999-4893/14/1/16 %U http://dx.doi.org/10.3390/a14010016 %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, image reconstruction, industrial engineering, tomography, Process Tomography %R 10.1109/ISDA.2010.5687299 %U http://sites.google.com/site/alaaalfeef/home/8.pdf %U http://dx.doi.org/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 10.1109/ICRERA.2016.7884553 %U http://dx.doi.org/10.1109/ICRERA.2016.7884553 %P 285-290 %0 Journal Article %T A Hybrid LSTM-Based Genetic Programming Approach for Short-Term Prediction of Global Solar Radiation Using Weather Data %A Al-Hajj, Rami %A Assi, Ali %A Fouad, Mohamad %A Mabrouk, Emad %J Processes %D 2021 %V 9 %N 7 %@ 2227-9717 %F al-hajj:2021:Processes %X The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance’s consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP in a post-processing stage to combine the LSTM models’ outputs to find the best prediction of the GSR. We have examined two versions of the GP in the proposed model: a standard version and a boosted version that incorporates a local search technique. We have shown an improvement in terms of performance provided by the proposed hybrid model. We have compared its performance to stacking techniques based on machine learning for combination. The results show that the suggested method provides significant improvement in terms of performance and consistency. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/pr9071187 %U https://www.mdpi.com/2227-9717/9/7/1187 %U http://dx.doi.org/10.3390/pr9071187 %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 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/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 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/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 10.1109/SSCI44817.2019.9002861 %U http://dx.doi.org/10.1109/SSCI44817.2019.9002861 %P 2395-2402 %0 Conference Proceedings %T Data Imputation for Symbolic Regression with Missing Values: A Comparative Study %A Al-Helali, Baligh %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %S 2020 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2020 %8 dec %F Al-Helali:2020:SSCI %X Symbolic regression via genetic programming is considered as a crucial machine learning tool for empirical modelling. However, in reality, it is common for real-world data sets to have some data quality problems such as noise, outliers, and missing values. Although several approaches can be adopted to deal with data incompleteness in machine learning, most studies consider the classification tasks, and only a few have considered symbolic regression with missing values. In this work, the performance of symbolic regression using genetic programming on real-world data sets that have missing values is investigated. This is done by studying how different imputation methods affect symbolic regression performance. The experiments are conducted using thirteen real-world incomplete data sets with different ratios of missing values. The experimental results show that although the performance of the imputation methods differs with the data set, CART has a better effect than others. This might be due to its ability to deal with categorical and numerical variables. Moreover, the superiority of the use of imputation methods over the commonly used deletion strategy is observed. %K genetic algorithms, genetic programming %R 10.1109/SSCI47803.2020.9308216 %U http://dx.doi.org/10.1109/SSCI47803.2020.9308216 %P 2093-2100 %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 10.1007/978-3-030-44094-7_1 %U http://dx.doi.org/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 10.1109/CEC48606.2020.9185526 %U http://dx.doi.org/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 use transfer learning for the symbolic regression task when the underlying data are incomplete. The current work addresses this point by presenting a transfer learning method for symbolic regression on data with high ratios of missing values. A multi-tree genetic programming algorithm based feature-based transformation is proposed for transferring data from a complete source domain to a different, incomplete target domain. The experimental work has been conducted on real-world data sets considering different transfer learning scenarios each is determined based on three factors: missingness ratio, domain difference, and task similarity. In most cases, the proposed method achieved positive transductive transfer learning in both homogeneous and heterogeneous domains. Moreover, even with less significant success, the obtained results show the applicability of the proposed approach for inductive transfer learning. %K genetic algorithms, genetic programming %R 10.1109/CEC48606.2020.9185670 %U http://dx.doi.org/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 10.1145/3377930.3390160 %U https://doi.org/10.1145/3377930.3390160 %U http://dx.doi.org/10.1145/3377930.3390160 %P 913-921 %0 Journal Article %T A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data %A Al-Helali, Baligh %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %J Soft Computing %D 2021 %8 apr %V 25 %N 8 %@ 1432-7643 %F DBLP:journals/soco/Al-Helali00021 %X Incompleteness is one of the problematic data quality challenges in real-world machine learning tasks. A large number of studies have been conducted for addressing this challenge. However, most of the existing studies focus on the classification task and only a limited number of studies for symbolic regression with missing values exist. a new imputation method for symbolic regression with incomplete data is proposed. The method aims to improve both the effectiveness and efficiency of imputing missing values for symbolic regression. This method is based on genetic programming (GP) and weighted K-nearest neighbors (KNN). It constructs GP-based models using other available features to predict the missing values of incomplete features. The instances used for constructing such models are selected using weighted KNN. The experimental results on real-world data sets show that the proposed method outperforms a number of state-of-the-art methods with respect to the imputation accuracy, the symbolic regression performance, and the imputation time. %K genetic algorithms, genetic programming, Symbolic regression, Incomplete data, KNN, Imputation %9 journal article %R 10.1007/s00500-021-05590-y %U https://doi.org/10.1007/s00500-021-05590-y %U http://dx.doi.org/10.1007/s00500-021-05590-y %P 5993-6012 %0 Journal Article %T Multi-Tree Genetic Programming with New Operators for Transfer Learning in Symbolic Regression with Incomplete Data %A Al-Helali, Baligh %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2021 %8 dec %V 25 %N 6 %@ 1089-778X %F Al-Helali:ieeeTEC %X Lack of knowledge is a common consequence of data incompleteness when learning from real-world data. To deal with such a situation, this work uses transfer learning to re-use knowledge from different (yet related) but complete domains. Due to its powerful feature construction ability, genetic programming is used to construct feature-based transformations that map the feature space of the source domain to that of the target domain such that their differences are reduced. Particularly, this work proposes a new multi-tree genetic programming-based feature construction approach to transfer learning in symbolic regression with missing values. It transfers knowledge related to the importance of the features and instances in the source domain to the target domain to improve the learning performance. Moreover, new genetic operators are developed to encourage minimising the distribution discrepancy between the transformed domain and the target domain. A new probabilistic crossover is developed to make the well-constructed trees in the individuals more likely to be mated than the other trees. A new mutation operator is designed to give more probability for the poorly-constructed trees to be mutated. The experimental results show that the proposed method not only achieves better performance compared with different traditional learning methods but also advances two recent transfer learning methods on real-world data sets with various incompleteness and learning scenarios. %K genetic algorithms, genetic programming, Symbolic Regression, Incomplete Data, Transfer Learning, Evolutionary Learning %9 journal article %R 10.1109/TEVC.2021.3079843 %U http://dx.doi.org/10.1109/TEVC.2021.3079843 %P 1049-1063 %0 Conference Proceedings %T Genetic Programming-Based Selection of Imputation Methods in Symbolic Regression with Missing Values %A Al-Helali, Baligh %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %Y Gallagher, Marcus %Y Moustafa, Nour %Y Lakshika, Erandi %S AI 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, Canberra, ACT, Australia, November 29-30, 2020, Proceedings %S Lecture Notes in Computer Science %D 2020 %V 12576 %I Springer %F DBLP:conf/ausai/Al-Helali00Z20 %K genetic algorithms, genetic programming %R 10.1007/978-3-030-64984-5_13 %U https://doi.org/10.1007/978-3-030-64984-5_13 %U http://dx.doi.org/10.1007/978-3-030-64984-5_13 %P 163-175 %0 Conference Proceedings %T GP with a Hybrid Tree-vector Representation for Instance Selection and Symbolic Regression on Incomplete Data %A Al-Helali, Baligh %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %Y Ong, Yew-Soon %S 2021 IEEE Congress on Evolutionary Computation (CEC) %D 2021 %8 28 jun 1 jul %C Krakow, Poland %F Al-Helali:2021:CEC %X Data incompleteness is a pervasive problem in symbolic regression, and machine learning in general. Unfortunately, most symbolic regression methods are only applicable when the given data is complete. One common approach to handling this situation is data imputation. It works by estimating missing values based on existing data. However, which existing data should be used for imputing the missing values? The answer to this question is important when dealing with incomplete data. To address this question, this work proposes a mixed tree-vector representation for genetic programming to perform instance selection and symbolic regression on incomplete data. In this representation, each individual has two components: an expression tree and a bit vector. While the tree component constructs symbolic regression models, the vector component selects the instances that are used to impute missing values by the weighted k-nearest neighbour (WKNN) imputation method. The complete imputed instances are then used to evaluate the GP-based symbolic regression model. The obtained experimental results show the applicability of the proposed method on real-world data sets with different missingness scenarios. When compared with existing methods, the proposed method not only produces more effective symbolic regression models but also achieves more efficient imputations. %K genetic algorithms, genetic programming, Computational modeling, Machine learning, Evolutionary computation, Regression tree analysis, Symbolic Regression, Incomplete Data, Imputation, Instance Selection %R 10.1109/CEC45853.2021.9504767 %U http://dx.doi.org/10.1109/CEC45853.2021.9504767 %P 604-611 %0 Journal Article %T Genetic Programming for Feature Selection Based on Feature Removal Impact in High-Dimensional Symbolic Regression %A Al-Helali, Baligh %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Emerging Topics in Computational Intelligence %D 2024 %8 jun %V 8 %N 3 %@ 2471-285X %F Al-Helali:ETCI %X Symbolic regression is increasingly important for discovering mathematical models for various prediction tasks. It works by searching for the arithmetic expressions that best represent a target variable using a set of input features. However, as the number of features increases, the search process becomes more complex. To address high-dimensional symbolic regression, this work proposes a genetic programming for feature selection method based on the impact of feature removal on the performance of SR models. Unlike existing Shapely value methods that simulate feature absence at the data level, the proposed approach suggests removing features at the model level. This approach circumvents the production of unrealistic data instances, which is a major limitation of Shapely value and permutation-based methods. Moreover, after calculating the importance of the features, a cut-off strategy, which works by injecting a number of random features and using their importance to automatically set a threshold, is proposed for selecting important features. The experimental results on artificial and real-world high-dimensional data sets show that, compared with state-of-the-art feature selection methods using the permutation importance and Shapely value, the proposed method not only improves the SR accuracy but also selects smaller sets of features. %K genetic algorithms, genetic programming, Feature extraction, Data models, Computational modelling, Task analysis, Predictive models, Machine learning, Feature selection, high dimensionality, symbolic regression %9 journal article %R 10.1109/TETCI.2024.3369407 %U http://dx.doi.org/10.1109/TETCI.2024.3369407 %P 2269-2282 %0 Journal Article %T Multitree Genetic Programming With Feature-Based Transfer Learning for Symbolic Regression on Incomplete Data %A Al-Helali, Baligh %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Cybernetics %D 2024 %8 jul %V 54 %N 7 %@ 2168-2275 %F Al-Helali:CYB %X Data incompleteness is a serious challenge in real-world machine-learning tasks. Nevertheless, it has not received enough attention in symbolic regression (SR). Data missingness exacerbates data shortage, especially in domains with limited available data, which in turn limits the learning ability of SR algorithms. Transfer learning (TL), which aims to transfer knowledge across tasks, is a potential solution to solve this issue by making amends for the lack of knowledge. However, this approach has not been adequately investigated in SR. To fill this gap, a multitree genetic programming-based TL method is proposed in this work to transfer knowledge from complete source domains (SDs) to incomplete related target domains (TDs). The proposed method transforms the features from a complete SD to an incomplete TD. However, having many features complicates the transformation process. To mitigate this problem, we integrate a feature selection mechanism to eliminate unnecessary transformations. The method is examined on real-world and synthetic SR tasks with missing values to consider different learning scenarios. The obtained results not only show the effectiveness of the proposed method but also show its training efficiency compared with the existing TL methods. Compared to state-of-the-art methods, the proposed method reduced an average of more than 2.58percent and 4percent regression error on heterogeneous and homogeneous domains, respectively. %K genetic algorithms, genetic programming, Task analysis, Feature extraction, Data models, Transfer learning, Contracts, Adaptation models, Routing, incomplete data, symbolic regression (SR), transfer learning (TL) %9 journal article %R 10.1109/TCYB.2023.3270319 %U http://dx.doi.org/10.1109/TCYB.2023.3270319 %P 4014-4027 %0 Journal Article %T Genetic Programming-based Feature Selection for Symbolic Regression on Incomplete Data %A Al-Helali, Baligh %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %J Evolutionary Computation %D XXXX %8 nov 21 2024 %@ 1063-6560 %F Al-Helali:EC %O Just Accepted %X High-dimensionality is one of the serious real-world data challenges in symbolic regression and it is more challenging if the data are incomplete. ... a genetic programming-based approach to select features directly from incomplete high-dimensional data to improve symbolic regression performance. We extend the concept of identity/neutral elements from mathematics into the function operators of genetic programming, thus they can handle the missing values in incomplete data. Experiments have been conducted on a number of data sets considering different missingness ratios in high-dimensional symbolic regression tasks. The results show that the proposed method leads to better symbolic regression results when compared with state-of-the-art methods that can select features directly from incomplete data. Further results show that our approach not only leads to better symbolic regression accuracy but also selects a smaller number of relevant features ... %K genetic algorithms, genetic programming, Symbolic Regression, Incomplete Data, Feature Selection, High-dimensionality %9 journal article %R 10.1162/evco_a_00362 %U http://dx.doi.org/10.1162/evco_a_00362 %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 Conference Proceedings %T Enhancing Hotel Performance Prediction in Oman’s Tourism Industry: Insights from Machine Learning, Feature Analysis, and Predictive Factors %A Al Jassim, Rasha S. %A Al Mansoory, Shqran %A Jetly, Karan %A AlMaqbali, Hilal %S 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS) %D 2024 %8 may %F Al-Jassim:2024:EAIS %X This paper introduces two novel fitness functions adapted to attraction attributes, extending the capabilities of the Linear Genetic Programming for Optimisation Decision Tree (LGPDT). A comparative analysis with XGBoost evaluates LGPDT’s performance using both traditional and tourism datasets, examining its predictive capacity for hotel performance in Oman. Using extensive experiments and analysis, the study proved the effectiveness of LGPDT in this context, revealing promising results with a mean accuracy of 72.0percent and a standard deviation of 7.7percent. This underscores LGPDT’s robustness and suitability for decision-making in the hospitality industry. Comparison with XGBoost demonstrates LGPDT’s slightly higher stability in accuracy, highlighting its potential as a predictive tool. Moreover, the evaluation of new fitness functions reveals that they are computationally efficient while maintaining similar quality standards compared to previously studied fitness functions. These findings underscore LGPDT’s efficacy for predicting hotel performance, offering valuable insights for industry stakeholders. %K genetic algorithms, genetic programming, Accuracy, Machine learning algorithms, Tourism industry, Stability criteria, Prediction algorithms, Robustness, Decision trees, Decision Tree, Evolutionary Algorithms, XG-Boost %R 10.1109/EAIS58494.2024.10570014 %U http://dx.doi.org/10.1109/EAIS58494.2024.10570014 %0 Conference Proceedings %T Enhancing Tourism Performance in Oman: A Case Study Using Correlation-Guided Linear Genetic Programming Decision Tree (C-LGPDT) %A Al Jassim, Rasha S. %A Al Mansoory, Shqran %A Jetly, Karan %A Abdullah AlMaqbali, Hilal Ali %A Mohammed Albalushi, Muna %S 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT) %D 2024 %8 jul %F Al-Jassim:2024:CoDIT %X This research examines the optimisation of decision tree induction techniques by integrating evolutionary algorithms. It focuses on the Linear Genetic Programming Decision Tree (LGPDT). LGPDT employs a linear program to encode decision trees, achieving an optimal balance between accuracy and interpretability. The study introduces C-LGPDT as an extension of LGPDT, aiming to enhance its efficiency through correlation-based feature selection. This integration reduces dataset dimensionality and eliminates irrelevant or redundant features, resulting in a more accurate and interpretable decision tree model. The performance of C-LGPDT is thoroughly examined, and it is shown that it consistently outperforms older approaches, especially C4.5, and that it is more robust and accurate. A tourism dataset is also used to evaluate the C-LGPDT’s performance, with an emphasis on its stability in recall and precision. Results show that C-LGPDT is effective at solving decision tree induction problems, making it a good candidate for machine learning classification tasks. %K genetic algorithms, genetic programming, Accuracy, Evolutionary computation, Machine learning, Feature extraction, Stability analysis, Decision trees, Information technology, Optimisation, Evolutionary Algorithms, Tourism, Decision Tree, Linear Genetic Programming, Classification %R 10.1109/CoDIT62066.2024.10708185 %U http://dx.doi.org/10.1109/CoDIT62066.2024.10708185 %P 1655-1660 %0 Journal Article %T Novel explicit models for assessing the frictional resistance of pipe piles subjected to seismic effects %A Al-Jeznawi, Duaa %A Sadik, Laith %A Alzabeebee, Saif %A Al-Janabi, Musab Aied Qissab %A Keawsawasvong, Suraparb %J Journal of Safety Science and Resilience %D 2025 %V 6 %N 1 %@ 2666-4496 %F Al-Jeznawi:2025:jnlssr %X This paper introduces novel explicit models to predict the frictional resistance of open and closed-ended pipe piles subjected to seismic loading. This research employs genetic programming (GP) and multiobjective genetic algorithm-based evolutionary polynomial regression (EPR-MOGA) to develop closed-form expressions for estimating pile frictional resistance, using widely used input parameters for enhanced practicality and applicability in engineering practice. The proposed models are developed using only three input variables: the corrected standard penetration test (SPT) blow count (N1)60, the pile slenderness ratio (L/D), and the peak ground acceleration (PGA). This deliberate reduction in input complexity significantly enhances the models’ applicability across a wide range of geotechnical scenarios and industries. The accuracy of the developed models was assessed via the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). In the case of the GP model, the evaluation metrics for the testing set for open-ended piles (R2, RMSE, and MAE values) are 0.89, 0.43, and 0.35, respectively, whereas the corresponding values for closed-ended piles are 0.93, 0.38, and 0.3, respectively. On the other hand, the EPR-MOGA approach achieves similarly encouraging results, with performance metrics of 0.92, 0.37, and 0.29 for open-ended piles and 0.91, 0.39, and 0.30 for closed-ended piles %K genetic algorithms, genetic programming, Seismic resilience, Evolutionary polynomial regression, Multiobjective genetic algorithm, Pipe piles, Frictional resistance, Seismic excitation, Corrected SPT test blow count %9 journal article %R 10.1016/j.jnlssr.2024.06.010 %U https://www.sciencedirect.com/science/article/pii/S2666449624000537 %U http://dx.doi.org/10.1016/j.jnlssr.2024.06.010 %P 29-37 %0 Journal Article %T Predicting Seismic-Induced Settlement of Pipelines Buried in Sandy Soil Reinforced with Concrete and FRP Micropiles: A Genetic Programming Approach %A Al-Jeznawi, Duaa %A Al-Janabi, Musab Aied Qissab %A Sadik, Laith %A Bernardo, Luis Filipe Almeida %A de Almeida Andrade, Jorge Miguel %J Journal of Composites Science %D 2025 %V 9 %N 5 %@ 2504-477X %F al-jeznawi:2025:JoCS %X Unstable sandy soils pose significant challenges for buried pipelines due to soil-infrastructure interaction, leading to settlement that increases the risk of displacement and stress-induced fractures. In earthquake-prone regions, seismic-induced ground deformation further threatens underground infrastructure. Fiber-reinforced polymer (FRP) composites have emerged as a sustainable alternative to conventional piling materials, addressing durability issues in deep foundations. This paper introduces novel explicit models for predicting the maximum settlement of oil pipelines supported by concrete or polymer micropiles under seismic loading. Using genetic programming (GP), this study develops closed-form expressions based on simplified input parameters–micropile dimensions, pile spacing, soil properties, and peak ground acceleration–improving the models’ practicality for engineering applications. The models were evaluated using a dataset of 610 data points and demonstrated good accuracy across different conditions, achieving coefficients of determination (R2) as high as 0.92, among good values for other evaluation metrics. These findings contribute to a robust, practical tool for mitigating seismic risks in pipeline design, highlighting the potential of FRP micropiles for enhancing infrastructure resilience under challenging geotechnical scenarios. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/jcs9050207 %U https://www.mdpi.com/2504-477X/9/5/207 %U http://dx.doi.org/10.3390/jcs9050207 %P ArticleNo.207 %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 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/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 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/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 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/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 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/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 10.1007/s10710-016-9272-x %U https://rdcu.be/dR8cf %U http://dx.doi.org/10.1007/s10710-016-9272-x %P 315-316 %0 Journal Article %T Detection of advanced persistent threat: A genetic programming approach %A Al Mamun, Abdullah %A Al-Sahaf, Harith %A Welch, Ian %A Mansoori, Masood %A Camtepe, Seyit %J Applied Soft Computing %D 2024 %V 167 %@ 1568-4946 %F Al-Mamun:2024:asoc %X Advanced Persistent Threats (APTs) are an intimidating class of cyberattacks known for their persistence, sophistication, and targeted nature. These attacks, coordinated by highly motivated adversaries, pose a grave risk to organizations and individuals, often operating stealthily and evading detection. While existing research primarily focuses on applying Machine Learning (ML) methods to analyse network traffic data for APT detection, this article introduces a novel approach that uses Genetic Programming (GP). The proposed method not only detects APT attacks but also identifies their specific life cycle stages through the evolutionary capabilities of GP. Its effectiveness lies in its ability to excel in detecting intricate patterns, even within classes with a limited number of instances, a feat that is often challenging for traditional ML techniques. The method involves evolving and optimising its models to effectively learn and adapt to complex APT behaviours. Experimentation with a publicly available dataset showcases the efficacy of the proposed method across diverse APT stages. The results demonstrate that the proposed method, GPC, achieves a 3.71percent improvement in balanced accuracy compared to the best-performing model from related works. Moreover, a thorough analysis of the best-evolved GP model uncovers valuable insights about identified features and significant patterns. This research advances the APT detection paradigm by leveraging GP’s capabilities, providing a fresh and effective perspective on countering these persistent threats %K genetic algorithms, genetic programming, APT, Advanced Persistent Threat, Evolutionary computation, Machine Learning, CKC %9 journal article %R 10.1016/j.asoc.2024.112447 %U https://www.sciencedirect.com/science/article/pii/S1568494624012213 %U http://dx.doi.org/10.1016/j.asoc.2024.112447 %P 112447 %0 Journal Article %T Genetic programming for enhanced detection of Advanced Persistent Threats through feature construction %A Al Mamun, Abdullah %A Al-Sahaf, Harith %A Welch, Ian %A Camtepe, Seyit %J Computer and Security %D 2025 %V 149 %@ 0167-4048 %F Al-Mamun:2025:cose %X Advanced Persistent Threats (APTs) pose considerable challenges in the realm of cybersecurity, characterised by their evolving tactics and complex evasion techniques. These characteristics often outsmart traditional security measures and necessitate the development of more sophisticated detection methods. This study introduces Feature Evolution using Genetic Programming (FEGP), a novel method that leverages multi-tree Genetic Programming (GP) to construct and enhance features for APT detection. While GP has been widely used for tackling various problems in different domains, our study focuses on the adaptation of GP to the multifaceted landscape of APT detection. The proposed method automatically constructs discriminative features by combining the original features using mathematical operators. By leveraging GP, the system adapts to the evolving tactics employed by APTs, enhancing the identification of APT activities with greater accuracy and reliability. To assess the efficacy of the proposed method, comprehensive experiments were conducted on widely used and publicly accessible APT datasets. Using the combination of constructed and original features on the DAPT-2020 dataset, FEGP achieved a balanced accuracy of 79.28percent, surpassing the best comparative methods by an average of 2.12percent in detecting APT stages. Additionally, using only constructed features on the Unraveled dataset, FEGP achieved a balanced accuracy of 83.14percent, demonstrating a 3.73percent improvement over the best comparative method. The findings presented in this paper underscore the importance of GP-based feature construction for APT detection, providing a pathway toward improved accuracy and efficiency in identifying APT activities. The comparative analysis of the proposed method against existing feature construction methods demonstrates FEGP’s effectiveness as a state-of-the-art method for multi-class APT classification. In addition to the performance evaluation, further analysis was conducted, encompassing feature importance analysis, and a detailed time analysis %K genetic algorithms, genetic programming, APT, Advanced Persistent Threat, Evolutionary computation, Feature learning, Machine learning, Explainable AI %9 journal article %R 10.1016/j.cose.2024.104185 %U https://www.sciencedirect.com/science/article/pii/S0167404824004905 %U http://dx.doi.org/10.1016/j.cose.2024.104185 %P 104185 %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 10.1109/ACCT.2012.57 %U http://dx.doi.org/10.1109/ACCT.2012.57 %P 69-71 %0 Generic %T Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers for Better Biocomposite Materials %A AL-Oqla, Faris M. %A Faris, Hossam %A Habib, Maria %A Castillo-Valdivieso, Pedro Angel %D 2024 %I arXiv %F DBLP:journals/corr/abs-2404-07213 %K genetic algorithms, genetic programming %R 10.48550/ARXIV.2404.07213 %U https://doi.org/10.48550/arXiv.2404.07213 %U http://dx.doi.org/10.48550/ARXIV.2404.07213 %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 10.4236/jsea.2011.48054 %U http://www.scirp.org/journal/PaperDownload.aspx?DOI=10.4236/jsea.2011.48054 %U http://dx.doi.org/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 10.1109/SSD.2010.5585505 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.3891 %U http://dx.doi.org/10.1109/SSD.2010.5585505 %0 Journal Article %T Employing Gene Expression Programming in Estimating Software Effort %A Al-Saati, Najla Akram %A Al-Reffaee, Taghreed Riyadh %J International Journal of Computer Applications %D 2018 %8 aug %V 182 %N 8 %I Foundation of Computer Science (FCS), NY, USA %C New York, USA %@ 0975-8887 %F Al-Saati:2018:IJCA %X The problem of estimating the effort for software packages is one of the most significant challenges encountering software designers. The precision in estimating the effort or cost can have a huge impact on software development. Various methods have been investigated in order to discover good enough solutions to this problem; lately evolutionary intelligent techniques are explored like Genetic Algorithms, Genetic Programming, Neural Networks, and Swarm Intelligence. In this work, Gene Expression Programming (GEP) is investigated to show its efficiency in acquiring equations that best estimates software effort. Datasets employed are taken from previous projects. The comparisons of learning and testing results are carried out with COCOMO, Analogy, GP and four types of Neural Networks, all show that GEP outperforms all these methods in discovering effective functions for the estimation with robustness and efficiency. %K genetic algorithms, genetic programming, Gene Expression Programming, Effort Estimation, Software Engineering, Artificial Intelligence %9 journal article %R 10.5120/ijca2018917619 %U http://www.ijcaonline.org/archives/volume182/number8/29837-2018917619 %U http://dx.doi.org/10.5120/ijca2018917619 %P 1-8 %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 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 10.1145/3319619.3322083 %U http://dx.doi.org/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, TPOT, AI, ANN, EML, GPU, EMO, autoML, artificial intelligence, machine learning, evolutionary computation, classification, regression, clustering, combinatorial optimisation, deep learning, transfer learning, ensemble learning %9 journal article %R 10.1080/03036758.2019.1609052 %U https://doi.org/10.1080/03036758.2019.1609052 %U http://dx.doi.org/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 10.5220/0006959000790085 %U https://doi.org/10.5220/0006959000790085 %U http://dx.doi.org/10.5220/0006959000790085 %P 79-85 %0 Thesis %T Multi-objective search-based approach for software project management %A Al-Zubaidi, Wisam Haitham Abbood %D 2019 %8 31 mar %C Wollongong, NSW 2522, Australia %C University of Wollongong %F Al-Zubaidi:thesis %X Project management covers the entire lifecycle of software, underpinning the success or failure of many software projects. Managing modern software projects often follows the incremental and iterative process where a software product is incrementally developed through a number of iterations. In each iteration, the development team needs to complete a number of issues, each of which can be implementing a new feature for the software, modifying an existing functionality, fixing a bug or conducting some other project tasks. Although this agile approach reduces the risk of project failures, managing projects at the level of issues and iterations is still highly difficult due to the inherent dynamic nature of software, especially in large-scale software projects. Challenges in this context can be in many forms such as making accurate estimations of the resolution time and effort of resolving issues or selecting suitable issues for upcoming iterations. These integral parts of planning is highly challenging since many factors need considering such as customer business value and the team historical estimations, capability and performance. Challenges also exist at the implementation level, such as managing the reviewing of code changes made to resolve issues. There is currently a serious lack of automated support which help project managers and software development teams address those challenges. This thesis aims to fill those gaps. We leverage a huge amount of historical data in software projects to generate valuable insight for dealing with those challenges in managing iterations and issues. We reformulate those project management problems as search-based optimization problems and employ a range of evolutionary meta-heuristics search techniques to solve them. The search is simultaneously guided by a number of multiple fitness functions that express different objectives (e.g. customer business value, developer expertise and workload, and complexity of estimation models) and constraints (e.g. a team historical capability and performance) in the context of modern software projects. Using this approach, we build novel models for estimating issue resolution time and effort, suggesting appropriate issues for upcoming iterations in iteration planning and recommending suitable reviewers for code changes made to resolve issues. An extensive empirical evaluation on a range of large software projects (including Mesos, Usergrid, Aurora, Slider, Kylin, Mahout, Common, Hdfs, MapReduce, Yarn, Apstud, Mule, Dnn, Timob, Tisud, Xd, Nexus, Android, LibreOffice, Qt, and Openstack) demonstrates the highly effective performance of our approach against other alternative techniques (improvement between 1.83 to 550 percent) to show the effectiveness of our approach. %K genetic algorithms, genetic programming, SBSE, Iteration Planning, Agile Development, Effort Estimation, MOGP %9 Ph.D. thesis %U https://ro.uow.edu.au/theses1/690/ %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 10.1109/AHS.2008.52 %U http://dx.doi.org/10.1109/AHS.2008.52 %P 19-26 %0 Journal Article %T Hybrid approach of using bi-objective genetic programming in well control optimization of waterflood management %A Al-Aghbari, Mohammed %A M. Gujarathi, Ashish %J Geoenergy Science and Engineering %D 2023 %V 228 %@ 2949-8910 %F ALAGHBARI:2023:geoen %X A new hybrid optimization approach is proposed by applying bi-objective genetic programming (BioGP) algorithm along with NSGA-II algorithm to expand the diversity of the Pareto solutions and speed up the convergence. The novel methodology is used in two distinct cases: the benchmark model for the Brugge field and a Middle Eastern oil-field sector model. The Brugge field includes twenty producing wells and ten injecting wells, but the real sector model has three injectors and four producers. The two primary objectives applied are to optimize the total volume of produced oil and reduce cumulative produced water. In the optimization process, the injection rate (qwi) and the bottom-hole pressure (BHP) are the control parameters for injection and producing wells, respectively. The hybrid technique of applying BioGP guided NSGA-II in the Brugge field model demonstrated a 50percent acceleration in the convergence speed when compared to the NSGA-II solution. The calculated Pareto solutions for the Middle-Eastern sector model by the proposed methodology at various generations exhibited better diversity and convergence in comparison to the NSGA-II solutions. The highest cumulative produced oil of 550.45 times 103 m3 is obtained by the proposed hybrid methodology in comparison to the NSGA-II’s highest cumulative of 522 times 103 m3. The two solution points A’ and B’ achieved using the BioGP guided NSGA-II have lower WOR by 17percent and 15percent, respectively, than A and B solutions established by NSGA-II alone. Pareto solution ranking is performed using the net flow method (NFM) and the best optimum solution determined for BioGP guided NSGA-II is 532.38 times 103 m3 oil using equal-based weight compared to 505.44 times 103 m3 using the entropy-based weights of 41percent oil & 59percent water. Overall, the optimal Pareto solutions achieved by the proposed methodology of using BioGP guided NSGA-II algorithm has better diversity with improvement in convergence speed in comparison to the NSGA-II %K genetic algorithms, genetic programming, Multi-objective optimization, Bi-objective genetic programming, BioGP, NSGA-II, Net-flow method, Waterflood optimization, Reservoir simulation %9 journal article %R 10.1016/j.geoen.2023.211967 %U https://www.sciencedirect.com/science/article/pii/S2949891023005547 %U http://dx.doi.org/10.1016/j.geoen.2023.211967 %P 211967 %0 Conference Proceedings %T Detection and Quantitative Prediction of Diplocarpon earlianum Infection Rate in Strawberry Leaves using Population-based Recurrent Neural Network %A Alajas, Oliver John %A Concepcion, Ronnie %A Bandala, Argel %A Sybingco, Edwin %A Vicerra, Ryan Rhay %A Dadios, Elmer P. %A Mendigoria, Christan Hail %A Aquino, Heinrick %A Ambata, Leonard %A Duarte, Bernardo %S 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) %D 2022 %8 jun %F Alajas:2022:IEMTRONICS %X Fragaria ananassa, a member of the rose family’s flowering plants, commonly recognized as strawberry, is prone to Diplocarpon earlianum infection that causes leaf scorch. Assessment via visual inspection of strawberries by farmers is normally ineffective, destructive, and laborious. To address this challenge, the use of integrated computer vision and machine learning techniques was done to classify a healthy from a scorch-infected strawberry leaf image and to estimate the leaf region infection rate (LRIR). A dataset made up of 204 normally healthy and 161 scorch-infected strawberry leaf images was used. Images were initially preprocessed and segmented via graph-cut segmentation to extract the region of interest for feature extraction and selection. The hybrid combination of neighborhood and principal component analysis (NCA-PCA) was used to select desirable features. Multigene genetic programming (MGGP) was used to formulate the fitness function that will be essential for determining the optimized neuron configurations of the recurrent neural network (RNN) through genetic algorithm (GA), and cuckoo search algorithm (CSA), and artificial bee colony (ABC). Four classification machine learning models were configured in which the classification tree (CTree) bested other detection models with an accuracy of 10percent and exhibited the shortest inference time of 14.746 s. The developed ABC-RNN3 model outperformed GA-RNN3 and CSA-RNN3 in performing non-invasive LRIR prediction with an R2 value of 0.948. With the use of the NCA-PCA-CTree3-ABC-RNN3 hybrid model, for crop disease detection and infection rate prediction, plant disease assessment proved to be more efficient and labor cost-effective than manual disease inspection methods. %K genetic algorithms, genetic programming %R 10.1109/IEMTRONICS55184.2022.9795744 %U http://dx.doi.org/10.1109/IEMTRONICS55184.2022.9795744 %0 Conference Proceedings %T Grape Phaeomoniella chlamydospora Leaf Blotch Recognition and Infected Area Approximation Using Hybrid Linear Discriminant Analysis and Genetic Programming %A Alajas, Oliver John %A Concepcion II, Ronnie %A Bandala, Argel %A Sybingco, Edwin %A Dadios, Elmer %A Mendigoria, Christan Hail %A Aquino, Heinrick %S 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) %D 2022 %8 January 4 dec %C Boracay Island, Philippines %F Alajas:2022:HNICEM %X Grapes, scientifically called Vitis vinifera, are vulnerable against Phaeomoniella chlamydospora, the microorganism that causes Esca (black measles) to the leaves, trunks, cordons, and fruit of a young vineyard. Manual visual examination via the naked eye can prove to be challenging especially if done in large-scale vineyards. To address this issue, merging the use of computer vision, image processing, and machine learning was employed as a means of performing blotch identification and leaf blotch area prediction. The dataset is made up of 543 images, comprised of healthy and Esca infected leaves which were captured by an RGB camera. Images were preprocessed and segmented to isolate the diseased pixels and compute the ground truth pixel area. Desirable leaf signatures (G, B, contrast, H, R, S, a*, b*, Cb, and Cr) derived from the feature extraction process using a classification tree. The LDA12 was able to accurately distinguish the healthy from the blotch-infected leaves with a whopping 98.77percent accuracy compared to NB, KNN, and SVM. The MGSR12, with an R2 of 0.9208, topped other models such as RTree, GPR, and RLinear. The hybrid CTree-LDA12-MGSR12 algorithm proved to be ideal in performing leaf health classification and blotched area assessment of grape phenotypes which is important in plant disease identification and fungal spread prevention. %K genetic algorithms, genetic programming, Support vector machines, SVM, Image segmentation, Visualization, Image recognition, Computational modelling, Pipelines, Process control, image processing, plant disease detection, machine learning, computer vision, soft computing, black measles %R 10.1109/HNICEM57413.2022.10109613 %U http://dx.doi.org/10.1109/HNICEM57413.2022.10109613 %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 Journal Article %T Process optimization, multi-gene genetic programming modeling and reliability assessment of bioactive extracts recovery from Phyllantus emblica %A Alanzi, Hamdan %A Alenezi, Hamoud %A Adeyi, Oladayo %A Adeyi, Abiola J. %A Olusola, Emmanuel %A Gan, Chee-Yuen %A Olalere, Olusegun Abayomi %J Journal of Engineering Research %D 2024 %@ 2307-1877 %F ALANZI:2024:jer %X This study investigates the feasibility of extracting bioactive antioxidants from Phyllantus emblica leaves using a combination of ethanol-water mixture (0-100percent) and heat-assisted extraction technology (HAE-T). Operating temperature (30-50degreeC), solid-to-liquid ratio (1:20-1:60g/mL), and extraction time (45-180min) were varied to determine their effects on extract total phenolic content (TPC), yield (EY), and antioxidant activity (AA). The Box-Behnken experimental design (BBD) within response surface methodology (RSM) was employed, with multi-objective process optimization using the desirability function algorithm to find the optimal process variables for maximizing TPC, EY, and AA simultaneously. The extraction process was modeled using BBD-RSM and multi-gene genetic programming (MGGP) algorithm, with model reliability assessed via Monte Carlo simulation. HPLC characterization identified betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid as bioactive constituents in the extract. The study found that a 50percent ethanol solution yielded the best extraction efficiency. The optimal process parameters for maximum EY (21.6565percent), TPC (67.116mg GAE/g), and AA (3.68583uM AAE/g) were determined as OT of 41.61degreeC, S:L of 1:60g/mL, and ET of 180min. Both BBD-RSM and MGGP-based models satisfactorily predicted the observed process responses, with BBD-RSM models showing slightly better performance. Reliability analysis indicated high certainty in the predictions, with BBD-RSM models achieving 99.985percent certainty for TPC, 97.569percent for EY, and 98.661percent for AA values %K genetic algorithms, genetic programming, leaf, bioactive extract, Heat-assisted technology, multi gene genetic programming, reliability assessment %9 journal article %R 10.1016/j.jer.2024.02.020 %U https://www.sciencedirect.com/science/article/pii/S2307187724000476 %U http://dx.doi.org/10.1016/j.jer.2024.02.020 %0 Journal Article %T Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete %A Alarfaj, Mohammed %A Qureshi, Hisham Jahangir %A Shahab, Muhammad Zubair %A Javed, Muhammad Faisal %A Arifuzzaman, Md %A Gamil, Yaser %J Case Studies in Construction Materials %D 2024 %V 20 %@ 2214-5095 %F ALARFAJ:2024:cscm %X The demand for concrete production has led to a significant annual requirement for raw materials, resulting in a substantial amount of waste concrete. In response, recycled aggregate concrete has emerged as a promising solution. However, it faces challenges due to the vulnerability of the hardened mortar attached to natural aggregates, leading to susceptibility to cracking and reduced strength. This study focuses on predicting the split tensile strength of fiber reinforced recycled aggregate concrete using five prediction models, including two deep neural network models DNN1 and DNN2, one optimizable Gaussian process regression (OGPR), and two genetic programming based GEP1 and GEP2 models. The models exhibited high accuracy in predicting spilt tensile strength with robust R2, RMSE, and MAE values. DNN2 has the highest R2 value of 0.94 and GEP1 has the lowest R2 value of 0.76. DNN2 model R2 was 3.3percent and 13.5percent higher than OGPR and GEP2. Similarly, DNN2 and GEP2 model performed 9.3percent and 9.21percent better than DNN1 and GEP1 respectively in terms of R2. DNN2 model performed 20.32percent and 31.5percent better than OGPR and GEP2 in terms of MAE. Similarly, GEP2 and DNN2 MAE were 13.1percent and 31.5percent better than GEP1 and DNN1. Sensitivity analysis using the relevance factor and permutation feature importance revealed that the most significant positive factors are cement, natural coarse aggregates, density of recycle aggregates, and superplasticizer while recycle aggregate concrete, max size, and water content of recycle aggregates and water content have the most negative effect on STS values. The proposed ML methods, especially DNN2 and OGPR can be effectively used in practical projects, saving time and cost for eco-friendly fiber reinforced recycled aggregate concrete mixes. However, it is required to study more input variables and use hybrid models to further enhance the accuracy and reliability of the models %K genetic algorithms, genetic programming, Gene expression programming, Fiber reinforced Recycled Aggregate Concrete, Machine Learning, Sustainability, Eco-friendly Concrete, Spilt Tensile Strength, Deep neural networks, ANN, Optimizable gaussian process regression %9 journal article %R 10.1016/j.cscm.2023.e02836 %U https://www.sciencedirect.com/science/article/pii/S2214509523010173 %U http://dx.doi.org/10.1016/j.cscm.2023.e02836 %P e02836 %0 Journal Article %T Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature %A Alaskar, Abdulaziz %A Alfalah, Ghasan %A Althoey, Fadi %A Abuhussain, Mohammed Awad %A Javed, Muhammad Faisal %A Deifalla, Ahmed Farouk %A Ghamry, Nivin A. %J Case Studies in Construction Materials %D 2023 %V 18 %@ 2214-5095 %F ALASKAR:2023:cscm %X The elevated temperature severely influences the mixed properties of concrete, causing a decrease in its strength properties. Accurate proportioning of concrete components for obtaining the required compressive strength (C-S) at elevated temperatures is a complicated and time-taking process. However, using evolutionary programming techniques such as gene expression programming (GEP) and multi-expression programming (MEP) provides the accurate prediction of concrete C-S. This article presents the genetic programming-based models (such as gene expression programming (GEP) and multi-expression programming (MEP)) for forecasting the concrete compressive strength (C-S) at elevated temperatures. In this regard, 207 C-S values at elevated temperatures were obtained from previous studies. In the model’s development, C-S was considered as the output parameter with the nine most influential input parameters, including; Nano silica, cement, fly ash, water, temperature, silica fume, superplasticizer, sand, and gravels. The efficacy and accuracy of the GEP and MEP-based models were assessed by using statistical measures such as mean absolute error (MAE), correlation coefficient (R2), and root mean square error (RMSE). Moreover, models were also evaluated for external validation using different validation criteria recommended by previous studies. In comparing GEP and MEP models, GEP gave higher R2 and lower RMSE and MAE values of 0.854, 5.331 MPa, and 0.018 MPa respectively, indicating a strong correlation between actual and anticipated outputs. Thus, the GEP-based model was used further for sensitivity analysis, which revealed that cement is the most influencing factor. In addition, the proposed GEP model provides simple mathematical expression that can be easily implemented in practice %K genetic algorithms, genetic programming, gene expression programming %9 journal article %R 10.1016/j.cscm.2023.e02199 %U https://www.sciencedirect.com/science/article/pii/S2214509523003790 %U http://dx.doi.org/10.1016/j.cscm.2023.e02199 %P e02199 %0 Journal Article %T Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure %A Alatefi, Saad %A Agwu, Okorie Ekwe %A Azim, Reda Abdel %A Alkouh, Ahmad %A Dzulkarnain, Iskandar %J Chemical Engineering Research and Design %D 2024 %@ 0263-8762 %F ALATEFI:2024:cherd %X multiple data-driven models for predicting CO2 minimum miscibility pressure (MMP). The aim is to address the issue of existing models lacking explicit presentation. With a database of 155 data points, five models were developed using artificial neural network (ANN), multigene genetic programming (MGGP), support vector regression (SVR), multivariate adaptive regression splines (MARS), and multiple linear regression (MLR). Comparative analysis was conducted using statistical metrics (R2, MSE, MAE, RMSE), and sensitivity analysis was performed on input variables. The results showed that ANN and SVR had comparable predictive performance (ANN: R2 = 0.982, MSE = 0.00676, MAE = 0.9765, RMSE = 0.082), SVR (R2 = 0.935, MSE = 0.0041, MAE = 0.72, RMSE = 0.064) followed by MARS, MLR, and MGGP. Sensitivity analysis revealed that reservoir temperature was the most influential parameter across all models, except for the MLR algorithm where injected CO2 amount was crucial. These models can be used for a wide range of CO2 MMP ranging from 940psi to 5830psi, thus rendering them useful for any reservoir globally. These models offer improved accuracy and computational efficiency compared to existing ones, potentially reducing costs associated with laboratory experiments and providing rapid and precise CO2 MMP predictions %K genetic algorithms, genetic programming, Artificial intelligence, CO2, Explicit models, Gas flooding, Minimum miscibility pressure %9 journal article %R 10.1016/j.cherd.2024.04.033 %U https://www.sciencedirect.com/science/article/pii/S0263876224002351 %U http://dx.doi.org/10.1016/j.cherd.2024.04.033 %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 10.1109/CT-IETA.2016.7868256 %U http://dx.doi.org/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 10.4203/ccp.89.86 %U http://www.civil-comp.com/pubs/catalog.htm?t=contents&f=26_3 %U http://dx.doi.org/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 10.4203/ccp.89.175 %U http://www.civil-comp.com/pubs/catalog.htm?t=contents&f=26_3 %U http://dx.doi.org/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 10.1002/hyp.7511 %U http://onlinelibrary.wiley.com/doi/10.1002/hyp.7511/abstract %U http://dx.doi.org/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 10.1007/s00366-009-0140-7 %U http://dx.doi.org/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 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/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 10.1108/02644401111118132 %U http://www.emeraldinsight.com/journals.htm?articleid=1912293 %U http://dx.doi.org/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 10.1016/j.conbuildmat.2010.09.010 %U http://dx.doi.org/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 10.1016/j.oceaneng.2010.06.003 %U http://www.sciencedirect.com/science/article/B6V4F-50DXD90-1/2/b2489a1aebf49e771abca1b27d3b24b4 %U http://dx.doi.org/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 10.1016/j.eswa.2011.04.049 %U http://www.sciencedirect.com/science/article/pii/S0957417411005653 %U http://dx.doi.org/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 10.1080/13632469.2010.526752 %U http://www.tandfonline.com/doi/abs/10.1080/13632469.2010.526752#.UlMR6NKc_G0 %U http://dx.doi.org/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 10.1016/j.gsf.2011.12.008 %U http://www.sciencedirect.com/science/article/pii/S167498711100137X %U http://dx.doi.org/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 10.1007/978-3-642-23424-8_11 %U http://dx.doi.org/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 10.1016/B978-0-12-398296-4.00012-X %U http://www.sciencedirect.com/science/article/pii/B978012398296400012X %U http://dx.doi.org/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 10.1007/s00521-012-1144-6 %U http://link.springer.com/article/10.1007%2Fs00521-012-1144-6 %U http://dx.doi.org/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 10.1016/j.gsf.2014.12.005 %U http://www.sciencedirect.com/science/article/pii/S1674987114001625 %U http://dx.doi.org/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 10.1016/j.gsf.2015.10.006 %U http://www.sciencedirect.com/science/article/pii/S1674987115001243 %U http://dx.doi.org/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 10.1016/j.acme.2016.06.004 %U http://www.sciencedirect.com/science/article/pii/S1644966516300814 %U http://dx.doi.org/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 Optimizing Diabetes Predictive Modeling with Automated Decision Trees %A Albalushi, Muna %A Al Jassim, Rasha %A Jetly, Karan %A Al Khayari, Raya %A Al Maqbali, Hilal %S 2023 IEEE Smart World Congress (SWC) %D 2023 %8 aug %F Albalushi:2023:SWC %X This paper introduces Linear Genetic Programming for Optimising Decision Tree (LGP-OptTree), a novel form of Genetic Programming (GP) aimed at enhancing diabetes detection. LGP-OptTree is designed to optimise the attributes and hyperparameters of decision trees by using a unique genotype and phenotype structure. The proposed method is evaluated on the Pima dataset and compared with other techniques. By fine-tuning the attributes and hyperparameters of decision trees using LGP-OptTree, this study aims to improve the accuracy and efficacy of diabetes detection. A performance metric is used to determine the effectiveness of the proposed method with respect to other approaches. The contribution of this research lies in providing general healthcare professionals with a new approach for enhancing diabetes detection accuracy through decision trees. %K genetic algorithms, genetic programming, Measurement, Medical services, Predictive models, Prediction algorithms, Diabetes, Decision trees, Evolutionary Algorithm %R 10.1109/SWC57546.2023.10449077 %U http://dx.doi.org/10.1109/SWC57546.2023.10449077 %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 10.1109/SIBGRAPI.2016.063 %U http://dx.doi.org/10.1109/SIBGRAPI.2016.063 %P 408-415 %0 Journal Article %T A Soft Computing Approach for Selecting and Combining Spectral Bands %A Albarracin, Juan F. H. %A Oliveira, Rafael S. %A Hirota, Marina %A dos Santos, Jefersson A. %A da S. Torres, Ricardo %J Remote Sensing %D 2020 %V 12 %N 14 %@ 2072-4292 %F albarracin:2020:RS %X We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimisation problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. Using time series defined in terms of the learnt spectral indices, we show that the GP framework leads to superior results than other indices that are used to discriminate and classify tropical biomes. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/rs12142267 %U https://www.mdpi.com/2072-4292/12/14/2267 %U http://dx.doi.org/10.3390/rs12142267 %0 Thesis %T On the spatial dilemma linking deep reenactment and learning from disentangled representations in video %A Hernandez Albarracin, Juan Felipe %D 2023 %8 17 jan %C Brazil %C Institute of Computing, State University of Campinas %F Hernandez-Albarracin:thesis %K genetic algorithms, genetic programming %9 Ph.D. thesis %U https://ic.unicamp.br/en/evento/defesa-de-doutorado-de-juan-felipe-hernandez-albarracin/ %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 10.1007/978-3-319-16501-1 %U http://dx.doi.org/10.1007/978-3-319-16501-1 %P 3-15 %0 Journal Article %T Multi-Dimensional Regression Models for Predicting the Wall Thickness Distribution of Corrugated Pipes %A Albrecht, Hanny %A Roland, Wolfgang %A Fiebig, Christian %A Berger-Weber, Gerald Roman %J Polymers %D 2022 %V 14 %N 17 %@ 2073-4360 %F albrecht:2022:Polymers %X Corrugated pipes offer both higher stiffness and higher flexibility while simultaneously requiring less material than rigid pipes. Production rates of corrugated pipes have therefore increased significantly in recent years. Due to rising commodity prices, pipe manufacturers have been driven to produce corrugated pipes of high quality with reduced material input. To the best of our knowledge, corrugated pipe geometry and wall thickness distribution significantly influence pipe properties. Essential factors in optimising wall thickness distribution include adaptation of the mold block geometry and structure optimisation. To achieve these goals, a conventional approach would typically require numerous iterations over various pipe geometries, several mold block geometries, and then fabrication of pipes to be tested experimentally—an approach which is very time-consuming and costly. To address this issue, we developed multi-dimensional mathematical models that predict the wall thickness distribution in corrugated pipes as functions of the mold geometry by using symbolic regression based on genetic programming (GP). First, the blow molding problem was transformed into a dimensionless representation. Then, a screening study was performed to identify the most significant influencing parameters, which were subsequently varied within wide ranges as a basis for a comprehensive, numerically driven parametric design study. The data set obtained was used as input for data-driven modelling to derive novel regression models for predicting wall thickness distribution. Finally, model accuracy was confirmed by means of an error analysis that evaluated various statistical metrics. With our models, wall thickness distribution can now be predicted and subsequently used for structural analysis, thus enabling digital mold block design and optimising the wall thickness distribution. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/polym14173455 %U https://www.mdpi.com/2073-4360/14/17/3455 %U http://dx.doi.org/10.3390/polym14173455 %P ArticleNo.3455 %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 10.1007/978-3-540-46239-2_1 %U http://dx.doi.org/10.1007/978-3-540-46239-2_1 %P 1-15 %0 Journal Article %T Predictive Models of Double-Vibropolishing in Bowl System Using Artificial Intelligence Methods %A Alcaraz, Joselito Yam II %A Ahluwalia, Kunal %A Yeo, Swee-Hock %J Journal of Manufacturing and Materials Processing %D 2019 %V 3 %N 1 %@ 2504-4494 %F alcaraz:2019:JMMP %X Vibratory finishing is a versatile and efficient surface finishing process widely used to finish components of various functionalities. Research efforts were focused in fundamental understanding of the process through analytical solutions and simulations. On the other hand, predictive modelling of surface roughness using computational intelligence (CI) methods are emerging in recent years, though CI methods have not been extensively applied yet to a new vibratory finishing method called double-vibropolishing. In this study, multi-variable regression, artificial neural networks, and genetic programming models were designed and trained with experimental data obtained from subjecting rectangular Ti-6Al-4V test coupons to double vibropolishing in a bowl system configuration. Model selection was done by comparing the mean-absolute percentage error and r-squared values from both training and testing datasets. Exponential regression was determined as the best model for the bowl double-vibropolishing system studied with a Test MAPE score of 6.1percent and a R-squared score of 0.99. A family of curves was generated using the exponential regression model as a potential tool in predicting surface roughness with time. %K genetic algorithms, genetic programming, vibratory finishing, double vibro-polishing, artificial intelligence, regression, neural network, ANN %9 journal article %R 10.3390/jmmp3010027 %U https://www.mdpi.com/2504-4494/3/1/27 %U http://dx.doi.org/10.3390/jmmp3010027 %0 Journal Article %T Thiophene Stability in Photodynamic Therapy: A Mathematical Model Approach %A Alcazar, Jackson J. %J International Journal of Molecular Sciences %D 2024 %8 21 feb %V 25 %N 5 %@ 1422-0067 %F alcazar:2024:IJMS %O Special Issue Molecular Aspects of Photodynamic Therapy %X Thiophene-containing photosensitizers are gaining recognition for their role in photodynamic therapy (PDT). However, the inherent reactivity of the thiophene moiety toward singlet oxygen threatens the stability and efficiency of these photosensitizers. This study presents a novel mathematical model capable of predicting the reactivity of thiophene toward singlet oxygen in PDT, using Conceptual Density Functional Theory (CDFT) and genetic programming. The research combines advanced computational methods, including various DFT techniques and symbolic regression, and is validated with experimental data. The findings underscore the capacity of the model to classify photosensitizers based on their photodynamic efficiency and safety, particularly noting that photosensitizers with a constant rate 1000 times lower than that of unmodified thiophene retain their photodynamic performance without substantial singlet oxygen quenching. Additionally, the research offers insights into the impact of electronic effects on thiophene reactivity. Finally, this study significantly advances thiophene-based photosensitizer design, paving the way for therapeutic agents that achieve a desirable balance between efficiency and safety in PDT. %K genetic algorithms, genetic programming, safe PDT, efficient PDT, thiophene-containing photosensitiser, singlet oxygen, conceptual DFT %9 journal article %R 10.3390/ijms25052528 %U https://www.mdpi.com/1422-0067/25/5/2528 %U http://dx.doi.org/10.3390/ijms25052528 %P ArticleNo.2528 %0 Conference Proceedings %T Evolving Monotone Conjunctions in Regimes Beyond Proved Convergence %A Alchirch, Pantia-Marina %A Diochnos, Dimitrios I. %A Papakonstantinopoulou, Katia %Y Medvet, Eric %Y Pappa, Gisele %Y Xue, Bing %S EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming %S LNCS %D 2022 %8 20 22 apr %V 13223 %I Springer Verlag %C Madrid, Spain %F Alchirch:2022:EuroGP %X Recently it was shown, using the typical mutation mechanism that is used in evolutionary algorithms, that monotone conjunctions are provably evolvable under a specific set of Bernoulli (p)n distributions. A natural question is whether this mutation mechanism allows convergence under other distributions as well. Our experiments indicate that the answer to this question is affirmative and, at the very least, this mechanism converges under Bernoulli (p)n distributions outside of the known proved regime. %K genetic algorithms, genetic programming: Poster, Evolvability, Monotone conjunctions, Distribution-specific learning, Bernoulli (p)**n distributions %R 10.1007/978-3-031-02056-8_15 %U http://dx.doi.org/10.1007/978-3-031-02056-8_15 %P 228-244 %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 10.1109/CEC.2018.8477951 %U http://dx.doi.org/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 %X The balance between approximation error and model complexity is an important trade-off for Symbolic Regression algorithms. This trade-off is achieved by means of specific operators for bloat control, modified operators, limits to the size of the generated expressions and multi-objective optimization. Recently, the representation Interaction-Transformation was introduced with the goal of limiting the search space to simpler expressions, thus avoiding bloating. This representation was used in the context of an Evolutionary Algorithm in order to find concise expressions resulting in small approximation errors competitive with the literature. Particular to this algorithm, two parameters control the complexity of the generated expression. This paper investigates the influence of those parameters w.r.t. the goodness-of-fit. Through some extensive experiments, we find that the maximum number of terms is more important to control goodness-of-fit but also that there is a limit to the extent that increasing its value renders any benefits. Second, the limit to the minimum and maximum value of the exponent has a smaller influence to the results and it can be set to a default value without impacting the final results. %K genetic algorithms, genetic programming %R 10.1109/CEC48606.2020.9185521 %U http://dx.doi.org/10.1109/CEC48606.2020.9185521 %P paperid24027 %0 Conference Proceedings %T Measuring Feature Importance of Symbolic Regression Models Using Partial Effects %A Aldeia, Guilherme Seidyo Imai %A Olivetti de Franca, Fabricio %Y Chicano, Francisco %Y Tonda, Alberto %Y Krawiec, Krzysztof %Y Helbig, Marde %Y Cleghorn, Christopher W. %Y Wilson, Dennis G. %Y Yannakakis, Georgios %Y Paquete, Luis %Y Ochoa, Gabriela %Y Bacardit, Jaume %Y Gagne, Christian %Y Mostaghim, Sanaz %Y Jourdan, Laetitia %Y Schuetze, Oliver %Y Posik, Petr %Y Segura, Carlos %Y Tinos, Renato %Y Cotta, Carlos %Y Heywood, Malcolm %Y Zhang, Mengjie %Y Trujillo, Leonardo %Y Miikkulainen, Risto %Y Xue, Bing %Y Neumann, Aneta %Y Allmendinger, Richard %Y Ishikawa, Fuyuki %Y Medina-Bulo, Inmaculada %Y Neumann, Frank %Y Sutton, Andrew M. %S Proceedings of the 2021 Genetic and Evolutionary Computation Conference %S GECCO ’21 %D 2021 %8 jul 10 14 %I Association for Computing Machinery %C internet %F Aldeia:2021:GECCO %X In explainable AI, one aspect of a prediction’s explanation is to measure each predictor’s importance to the decision process.The importance can measure how much variation a predictor promotes locally or how much the predictor contributes to the deviation from a reference point (Shapley value). If we have the ground truth analytical model, we can calculate the former using the Partial Effect, calculated as the predictor’s partial derivative. Also, we can estimate the latter by calculating the average partial effect multiplied by the difference between the predictor and the reference value. Symbolic Regression is a gray-box model for regression problems that returns an analytical model approximating the input data. Although it is often associated with interpretability, few works explore this property. We will investigate the use of Partial Effect with the analytical models generated by the Interaction-Transformation Evolutionary Algorithm symbolic regressor (ITEA). We show that the regression models returned by ITEA coupled with Partial Effect provide the closest explanations to the ground truth and a close approximation to Shapley values. These results openup new opportunities to explain symbolic regression modelscompared to the approximations provided by model agnostic approaches. %K genetic algorithms, genetic programming, XAI, explainable AI, symbolic regression, interaction-transformation, Supervised learning, SHAP, Shapley value %R 10.1145/3449639.3459302 %U http://dx.doi.org/10.1145/3449639.3459302 %P 750-758 %0 Conference Proceedings %T Interaction-Transformation Evolutionary Algorithm with coefficients optimization %A Aldeia, Guilherme Seidyo Imai %A Olivetti de Franca, Fabricio %Y Trautmann, Heike %Y Doerr, Carola %Y Moraglio, Alberto %Y Bartz-Beielstein, Thomas %Y Filipic, Bogdan %Y Gallagher, Marcus %Y Ong, Yew-Soon %Y Gupta, Abhishek %Y Kononova, Anna V. %Y Wang, Hao %Y Emmerich, Michael %Y Bosman, Peter A. N. %Y Zaharie, Daniela %Y Caraffini, Fabio %Y Dreo, Johann %Y Auger, Anne %Y Dietric, Konstantin %Y Dufosse, Paul %Y Glasmachers, Tobias %Y Hansen, Nikolaus %Y Mersmann, Olaf %Y Posik, Petr %Y Tusar, Tea %Y Brockhoff, Dimo %Y Eftimov, Tome %Y Kerschke, Pascal %Y Naujoks, Boris %Y Preuss, Mike %Y Volz, Vanessa %Y Derbel, Bilel %Y Li, Ke %Y Li, Xiaodong %Y Zapotecas, Saul %Y Zhang, Qingfu %Y Coletti, Mark %Y Schuman, Catherine (Katie) %Y Scott, Eric “Siggy” %Y Patton, Robert %Y Wiegand, Paul %Y Bassett, Jeffrey K. %Y Gunaratne, Chathika %Y Chugh, Tinkle %Y Allmendinger, Richard %Y Hakanen, Jussi %Y Tauritz, Daniel %Y Woodward, John %Y Lopez-Ibanez, Manuel %Y McCall, John %Y Bacardit, Jaume %Y Brownlee, Alexander %Y Cagnoni, Stefano %Y Iacca, Giovanni %Y Walker, David %Y Toutouh, Jamal %Y O’Reilly, UnaMay %Y Machado, Penousal %Y Correia, Joao %Y Nesmachnow, Sergio %Y Ceberio, Josu %Y Villanueva, Rafael %Y Hidalgo, Ignacio %Y Fernandez de Vega, Francisco %Y Paolo, Giuseppe %Y Coninx, Alex %Y Cully, Antoine %Y Gaier, Adam %Y Wagner, Stefan %Y Affenzeller, Michael %Y Bruce, Bobby R. %Y Nowack, Vesna %Y Blot, Aymeric %Y Winter, Emily %Y Langdon, William B. %Y Petke, Justyna %Y Fernandez Alzueta, Silvino %Y Valledor Pellicer, Pablo %Y Stuetzle, Thomas %Y Paetzel, David %Y Wagner, Alexander %Y Heider, Michael %Y Veerapen, Nadarajen %Y Malan, Katherine %Y Liefooghe, Arnaud %Y Verel, Sebastien %Y Ochoa, Gabriela %Y Omidvar, Mohammad Nabi %Y Sun, Yuan %Y Tarantino, Ernesto %Y Ivanoe, De Falco %Y Della Cioppa, Antonio %Y Umberto, Scafuri %Y Rieffel, John %Y Mouret, Jean-Baptiste %Y Doncieux, Stephane %Y Nikolaidis, Stefanos %Y Togelius, Julian %Y Fontaine, Matthew C. %Y Georgescu, Serban %Y Chicano, Francisco %Y Whitley, Darrell %Y Kyriienko, Oleksandr %Y Dahl, Denny %Y Shir, Ofer %Y Spector, Lee %Y Rahat, Alma %Y Everson, Richard %Y Fieldsend, Jonathan %Y Wang, Handing %Y Jin, Yaochu %Y Hemberg, Erik %Y Elsayed, Marwa A. %Y Kommenda, Michael %Y La Cava, William %Y Kronberger, Gabriel %Y Gustafson, Steven %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F aldeia:2022:SymReg %X Symbolic Regression is the task of finding a mathematical expression to describe the relationship between one or more independent variables with a dependent variable. The search space can be vast and include any algebraic function; thus, finding optimal values for coefficients may not be a trivial task. The Interaction-Transformation representation alleviates this problem enforcing that the coefficients of the expression is part of a linear transformation, allowing the application of least squares. But this solution also limits the search space of the expressions. This paper proposes four different strategies to optimize the coefficients of the nonlinear part of the Interaction-Transformation representation. We benchmark the proposed strategies by applying the Interaction-Transformation Evolutionary Algorithm (ITEA) to six well-known data sets to evaluate four optimization heuristics combining linear and non-linear methods. The results show that optimizing the non-linear and linear coefficients separately was the best strategy to find better-performing expressions with a higher run-time and expression size. The non-linear optimization method alone was the worst-performing method. %K genetic algorithms, genetic programming, Representation of mathematical functions, symbolic regression, coefficient optimization, benchmark, evolutionary algorithm %R 10.1145/3520304.3533987 %U http://dx.doi.org/10.1145/3520304.3533987 %P 2274-2281 %0 Journal Article %T Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set %A Aldeia, Guilherme Seidyo Imai %A Olivetti de Franca, Fabricio %J Genetic Programming and Evolvable Machines %D 2022 %8 sep %V 23 %N 3 %@ 1389-2576 %F Aldeia:2022:GPEM %O Special Issue: Highlights of Genetic Programming 2021 Events %X In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the prediction model, verify some of its properties, or even enforce them to improve fairness. Many model-agnostic explanatory methods exists to provide explanations for black-box models. In the regression task, the practitioner can use white-boxes or gray-boxes models to achieve more interpretable results, which is the case of symbolic regression. When using an explanatory method, and since interpretability lacks a rigorous definition, there is a need to evaluate and compare the quality and different explainers. This paper proposes a benchmark scheme to evaluate explanatory methods to explain regression models, mainly symbolic regression models. Experiments were performed using 100 physics equations with different interpretable and non-interpretable regression methods and popular explanation methods, evaluating the performance of the explainers performance with several explanation measures. In addition, we further analyzed four benchmarks from the GP community. The results have shown that Symbolic Regression models can be an interesting alternative to white-box and black-box models that is capable of returning accurate models with appropriate explanations. Regarding the explainers, we observed that Partial Effects and SHAP were the most robust explanation models, with Integrated Gradients being unstable only with tree-based models. This benchmark is publicly available for further experiments. %K genetic algorithms, genetic programming, Symbolic regression, Explanatory methods, Feature importance attribution, Benchmark %9 journal article %R 10.1007/s10710-022-09435-x %U http://dx.doi.org/10.1007/s10710-022-09435-x %P 309-349 %0 Generic %T Call for Action: towards the next generation of symbolic regression benchmark %A Aldeia, Guilherme S. Imai %A Zhang, Hengzhe %A Bomarito, Geoffrey %A Cranmer, Miles %A Fonseca, Alcides %A Burlacu, Bogdan %A La Cava, William G. %A Olivetti de Franca, Fabricio %D 2025 %8 June %F Aldeia:2025:SymReg %X Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity of algorithms, datasets, and evaluation criteria. In this work, we present an updated version of SRBench. Our benchmark expands the previous one by nearly doubling the number of evaluated methods, refining evaluation metrics, and using improved visualizations of the results to understand the performances. Additionally, we analyze trade-offs between model complexity, accuracy, and energy consumption. Our results show that no single algorithm dominates across all datasets. We propose a call for action from SR community in maintaining and evolving SRBench as a living benchmark that reflects the state-of-the-art in symbolic regression, by standardizing hyperparameter tuning, execution constraints, and computational resource allocation. We also propose deprecation criteria to maintain the benchmark’s relevance and discuss best practices for improving SR algorithms, such as adaptive hyperparameter tuning and energy-efficient implementations. %K genetic algorithms, genetic programming, Machine Learning, cs.LG, Neural and Evolutionary Computing, cs.NE, symbolic regression, benchmark, srbench %U https://doi.org/10.48550/arXiv.2505.03977 %U http://arxiv.org/abs/2505.03977v1 %0 Thesis %T Current Challenges of Symbolic Regression: Optimization, Selection, Model Simplification, and Benchmarking %A Aldeia, Guilherme Seidyo Imai %D 2025 %8 January %C Santo Andre, Sao Paulo, Brazil %C Computer Science of the Federal University of ABC %F Aldeia:thesis %X Symbolic Regression (SR) is a regression method that aims to discover mathematical expressions that describe the relationship between variables, and it is often implemented through Genetic Programming, a metaphor for the process of biological evolution. Its appeal lies in combining predictive accuracy with interpretable models, but its promise is limited by several long-standing challenges: parameters are difficult to optimize, the selection of solutions can affect the search, and models often grow unnecessarily complex. In addition, current methods must be constantly re-evaluated to understand the SR landscape. This thesis addresses these challenges through a sequence of studies conducted throughout the doctorate, each focusing on an important aspect of the SR search process. First, I investigate parameter optimization, obtaining insights into its role in improving predictive accuracy, albeit with trade-offs in runtime and expression size. Next, I study parent selection, exploring epsilon-lexicase to select parents more likely to generate good performing offspring. The focus then turns to simplification, where I introduce a novel method based on memoization and locality-sensitive hashing that reduces redundancy and yields simpler, more accurate models. All of these contributions are implemented into a multi-objective evolutionary SR library, which achieves Pareto-optimal performance in terms of accuracy and simplicity on benchmarks of real-world and synthetic problems, outperforming several contemporary SR approaches. The thesis concludes by proposing changes to a famous large-scale symbolic regression benchmark suite, then running the experiments to assess the symbolic regression landscape, demonstrating that a SR method with the contributions presented in this thesis achieves Pareto-optimal performance. %K genetic algorithms, genetic programming, symbolic regression, simplification, e-lexicase selection, non-linear optimization, multi-objective optimization, ITEA, FEAT, Pareto, MOGP, NSGA2, PTC2 %9 Ph.D. thesis %U https://arxiv.org/abs/2512.01682 %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 Journal Article %T Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches %A Aldrees, Ali %A Khan, Majid %A Taha, Abubakr Taha Bakheit %A Ali, Mujahid %J Journal of Water Process Engineering %D 2024 %V 58 %@ 2214-7144 %F ALDREES:2024:jwpe %X Water quality indexes (WQI) are pivotal in assessing aquatic systems. Conventional modeling approaches rely on extensive datasets with numerous unspecified inputs, leading to time-consuming WQI assessment procedures. Numerous studies have used machine learning (ML) methods for WQI analysis but often lack model interpretability. To address this issue, this study developed five interpretable predictive models, including two gene expression programming (GEP) models, two deep neural networks (DNN) models, and one optimizable Gaussian process regressor (OGPR) model for estimating electrical conductivity (EC) and total dissolved solids (TDS). For the model development, a total of 372 records on a monthly basis were collected in the Upper Indus River at two outlet stations. The efficacy and accuracy of the models were assessed using various statistical measures, such as correlation (R), mean square error (MAE), root mean square error (RMSE), and 5-fold cross-validation. The DNN2 model demonstrated outstanding performance compared to the other five models, exhibiting R-values closer to 1.0 for both EC and TDS. However, the genetic programming-based models, GEP1 and GEP2, exhibited comparatively lower accuracy in predicting the water quality indexes. The SHapely Additive exPlanation (SHAP) analysis revealed that bicarbonate, calcium, and sulphate jointly contribute approximately 78 percent to EC, while the combined presence of sodium, bicarbonate, calcium, and magnesium accounts for around 87 percent of TDS in water. Notably, the influence of pH and chloride was minimal on both water quality indexes. In conclusion, the study highlights the cost-effective and practical potential of predictive models for EC and TDS in assessing and monitoring river water quality %K genetic algorithms, genetic programming, Gene expression programming, Water quality indexes, ANN, Deep neural networks, Optimizable Gaussian process regressor, SHAP %9 journal article %R 10.1016/j.jwpe.2024.104789 %U https://www.sciencedirect.com/science/article/pii/S2214714424000199 %U http://dx.doi.org/10.1016/j.jwpe.2024.104789 %P 104789 %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 10.1145/2330784.2330859 %U http://dx.doi.org/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 10.1145/2464576.2482689 %U http://dx.doi.org/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 10.1134/S1064230713020020 %U http://dx.doi.org/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 10.3390/a11070108 %U http://www.mdpi.com/1999-4893/11/7/108 %U http://dx.doi.org/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 August 12 jul %C Hong Kong %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 10.1109/AIM.2019.8868610 %U http://dx.doi.org/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 10.3390/a13020048 %U https://www.mdpi.com/1999-4893/13/2/48/pdf %U http://dx.doi.org/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 10.1016/j.enggeo.2015.12.002 %U http://www.sciencedirect.com/science/article/pii/S0013795215300971 %U http://dx.doi.org/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 10.1007/BFb0055928 %U http://dx.doi.org/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 10.1007/BFb0040753 %U http://dx.doi.org/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 10.1109/CEC.2001.934330 %U http://scalab.uc3m.es/~dborrajo/papers/cec01.ps.gz %U http://dx.doi.org/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 10.1162/10636560152642841 %U http://www.mitpressjournals.org/doi/pdf/10.1162/10636560152642841 %U http://dx.doi.org/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 10.1016/S0004-3702(02)00246-1 %U http://scalab.uc3m.es/~dborrajo/papers/aij-evock.ps.gz %U http://dx.doi.org/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 10.1109/CEC.2011.5949749 %U http://dx.doi.org/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 10.1109/CEC.2009.4983083 %U P395.pdf %U http://dx.doi.org/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 10.1007/978-3-319-10762-2_38 %U http://dx.doi.org/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 10.1007/978-3-319-45823-6_3 %U http://dx.doi.org/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 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/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 10.1016/j.ijforecast.2016.07.002 %U http://www.sciencedirect.com/science/article/pii/S0169207016300711 %U http://dx.doi.org/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 %X En este articulo presentamos una nueva manera de aplicar programacion genetica al problema de clasificacion de series temporales. Eneste caso las series de datos usadas son senalesde electroencefalograma. Se han implementado dos tipos de algoritmos de programaciongenetica: uno de ellos usa programacion distribuida mientras que el otro aplica una tecnica de muestreo aleatorio para evitar el problema de la sobreadaptacion. Los arboles resultantes obtienen porcentajes de aciertos en la clasificacion equivalentes a los que se obtienen usando metodos de clasifficacion tradicionales %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 10.1007/978-3-540-31989-4_22 %U http://dx.doi.org/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 16 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 10.1109/CEC.2006.1688316 %U http://dx.doi.org/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 10.1007/978-3-540-71805-5_19 %U http://dx.doi.org/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 10.1007/978-3-540-78761-7_13 %U http://dx.doi.org/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 10.1109/CEC.2008.4631188 %U EC0649.pdf %U http://dx.doi.org/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 10.1109/TITS.2008.922932 %U http://results.ref.ac.uk/Submissions/Output/2145080 %U http://dx.doi.org/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 10.1109/HIS.2008.127 %U http://dx.doi.org/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 10.1007/978-3-540-77477-8_9 %U http://dx.doi.org/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 10.1007/978-3-642-01129-0_73 %U http://dx.doi.org/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 10.1145/1570256.1570309 %U http://dx.doi.org/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 10.1162/evco.2010.18.2.18206 %U http://dx.doi.org/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 10.1162/EVCO_a_00110 %U http://dx.doi.org/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 10.1023/B:GENP.0000036057.27304.5b %U https://rdcu.be/dR8co %U http://dx.doi.org/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 Journal Article %T Optimization of multi-energy storage in urban building clusters %A Algburi, Sameer %A Mohammed, Aymen %A Fakhruldeen, Hassan Falah %A Abdullah, Ibrahim %A Alhani, Israa %A Khudhair, Ali %A Hassan, Qusay %A Ssebunya, Michael %A Jabbar, Feryal Ibrahim %J Results in Engineering %D 2025 %V 26 %@ 2590-1230 %F Algburi:2025:rineng %X A data-driven optimisation framework was developed to enhance energy performance in building clusters through multi-energy storage systems, combining electrical and thermal solutions. The approach used surrogate modelling, symbolic regression, and genetic programming to simulate energy consumption, integrate weather and tariff data, and refine storage strategies across varied building types. Applied to a cluster in Bismayah city, Iraq, the methodology evaluated HVAC configurations, facade designs, and thermal mass levels to tailor storage capacity recommendations. Results revealed a 38 percent reduction in peak grid import and a 24.6 percent drop in overall energy consumption when hybrid energy storage was implemented. A thermal energy storage tank capacity of 4651 kWh, coupled with a battery storage unit of 342 kWh, demonstrated a 35 percent decrease in energy costs. Demand response participation increased by 45 percent through strategic use of pre-cooling routines and temperature reset controls. TES-only configurations achieved energy usage of 16.3 kWh/msquared, while hybrid configurations further reduced to 8.7 kWh/msquared. Budget analyses showed that investments ranging from $2.6 million to $10.4 million proportionally enhanced system performance without over-sizing. The integration of Battery and Thermal Energy Storage with Phase Change Materials further supported passive thermal control, reducing HVAC reliance during peak hours %K genetic algorithms, genetic programming, Energy management, Storage systems, Optimization, Demand response, Efficiency %9 journal article %R 10.1016/j.rineng.2025.105427 %U https://www.sciencedirect.com/science/article/pii/S2590123025014975 %U http://dx.doi.org/10.1016/j.rineng.2025.105427 %P 105427 %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 10.1145/3319619.3326814 %U https://arxiv.org/abs/1905.02258 %U http://dx.doi.org/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 10.1109/INISTA.2015.7276734 %U http://dx.doi.org/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 10.1109/CEC.2016.7744083 %U https://www.dora.dmu.ac.uk/handle/2086/11896 %U http://dx.doi.org/10.1109/CEC.2016.7744083 %P 2381-2388 %0 Thesis %T Gene expression programming for efficient time-series financial forecasting %A Alghieth, Manal %D 2016 %8 January %C UK %C School of Computer Science and Informatics, De Montfort University %F Alghieth:thesis %X Stock market prediction is of immense interest to trading companies and buyers due to high profit margins. The majority of successful buying or selling activities occur close to stock price turning trends. This makes the prediction of stock indices and analysis a crucial factor in the determination that whether the stocks will increase or decrease the next day. Additionally, precise prediction of the measure of increase or decrease of stock prices also plays an important role in buying/selling activities. This research presents two core aspects of stock-market prediction. Firstly, it presents a Network based Fuzzy Inference System (ANFIS) methodology to integrate the capabilities of neural networks with that of fuzzy logic. A specialised extension to this technique is 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 thesis aims at the modeling 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 gene-expression-programming (GEP) techniques to tune algebraic functions representing the fittest equation for stock market activities. The technology achieves novelty by proposing a fractional adaptive mutation rate Elitism (GEP-FAMR) technique to initiate a balance between varied mutation rates between varied-fitness chromosomes thereby improving prediction accuracy and fitness improvement rate. The methodology is evaluated against five 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 95.96 percent for short-term 5-day and 95.35 percent for medium-term 56-day trading periods. The contribution of this research to theory is that it presented a novel evolutionary methodology with modified selection operators for the prediction of stock exchange data via Gene expression programming. The methodology dynamically adapts the mutation rate of different fitness groups in each generation to ensure a diversification II balance between high and low fitness solutions. The GEP-FAMR approach was preferred to Neural and Fuzzy approaches because it can address well-reported problems of over-fitting, algorithmic black-boxing, and data-snooping issues via GP and GEP algorithms %K genetic algorithms, genetic programming, Gene Expression Programming, GEP, Computer Science, Business, ANN, FIS, ANFIS, RBS %9 Ph.D. thesis %U https://api.semanticscholar.org/CorpusID:67307411 %0 Journal Article %T Exergoeconomic analysis and optimization of a solar energy-based integrated system with oxy-combustion for combined power cycle and carbon capturing %A Al-Hamed, Khaled H. M. %A Dincer, Ibrahim %J Energy %D 2022 %V 250 %@ 0360-5442 %F ALHAMED:2022:energy %X This work presents a newly developed integrated system that produces multiple useful products, namely electricity, space cooling, freshwater, and ammonium bicarbonate. The two sources of energy for this integrated system are solar energy and natural gas. The natural gas is consumed in an oxy-combustion Brayton cycle to produce electricity, while the solar energy provides electric power to the carbon capturing unit to produce ammonium bicarbonate as a valuable chemical product to compensate for the operation costs of carbon capture. This integrated system is studied using the exergoeconomic analysis and the multi-objective optimization method of genetic programming and genetic algorithm to enhance the thermodynamic and economic aspects of this system. Applying such an analysis to this integrated system adds more understanding and knowledge on how effectively and efficiently this carbon capture system operates and whether or not it is financially viable to pursue this integrated system for further prototyping and concept demonstration. The results of this exergoeconomic analysis show that the production cost of ammonium bicarbonate per 1 kg in this integrated system is 0.0687 $ kg-1, and this is much lower than the market price. This means that producing ammonium bicarbonate as a way to capture carbon dioxide is feasible financially. Furthermore, the optimization results show that the overall exergy destruction rate and the overall unit cost of products are 86,000 kW and 5.19 times 10-3 $ kJ-1, respectively, when operated under optimum conditions %K genetic algorithms, genetic programming, Ammonia, Carbon capture, Energy, Exergoeconomic analysis, Gas turbine, Optimization %9 journal article %R 10.1016/j.energy.2022.123814 %U https://www.sciencedirect.com/science/article/pii/S0360544222007174 %U http://dx.doi.org/10.1016/j.energy.2022.123814 %P 123814 %0 Conference Proceedings %T Ghost Swarms: Learning Swarm Rules from Environmental Changes Alone %A Alharthi, Khulud %A Abdallah, S. Zahraa %A Hauert, Sabine %Y Xue, Bing %Y Manzoni, Luca %Y Bakurov, Illya %S European Conference on Genetic Programming, EuroGP 2025 %S LNCS %D 2025 %8 23 25 apr %V 15609 %I Springer Nature %C Trieste %F alharthi:2025:EuroGP %X Swarm behaviours emerge from agents interacting with their local environment following simple rules. While directly observing each agent can be challenging, their collective behaviour leaves detectable environmental imprints that could offer insights into the underlying swarm dynamics. However, this task is complex due to the hidden and interconnected relationships between the rules governing agent interactions, the emergent swarm behaviour, and the environmental changes generated by this behaviour. In this work, we propose a method for extracting human-readable controllers from demonstrations showing only observable environmental imprints caused by the swarm. This approach explores whether these environmental imprints can reveal the swarm’s actions, even when the individual agents are challenging to track. Our approach eliminates the need for prior knowledge about the controller or its structure, enabling the successful learning of controllers from a single demonstration. We provide a novel method for understanding and managing both natural and engineered swarms by using the environmental imprints left by swarm behaviours, even when direct observation of the swarm’s actions is not feasible. %K genetic algorithms, genetic programming, Swarm Behaviour, Imitation Learning, Environmental Imprints %R 10.1007/978-3-031-89991-1_1 %U http://dx.doi.org/10.1007/978-3-031-89991-1_1 %P 1-17 %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 10.1109/UKCI.2010.5625586 %U http://dx.doi.org/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 10.1109/CIG.2011.6031997 %U http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper31.pdf %U http://dx.doi.org/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 November 13 aug %C Niagara Falls, Canada %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 10.1109/CIG.2013.6633639 %U http://dx.doi.org/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 10.1023/B:GENP.0000017009.11392.e2 %U http://dx.doi.org/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 10.1007/978-0-387-87623-8_16 %U http://dx.doi.org/10.1007/978-0-387-87623-8_16 %P 249-269 %0 Journal Article %T Difficult first strategy GP: an inexpensive sampling technique to improve the performance of genetic programming %A Ali, Muhammad Quamber %A Majeed, Hammad %J Evol. Intell. %D 2020 %V 13 %N 4 %F DBLP:journals/evi/AliM20 %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s12065-020-00355-2 %U https://doi.org/10.1007/s12065-020-00355-2 %U http://dx.doi.org/10.1007/s12065-020-00355-2 %P 537-549 %0 Journal Article %T Multi-objective Lyapunov-based controller design for nonlinear systems via genetic programming %A Ali, Mir Masoud Ale %A Jamali, Ali %A Asgharnia, A. %A Ansari, R. %A Mallipeddi, Rammohan %J Neural Computing and Applications %D 2022 %8 jan %V 34 %N 2 %@ 0941-0643 %F DBLP:journals/nca/AliJAAM22 %X In system control, stability is considered the most important factor as unstable system is impractical or dangerous to use. Lyapunov direct method, one of the most useful tools in the stability analysis of nonlinear systems, enables the design of a controller by determining the region of attraction (ROA). However, the two main challenges posed are (1) it is hard to determine the scalar function referred to as Lyapunov function, and (2) the optimality of the designed controller is generally questionable. In this paper, multi-objective genetic programming (MOGP)-based framework is proposed to obtain both optimal Lyapunov and control functions at the same time. In other words, MOGP framework is employed to minimize several time-domain performances as well as the ROA radius to find the optimal Lyapunov and control functions. The proposed framework is tested in several nonlinear benchmark systems, and the control performance is compared with state-of-the-art algorithms. %K genetic algorithms, genetic programming, Lyapunov function, Stability, Region of attraction, Pareto %9 journal article %R 10.1007/s00521-021-06453-1 %U https://rdcu.be/dl3Cd %U http://dx.doi.org/10.1007/s00521-021-06453-1 %P 1345-1357 %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 10.1016/j.agrformet.2018.09.002 %U http://www.sciencedirect.com/science/article/pii/S0168192318302971 %U http://dx.doi.org/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 10.1016/B978-0-12-816514-0.00002-3 %U http://www.sciencedirect.com/science/article/pii/B9780128165140000023 %U http://dx.doi.org/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 10.1016/j.jbi.2015.01.004 %U http://www.sciencedirect.com/science/article/pii/S1532046415000064 %U http://dx.doi.org/10.1016/j.jbi.2015.01.004 %P 256-269 %0 Thesis %T Intelligent Decision Making Ensemble Classification System for Breast Cancer Prediction %A Ali, Safdar %D 2015 %8 27 jul %C Nilore, Islamabad, Pakistan %C Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences %F Ali:thesis %X Breast cancer is a complex and heterogeneous disease which seriously impacts women’s health. The diagnostic of breast cancer is an intricate process. Therefore, an accurate and reliable prediction system for breast cancer is indispensable to avoid misleading results.In this regard, improved decision support systems are essential for breast cancer prediction. Consequently, this thesis focuses on the development of intelligent decision making systems using ensemble classification for the early prediction of breast cancer. Proteins of a breast tissue generally reflect the initial changes caused by successive genetic mutations, which may lead to cancer. In this research, such changes in protein sequences are exploited for the early diagnosis of breast cancer. It is found that substantial variation of Proline, Serine, Tyrosine, Cysteine, Arginine, and Asparagine amino acid molecules in cancerous proteins offer high discrimination for cancer diagnostic. Molecular descriptors derived from physicochemical properties of amino acids are used to transform primary protein sequences into feature spaces of amino acid composition (AAC), split amino acid composition (SAAC), pseudo amino acid composition-series (PseAAC-S), and pseudo amino acid composition-parallel (PseAAC-P). The research work in this thesis is divided in two phases. In the first phase, the basic framework is established to handle imbalanced dataset in order to enhance true prediction performance. In this phase, conventional individual learning algorithms are employed to develop different prediction systems. Firstly, in conjunction with oversampling based Mega-Trend-Diffusion (MTD) technique, individual prediction systems are developed. Secondly, homogeneous ensemble systems CanPro-IDSS and Can-CSCGnB are developed using MTD and cost-sensitive classifier (CSC) techniques, respectively. It is found that assimilation of MTD technique for the CanPro-IDSS system is superior than CSC based technique to handle imbalanced dataset of protein sequences. In this connection, a web based CanPro-IDSS cancer prediction system is also developed. Lastly, a novel heterogeneous ensemble system called IDMS-HBC is developed for breast cancer detection. The second phase of this research focuses on the exploitation of variation of amino acid molecules in cancerous protein sequences using physicochemical properties. In this phase, unlike traditional ensemble prediction approaches, the proposed IDM-PhyChm-Ens ensemble system is developed by combining the decision spaces of a specific classifier trained on different feature spaces. This intelligent ensemble system is constructed using diverse learning algorithms of Random Forest(RF), Support Vector Machines, K-Nearest Neighbor, and Naive Bayes (NB). It is observed that the combined spaces of SAAC+PseAAC-S and AAC+SAAC possess the best discrimination using ensemble-RF and ensemble-NB. Lastly, a novel classifier stacking based evolutionary ensemble system Can-Evo-Ens is also developed, whereby Genetic programming is used as the ensemble method. This study revealed that PseAAC-S feature space carries better discrimination power compared to AAC, SAAC, and PseAAC-P based feature extraction strategies. Intensive experiments are performed to evaluate the performance of the proposed intelligent decision making systems for cancer/non-cancer and breast/non-breast cancer datasets. The proposed approaches have demonstrated improvement over previous state-of-the-art approaches. The proposed systems maybe useful for academia, practitioners, and clinicians for the early diagnosis of breast cancer using protein sequences. Finally, it is expected that the findings of this research would have positive impact on diagnosis, prevention, treatment, and management of cancer %K genetic algorithms, genetic programming, Can-Evo-Ens %9 Ph.D. thesis %U http://faculty.pieas.edu.pk/abdulmajid/ %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 10.1109/TSE.2009.52 %U http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5210118&isnumber=4359463 %U http://dx.doi.org/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 10.1109/ICCNIT.2011.6020945 %U http://dx.doi.org/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 %8 21 24 mar %C Sousse, Tunisia %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 10.1109/SETIT.2012.6482043 %U http://dx.doi.org/10.1109/SETIT.2012.6482043 %P 962-975 %0 Conference Proceedings %T AutoGE: A Tool for Estimation of Grammatical Evolution Models %A Ali, Muhammad Sarmad %A Kshirsagar, Meghana %A Naredo, Enrique %A Ryan, Conor %Y Rocha, Ana Paula %Y Steels, Luc %Y van den Herik, H. Jaap %S Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021 %D 2021 %8 feb 4 6 %V 2 %I SCITEPRESS %C Online %F conf/icaart/Ali0NR21 %K genetic algorithms, genetic programming, grammatical evolution %R 10.5220/0010393012741281 %U http://dx.doi.org/10.5220/0010393012741281 %P 1274-1281 %0 Conference Proceedings %T Towards Automatic Grammatical Evolution for Real-world Symbolic Regression %A Ali, Muhammad Sarmad %A Kshirsagar, Meghana %A Naredo, Enrique %A Ryan, Conor %Y Baeck, Thomas %Y Wagner, Christian %Y Garibaldi, Jonathan M. %Y Lam, H. K. %Y Cottrell, Marie %Y Merelo, Juan Julian %Y Warwick, Kevin %S Proceedings of the 13th International Joint Conference on Computational Intelligence, IJCCI %D 2021 %8 oct 25 27 %I SCITEPRESS %C Online %F conf/ijcci/Ali0NR21 %X AutoGE (Automatic Grammatical Evolution) is a tool designed to aid users of GE for the automatic estimation of Grammatical Evolution (GE) parameters, a key one being the grammar. The tool comprises of a rich suite of algorithms to assist in fine tuning a BNF (Backus-Naur Form) grammar to make it adaptable across a wide range of problems. It primarily facilitates the identification of better grammar structures and the choice of function sets to enhance existing fitness scores at a lower computational overhead. we discuss and report experimental results for our Production Rule Pruning algorithm from AutoGE which employs a simple frequency-based approach for eliminating less useful productions. It captures the relationship between production rules and function sets involved in the problem domain to identify better grammar. The experimental study incorporates an extended function set and common grammar structures for grammar definition. Preliminary results based on ten popular real-world regression datasets demonstrate that the proposed algorithm not only identifies suitable grammar structures, but also prunes the grammar which results in shorter genome length for every problem, thus optimising memory usage. Despite using a fraction of budget in pruning, AutoGE was able to significantly enhance test scores for 3 problems. %K genetic algorithms, genetic programming, grammatical evolution, grammar pruning, effective genome length %R 10.5220/0010691500003063 %U http://dx.doi.org/10.5220/0010691500003063 %P 68-78 %0 Conference Proceedings %T Automated Grammar-based Feature Selection in Symbolic Regression %A Ali, Muhammad Sarmad %A Kshirsagar, Meghana %A Naredo, Enrique %A Ryan, Conor %Y Rahat, Alma %Y Fieldsend, Jonathan %Y Wagner, Markus %Y Tari, Sara %Y Pillay, Nelishia %Y Moser, Irene %Y Aleti, Aldeida %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Hemberg, Erik %Y Cleghorn, Christopher %Y Sun, Chao-li %Y Yannakakis, Georgios %Y Bredeche, Nicolas %Y Ochoa, Gabriela %Y Derbel, Bilel %Y Pappa, Gisele L. %Y Risi, Sebastian %Y Jourdan, Laetitia %Y Sato, Hiroyuki %Y Posik, Petr %Y Shir, Ofer %Y Tinos, Renato %Y Woodward, John %Y Heywood, Malcolm %Y Wanner, Elizabeth %Y Trujillo, Leonardo %Y Jakobovic, Domagoj %Y Miikkulainen, Risto %Y Xue, Bing %Y Neumann, Aneta %Y Allmendinger, Richard %Y Medina-Bulo, Inmaculada %Y Bechikh, Slim %Y Sutton, Andrew M. %Y Oliveto, Pietro Simone %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F ali:2022:GECCO %X With the growing popularity of machine learning (ML), regression problems in many domains are becoming increasingly high-dimensional. Identifying relevant features from a high-dimensional dataset still remains a significant challenge for building highly accurate machine learning models.Evolutionary feature selection has been used for high-dimensional symbolic regression using Genetic Programming (GP). While grammar based GP, especially Grammatical Evolution (GE), has been extensively used for symbolic regression, no systematic grammar-based feature selection approach exists. This work presents a grammar-based feature selection method, Production Ranking based Feature Selection (PRFS), and reports on the results of its application in symbolic regression.The main contribution of our work is to demonstrate that the proposed method can not only consistently select the most relevant features, but also significantly improves the generalization performance of GE when compared with several state-of-the-art ML-based feature selection methods. Experimental results on benchmark symbolic regression problems show that the generalization performance of GE using PRFS was significantly better than that of a state-of-the-art Random Forest based feature selection in three out of four problems, while in fourth problem the performance was the same. %K genetic algorithms, genetic programming, feature selection, symbolic regression, production ranking, grammatical evolution, grammar pruning %R 10.1145/3512290.3528852 %U http://dx.doi.org/10.1145/3512290.3528852 %P 902-910 %0 Thesis %T Automatic Production Selection in Grammatical Evolution %A Ali, Muhammad Sarmad %D 2023 %8 23 mar %C Ireland %C Lero, Faculty of Science and Engineering, Department of Computer Science & Information Systems, University of Limerick %F Muhammad_Sarmad_Ali:thesis %X By the very nature of its representation, symbolic regression through Grammatical Evolution (GE) stands a chance of being interpretable. However, while GE builds solutions using available building blocks (grammar productions), striving to achieve better approximation can sometimes compromise program size and hence interpretability. On the other hand, increased data dimensionality in regression problems poses a significant challenge when attempting to achieve better performance. This research addresses both issues and demonstrates that choosing the right set of grammar productions through production selection for a given problem not only lets GE perform dimensionality reduction but also reduces program sizes while maintaining the most important performance criterion, generalisation. Grammar design, especially the choice of productions, has largely been a subject of expert judgement or trial and error. We hypothesise that evolution convergence carries information which can be exploited to distinguish between worthy and less useful productions. To test this hypothesis, we devise a production ranking scheme to rank grammar productions used in solution derivations based on structural analysis. The ranking profile of productions provides rich information for production selection, and further development affirmed the effectiveness of the ranking approach. Grammar is not a static artifact in this research but rather adapts to a given problem. At different stages during evolution, productions which appear not to improve evolvability are pruned from the grammar. We develop two grammar pruning approaches: static pruning and dynamic pruning. While static pruning removes productions across subexperiments, dynamic pruning prunes the grammar across generations. The developed approaches of production ranking and grammar pruning are shown to achieve significantly smaller solutions while maintaining accuracy on a variety of synthetic as well as real-world regression problems. Algorithms developed in this research, with an extensive set of experimentation, analysis, and comparison, are integrated into an automated tool, AutoGE, which not only aids in primitive set selection but also in feature selection. Feature selection has been a challenging task, especially in high-dimensional symbolic regression. Using linear scaling to build the ranking profile of features, it is demonstrated that feature selection with AutoGE helps improve generalisation performance in high-dimensional problems compared to state-of-the-art machine learning approaches. %K genetic algorithms, genetic programming, Grammatical Evolution, GE, AutoGE, grammar productions, grammar design, Engineering %9 Ph.D. thesis %R 10.34961/researchrepository-ul.24083970 %U https://researchrepository.ul.ie/handle/10344/12127 %U http://dx.doi.org/10.34961/researchrepository-ul.24083970 %0 Journal Article %T Dynamic Grammar Pruning for Program Size Reduction in Symbolic Regression %A Ali, Muhammad Sarmad %A Kshirsagar, Meghana %A Naredo, Enrique %A Ryan, Conor %J SN Computer Science %D 2023 %V 4 %F Ali:2023:SNCS %X ... program size reduction without compromising generalization performance. We tested our approach on 13 standard symbolic regression datasets with Grammatical Evolution. Using a grammar embodying a well-defined function set as a baseline, we compare effective genome length and test performance with our approach. Dynamic grammar pruning achieved significantly better genome lengths for all datasets, while significantly improving generalization performance on three datasets, although it worsened in five datasets. When we used linear scaling during the production ranking stages (the first 20 generations) the results dramatically improved. Not only were the programs smaller in all datasets, but generalization scores were also significantly better than the baseline in 6 out of 13 datasets, and comparable in the rest. When the baseline was also linearly scaled as well, the program size was still smaller with the Production Ranking approach, while generalization scores dropped in only three datasets without any significant compromise in the rest. %K genetic algorithms, genetic programming, Grammatical evolution, Grammar pruning, Production ranking, Effective genome length %9 journal article %R 10.1007/s42979-023-01840-y %U https://rdcu.be/evF9r %U http://dx.doi.org/10.1007/s42979-023-01840-y %P Articleno:402 %0 Conference Proceedings %T Optimization of the Number and Placement of Routers in Wireless Mesh Networks %A Ali, Mohammed Sadeq Ali %A Cevik, Mesut %S 2022 International Conference on Artificial Intelligence of Things (ICAIoT) %D 2022 %8 29 30 dec %C Istanbul, Turkey %F Ali:2022:ICAIoT %X Wireless Mesh Networks (WMNs) are a new type of wireless network that has been growing in popularity. These networks consist of routers and clients. The routers are called mesh routers (MRs) and the clients are called mesh clients. WMNs have several advantages over traditional wireless networks, such as more reliable coverage and faster speeds. Many different types of algorithms can be used to determine the best placement for these routers, with some algorithms being better than others depending on the environment or situation. One algorithm is called a Genetic Algorithm (GA), which uses genetic programming to find an optimal solution for router placement. GA is used to find the best placement of the router so that it can provide the most coverage possible for a specific area GA or evolutionary algorithms are based on a biological theory known as Darwin’s theory. In evolutionary algorithms, it is since the information of the problem becomes chromosomes, and then the problem is solved by special problem-solving techniques in the evolutionary algorithm. The suggested method was implemented using the C++ programming environment and the NS2 software suite. Using a benchmark of produced instances, the experimental outcomes have been analysed. Variable sets of produced instances ranging in size from small to big have been explored. Consequently, several properties of WMNs, including the topological placement of mesh clients, have been recorded. %K genetic algorithms, genetic programming, Wireless networks, Wireless mesh networks, Evolutionary computation, Software, Reliability, Problem-solving, Internet of Things, IOT, routers, wireless network, WMNs %R 10.1109/ICAIoT57170.2022.10121861 %U http://dx.doi.org/10.1109/ICAIoT57170.2022.10121861 %0 Journal Article %T Artificial intelligent techniques for prediction of rock strength and deformation properties - A review %A Ali, Mujahid %A Hin Lai, Sai %J Structures %D 2023 %V 55 %@ 2352-0124 %F ALI:2023:istruc %X In rock design projects, a number of mechanical properties are frequently employed, particularly unconfined compressive strength (UCS) and deformation (E). The researchers attempt to conduct an indirect investigation since direct measurement of UCS and E is time-consuming, expensive, and requires more expertise and methodologies. Recent and past studies investigate the UCS and E from rock index tests mainly P-wave velocity (Vp), slake durability index, Density, Shore hardness, Schmidt hammer Rebound number (Rn), unit weight, porosity (e) point load strength (Is(50)), and block punch strength index test as its economical and easy to use. The evaluation of these properties is the essential input into modern design methods that routinely adopt some form of numerical modeling, such as machine learning (ML), Artificial Neural Networking (ANN), finite element modeling (FEM), and finite difference methods. Besides, several researchers evaluate the correlation between the input parameters using statistical analysis tools before using them for intelligent techniques. The current study compared the results of laboratory tests, statistical analysis, and intelligent techniques for UCS and E estimation including ANN and adaptive neuro-fuzzy inference system (ANFIS), Genetic Programming (GP), Genetic Expression Programming (GEP), and hybrid models. Following the execution of the relevant models, numerous performance indicators, such as root mean squared error, coefficient of determination (R2), variance account for, and overall ranking, are reviewed to choose the best model and compare the acquired results. Based on the current review, it is concluded that the same rock types from different countries show different mechanical properties due to weathering, size, texture, mineral composition, and temperature. For instance, in the UCS of strong rock (granite) in Spain, ranges from 24 MPa to 278 MPa, whereas in Malaysian rocks, it shows 39 MPa to 212 MPa. On the other side, the coefficient of determination (R2) correlation for the UCS also varies from country to country; while using different modern techniques, the R2 values improved. Finally, recommendations on material properties and modern techniques have been suggested %K genetic algorithms, genetic programming, Deformation, Unconfined Compressive Strength (UCS), Intelligent techniques, ANN, Statistical analysis %9 journal article %R 10.1016/j.istruc.2023.06.131 %U https://www.sciencedirect.com/science/article/pii/S2352012423008901 %U http://dx.doi.org/10.1016/j.istruc.2023.06.131 %P 1542-1555 %0 Journal Article %T Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures %A Ali, Mohsin %A Chen, Li %A Qureshi, Qadir Bux Alias Imran Latif %A Alsekait, Deema Mohammed %A Khan, Adil %A Arif, Kiran %A Luqman, Muhammad %A Elminaam, Diaa Salama Abd %A Hamza, Amir %A Khan, Majid %J Composites Part C: Open Access %D 2024 %V 15 %@ 2666-6820 %F Ali:2024:jcomc %X Steel-fiber-reinforced concrete (SFRC) has replaced traditional concrete in the construction sector, improving fracture resistance and post-cracking performance. However, extreme temperatures degrade concrete’s material characteristics including stiffness and strength. The construction industry increasingly embraces machine learning (ML) to estimate concrete properties and optimise cost and time accurately. This study employs independent ML methods, gene expression programming (GEP), multi-expression programming (MEP), XGBoost, and Bayesian estimation model (BES) to predict SFRC compressive strength (CS) at high temperatures. 307 experimental data points from published studies were used to develop the models. The models were trained using 70 percent of the dataset, with 15 percent for validation and 15 percent for testing. Iterative hyperparameter adjustment and trial-and-error refining achieved optimum predictions. All the models were evaluated using correlation (R) values for training, validation, and testing datasets. MEP showed slightly lower R-values of 0.923, 0.904, and 0.949 than GEP, which performed consistently with 0.963, 0.967, and 0.961. XGBoost had the greatest training R-value of 0.997 but dropped in validation (0.918) and testing (0.896). BES model exhibited commendable performance with scores of 0.986, 0.944, and 0.897. GEP and XGBoost exhibited great accuracy, with GEP sustaining constant accuracy across all datasets, highlighting its potency in predicting CS. Interpreting model predictions using SHapley Additive exPlanation (SHAP) highlighted temperature over heating rate. CS improved significantly as the steel fiber volume fraction (Vf) reached 1.5 percent, plateauing thereafter. The proposed models are valid and accurate, providing designers and builders with a practical and adaptable method for estimating strength in SFRC structural applications, particularly under high-temperature conditions %K genetic algorithms, genetic programming, SFRC, Elevated temperature, Machine learning, Compressive strength, Predictive model, gene expression programming %9 journal article %R 10.1016/j.jcomc.2024.100529 %U https://www.sciencedirect.com/science/article/pii/S2666682024000987 %U http://dx.doi.org/10.1016/j.jcomc.2024.100529 %P 100529 %0 Conference Proceedings %T Advancing Structural Safety: Genetic Programming Approaches to Steel Fiber-Reinforced Concrete (SFRC) Blast Response Prediction %A Ali, Mohsin %A Rusho, Maher Ali %A Chen3, Li %A Cruz, Dany Marcelo Tasan %S 2025 17th International Conference on Computer and Automation Engineering (ICCAE) %D 2025 %8 mar %F Ali:2025:ICCAE %X Steel Fiber-Reinforced Concrete (SFRC) has emerged as a preferred material for blast-resistant structures due to its exceptional mechanical properties and energy absorption capabilities. This study introduces a machine learning-based framework to predict the maximum displacement of SFRC structural members under blast loading. Using 107 experimental data points, split into $\mathbf7 0 \percent$ for training and 15 percent each for validation and testing, Gene Expression Programming (GEP) and Multi-Expression Programming (MEP) were applied. The GEP model exhibited superior predictive performance with R -values of 0.964 (training), 0.968 (validation), and 0.960 (testing), while the MEP model achieved reasonable accuracy with $R$-values of 0.922, 0.905, and 0.948, respectively. Additionally, parametric analysis revealed the influence of fiber properties on SFRC behaviour. This approach not only simplifies predictive modelling but also enhances its reliability, offering valuable insights for optimising SFRC design under extreme conditions and contributing to the advancement of resilient structural systems. %K genetic algorithms, genetic programming, Training, Accuracy, Loading, Machine learning, Predictive models, Mathematical models, Concrete, Steel, Load modelling, Steel Fiber Reinforced Concrete (SFRC), Blast loading, gene expression programming %R 10.1109/ICCAE64891.2025.10980530 %U http://dx.doi.org/10.1109/ICCAE64891.2025.10980530 %P 183-187 %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 10.1109/CDC.2016.7798684 %U http://dx.doi.org/10.1109/CDC.2016.7798684 %P 2789-2795 %0 Thesis %T Symbolic Regression for Reinforcement Learning in Continuous Spaces %A Alibekov, Eduard %D 2021 %8 aug %C Czech Republic %C F3 Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague %F Alibekov:thesis %X Reinforcement Learning (RL) algorithms can optimally solve dynamic decision and control problems in engineering, economics, medicine, artificial intelligence, and other disciplines.However, state-of-the-art RL methods still have not solved the transition from a small set of discrete states to fully continuous spaces. They have to rely on numerical function approximators, such as radial basis functions or neural networks, to represent the value function or policy mappings. While these numerical approximators are well-developed, the choice of a suitable architecture is a difficult step that requires significant trial-and-error tuning. Moreover, numerical approximators frequently exhibit uncontrollable surface artifacts that damage the overall performance of the controlled system. Symbolic Regression (SR) is an evolutionary optimization technique that automatically, without human intervention, generates analytical expressions to fit numerical data. The method has gained attention in the scientific community not only for its ability to recover known physical laws, but also for suggesting yet unknown but physically plausible and interpretable relationships. Additionally, the analytical nature of the result approximators allows to unleash the full power of mathematical apparatus. This thesis aims to develop methods to integrate SR into RL in a fully continuous case. To accomplish this goal, the following original contributions to the field have been developed. (i) Introduction of policy derivation methods. Their main goal is to exploit the full potential of using continuous action spaces, contrary to the state-of-the-art discretised set of actions. (ii) Quasi-symbolic policy derivation (QSPD) algorithm, specifically designed to be used with a symbolic approximation of the value function. The goal of the proposed algorithm is to efficiently derive continuous policy out of symbolic approximator. The experimental evaluation indicated the superiority of QSPD over state-of-the-art methods. (iii) Design of a symbolic proxy-function concept. Such a function is successfully used to alleviate the negative impacts of approximation artifacts on policy derivation. (iv) Study on fitness criterion in the context of SR for RL. The analysis indicated a fundamental flaw with any other symmetric error functions, including commonly used mean squared error. Instead, a new error function procedure has been proposed alongside with a novel fitting procedure. The experimental evaluation indicated dramatic improvement of the approximation quality for both numerical and symbolic approximators. (v) Robust symbolic policy derivation (RSPD) algorithm, which adds an extra level of robustness against imperfections in symbolic approximators. The experimental evaluation demonstrated significant improvements in the reachability of the goal state. All these contributions are then combined into a single,efficient SR for RL (ESRL) framework. Such a framework is able to tackle high-dimensional, fully-continuous RL problems out-of-the-box. The proposed framework has been tested on three bench-marks: pendulum swing-up, magnetic manipulation, and high-dimensional drone strike benchmark. %K genetic algorithms, genetic programming, Single Node Genetic Programming, reinforcement learning, optimal control, function approximation,evolutionary optimization, symbolic regression, robotics, autonomous systems %9 Ph.D. thesis %U https://cyber.felk.cvut.cz/news/eduard-alibekov-defended-his-ph-d-thesis/ %0 Journal Article %T Prediction of the shear modulus of municipal solid waste (MSW): An application of machine learning techniques %A Alidoust, Pourya %A Keramati, Mohsen %A Hamidian, Pouria %A Amlashi, Amir Tavana %A Gharehveran, Mahsa Modiri %A Behnood, Ali %J Journal of Cleaner Production %D 2021 %V 303 %@ 0959-6526 %F ALIDOUST:2021:JCP %X The dynamic properties of Municipal Solid Waste (MSW) are site-specific and need to be evaluated separately in different regions. The laboratory-based evaluation of MSW has difficulties such as an unpleasant aroma or degradability of MSW, making the testing procedure unfavorable. Moreover, these evaluations are time- and cost-intensive, which may also require trained personnel to conduct the tests. To address this concern, alternatively, the shear modulus of MSW can be estimated through some predictive models. In this study, the shear modulus was evaluated using 153 cyclic triaxial tests. For this purpose, the effects of various factors, including the shear strain (ShS), age of the MSW (Age), percentage of plastic (POP), confining pressure (CP), unit weight (UW), and loading frequency (F) on the shear modulus of MSW were evaluated. The data obtained through laboratory experiments was then employed to model the dynamic response of MSW using four different machine learning techniques including Artificial Neural Networks (ANN), Multivariate Adaptive Regression Splines (MARS), Multi-Gene Genetic Programming (MGGP), and M5 model Tree (M5Tree). A comparison of the performance of developed models indicated that the ANN model outperformed the other models. More specifically, for ANN, MARS, MGGP, and M5Tree models, the corresponding values of R-squared equal to 0.9897, 0.9640, 0.9617, and 0.8482 for the training dataset, while the values for the testing dataset for ANN, MARS, MGGP, and M5Tree are 0.9812, 0.9551, 0.9574, and 0.8745. Furthermore, although the developed models using MARS and MGGP techniques resulted in more errors compared to the ANN technique, they were found to produce reliable predictions. To further compare the performance and efficiency of the developed models and study the effects of each input variable on the output variable (i.e., shear modulus), model validity, parametric study, and sensitivity analysis were performed %K genetic algorithms, genetic programming, Municipal solid waste, Cyclic triaxial test, Shear modulus, Artificial neural network (ANN), Multivariate adaptive regression splines (MARS), Multi-gene genetic programming (MGGP), M5 model tree (M5Tree) %9 journal article %R 10.1016/j.jclepro.2021.127053 %U https://www.sciencedirect.com/science/article/pii/S0959652621012725 %U http://dx.doi.org/10.1016/j.jclepro.2021.127053 %P 127053 %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 10.1109/SKIMA.2014.7083391 %U http://dx.doi.org/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 10.1016/j.cageo.2009.09.014 %U http://www.sciencedirect.com/science/article/B6V7D-4YCS020-1/2/514d629e145e62f37dbf599a1a7608a9 %U http://dx.doi.org/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 10.5772/47801 %U http://dx.doi.org/10.5772/47801 %P 255-284 %0 Journal Article %T Predictive models of laminar flame speed in NH3/H2/O3/air mixtures using multi-gene genetic programming under varied fuelling conditions %A Ali Shah, Zubair %A Marseglia, G. %A De Giorgi, M. G. %J Fuel %D 2024 %V 368 %@ 0016-2361 %F ALISHAH:2024:fuel %X The primary aim of this study is to develop and validate a novel multi-gene genetic programming approach for accurately predicting Laminar Flame Speed (LFS) in ammonia (NH3)/hydrogen (H2)/air mixtures, a key aspect in the advancement of carbon-free fuel technologies. Ammonia, particularly when blended with hydrogen, presents significant potential as a carbon-free fuel due to its enhanced reactivity. This research not only investigates the effects of hydrogen concentration, initial temperature, and pressure on LFS and Ignition Delay Time (IDT) but also explores the impact of oxidizing agents like ozone (O3) in augmenting NH3 combustion. A modified reaction mechanism was implemented and validated through parametric analysis. Main findings demonstrate that IDT decreases with higher hydrogen concentrations, increased initial temperature, and initial pressure, although the influence of pressure decreases above 10 atm. Conversely, at lower temperatures (below 1200 K) and higher hydrogen concentrations (30 percent and 50 percent), the dominance of H2 chemistry can negatively impact initial pressure. LFS increases with higher temperature and hydrogen concentration, but decreases under elevated pressure, with its effect becoming negligible above 5 atm. An optimized equivalence ratio (?) range of 1.10 - 1.15 is identified for efficient combustion. Introducing ozone into the oxidizer notably improves LFS in NH3/H2/air mixtures, with the addition of 0.01 ozone mirroring the effect of a 10 percent hydrogen addition under normal conditions. The study’s fundamental contribution is the development of a multi-gene genetic algorithm, showcasing the correlation between predicted LFS values and actual values derived from chemkin simulations. The successful validation of this methodology across various case studies underscores its potential as a robust tool in zero-carbon combustion applications, marking a significant stride in the field %K genetic algorithms, genetic programming, NH, H, Laminar Flame Speed (LFS), Ignition Delay Time (IDT), Ozone (O), Multi-gene genetic programming %9 journal article %R 10.1016/j.fuel.2024.131652 %U https://www.sciencedirect.com/science/article/pii/S0016236124008007 %U http://dx.doi.org/10.1016/j.fuel.2024.131652 %P 131652 %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 10.1145/3377930.3390224 %U https://doi.org/10.1145/3377930.3390224 %U http://dx.doi.org/10.1145/3377930.3390224 %P 157-165 %0 Conference Proceedings %T A Neural Approach to Generation of Constructive Heuristics %A Alissa, Mohamad %A Sim, Kevin %A Hart, Emma %Y Ong, Yew-Soon %S 2021 IEEE Congress on Evolutionary Computation (CEC) %D 2021 %8 28 jun 1 jul %C Krakow, Poland %F Alissa:2021:CEC %X Both algorithm-selection methods and hyper-heuristic methods rely on a pool of complementary heuristics. Improving the pool with new heuristics can improve performance, however, designing new heuristics can be challenging. Methods such as genetic programming have proved successful in automating this process in the past. Typically, these make use of problem state-information and existing heuristics as components. Here we propose a novel neural approach for generating constructive heuristics, in which a neural network acts as a heuristic by generating decisions. We evaluate two architectures, an Encoder-Decoder LSTM and a Feed-Forward Neural Network. Both are trained using the decisions output from existing heuristics on a large set of instances. We consider streaming instances of bin-packing problems in a continual stream that must be packed immediately in strict order and using a limited number of resources. We show that the new heuristics generated are capable of solving a subset of instances better than the well-known heuristics forming the original pool, and hence the overall value of the pool is improved w.r.t. both Falkenauers performance metric and the number of bins used. %K genetic algorithms, genetic programming, Measurement, Navigation, Heuristic algorithms, Neural networks, ANN, Evolutionary computation, Dynamic scheduling, Automatic Heuristics Generation, Hyper-Heuristics, Encoder-Decoder LSTM, Streaming Bin-packing %R 10.1109/CEC45853.2021.9504989 %U http://dx.doi.org/10.1109/CEC45853.2021.9504989 %P 1147-1154 %0 Conference Proceedings %T Firefly Programming For Symbolic Regression Problems %A Aliwi, Mohamed %A Aslan, Selcuk %A Demirci, Sercan %S 2020 28th Signal Processing and Communications Applications Conference (SIU) %D 2020 %8 oct %F Aliwi:2020:SIU %X Symbolic regression is the process of finding a mathematical formula that fits a specific set of data by searching in different mathematical expressions. This process requires great accuracy in order to reach the correct formula. In this paper, we will present a new method for solving symbolic regression problems based on the firefly algorithm. This method is called Firefly Programming (FP). The results of applying firefly programming algorithm to some symbolic regression benchmark problems will be compared to the results of Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods. %K genetic algorithms, genetic programming, Optimization, Statistics, Sociology, Linear programming, Brightness, firefly algorithm, symbolic regression, automatic programming %R 10.1109/SIU49456.2020.9302201 %U http://dx.doi.org/10.1109/SIU49456.2020.9302201 %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 10.1109/EAIS.2011.5945924 %U http://dx.doi.org/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 10.1109/AICCSA.2010.5586985 %U http://dx.doi.org/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 10.3844/jcssp.2011.1574.1580 %U http://www.thescipub.com/pdf/10.3844/jcssp.2011.1574.1580 %U http://dx.doi.org/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 Conference Proceedings %T Hate Speech Detection Using Genetic Programming %A Aljero, Mona Khalifa A. %A Dimililer, Nazife %S 2020 International Conference on Advanced Science and Engineering (ICOASE) %D 2020 %8 dec %F Aljero:2020:ICOASE %X There has been a steep increase in the use of social media in our everyday lives in recent years. Along with this, there has been an increase in hate speech disseminated on these platforms, due to the anonymity of the users as well as the ease of use. Social media platforms need to filter and prevent the spread of hate speech to protect their users and society. Due to the high traffic, automatic detection of hate speech is necessary. Hate speech detection is one of the most difficult classification challenges in text mining. Research in this domain focuses on the use of supervised machine learning approaches, such as support vector machine, logistic regression, convolutional neural network, and random forest. Ensemble techniques have also been employed. However, the performance of these approaches has not yet reached an acceptable level. In this paper, we propose the use of the Genetic Programming (GP) approach for binary classification of hate speech on social media platforms. Each individual in the GP framework represents a classifier that is evolved to optimize Fl-score. Experimental results show the effectiveness of our GP approach; the proposed approach outperforms the state-of-the-art using the same dataset HatEval. %K genetic algorithms, genetic programming %R 10.1109/ICOASE51841.2020.9436621 %U http://dx.doi.org/10.1109/ICOASE51841.2020.9436621 %0 Journal Article %T Genetic Programming Approach to Detect Hate Speech in Social Media %A Aljero, Mona Khalifa A. %A Dimililer, Nazife %J IEEE Access %D 2021 %V 9 %@ 2169-3536 %F Aljero:2021:A %X Social media sites, which became central to our everyday lives, enable users to freely express their opinions, feelings, and ideas due to a certain level of depersonalization and anonymity they provide. If there is no control, these platforms may be used to propagate hate speech. In fact, in recent years, hate speech has increased on social media. Therefore, there is a need to monitor and prevent hate speech on these platforms. However, manual control is not feasible due to the high traffic of content production on social media sites. Moreover, the language used and the length of the messages provide a challenge when using classical machine learning approaches as prediction methods. This paper presents a genetic programming (GP) model for detecting hate speech where each chromosome represents a classifier employing a universal sentence encoder as a feature. A novel mutation technique that affects only the feature values in combination with the standard one-point mutation technique improved the performance of the GP model by enriching the offspring pool with alternative solutions. The proposed GP model outperformed all state-of-the-art systems for the four publicly available hate speech datasets. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/ACCESS.2021.3104535 %U http://dx.doi.org/10.1109/ACCESS.2021.3104535 %P 115115-115125 %0 Journal Article %T Binary text classification using genetic programming with crossover-based oversampling for imbalanced datasets %A Aljero, Mona Khalifa A. %A Dimililer, Nazife %J Turkish J. Electr. Eng. Comput. Sci. %D 2023 %V 31 %N 1 %F DBLP:journals/elektrik/AljeroD23 %K genetic algorithms, genetic programming %9 journal article %R 10.55730/1300-0632.3978 %U https://doi.org/10.55730/1300-0632.3978 %U http://dx.doi.org/10.55730/1300-0632.3978 %P 180-192 %0 Journal Article %T Ensemble Optimization for Invasive Ductal Carcinoma (IDC) Classification Using Differential Cartesian Genetic Programming %A Alkhaldi, Eid %A Salari, Ezzatollah %J IEEE Access %D 2022 %V 10 %@ 2169-3536 %F Alkhaldi:2022:IEEEAccess %X The high cost of acquiring annotated histological slides for breast specimens entails exploiting an ensemble of models appropriately trained on small datasets. Histological Image Classification ensembles strive to accurately detect abnormal tissues in the breast samples by determining the correlation between the predictions of its weak learners. Nonetheless, the state-of-the-art ensemble methods, such as boosting and bagging, count merely on manipulating the dataset and lack intelligent ensemble decision making. Furthermore, the methods mentioned above are short of the diversity of the weak models of the ensemble. Likewise, other commonly used voting strategies, such as weighted averaging, are limited to how the classifiers’ diversity and accuracy are balanced. Hence, In this paper, we assemble a Neural Network ensemble that integrates the models trained on small datasets by employing biologically-inspired methods. Our procedure is comprised of two stages. First, we train multiple heterogeneous pre-trained models on the benchmark Breast Histopathology Images for Invasive Ductal Carcinoma (IDC) classification dataset. In the second meta-training phase, we use the differential Cartesian Genetic Programming (dCGP) to generate a Neural Network that merges the trained models optimally. We compared our empirical outcomes with other state-of-the-art techniques. Our results demonstrate that improvising a Neural Network ensemble using Cartesian Genetic Programming transcended formerly published algorithms on slim datasets. %K genetic algorithms, genetic programming, Cartesian genetic programming %9 journal article %R 10.1109/ACCESS.2022.3228176 %U http://dx.doi.org/10.1109/ACCESS.2022.3228176 %P 128790-128799 %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 Department of Civil Engineering, Faculty of Engineering and Computing, Curtin University %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, GEP, modelling pile capacity, construction, load-settlement behaviour of piles, artificial intelligence, artificial neural networks, ANN, numerical modelling techniques %9 Ph.D. thesis %U http://espace.library.curtin.edu.au/Modelling.pdf %0 Book %T Modelling pile capacity & load-settlement behaviour from CPT data: For piles in sand and mixed soils using artificial intelligence %A Alkroosh, Iyad %D 2012 %8 23 may %I Lambert Academic Publishing %F Alkroosh:book %X This work involves the presentation of new approach attempted to predict the axial capacity and load-settlement behaviour of piles embedded in sand and mixed soils. Two artificial intelligence techniques including Gene Expression Programming (GEP) and Artificial Neural Networks (ANNs) have been used in the approach. The work begins with the definitions of the two techniques and explanation of their terminology and the theories which each of them is based on. The work also comprises extensive literature review of the proposed procedures for evaluating pile capacity and load settlement behaviour. The application of the artificial intelligence in the work begins with the use of the GEP for modelling the pile capacity. The modelling involves data collection, selection of input variables, data division, determination of setting parameters and GEP model selection and model formulation and validation. Two models are developed, a model for bored piles and two others for driven piles. In the second phase of this work, the artificial neural network used for modelling the load-settlement behaviour of the piles. %K genetic algorithms, genetic programming, Gene Expression Programming, ANN %U https://www.amazon.co.uk/Modelling-pile-capacity-load-settlement-behaviour/dp/3848436906 %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 10.1016/j.sandf.2014.02.013 %U http://www.sciencedirect.com/science/article/pii/S0038080614000213 %U http://dx.doi.org/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 10.1038/nbt823 %U http://dbkgroup.org/Papers/NatureBiotechnology21(692-696).pdf %U http://dx.doi.org/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 10.1128/AEM.70.10.6157-6165.2004 %U http://dx.doi.org/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 10.1145/1569901.1570029 %U http://dx.doi.org/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 10.1007/0-387-28111-8_12 %U http://dx.doi.org/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 10.1145/1143997.1144040 %U http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p239.pdf %U http://dx.doi.org/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 10.1007/978-0-387-76308-8_9 %U http://dx.doi.org/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 10.1007/978-0-387-87623-8_2 %U http://dx.doi.org/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 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/10.1145/3372787.3390439 %P 43-54 %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 2020 ACM/IEEE 15th International Conference on Global Software Engineering (ICGSE) %D 2020 %8 may %F Almarimi:2020:ICGSE %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 sub-optimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an auto-mated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterise the existence of community smell in-stances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89percent achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterise community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community. %K genetic algorithms, genetic programming, Search-based software engineering, SBSE, Costs, Social networking (online), Standards organizations, Collaboration, Transforms, Software quality, Community smells, Social debt, Socio-technical factors, Multi-label learning %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=10148849 %P 43-54 %0 Conference Proceedings %T Genetic Programming with External Memory in Sequence Recall Tasks %A Al Masalma, Mihyar %A Heywood, Malcolm %Y Trautmann, Heike %Y Doerr, Carola %Y Moraglio, Alberto %Y Bartz-Beielstein, Thomas %Y Filipic, Bogdan %Y Gallagher, Marcus %Y Ong, Yew-Soon %Y Gupta, Abhishek %Y Kononova, Anna V. %Y Wang, Hao %Y Emmerich, Michael %Y Bosman, Peter A. N. %Y Zaharie, Daniela %Y Caraffini, Fabio %Y Dreo, Johann %Y Auger, Anne %Y Dietric, Konstantin %Y Dufosse, Paul %Y Glasmachers, Tobias %Y Hansen, Nikolaus %Y Mersmann, Olaf %Y Posik, Petr %Y Tusar, Tea %Y Brockhoff, Dimo %Y Eftimov, Tome %Y Kerschke, Pascal %Y Naujoks, Boris %Y Preuss, Mike %Y Volz, Vanessa %Y Derbel, Bilel %Y Li, Ke %Y Li, Xiaodong %Y Zapotecas, Saul %Y Zhang, Qingfu %Y Coletti, Mark %Y Schuman, Catherine (Katie) %Y Scott, Eric “Siggy” %Y Patton, Robert %Y Wiegand, Paul %Y Bassett, Jeffrey K. %Y Gunaratne, Chathika %Y Chugh, Tinkle %Y Allmendinger, Richard %Y Hakanen, Jussi %Y Tauritz, Daniel %Y Woodward, John %Y Lopez-Ibanez, Manuel %Y McCall, John %Y Bacardit, Jaume %Y Brownlee, Alexander %Y Cagnoni, Stefano %Y Iacca, Giovanni %Y Walker, David %Y Toutouh, Jamal %Y O’Reilly, UnaMay %Y Machado, Penousal %Y Correia, Joao %Y Nesmachnow, Sergio %Y Ceberio, Josu %Y Villanueva, Rafael %Y Hidalgo, Ignacio %Y Fernandez de Vega, Francisco %Y Paolo, Giuseppe %Y Coninx, Alex %Y Cully, Antoine %Y Gaier, Adam %Y Wagner, Stefan %Y Affenzeller, Michael %Y Bruce, Bobby R. %Y Nowack, Vesna %Y Blot, Aymeric %Y Winter, Emily %Y Langdon, William B. %Y Petke, Justyna %Y Fernandez Alzueta, Silvino %Y Valledor Pellicer, Pablo %Y Stuetzle, Thomas %Y Paetzel, David %Y Wagner, Alexander %Y Heider, Michael %Y Veerapen, Nadarajen %Y Malan, Katherine %Y Liefooghe, Arnaud %Y Verel, Sebastien %Y Ochoa, Gabriela %Y Omidvar, Mohammad Nabi %Y Sun, Yuan %Y Tarantino, Ernesto %Y Ivanoe, De Falco %Y Della Cioppa, Antonio %Y Umberto, Scafuri %Y Rieffel, John %Y Mouret, Jean-Baptiste %Y Doncieux, Stephane %Y Nikolaidis, Stefanos %Y Togelius, Julian %Y Fontaine, Matthew C. %Y Georgescu, Serban %Y Chicano, Francisco %Y Whitley, Darrell %Y Kyriienko, Oleksandr %Y Dahl, Denny %Y Shir, Ofer %Y Spector, Lee %Y Rahat, Alma %Y Everson, Richard %Y Fieldsend, Jonathan %Y Wang, Handing %Y Jin, Yaochu %Y Hemberg, Erik %Y Elsayed, Marwa A. %Y Kommenda, Michael %Y La Cava, William %Y Kronberger, Gabriel %Y Gustafson, Steven %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F alMasalma:2022:GECCOcomp %X Partially observable tasks imply that a learning agent has to recall previous state in order to make a decision in the present. Recent research with neural networks have investigated both internal and external memory mechanisms for this purpose, as well as proposing benchmarks to measure their effectiveness. These developments motivate our investigation using genetic programming and an external linked list memory model. A thorough empirical evaluation using a scalable sequence recall benchmark establishes the underlying strength of the approach. In addition, we assess the impact of decisions made regarding the instruction set and characterize the sensitivity to noise / obfuscation in the definition of the benchmarks. Compared to neural solutions to these benchmarks, GP extends the state-of-the-art to greater task depths than previously possible. %K genetic algorithms, genetic programming, modularity, partially observable, external memory %R 10.1145/3520304.3528883 %U http://dx.doi.org/10.1145/3520304.3528883 %P 518-521 %0 Journal Article %T Benchmarking ensemble genetic programming with a linked list external memory on scalable partially observable tasks %A Al Masalma, Mihyar %A Heywood, Malcolm %J Genetic Programming and Evolvable Machines %D 2022 %8 30 nov %V 23 %N Suppl 1 %@ 1389-2576 %F alMasalma:2023:GPEM %X Reactive learning agents cannot solve partially observable sequential decision-making tasks as they are limited to defining outcomes purely in terms of the observable state. However, augmenting reactive agents with external memory might provide a path for addressing this limitation. In this work, external memory takes the form of a linked list data structure that programs have to learn how to use. We identify conditions under which additional recurrent connectivity from program output to input is necessary for state disambiguation. Benchmarking against recent results from the neural network literature on three scalable partially observable sequential decision-making tasks demonstrates that the proposed approach scales much more effectively. Indeed, solutions are shown to generalize to far more difficult sequences than those experienced under training conditions. Moreover, recommendations are made regarding the instruction set and additional benchmarking is performed with input state values designed to explicitly disrupt the identification of useful states for later recall. The protected division operator appears to be particularly useful in developing simple solutions to all three tasks. %K genetic algorithms, genetic programming, External memory, Partial observability, Internal state, Ensembles, noop, pop_head, pop_tail, DIV %9 journal article %R 10.1007/s10710-022-09446-8 %U https://rdcu.be/daFLX %U http://dx.doi.org/10.1007/s10710-022-09446-8 %P s1-s29 %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 10.1109/LGRS.2017.2719033 %U http://dx.doi.org/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 10.1016/j.ecoinf.2015.01.003 %U http://www.sciencedirect.com/science/article/pii/S1574954115000114 %U http://dx.doi.org/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 10.1109/SIBGRAPI.2016.029 %U http://dx.doi.org/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 10.3233/ICA-180561 %U http://dx.doi.org/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 Thesis %T Investigation of the performance of cracked steel members strengthened with carbon fibre reinforced polymers under impact loads %A Al-Mosawe, Alaa %D 2016 %C Melbourne, Australia %C Faculty of Science, Engineering and Technology, Swinburne University of Technology %F AlMosawe:thesis %X Generally, steel structures are subjected to different types of loadings during their life-time. Over time these structures sustain fewer loads than those for which they were designed. The reduction in structural capacity might occur as a result of various parameters, including aging, changes in use, increases in applied loads, and as a result of environmental effects causing corrosion. These structures need to be strengthened or repaired in order to be able to carry the different applied loads. Carbon fibre reinforced polymers (CFRPs) are a new method of strengthening, the use of which has grown in the last few decades. This method of strengthening has attracted structural engineers due to its ease of application, light weight and very high tensile strength. The bond between CFRP and steel members is the main issue in understanding the bond behaviour. This thesis presents the effect of impact loading on the bond behaviour of CFRP-steel double strap joints. The results of comprehensive experimental tests are presented in this project on the basis of testing large numbers of CFRP-steel double strap joints under both static and dynamic loadings. Another series of tests was conducted to investigate the mechanical properties of the composite material itself. The mechanical properties were investigated under different loading rates, starting from quasi-static loading at 2mm/min, to impact loadings of 201000mm/minute, 258000mm/minute and 300000mm/minute. The experimental results showed that loading rate has a significant effect on the material properties, and a significant increase was shown in tensile strength and modulus of elasticity. The results of another series of tests are presented in this thesis. A number of CFRP-steel double strap joints were prepared and tested under quasi-static loads. Three different types of CFRP modulus (low modulus 165 GPa, normal modulus 205GPa and ultra-high CFRP modulus 460 GPa) were used, to study the effect of CFRP modulus on the bond behaviour between steel and CFRP laminates. In order to investigate the effect of CFRP geometry on the bond properties, two different CFRP sections were used (20 by 1.4mm and 10 by 1.4mm). The results showed a significant influence on the bond strength, strain distribution along the bond, effective bond length and failure mode for specimens with different CFRP modulus. The results also showed that a small CFRP section is sensitive to any little movement. Further tests were also conducted on CFRP-steel double strap specimens with different CFRP moduli under high impact loading rates. The load rates used in this project were 201m per minute, 258m per minute and 300m/min. The aim of this test was to find the degree of joint enhancement under dynamic loadings compared to quasi-static loads. The results showed a significant increase in load-carrying capacity, and strain distribution along the bond. However, a significant decrease in the effective bond length under impact loads was observed compared to quasi-static testing. Different failure modes were shown compared to specimens tested under quasi-static loadings. Finite element analysis was conducted in this research to model the CFRP-steel double strap joint under both quasi-static and dynamic loads. The individual components of the joint (CFRP laminate, Araldite 420 adhesive and steel plates) were first modeled and analysed under the four loading rates. The CFRP-steel double strap joints were modelled using non-linear finite element analysis using the commercial software ABAQUS 6.13. The results showed good prediction of material properties and joint behaviour using non-linear finite element analysis, and the results of tensile joint strength, strain distribution along the bond, effective bond length and failure modes were close to those tested experimentally. This thesis also shows a new formulation of CFRP-steel double strap joints using genetic programming; the data from the experimental and numerical analysis were analysed using genetic programming software. Three different parameters were used: bond length, loading rate and the CFRP modulus. The outcomes of this analysis are showing an expression tree and a new equation to express the bond strength of these types of joints. The results are assumed to be used for the range of parameters used as input data in the programming. Finally, some suggestions on future work to continue the investigation of the bond behaviour between CFRP and steel in the double strap joints are provided. %K genetic algorithms, genetic programming, CFRP, Fe %9 Ph.D. thesis %U http://hdl.handle.net/1959.3/414765 %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 10.1016/j.compstruct.2016.11.016 %U http://www.sciencedirect.com/science/article/pii/S0263822316317767 %U http://dx.doi.org/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 10.1109/IEMBS.2009.5335368 %U http://dx.doi.org/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 10.1016/j.medengphy.2010.11.008 %U http://dx.doi.org/10.1016/j.medengphy.2010.11.008 %P 411-417 %0 Conference Proceedings %T Genetic Improvement of Shoreline Evolution Forecasting Models %A Al Najar, Mahmoud %A Almar, Rafael %A Bergsma, Erwin W. J. %A Delvit, Jean-Marc %A Wilson, Dennis G. %Y Bruce, Bobby R. %Y Nowack, Vesna %Y Blot, Aymeric %Y Winter, Emily %Y Langdon, W. B. %Y Petke, Justyna %S GI @ GECCO 2022 %D 2022 %8 September %I Association for Computing Machinery %C Boston, USA %F AlNajar:2022:GI %X Coastal development and climate change are changing the geography of our coasts, while more and more people are moving towards the coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Cartesian Genetic Programming (CGP) is a form of Genetic Programming (GP) that has been successfully used in a large variety of tasks including data-driven symbolic regression. We formulate the problem of shoreline evolution forecasting as a Genetic Improvement (GI) problem using CGP to encode and improve upon ShoreFor, an equilibrium shoreline prediction model, to study the effectiveness of CGP in GI in forecasting tasks. This work presents an empirical study of the sensitivity of CGP to a number of evolutionary configurations and constraints and compares the performances of the evolved models to the base ShoreFor model. %K genetic algorithms, genetic programming, genetic improvement, Cartesian Genetic Programming, RCGP, symbolic regression, forecasting, shoreline evolution, earth observation salellite, CGP-ShoreFor, ShorefFor, physical sciences, geography, coastal erosion, Tairua New Zealand, Mielke correlation coefficient %R 10.1145/3520304.3534041 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/AlNajar_2022_GI.pdf %U http://dx.doi.org/10.1145/3520304.3534041 %P 1916-1923 %0 Conference Proceedings %T Improving a Shoreline Forecasting Model with Symbolic Regression %A Al Najar, Mahmoud %A Almar, Rafael %A Bergsma, Erwin W. J. %A Delvit, Jean-Marc %A Wilson, Dennis G. %S ICLR 2023 Workshop on Tackling Climate Change with Machine Learning %D 2023 %8 April %C Kigali Rwanda %F alnajar2023improving %X Given the current context of climate change and the increasing population densities at coastal zones around the globe, there is an increasing need to be able to predict the development of our coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Symbolic Regression (SR) is a type of Machine Learning algorithm that aims to find interpretable symbolic expressions that can explain relations in the data. In this work, we aim to study the problem of forecasting shoreline change using SR. We make use of Cartesian Genetic Programming (CGP) in order to encode and improve upon ShoreFor, a physical shoreline prediction model. During training, CGP individuals are evaluated and selected according to their predictive score at five different coastal sites. This work presents a comparison between a CGP-evolved model and the base ShoreFor model. In addition to evolution’s ability to produce well-performing models, it demonstrates the usefulness of SR as a research tool to gain insight into the behaviors of shorelines in various geographical zones. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, Machine Learning, Interpretable ML, XAI, Symbolic Computation, Earth Observation & Monitoring, Extreme Weather, Ocean, Atmosphere, Hybrid Physical Models, Time-series Analysis %U https://www.climatechange.ai/papers/iclr2023/21 %0 Thesis %T Estimating Coastal Evolution with Machine Learning %A Al Najar, Mahmoud %D 2023 %8 30 nov %C France %C Institut Superieur de l’Aeronautique et de l’Espace, University of Toulouse %F AlNajar:thesis %X Forecasting coastal evolution is a prerequisite for effective coastal management and has been a fundamental goal of coastal research for decades. However, coastal evolution is a complex process, and predicting its development through time remains challenging. The absence of representative datasets which accurately track the state and evolution of coastal systems greatly limits our ability to study these processes given different natural and anthropological scenarios. While traditional field surveys have been used extensively in the literature and have served as important assets in advancing our knowledge of these systems, the high operational costs of traditional field surveys limit their use to local and sparse spatio-temporal scales. Satellite-based Remote Sensing (RS) provides the opportunity for frequently monitoring the Earth at high temporal resolutions and scales, but requires the development of novel data processing methodologies for large streams of Earth Observation data. Machine Learning (ML) is a subfield of Artificial Intelligence which aims at constructing algorithms able to leverage large amounts of example data in order to automatically construct predictive models, and has been a critical component of many scientific advancements in recent years. This thesis examines the potential and capability of modern ML in two important problems in Coastal Science where ML remains unexplored. Deep Learning and Interpretable Machine Learning are applied to the problems of satellite-derived bathymetry and shoreline evolution modelling. The work demonstrates that ML is competitive with current physics-based baselines on both tasks, and shows the potential of ML in automating many of our large-scale coastal data analysis towards gaining a global understanding of coastal evolution. %K genetic algorithms, genetic programming, genetic improvement, Cartesian Genetic Programming, Deep Learning, Earth Observation, Shoreline forecasting, Bathymetry inversion, Bathymetrie, Littoraux – Modifications, Programmation genetique (informatique), Apprentissage profond, Teledetection en sciences de la Terre %9 Ph.D. thesis %U https://doctorat.univ-toulouse.fr/as/ed/detailResp.pl?resp=31559 %0 Journal Article %T Optimizing the Energy Consumption of Blockchain-Based Systems Using Evolutionary Algorithms: A New Problem Formulation %A Alofi, Akram %A Bokhari, Mahmoud A. %A Bahsoon, Rami %A Hendley, Robert J. %J IEEE Transactions on Sustainable Computing %D 2022 %V 7 %N 4 %F DBLP:journals/tsusc/AlofiBBH22 %X ... we represent the problem as a subset selection problem of miners in blockchain-based systems. We formulate the problem of blockchain energy consumption as an optimisation problem with four conflicting objectives: energy consumption, carbon emission, decentralisation and trust. We propose a model composed of different fitness functions ... %K genetic algorithms, genetic programming, genetic improvement, Search-Based Software Engineering, SBSE, Blockchain, Mining, Optimization, Evolutionary Algorithms, Sustainability %9 journal article %R 10.1109/TSUSC.2022.3160491 %U https://research.birmingham.ac.uk/en/publications/optimizing-the-energy-consumption-of-blockchain-based-systems-usi %U http://dx.doi.org/10.1109/TSUSC.2022.3160491 %P 910-922 %0 Journal Article %T Physics-based models, surrogate models and experimental assessment of the vehicle-bridge interaction in braking conditions %A Aloisio, Angelo %A Contento, Alessandro %A Alaggio, Rocco %A Quaranta, Giuseppe %J Mechanical Systems and Signal Processing %D 2023 %V 194 %@ 0888-3270 %F ALOISIO:2023:ymssp %X The dynamics of roadway bridges crossed by vehicles moving at variable speed has attracted far less attention than that generated by vehicles travelling at constant velocity. Consequently, the role of some parameters and the combination thereof, as well as influence and accuracy of the modelling strategies, are not fully understood yet. Therefore, a large statistical analysis is performed in the present study to provide novel insights into the dynamic vehicle-bridge interaction (VBI) in braking conditions. To this end, an existing mid-span prestressed concrete bridge is selected as case study. First, several numerical simulations are performed considering alternative vehicle models (i.e., single and two degrees-of-freedom models) and different braking scenarios (i.e., soft and hard braking conditions, with both stationary and nonstationary road roughness models in case of soft braking). The statistical appraisal of the obtained results unfolds some effects of the dynamic VBI modelling in braking conditions that have not been reported in previous studies. Additionally, the use of machine learning techniques is explored for the first time to develop surrogate models able to predict the effect of the dynamic VBI in braking conditions efficiently. These surrogate models are then employed to obtain the fragility curve for the selected prestressed concrete bridge, where the attainment of the decompression moment is considered as relevant limit state. Whilst the derivation of the fragility curve using numerical simulations turned out to be almost unpractical using standard computational resources, the proposed approach that exploits surrogate models carried out via machine learning techniques was demonstrated accurate despite the dramatic reduction of the total elaboration time. Finally, the accuracy of the numerical (physics-based and surrogate) models is evaluated on a statistical basis through comparisons with experimental data %K genetic algorithms, genetic programming, Bouncing, Braking, Bridge, Fragility curve, Machine learning, Moving load, Neural network, ANN, Pitching, Roughness, Surrogate model, Vehicle-bridge interaction %9 journal article %R 10.1016/j.ymssp.2023.110276 %U https://www.sciencedirect.com/science/article/pii/S0888327023001838 %U http://dx.doi.org/10.1016/j.ymssp.2023.110276 %P 110276 %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 10.1109/ICTAI.2008.14 %U http://dx.doi.org/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 10.1142/S0218213009000391 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.301.3133 %U http://dx.doi.org/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 10.1109/ICTAI.2009.35 %U http://dx.doi.org/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 Proceedings of the 5th International Joint Conference on Computational Intelligence, IJCCI 2013 %D 2013 %8 20 22 sep %I SciTePress %C Vilamoura, Algarve, Portugal %F conf/ijcci/AlonsoMB13 %X In this paper we propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials) and real problems, show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalisation error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria. %K genetic algorithms, genetic programming %R 10.5220/0004554100250036 %U https://ijcci.scitevents.org/Abstract.aspx?idEvent=0fEvcjBHBM8= %U http://dx.doi.org/10.5220/0004554100250036 %P 25-36 %0 Book Section %T Genetic Programming Model Regularization %A Alonso, Cesar L. %A Montana, Jose Luis %A Borges, Cruz Enrique %E Madani, Kurosh %E Dourado, Antonio %E Rosa, Agostinho %E Filipe, Joaquim %E Kacprzyk, Janusz %B Computational Intelligence %S Springer Professional Technik %D 2016 %I Springer %F Alonso:2016:CI %O Selected extended papers from the fifth International Joint Conference on Computational Intelligence (IJCCI 2013), held in Vilamoura, Algarve, Portugal, from 20 to 22 September 2013 %X We propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials), show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalization error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria. %K genetic algorithms, genetic programming, VC dimension %R 10.1007/978-3-319-23392-5_6 %U https://www.springerprofessional.de/en/genetic-programming-model-regularization/6856568 %U http://dx.doi.org/10.1007/978-3-319-23392-5_6 %P 105-120 %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 10.1007/978-3-540-70720-2_3 %U http://dx.doi.org/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 10.1145/1830483.1830664 %U http://dx.doi.org/10.1145/1830483.1830664 %P 989-990 %0 Journal Article %T Symbolic Regression Model for Predicting Compression Strength of Prismatic Masonry Columns Confined by FRP %A Alotaibi, Khalid Saqer %A Islam, A. B. M. Saiful %J Buildings %D 2023 %V 13 %N 2 %@ 2075-5309 %F alotaibi:2023:Buildings %X The use of Fiber Reinforced Polymer (FRP) materials for the external confinement of existing concrete or masonry members is now an established technical solution. Several studies in the scientific literature show how FRP wrapping can improve the mechanical properties of members. Though there are numerous methods for determining the compressive strength of FRP confined concrete, no generalised formulae are available because of the greater complexity and heterogeneity of FRP-confined masonry. There are two main objectives in this analytical study: (a) proposing an entirely new mathematical expression to estimate the compressive strength of FRP confined masonry columns using symbolic regression model approach which can outperform traditional regression models, and (b) evaluating existing formulas. Over 198 tests of FRP wrapped masonry were compiled in a database and used to train the model. Several formulations from the published literature and international guidelines have been compared against experimental data. It is observed that the proposed symbolic regression model shows excellent performance compared to the existing models. The model is easier, has no restriction and thereby it can be feasibly employed to foresee the behaviour of FRP confined masonry elements. The coefficient of determination for the proposed symbolic regression model is determined as 0.91. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/buildings13020509 %U https://www.mdpi.com/2075-5309/13/2/509 %U http://dx.doi.org/10.3390/buildings13020509 %P ArticleNo.509 %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 Modeling the High-Frequency FX Market: An Agent-Based Approach %A Aloud, Monira %A Fasli, Maria %A Tsang, Edward %A Dupuis, Alexander %A Olsen, Richard %J Computational Intelligence %D 2017 %8 nov %V 33 %N 4 %@ 1467-8640 %F aloud:2017:coin %X The development of computational intelligence-based strategies for electronic markets has been the focus of intense research. To be able to design efficient and effective automated trading strategies, one first needs to understand the workings of the market, the strategies that traders use, and their interactions as well as the patterns emerging as a result of these interactions. In this article, we develop an agent-based model of the foreign exchange (FX) market, which is the market for the buying and selling of currencies. Our agent-based model of the FX market comprises heterogeneous trading agents that employ a strategy that identifies and responds to periodic patterns in the price time series. We use the agent-based model of the FX market to undertake a systematic exploration of its constituent elements and their impact on the stylised facts (statistical patterns) of transactions data. This enables us to identify a set of sufficient conditions that result in the emergence of the stylized facts similarly to the real market data, and formulate a model that closely approximates the stylized facts. We use a unique high-frequency data set of historical transactions data that enables us to run multiple simulation runs and validate our approach and draw comparisons and conclusions for each market setting. %K genetic algorithms, genetic programming, agent-based modeling, agent-based simulation, electronic markets, FX markets, stylized facts. %9 journal article %R 10.1111/coin.12114 %U http://repository.essex.ac.uk/18823/ %U http://dx.doi.org/10.1111/coin.12114 %P 771-825 %0 Journal Article %T Robust group intelligent models for predicting hydrogen density and viscosity: Implication for hydrogen production, transportation, and storage %A Alqahtani, Fahd Mohamad %A Youcefi, Mohamed Riad %A Nait Amar, Menad %A Djema, Hakim %A Ghasemi, Mohammad %J Journal of the Taiwan Institute of Chemical Engineers %D 2025 %V 168 %@ 1876-1070 %F Alqahtani:2025:jtice %X Backgrou %K genetic algorithms, genetic programming, Hydrogen, Viscosity, Density, Machine learning, Committee machine intelligent system, Multi-gene genetic programming %9 journal article %R 10.1016/j.jtice.2024.105949 %U https://www.sciencedirect.com/science/article/pii/S1876107024006072 %U http://dx.doi.org/10.1016/j.jtice.2024.105949 %P 105949 %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 10.1093/comjnl/bxh134 %U http://comjnl.oxfordjournals.org/cgi/content/full/49/1/129 %U http://dx.doi.org/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 10.1109/CIES.2013.6611741 %U http://dx.doi.org/10.1109/CIES.2013.6611741 %P 141-148 %0 Journal Article %T Integrated finite element analysis and machine learning approach for propagation pressure prediction in hybrid Steel-CFRP subsea pipelines %A Alrsai, Mahmoud %A Alsahalen, Ala’ %A Karampour, Hassan %A Alhawamdeh, Mohammad %A Alajarmeh, Omar %J Ocean Engineering %D 2024 %V 311 %@ 0029-8018 %F Alrsai:2024:oceaneng %X Accurate prediction of the propagation pressure (PP) in hybrid steel-CFRP pipe systems presents a substantial challenge due to intricate interactions and complex collapse failure modes. An efficient FE-based algorithm is programmed using ANSYS to numerically estimate the PP of hybrid steel-CFRP pipe, subjected to external pressure. This study employs a machine learning (ML) framework, addressing the inherent complexity with a three-phase approach: Parameter Design, Buckle Propagation Analysis, and ML Model Development. The dataset, encompassing about two thousand observations with four key features, undergoes k-fold cross-validation and min-max normalization for robust ML performance. Five ML models-Random Forest (RF), K-Nearest Neighbors (KNN), Genetic Programming (GP), Multi-layer Perceptron (MLP), and Support Vector Machine (SVM)-are developed and evaluated. The results revealed a significant influence of Ds/ts, a three-phase relationship with ts/tc, and a substantial decrease in PPh/PPs with increasing sigmays/sigmauc, predominantly exhibiting U-shaped or dog-bone failure modes in different scenarios. Proven that GP, KNN, and RF are the superior performers, ranking ahead of SVM with Gaussian Kernel (SVM-GK), MLP, and SVM with Linear Kernel (SVM-LK). Statistical metrics, Taylor Diagram analysis, and comparisons with FE results emphasize the effectiveness of GP, KNN, and RF. Additionally, normality tests and feature importance analysis provide nuanced insights %K genetic algorithms, genetic programming, Hybrid steel-CFRP pipe, Buckle propagation, U-shape failure, Collapse, Machine learning %9 journal article %R 10.1016/j.oceaneng.2024.118808 %U https://www.sciencedirect.com/science/article/pii/S0029801824021462 %U http://dx.doi.org/10.1016/j.oceaneng.2024.118808 %P 118808 %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 10.1016/j.conbuildmat.2018.11.219 %U http://www.sciencedirect.com/science/article/pii/S0950061818329143 %U http://dx.doi.org/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 10.1109/ICARA.2011.6144874 %U http://dx.doi.org/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 10.1109/CEC.2012.6256412 %U http://dx.doi.org/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 10.1016/j.eswa.2012.02.123 %U http://www.sciencedirect.com/science/article/pii/S0957417412003867 %U http://dx.doi.org/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 10.1109/CEC.2013.6557889 %U http://dx.doi.org/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 10.1109/IVCNZ.2013.6727019 %U http://dx.doi.org/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 10.1007/978-3-319-03680-9_13 %U http://dx.doi.org/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 10.1145/2683405.2683418 %U http://dl.acm.org/citation.cfm?id=2683405 %U http://dx.doi.org/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 10.1109/CEC.2015.7257190 %U http://dx.doi.org/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 10.1145/2739480.2754661 %U http://doi.acm.org/10.1145/2739480.2754661 %U http://dx.doi.org/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 10.1162/EVCO_a_00146 %U https://figshare.com/articles/journal_contribution/Binary_image_classification_A_genetic_programming_approach_to_the_problem_of_limited_training_instances/13150958?file=25287002 %U http://dx.doi.org/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 10.1109/TEVC.2016.2577548 %U http://dx.doi.org/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 10.1109/TEVC.2016.2577548 %U http://dx.doi.org/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 10.1145/3067695.3076039 %U http://doi.acm.org/10.1145/3067695.3076039 %U http://dx.doi.org/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 10.1007/978-3-319-68759-9_41 %U http://dx.doi.org/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 10.1109/TEVC.2017.2685639 %U http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7885048 %U http://dx.doi.org/10.1109/TEVC.2017.2685639 %P 825-844 %0 Thesis %T Genetic Programming for Automatically Synthesising Robust Image Descriptors with A Small Number of Instances %A Al-Sahaf, Harith %D 2017 %C New Zealand %C School of Engineering and Computer Science, Victoria University of Wellington %F Al-Sahaf:thesis %X Image classification is a core task in many applications of computer vision, including object detection and recognition. It aims at analysing the visual content and automatically categorising a set of images into different groups. Performing image classification can largely be affected by the features used to perform this task. Extracting features from images is a challenging task due to the large search space size and practical requirements such as domain knowledge and human intervention. Human intervention is usually needed to identify a good set of keypoints (regions of interest), design a set of features to be extracted from those keypoints such as lines and corners, and develop a way to extract those features. Automating these tasks has great potential to dramatically decrease the time and cost, and may potentially improve the performance of the classification task. There are two well-recognised approaches in the literature to automate the processes of identifying keypoints and extracting image features. Designing a set of domain-independent features is the first approach, where the focus is on dividing the image into a number of predefined regions and extracting features from those regions. The second approach is synthesising a function or a set of functions to form an image descriptor that aims at automatically detecting a set of keypoints such as lines and corners, and performing feature extraction. Although employing image descriptors is more effective and very popular in the literature, designing those descriptors is a difficult task that in most cases requires domain-expert intervention. The overall goal of this thesis is to develop a new domain independent Genetic Programming (GP) approach to image classification by using GP to evolve programs that are capable of automatically detecting diverse and informative keypoints, designing a set of features, and performing feature extraction using only a small number of training instances to facilitate image classification, and are robust to different image changes such as illumination and rotation. This thesis focuses on incorporating a variety of simple arithmetic operators and first-order statistics (mid-level features) into the evolutionary process and on representation of GP to evolve programs that are robust to image changes for image classification. This thesis proposes methods for domain-independent binary classification in images using GP to automatically identify regions within an image that have the potential to improve classification while considering the limitation of having a small training set. Experimental results show that in over 67percent of cases the new methods significantly outperform the use of existing hand-crafted features and features automatically detected by other methods. This thesis proposes the first GP approach for automatically evolving an illumination-invariant dense image descriptor that detects automatically designed keypoints, and performs feature extraction using only a few instances of each class. The experimental results show improvement of 86percent on average compared to two GP-based methods, and can significantly outperform domain-expert hand-crafted descriptors in more than 89percent of the cases. This thesis also considers rotation variation of images and proposes a method for automatically evolving rotation-invariant image descriptors through integrating a set of first-order statistics as terminals. Compared to hand-crafted descriptors, the experimental results reveal that the proposed method has significantly better performance in more than 83percent of the cases. This thesis proposes a new GP representation that allows the system to automatically choose the length of the feature vector side-by-side with evolving an image descriptor. Automatically determining the length of the feature vector helps to reduce the number of the parameters to be set. The results show that this method has evolved descriptors with a very small feature vector which yet still significantly outperform the competitive methods in more than 91percent of the cases. This thesis proposes a method for transfer learning by model in GP, where an image descriptor evolved on instances of a related problem (source domain) is applied directly to solve a problem being tackled (target domain). The results show that the new method evolves image descriptors that have better generalisability compared to hand-crafted image descriptors. Those automatically evolved descriptors show positive influence on classifying the target domain datasets in more than 56percent of the cases. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://hdl.handle.net/10063/6177 %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 %D 2021 %8 Fall %V 29 %N 3 %@ 1063-6560 %F Al-Sahaf:EC %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, for example, corners, line-segments, and shapes, in an image and extracting features from those key points. In this article, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by using a multitree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the few-shot setting and used to assess the performance of the proposed method and compared against six handcrafted and one evolutionary computation-based image descriptor as well as three convolutional neural network (CNN) based methods. The experimental results show that the new method has significantly outperformed the competitor image descriptors and CNN-based methods. Furthermore, different patterns have been identified from analysing the evolved programs. %K genetic algorithms, genetic programming, ANN, image descriptor, multi-tree, image classification, feature extraction %9 journal article %R 10.1162/evco_a_00284 %U https://doi.org/10.1162/evco_a_00284 %U http://dx.doi.org/10.1162/evco_a_00284 %P 331-366 %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 10.1007/978-3-540-31989-4_3 %U http://dx.doi.org/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 %8 29 dec %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 10.1016/S0169-7439(00)00101-5 %U http://dx.doi.org/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 10.1007/978-3-319-99241-9_1 %U https://discovery.ucl.ac.uk/id/eprint/10060107/ %U http://dx.doi.org/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 10.1109/GI.2019.00010 %U https://doi.org/10.1109/GI.2019.00010 %U http://dx.doi.org/10.1109/GI.2019.00010 %P 1 %0 Conference Proceedings %T Software Testing Research Challenges: An Industrial Perspective %A Alshahwan, Nadia %A Harman, Mark %A Marginean, Alexandru %Y Sampath, Sreedevi %S 16th IEEE International Conference on Software Testing, Verification and Validation (ICST 2023) %D 2023 %8 16 20 apr %C Dublin, Ireland %F Alshahwan:2023:ICST %O Keynote %X There have been rapid recent developments in automated software test design, repair and program improvement. Advances in artificial intelligence also have great potential impact to tackle software testing research problems. we highlight open research problems and challenges from an industrial perspective. This perspective draws on our experience at Meta Platforms, which has been actively involved in software testing research and development for approximately a decade. As we set out here, there are many exciting opportunities for software testing research to achieve the widest and deepest impact on software practice. With this overview of the research landscape from an industrial perspective, we aim to stimulate further interest in the deployment of software testing research. We hope to be able to collaborate with the scientific community on some of these research challenges. %K genetic algorithms, genetic programming, Genetic Improvement, SBSE, Automated Software Engineering, Software Testing, Automated Program Repair, APR, Artificial Intelligence, AI, Automated Remediation, regression testing %R 10.1109/ICST57152.2023.00008 %U https://research.facebook.com/file/1235985840680898/Software-Testing-Research-Challenges--An-Industrial-Perspective.pdf %U http://dx.doi.org/10.1109/ICST57152.2023.00008 %P 1-10 %0 Generic %T Assured LLM-Based Software Engineering %A Alshahwan, Nadia %A Harman, Mark %A Harper, Inna %A Marginean, Alexandru %A Sengupta, Shubho %A Wang, Eddy %D 2024 %8 June %I arXiv %C Lisbon, Portugal %F alshahwan2024assured %O InteNSE 2024 Keynote %X How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engineering; a generate-and-test approach, inspired by Genetic Improvement. Assured LLMSE applies a series of semantic filters that discard code that fails to meet these twin guarantees. This overcomes the potential problem of LLM’s propensity to hallucinate. It allows us to generate code using LLMs, independently of any human. The human plays the role only of final code reviewer, as they would do with code generated by other human engineers. This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal %K genetic algorithms, genetic programming, genetic improvement, ANN, LLMSE, SBSE, prompt search space, facebook, automatic test oracle, refactoring, APR, searchable prompting language %U https://arxiv.org/abs/2402.04380 %0 Conference Proceedings %T Automated Unit Test Improvement using Large Language Models at Meta %A Alshahwan, Nadia %A Chheda, Jubin %A Finogenova, Anastasia %A Gokkaya, Beliz %A Harman, Mark %A Harper, Inna %A Marginean, Alexandru %A Sengupta, Shubho %A Wang, Eddy %S Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering %S FSE 2024 %D 2024 %8 jul 15 19 %I ACM %C Porto de Galinhas, Brazil %F Alshahwan:2024:FSEcomp %X This paper describes Meta TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests. TestGen-LLM verifies that its generated test classes successfully clear a set of filters that assure measurable improvement over the original test suite, thereby eliminating problems due to LLM hallucination. We describe the deployment of TestGen-LLM at Meta test-a-thons for the Instagram and Facebook platforms. In an evaluation on Reels and Stories products for Instagram, 75percent of TestGen-LLMs test cases built correctly, 57percent passed reliably, and 25percent increased coverage. During Meta Instagram and Facebook test-a-thons, it improved 11.5percent of all classes to which it was applied, with 73percent of its recommendations being accepted for production deployment by Meta software engineers. We believe this is the first report on industrial scale deployment of LLM-generated code backed by such assurances of code improvement. %K TestGen-LLM, Automated Test Generation, Genetic Improvement, LLMs, Large Language Models, ANN, SBSE, Unit Testing %R 10.1145/3663529.3663839 %U https://doi.org/10.1145/3663529.3663839 %U http://dx.doi.org/10.1145/3663529.3663839 %P 185-196 %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 10.1145/1570256.1570358 %U http://dx.doi.org/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 10.1109/CEC.2010.5586288 %U http://dx.doi.org/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 10.1109/CNSM.2010.5691210 %U http://dx.doi.org/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 10.1109/CEC.2011.5949799 %U http://dx.doi.org/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 10.1016/j.jksuci.2014.03.013 %U http://www.sciencedirect.com/science/article/pii/S1319157814000561 %U http://dx.doi.org/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 10.1016/j.energy.2015.11.079 %U http://www.sciencedirect.com/science/article/pii/S0360544215016424 %U http://dx.doi.org/10.1016/j.energy.2015.11.079 %P 266-273 %0 Journal Article %T Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings %A Alsharif, Rashed %A Arashpour, Mehrdad %A Golafshani, Emadaldin Mohammadi %A Hosseini, M. Reza %A Chang, Victor %A Zhou, Jenny %J Journal of Building Engineering %D 2022 %V 46 %@ 2352-7102 %F ALSHARIF:2022:JBE %X The housing sector consumes a significant amount of energy worldwide, which is mainly attributed to operating energy systems for the provision of thermally comfortable indoor environments. Although the literature in this field has focused on investigating critical factors in energy consumption, only a few studies have conducted a quantitative sensitivity analysis for thermal occupant factors (TOF) (i.e., metabolic rate and clothing level). Therefore, this paper introduces a framework for testing the criticality of TOF with a cross-comparison against building-related factors, considering the constraint of occupant thermal comfort. Using a building energy simulation model, the energy consumption of a case study is simulated, and building energy model alternatives are generated. The scope includes TOF and building envelope factors, with an established orthogonal experimental design. A popular branch of machine learning (ML) called linear genetic programming (LGP) is used to analyse the generated data from the experiment. Finally, a sensitivity analysis is conducted using the developed LGP model to determine and rank the criticality of the considered factors. The findings reveal that occupants’ metabolic rate and clothing level have relevancy factors of -0.48 and -0.38 respectively, which ranked them 2nd and 3rd against building envelope factors for achieving energy-efficient comfortable houses. This research contributes to the literature by introducing a framework that couples orthogonal experiment design with ML techniques to quantify the criticality of TOF and rank them against building-envelope factors %K genetic algorithms, genetic programming, Artificial Intelligence, Energy simulation, Metabolic rate, Predicted mean vote (PMV), Sustainability %9 journal article %R 10.1016/j.jobe.2021.103846 %U https://www.sciencedirect.com/science/article/pii/S2352710221017046 %U http://dx.doi.org/10.1016/j.jobe.2021.103846 %P 103846 %0 Journal Article %T Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization %A Alshayeb, Suhaib %A Stevanovic, Aleksandar %A Park, B. Brian %J Energies %D 2021 %V 14 %N 21 %@ 1996-1073 %F alshayeb:2021:Energies %X Transportation agencies optimise signals to improve safety, mobility, and the environment. One commonly used objective function to optimise signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimise fuel consumption (FC). The critical component of the PI is the stop penalty K, which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is used to develop prediction models for the K-factor. The proposed K-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behaviour, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models’ quality in capturing the individual impact of the included parameters on the K-factor. The developed models showed an excellent performance in estimating the K-factor under multiple conditions. Future research shall evaluate the findings by using field-based K-values in optimising signals to reduce FC. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/en14217431 %U https://www.mdpi.com/1996-1073/14/21/7431 %U http://dx.doi.org/10.3390/en14217431 %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 10.1109/CEC.2012.6256155 %U http://dx.doi.org/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 10.1109/SSCI.2015.148 %U http://dx.doi.org/10.1109/SSCI.2015.148 %P 1021-1028 %0 Thesis %T Models for the prediction and management of complex systems in industrial and dynamic environments %A Alsina, Emanuel Federico %D 2016 %C Italy %C Universita degli studi di Modena e Reggio Emilia %F Alsina:thesis %X The world in which we live is becoming more and more complex. Modelling the reality means to create simplifications and abstractions of that, in order to figure out what is going on in this modern and complex world in which we live. Nowadays, models have become crucial to make better decisions. Models help us to be clearer thinkers, and to understand how to transform data in useful information. There are too many data out there, models take these data and structure them into information, and then into knowledge. Two main topics are discussed in this work: (1) how to model complex systems, and (2) how to make predictions within complex systems, in industrial and dynamic environments. The purpose of this thesis is to present a series of models developed to support the decision makers in the complexity management. The first topic is addressed presenting some models concerning the balancing of assembly lines, machine degradation in production lines, operation schedule, and the positing of cranes in automated warehousing. In particular, concerning the assembly lines, two bio-inspired models which optimize the global picking time of the components considering their physical allocation are presented. Moreover, the use of a multi-agent model able to simultaneously consider different factors that affect machines in a production line is analysed. This approach takes into account the ageing and the degradation of the machines, the repairs, the replacement, and the preventive maintenance activities. Furthermore, in order to present how to manage the complexity intrinsic into the operations scheduling, a model inspired by the behaviour of an ant colony is showed. Finally, another multi-agent model is showed, which is able to find the optimal dwell point in automated storage retrieval systems exploiting an idea deriving from force-fields. After that, an entire chapter is dedicated to the prediction in complex systems. Prediction in industrial and dynamic environments is a challenge that professionals and academics have to face more and more. Some models able to capture non-linear relationships between temporal events are presented. These models are applied to different fields, from the reliability of mechanical and electrical components, to renewable energy. In the final analysis, models able to predict the users behaviors within online social communities are introduced. In these cases, various machine learning approaches (such as artificial neural networks, logistic regressions, and random trees) are detailed. This thesis want to be an inspiration for those people which have to manage the complexity in industrial and dynamic environments, showing examples and results, in order to explain how to make this world a little more understandable. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U https://morethesis.unimore.it/theses/available/etd-11262015-110057/ %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 10.1109/CISDA.2009.5356540 %U http://dx.doi.org/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 10.1109/CISDA.2012.6291531 %U http://dx.doi.org/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 10.1145/2457450.2457453 %U http://doi.acm.org/http://dx.doi.org/10.1145/2457450.2457453 %U http://dx.doi.org/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 10.1109/HAVE.2013.6679618 %U http://dx.doi.org/10.1109/HAVE.2013.6679618 %P 98-102 %0 Thesis %T Towards a Continuous User Authentication Using Haptic Information %A Alsulaiman, Fawaz Abdulaziz A. %D 2013 %C Canada %C School of Electrical Engineering and Computer Science, University of Ottawa %F Alsulaiman_Fawaz_Abdulaziz_A_2013_thesis %X With the advancement in multimedia systems and the increased interest in haptics to be used in interpersonal communication systems, where users can see, show, hear, tell, touch and be touched, mouse and keyboard are no longer dominant input devices. Touch, speech and vision will soon be the main methods of human computer interaction. Moreover, as interpersonal communication usage increases, the need for securing user authentication grows. In this research, we examine a user’s identification and verification based on haptic information. We divide our research into three main steps. The first step is to examine a pre-defined task, namely a handwritten signature with haptic information. The user target in this task is to mimic the legitimate signature in order to be verified. As a second step, we consider the user’s identification and verification based on user drawings. The user target is predefined, however there are no restrictions imposed on the order or on the level of details required for the drawing. Lastly, we examine the feasibility and possibility of distinguishing users based on their haptic interaction through an interpersonal communication system. In this third step, there are no restrictions on user movements, however a free movement to touch the remote party is expected. In order to achieve our goal, many classification and feature reduction techniques have been discovered and some new ones were proposed. Moreover, in this work we use evolutionary computing in user verification and identification. Analysis of haptic features and their significance on distinguishing users is hence examined. The results show a use of visual features by Genetic Programming (GP) towards identity verification, with a probability equal to 50percent while the remaining haptic features were used with a probability of approximately 50percent. Moreover, with a handwritten signature application, a verification success rate of 97.93percent with False Acceptance Rate (FAR) of 1.28percent and 11.54percent False Rejection Rate (FRR) is achieved with the use of genetic programming enhanced with the random over sampled data set. In addition, with a totally free user movement in a haptic-enabled interpersonal communication system, an identification success rate of 83.3percent is achieved when random forest classifier is used. %K genetic algorithms, genetic programming, User Authentication, Identity Verification, User Identification, Haptics, Haptic-enabled Interpersonal Communication System %9 Ph.D. thesis %U https://ruor.uottawa.ca/bitstream/10393/23946/3/Alsulaiman_Fawaz_Abdulaziz_A_2013_thesis.pdf %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 10.1109/TLA.2011.6030978 %U http://dx.doi.org/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 10.1109/CLEI.2013.6670618 %U http://dx.doi.org/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 %R 10.7551/mitpress/1108.003.0009 %U http://dynamics.org/~altenber/PAPERS/EEGP/ %U http://dx.doi.org/10.7551/mitpress/1108.003.0009 %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 10.1016/B978-1-55860-356-1.50006-6 %U https://dynamics.org/Altenberg/PAPERS/STPT/ %U http://dx.doi.org/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 %S Lecture Notes in Computer Science %D 1992 %8 jul %V 899 %I Springer-Verlag %C Monterey, California, USA %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 %R 10.1007/3-540-59046-3_11 %U http://dynamics.org/~altenber/PAPERS/GGEGPM/ %U http://dx.doi.org/10.1007/3-540-59046-3_11 %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 10.1007/1-4020-7782-3_4 %U http://dynamics.org/Altenberg/FILES/LeeOPSAED.pdf %U http://dx.doi.org/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 10.1162/106454605774270633 %U http://dx.doi.org/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 10.1007/s10710-013-9198-5 %U http://dx.doi.org/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 10.1007/s10710-014-9223-3 %U http://dx.doi.org/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 10.1016/B978-0-12-800049-6.00307-3 %U https://www.sciencedirect.com/science/article/pii/B9780128000496003073 %U http://dx.doi.org/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 10.1007/s10710-017-9290-3 %U http://dx.doi.org/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 Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study %A Althoey, Fadi %A Akhter, Muhammad Naveed %A Nagra, Zohaib Sattar %A Awan, Hamad Hassan %A Alanazi, Fayez %A Khan, Mohsin Ali %A Javed, Muhammad Faisal %A Eldin, Sayed M. %A Ozkilic, Yasin Onuralp %J Case Studies in Construction Materials %D 2023 %V 18 %@ 2214-5095 %F ALTHOEY:2023:cscm %X This research study uses four machine learning techniques, i.e., Multi Expression programming (MEP), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Ensemble Decision Tree Bagging (DT-Bagging) for the development of new and advanced models for prediction of Marshall Stability (MS), and Marshall Flow (MF) of asphalt mixes. A comprehensive and detailed database of 343 data points was established for both MS and MF. The predicting variables were chosen among the four most influential, and easy-to-determine parameters. The models were trained, tested, validated, and the outcomes of the newly developed models were compared with actual outcomes. The root squared error (RSE), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), regression coefficient (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed that in the case of MS, the rising order of input significance was bulk specific gravity of compacted aggregate, Gmb (38.56 percent) > Percentage of Aggregates, Ps (19.84 percent) > Bulk Specific Gravity of Aggregate, Gsb (19.43 percent) > maximum specific gravity paving mix, Gmm (7.62 percent), while in case of MF the order followed was: Ps (36.93 percent) > Gsb (14.11 percent) > Gmb (10.85 percent) > Gmm (10.19 percent). The outcomes of parametric analysis (PA) consistency of results in relation to previous research findings. The DT-Bagging model outperformed all other models with values of 0.971 and 0.980 (R), 16.88 and 0.24 (MAE), 28.27 and 0.36 (RMSE), 0.069 and 0.041 (RSE), 0.020 and 0.032 (RRMSE), 0.010 and 0.016 (PI), 0.931 and 0.959 (NSE), for MS and MF, respectively. The results of the comparison analysis showed that ANN, ANFIS, MEP, and DT-Bagging are all effective and reliable approaches for the estimation of MS and MF. The MEP-derived mathematical expressions represent the novelty of MEP and are relatively simple and reliable. Roverall values for MS and MF were in the order of DT-Bagging >MEP >ANFIS >ANN with all values exceeding the permitted range of 0.80 for both MS and MF. Hence, all the modeling approaches showed higher performance, possessed high generalization and predication capabilities, and assess the relative significance of input parameters in the prediction of MS and MF. Hence, the findings of this research study would assist in the safer, faster, and sustainable prediction of MS and MF, from the standpoint of resources and time required to perform the Marshall tests %K genetic algorithms, genetic programming, Marshall Mix Parameter, Deep Learning, Prediction models, Asphalt, Bio-Inspired models %9 journal article %R 10.1016/j.cscm.2022.e01774 %U https://www.sciencedirect.com/science/article/pii/S2214509522009068 %U http://dx.doi.org/10.1016/j.cscm.2022.e01774 %P e01774 %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 10.2166/hydro.2012.219 %U https://iwaponline.com/jh/article-pdf/15/3/763/387059/763.pdf %U http://dx.doi.org/10.2166/hydro.2012.219 %P 763-779 %0 Journal Article %T Determination of Semi-Empirical Models for Mean Wave Overtopping Using an Evolutionary Polynomial Paradigm %A Altomare, Corrado %A Laucelli, Daniele B. %A Mase, Hajime %A Gironella, Xavi %J Journal of Marine Science and Engineering %D 2020 %V 8 %N 8 %@ 2077-1312 %F altomare:2020:JMSE %X The present work employs the so-called Evolutionary Polynomial Regression (EPR) algorithm to build up a formula for the assessment of mean wave overtopping discharge for smooth sea dikes and vertical walls. EPR is a data-mining tool that combines and integrates numerical regression and genetic programming. This technique is here employed to dig into the relationship between the mean discharge and main hydraulic and structural parameters that characterise the problem under study. The parameters are chosen based on the existing and most used semi-empirical formulas for wave overtopping assessment. Besides the structural freeboard or local wave height, the unified models highlight the importance of local water depth and wave period in combination with foreshore slope and dike slope on the overtopping phenomena, which are combined in a unique parameter that is defined either as equivalent or imaginary slope. The obtained models aim to represent a trade-off between accuracy and parsimony. The final formula is simple but can be employed for a preliminary assessment of overtopping rates, covering the full range of dike slopes, from mild to vertical walls, and of water depths from the shoreline to deep water, including structures with emergent toes. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/jmse8080570 %U https://www.mdpi.com/2077-1312/8/8/570 %U http://dx.doi.org/10.3390/jmse8080570 %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 10.1109/CIFEr.2014.6924092 %U http://dx.doi.org/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 10.1007/s10845-018-1432-9 %U http://link.springer.com/article/10.1007/s10845-018-1432-9 %U http://dx.doi.org/10.1007/s10845-018-1432-9 %P 19-32 %0 Journal Article %T Multi-objective optimization of an aluminum torch brazing process by means of genetic programming and R-NSGA-II %A Alvarado-Iniesta, Alejandro %A Tlapa-Mendoza, Diego A. %A Limon-Romero, Jorge %A Mendez-Gonzalez, Luis C. %J The International Journal of Advanced Manufacturing Technology %D 2017 %V 91 %N 9 - 12 %F alvarado-iniesta:2017:IJAMT %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s00170-017-0102-y %U http://link.springer.com/article/10.1007/s00170-017-0102-y %U http://dx.doi.org/10.1007/s00170-017-0102-y %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 10.1016/j.jmarsys.2005.11.017 %U http://dx.doi.org/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 10.1145/2908812.2908813 %U http://dx.doi.org/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 10.1080/13504850210158250 %U http://dx.doi.org/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, ANN, exchange-rate forecasting %9 journal article %R 10.1007/s00181-005-0249-5 %U http://dx.doi.org/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 10.1007/s10818-005-0494-x %U http://dx.doi.org/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 10.1016/j.econmod.2007.05.001 %U http://www.sciencedirect.com/science/article/B6VB1-4P0VD80-1/2/c0bb8da3af64aa1ea6b0a4f90e4790b0 %U http://dx.doi.org/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 10.1504/IJCEE.2009.029153 %U http://www.inderscience.com/link.php?id=29153 %U http://dx.doi.org/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 Saavedra Gonzalez, Angeles %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 10.1016/j.jfe.2009.02.002 %U http://dx.doi.org/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 %@ 1350-4851 %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 10.1080/13504850801987217 %U http://hdl.handle.net/10261/54902 %U http://dx.doi.org/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 10.1080/09603100903459782 %U http://dx.doi.org/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 10.1002/env.1025 %U https://doi.org/10.1002/env.1025 %U http://dx.doi.org/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 10.3390/forecast1010007 %U https://www.mdpi.com/2571-9394/1/1/7/ %U http://dx.doi.org/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 10.1007/s00181-019-01665-w %U http://dx.doi.org/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 10.1016/j.fuel.2019.116844 %U http://www.sciencedirect.com/science/article/pii/S0016236119321982 %U http://dx.doi.org/10.1016/j.fuel.2019.116844 %P 116844 %0 Journal Article %T Regressions of the dielectric constant and speed of sound of vegetable oils from their composition and temperature using genetic programming %A Alviso, Dario %A Zarate, Cristhian %A Artana, Guillermo %A Duriez, Thomas %J Journal of Food Composition and Analysis %D 2021 %V 104 %@ 0889-1575 %F ALVISO:2021:JFCA %X The dielectric constant (DC) and speed of sound (SoS) have been measured in many studies on vegetable oils (VOs). These measurements can be applied for quality control, for the detection of contaminants, and in works related to heated and frying VOs. There are several hundreds of VOs with potential use in the food industry, and for most of them, the DC and SoS values are not yet available. This paper proposes regression models of the DC and SoS of VOs as a function of their composition (saturated and unsaturated fatty acids) and the temperature. A regression study was conducted using available experimental databases including a total of 57 and 56 data in the range of 20-50 degreeC for the DC and SoS, respectively. The equations are obtained using genetic programming (GP). The goal is to minimize the mean absolute error (MAE) between the values of the measured and predicted DC and SoS for several VOs. The resulting GP regression equations reproduce correctly the dependencies of the DC and SoS of VOs on the saturated and unsaturated fatty acids. The validation of these equations is carried out by comparing their results to those of the experimental databases. The MAE values of the regression equations concerning the databases for DC and SoS of VOs are 0.02 and 1.0 m/s, respectively %K genetic algorithms, genetic programming, Vegetable oils, Regression, Dielectric constant, Speed of sound, Fatty acid %9 journal article %R 10.1016/j.jfca.2021.104175 %U https://www.sciencedirect.com/science/article/pii/S0889157521003756 %U http://dx.doi.org/10.1016/j.jfca.2021.104175 %P 104175 %0 Journal Article %T Modeling of vegetable oils cloud point, pour point, cetane number and iodine number from their composition using genetic programming %A Alviso, Dario %A Zarate, Cristhian %A Duriez, Thomas %J Fuel %D 2021 %V 284 %@ 0016-2361 %F ALVISO:2021:Fuel %X Vegetable oils (VOs) are composed of 90-98percent of triglycerides, i.e. esters composed of three fatty acids and glycerol, and small amounts of mono- and di-glycerides. Due to their physico-chemical properties, VOs have been considered for uses especially in large ships, in stationary engines and low and medium speed diesel engines, in pure form or in blends with fuel oil, diesel, biodiesel and alcohols. There are about 350 VOs with potential as fuel sources, and for most of them, physico-chemical properties values have not yet been measured. In this context, regression models using only VOs fatty acid composition are very useful. In the present paper, regression analysis of VOs cloud point (CP), pour point (PP), cetane number (CN) and iodine number (IN) as a function of saturated and unsaturated fatty acids is conducted. The study is done by using 4 experimental databases including 88 different data of VOs. Concerning the regression technique, genetic programming (GP) has been chosen. The cost function of GP is defined to minimize the Mean Absolute Error (MAE) between experimental and predicted values of each property. The resulting GP models consisting of terms including saturated and unsaturated fatty acids reproduce correctly the dependencies of all four properties on those acids. And they are validated by showing that their results are in good agreement to the experimental databases. In fact, MAE values of the proposed models with respect to the databases for CP, PP, CN and IN are lower than 4.51 degreeC, 4.54 degreeC, 3.64 and 8.01, respectively %K genetic algorithms, genetic programming, Vegetable oils, Fatty acid, Cetane number, Iodine number, Cloud point, Pour point %9 journal article %R 10.1016/j.fuel.2020.119026 %U https://www.sciencedirect.com/science/article/pii/S0016236120320226 %U http://dx.doi.org/10.1016/j.fuel.2020.119026 %P 119026 %0 Journal Article %T Evolution of Software Reliability Growth Models: A Comparison of Auto-Regression and Genetic Programming Models %A Alweshah, Mohammed %A Ahmed, Walid %A Aldabbas, Hamza %J International Journal of Computer Applications %D 2015 %8 sep %V 125 %N 3 %I Foundation of Computer Science (FCS), NY, USA %C New York, USA %@ 0975-8887 %F Alweshah:2015:IJCA %X Building reliability growth models to predict software reliability and identify and remove errors is both a necessity and a challenge for software testing engineers and project managers. Being able to predict the number of faults in software helps significantly in determining the software release date and in effectively managing project resources. Most of the growth models consider two or three parameters to estimate the accumulated faults in the testing process. Interest in using evolutionary computation to solve prediction and modeling problems has grown in recent years. In this paper, we explore the use of genetic programming (GP) as a tool to help in building growth models that can accurately predict the number of faults in software early on in the testing process. The proposed GP model is based on a recursive relation derived from the history of measured faults. The developed model is tested on real-time control, military, and operating system applications. The dataset was developed by John Musa of Bell Telephone Laboratories. The results of a comparison of the GP model with the traditional and simpler auto-regression model are presented. %K genetic algorithms, genetic programming %9 journal article %R 10.5120/ijca2015905864 %U https://www.ijcaonline.org/archives/volume125/number3/22413-2015905864 %U http://dx.doi.org/10.5120/ijca2015905864 %P 20-25 %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 %D 2018 %8 sep 18 20 %I SciTePress %C Seville, Spain %F DBLP:conf/ijcci/AlyasiriCK18 %X With cyber-attacks becoming a regular feature in daily business and attackers continuously evolving their techniques, we are witnessing ever more sophisticated and targeted threats. Various artificial intelligence algorithms have been deployed to analyse such incidents. Extracting knowledge allows the discovery of new attack methods, intrusion scenarios, and attackers objectives and strategies, all of which can help distinguish attacks from legitimate behaviour. Among those algorithms, Evolutionary Computation (EC) techniques have seen significant application. Research has shown it is possible to use EC methods to construct IDS detection rules. we show how Cartesian Genetic Programming (CGP) can construct the behaviour rule upon which an intrusion detection will be able to make decisions regarding the nature of the activity observed in the system. The CGP framework evolves human readable solutions that provide an explanation of the logic behind its evolved decisions. Experiments are conducted on up-to-date cybersecurity datasets and compared with state of the art paradigms. We also introduce ensemble learning paradigm, indicating how CGP can be used as stacking technique to improve the learning performance. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, Intrusion Detection System, Stacking Ensemble %R 10.5220/0006925901760183 %U https://www.scitepress.org/Papers/2018/69259/69259.pdf %U http://dx.doi.org/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 Conference Proceedings %T Evolving Rules for Detecting Cross-Site Scripting Attacks Using Genetic Programming %A Alyasiri, Hasanen %Y Anbar, Mohammed %Y Abdullah, Nibras %Y Manickam, Selvakumar %S 2nd International Conference on Advances in Cyber Security, ACeS 2020 %S Communications in Computer and Information Science %D 2020 %8 dec 8 9 %V 1347 %I Springer %C Penang, Malaysia %F alyasiri2020evolving %O Revised Selected Papers %X Web services are now a critical element of many of our day-to-day activities. Their applications are one of the fastest-growing industries around. The security issues related to these services are a major concern to their providers and are directly relevant to the everyday lives of system users. Cross-Site Scripting (XSS) is a standout amongst common web application security attacks. Protection against XSS injection attacks needs more work. Machine learning has considerable potential to provide protection in this critical domain. In this article, we show how genetic programming can be used to evolve detection rules for XSS attacks. We conducted our experiments on a publicly available and up-to-date dataset. The experimental results showed that the proposed method is an effective countermeasure against XSS attacks. We then investigated the computational cost of the detection rules. The best-evolved rule has a processing time of 177.87 ms and consumes memory of 8600 bytes. %K genetic algorithms, genetic programming %R 10.1007/978-981-33-6835-4_42 %U https://link.springer.com/chapter/10.1007/978-981-33-6835-4_42 %U http://dx.doi.org/10.1007/978-981-33-6835-4_42 %P 642-656 %0 Conference Proceedings %T Grammatical Evolution for Detecting Cyberattacks in Internet of Things Environments %A Alyasiri, Hasanen %A Clark, John A. %A Malik, Ali %A de Frein, Ruairi %S 2021 International Conference on Computer Communications and Networks (ICCCN) %D 2021 %8 19 22 jul %I IEEE %C Athens, Greece %F alyasiri2021grammatical %X The Internet of Things (IoT) is revolutionising nearly every aspect of modern life, playing an ever greater role in both industrial and domestic sectors. The increasing frequency of cyber-incidents is a consequence of the pervasiveness of IoT. Threats are becoming more sophisticated, with attackers using new attacks or modifying existing ones. Security teams must deal with a diverse and complex threat landscape that is constantly evolving. Traditional security solutions cannot protect such systems adequately and so researchers have begun to use Machine Learning algorithms to discover effective defense systems. we investigate how one approach from the domain of evolutionary computation, grammatical evolution, can be used to identify cyberattacks in IoT environments. The experiments were conducted on up-to-date datasets and compared with state-of-the-art algorithms. The potential application of evolutionary computation-based approaches to detect unknown attacks is also examined and discussed. %K genetic algorithms, genetic programming, Grammatical Evolution %R 10.1109/ICCCN52240.2021.9522283 %U https://ieeexplore.ieee.org/abstract/document/9522283 %U http://dx.doi.org/10.1109/ICCCN52240.2021.9522283 %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 10.1007/s10710-018-9322-7 %U http://dx.doi.org/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 10.1016/j.jngse.2020.103271 %U https://hal.archives-ouvertes.fr/hal-02534736 %U http://dx.doi.org/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, gamultiobj, matlab, GP surrogate models, in silico modeling %9 journal article %R 10.1039/C9SC01844A %U https://pubs.rsc.org/en/content/articlepdf/2019/sc/c9sc01844a %U http://dx.doi.org/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 10.17863/CAM.35535 %U https://www.repository.cam.ac.uk/handle/1810/288220 %U http://dx.doi.org/10.17863/CAM.35535 %0 Conference Proceedings %T Benchmarking Genetic Programming in a Multi-action Reinforcement Learning Locomotion Task %A Amaral, Ryan %A Ianta, Alexandru %A Bayer, Caleidgh %A Smith, Robert %A Heywood, Malcolm %Y Trautmann, Heike %Y Doerr, Carola %Y Moraglio, Alberto %Y Bartz-Beielstein, Thomas %Y Filipic, Bogdan %Y Gallagher, Marcus %Y Ong, Yew-Soon %Y Gupta, Abhishek %Y Kononova, Anna V. %Y Wang, Hao %Y Emmerich, Michael %Y Bosman, Peter A. N. %Y Zaharie, Daniela %Y Caraffini, Fabio %Y Dreo, Johann %Y Auger, Anne %Y Dietric, Konstantin %Y Dufosse, Paul %Y Glasmachers, Tobias %Y Hansen, Nikolaus %Y Mersmann, Olaf %Y Posik, Petr %Y Tusar, Tea %Y Brockhoff, Dimo %Y Eftimov, Tome %Y Kerschke, Pascal %Y Naujoks, Boris %Y Preuss, Mike %Y Volz, Vanessa %Y Derbel, Bilel %Y Li, Ke %Y Li, Xiaodong %Y Zapotecas, Saul %Y Zhang, Qingfu %Y Coletti, Mark %Y Schuman, Catherine (Katie) %Y Scott, Eric “Siggy” %Y Patton, Robert %Y Wiegand, Paul %Y Bassett, Jeffrey K. %Y Gunaratne, Chathika %Y Chugh, Tinkle %Y Allmendinger, Richard %Y Hakanen, Jussi %Y Tauritz, Daniel %Y Woodward, John %Y Lopez-Ibanez, Manuel %Y McCall, John %Y Bacardit, Jaume %Y Brownlee, Alexander %Y Cagnoni, Stefano %Y Iacca, Giovanni %Y Walker, David %Y Toutouh, Jamal %Y O’Reilly, UnaMay %Y Machado, Penousal %Y Correia, Joao %Y Nesmachnow, Sergio %Y Ceberio, Josu %Y Villanueva, Rafael %Y Hidalgo, Ignacio %Y Fernandez de Vega, Francisco %Y Paolo, Giuseppe %Y Coninx, Alex %Y Cully, Antoine %Y Gaier, Adam %Y Wagner, Stefan %Y Affenzeller, Michael %Y Bruce, Bobby R. %Y Nowack, Vesna %Y Blot, Aymeric %Y Winter, Emily %Y Langdon, William B. %Y Petke, Justyna %Y Fernandez Alzueta, Silvino %Y Valledor Pellicer, Pablo %Y Stuetzle, Thomas %Y Paetzel, David %Y Wagner, Alexander %Y Heider, Michael %Y Veerapen, Nadarajen %Y Malan, Katherine %Y Liefooghe, Arnaud %Y Verel, Sebastien %Y Ochoa, Gabriela %Y Omidvar, Mohammad Nabi %Y Sun, Yuan %Y Tarantino, Ernesto %Y Ivanoe, De Falco %Y Della Cioppa, Antonio %Y Umberto, Scafuri %Y Rieffel, John %Y Mouret, Jean-Baptiste %Y Doncieux, Stephane %Y Nikolaidis, Stefanos %Y Togelius, Julian %Y Fontaine, Matthew C. %Y Georgescu, Serban %Y Chicano, Francisco %Y Whitley, Darrell %Y Kyriienko, Oleksandr %Y Dahl, Denny %Y Shir, Ofer %Y Spector, Lee %Y Rahat, Alma %Y Everson, Richard %Y Fieldsend, Jonathan %Y Wang, Handing %Y Jin, Yaochu %Y Hemberg, Erik %Y Elsayed, Marwa A. %Y Kommenda, Michael %Y La Cava, William %Y Kronberger, Gabriel %Y Gustafson, Steven %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F amaral:2022:GECCOcomp %X Reinforcement learning (RL) requires an agent to interact with an environment to maximize the cumulative rather than the immediate reward. Recently, there as been a significant growth in the availability of scalable RL tasks, e.g. OpenAI gym. However, most benchmarking studies concentrate on RL solutions based on some form of deep learning. In this work, we benchmark a family of linear genetic programming based approaches to the 2-d biped walker problem. The biped walker is an example of a RL environment described in terms of a multi-dimensional, real-valued 24-d input and 4-d action space. Specific recommendations are made regarding mechanisms to adopt that are able to consistently produce solutions, in this case using transfer from periodic restarts. %K genetic algorithms, genetic programming, real-valued actions, continuous control, reinforcement learning %R 10.1145/3520304.3528766 %U http://dx.doi.org/10.1145/3520304.3528766 %P 522-525 %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 %R 10.7551/mitpress/1429.003.0093 %U http://ncra.ucd.ie/papers/alife2004.pdf %U http://dx.doi.org/10.7551/mitpress/1429.003.0093 %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, genotype-phenotype mapping %R 10.1109/CEC.2005.1554779 %U http://dx.doi.org/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 10.1016/j.enbuild.2015.01.008 %U http://www.sciencedirect.com/science/article/pii/S0378778815000110 %U http://dx.doi.org/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 10.1016/j.energy.2018.05.155 %U http://www.sciencedirect.com/science/article/pii/S036054421830999X %U http://dx.doi.org/10.1016/j.energy.2018.05.155 %P 886-893 %0 Conference Proceedings %T GPStar4: A Flexible Framework for Experimenting with Genetic Programming %A Amblard, Julien %A Filman, Robert %A Kopito, Gabriel %Y Kalkreuth, Roman %Y Baeck, Thomas %Y Wilson, Dennis G. %Y Kaufmann, Paul %Y Sotto, Leo Francoso Dal Piccol %Y Aktinson, Timothy %S Graph-based Genetic Programming %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F amblard:2023:GGP %X GPStar4 is a flexible workbench for experimenting with population algorithms. It is a framework that defines a genetic cycle, with inflection points for implementing an algorithm’s specific behaviors; it also provides a variety of implementations for these inflection points. A user of the system can select from the provided implementations and customize the places where alternative behavior is desired, or even create their own implementations. Components interact through a context mechanism that enables both mutable and immutable information sharing, type checking, computed defaults and event listeners.Interesting predefined components included in GPStar4 are implementations for classical tree-based expression structures; acyclic multigraphs with named ports, type systems for flat, hierarchical and attribute types, recursively defined populations using both subpopulation and build-from-parts semantics, and numeric and multi-objective fitnesses. Key enabling technologies for this flexibility include context mechanisms, choosers, and a variety of caches.GPStar4 can be run as an API library for other applications, as a command-line application, or as a stand-alone application with its own GUI. %K genetic algorithms, genetic programming, experimental framework, directed acyclic graph representations, population algorithms %R 10.1145/3583133.3596369 %U http://dx.doi.org/10.1145/3583133.3596369 %P 1910-1915 %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 10.1016/j.compstruct.2020.112782 %U http://www.sciencedirect.com/science/article/pii/S0263822320327082 %U http://dx.doi.org/10.1016/j.compstruct.2020.112782 %P 112782 %0 Journal Article %T Investigating the Bond Strength of FRP Rebars in Concrete under High Temperature Using Gene-Expression Programming Model %A Amin, Muhammad Nasir %A Iqbal, Mudassir %A Althoey, Fadi %A Khan, Kaffayatullah %A Faraz, Muhammad Iftikhar %A Qadir, Muhammad Ghulam %A Alabdullah, Anas Abdulalim %A Ajwad, Ali %J Polymers %D 2022 %V 14 %N 15 %@ 2073-4360 %F amin:2022:Polymers %X In recent times, the use of fibre-reinforced plastic (FRP) has increased in reinforcing concrete structures. The bond strength of FRP rebars is one of the most significant parameters for characterising the overall efficacy of the concrete structures reinforced with FRP. However, in cases of elevated temperature, the bond of FRP-reinforced concrete can deteriorate depending on a number of factors, including the type of FRP bars used, its diameter, surface form, anchorage length, concrete strength, and cover thickness. Hence, accurate quantification of FRP rebars in concrete is of paramount importance, especially at high temperatures. In this study, an artificial intelligence (AI)-based genetic-expression programming (GEP) method was used to predict the bond strength of FRP rebars in concrete at high temperatures. In order to predict the bond strength, we used failure mode temperature, fibre type, bar surface, bar diameter, anchorage length, compressive strength, and cover-to-diameter ratio as input parameters. The experimental dataset of 146 tests at various elevated temperatures were established for training and validating the model. A total of 70percent of the data was used for training the model and remaining 30percent was used for validation. Various statistical indices such as correlation coefficient (R), the mean absolute error (MAE), and the root-mean-square error (RMSE) were used to assess the predictive veracity of the GEP model. After the trials, the optimum hyperparameters were 150, 8, and 4 as number of chromosomes, head size and number of genes, respectively. Different genetic factors, such as the number of chromosomes, the size of the head, and the number of genes, were evaluated in eleven separate trials. The results as obtained from the rigorous statistical analysis and parametric study show that the developed GEP model is robust and can predict the bond strength of FRP rebars in concrete under high temperature with reasonable accuracy (i.e., R, RMSE and MAE 0.941, 2.087, and 1.620, and 0.935, 2.370, and 2.046, respectively, for training and validation). More importantly, based on the FRP properties, the model has been translated into traceable mathematical formulation for easy calculations. %K genetic algorithms, genetic programming, gene expression programming %9 journal article %R 10.3390/polym14152992 %U https://www.mdpi.com/2073-4360/14/15/2992 %U http://dx.doi.org/10.3390/polym14152992 %P ArticleNo.2992 %0 Journal Article %T Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming %A Amin, Muhammad Nasir %A Raheel, Muhammad %A Iqbal, Mudassir %A Khan, Kaffayatullah %A Qadir, Muhammad Ghulam %A Jalal, Fazal E. %A Alabdullah, Anas Abdulalim %A Ajwad, Ali %A Al-Faiad, Majdi Adel %A Abu-Arab, Abdullah Mohammad %J Materials %D 2022 %V 15 %N 19 %@ 1996-1944 %F amin:2022:Materials %X The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach. The number of chromosomes (Nc), genes (Ng) and, the head size (Hs) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R2, RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the Nc = 100, Hs = 8 and Ng = 3. This model exhibits an R2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results. %K genetic algorithms, genetic programming, gene expression programming %9 journal article %R 10.3390/ma15196959 %U https://www.mdpi.com/1996-1944/15/19/6959 %U http://dx.doi.org/10.3390/ma15196959 %P ArticleNo.6959 %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 10.1007/s10489-019-01601-6 %U https://doi.org/10.1007/s10489-019-01601-6 %U http://dx.doi.org/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 10.1007/s00521-011-0689-0 %U http://dx.doi.org/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 10.1007/s00521-012-1138-4 %U http://link.springer.com/article/10.1007%2Fs00521-012-1138-4 %U http://dx.doi.org/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 10.1007/978-3-319-00930-8_4 %U http://dx.doi.org/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 10.1007/s10710-014-9220-6 %U http://dx.doi.org/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 10.1016/j.asoc.2017.06.050 %U http://dx.doi.org/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 10.3233/IFS-130950 %U http://dx.doi.org/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, Mohammad %A Eftekhari, Mahdi %A Dehestani, Maryam %A Tajaddini, Azita %J Scientia Iranica %D 2013 %V 20 %N 3 %@ 1026-3098 %F Amiri:2013:SI %X 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, GPTIPS, Matlab %9 journal article %R 10.1016/j.scient.2012.12.040 %U https://core.ac.uk/download/pdf/81997689.pdf %U http://dx.doi.org/10.1016/j.scient.2012.12.040 %P 543-548 %0 Journal Article %T Evaluating the synergic effect of waste rubber powder and recycled concrete aggregate on mechanical properties and durability of concrete %A Amiri, Mostafa %A Hatami, Farzad %A Golafshani, Emadaldin Mohammadi %J Case Studies in Construction Materials %D 2021 %V 15 %@ 2214-5095 %F AMIRI:2021:CSCM %X The use of waste materials in the concrete mixture can help human beings to preserve the environment and achieve environmentally-friendly concrete. In this study, the influences of simultaneous replacements of cement by waste rubber powder (WRP) and coarse aggregate by recycled concrete aggregate (RCA) on the mechanical properties and durability of concrete were investigated experimentally. To do so, concrete specimens containing the WRP with the replacement ratios of percent, 2.5 percent, and 5 percent by weight of cement, and the RCA with the replacement levels of percent, 25 percent, and 50 percent of coarse aggregate were prepared. Moreover, different water to binder ratios and binder content were used. Mechanical properties of the concrete specimens consisting of compressive, flexural, and tensile strengths and the durability test of rapid chloride migration test (RCMT) were carried out at different ages. It was observed that the mechanical properties of concrete decrease by raising the proportions of recycled materials in all replacement ratios. Because of the negative effects of the WRP and RCA on, respectively, the cement matrix and the interfacial transition zone, the reduction of the mechanical properties are higher for the concrete specimens with both recycled materials. In the case of durability, the migration rate of chloride ions in concrete reduces by increasing the WRP rates due to the blockage of micro-pores connections. However, adding the RCA has a negative effect on the durability performance of concrete. Finally, four equations were proposed and evaluated for the compressive, tensile, flexural strength reduction and durability factors of concrete containing the WRP and RCA using the genetic programming %K genetic algorithms, genetic programming, Waste rubber powder, Recycled concrete aggregate, Green concrete, Mechanical properties, Durability %9 journal article %R 10.1016/j.cscm.2021.e00639 %U https://www.sciencedirect.com/science/article/pii/S2214509521001546 %U http://dx.doi.org/10.1016/j.cscm.2021.e00639 %P e00639 %0 Journal Article %T Evaluating Simultaneous Impact of Slag and Tire Rubber Powder on Mechanical Characteristics and Durability of Concrete %A Amiri, Mostafa %A Hatami, Farzad %A Golafshani, Emadaldin Mohammadi %J Journal of Renewable Materials %D 2022 %V 10 %N 8 %@ 2164-6325 %F Amiri:2022:JRM %X In this experimental study, the impact of Portland cement replacement by ground granulated blast furnace slag (GGBFS) and micronized rubber powder (MRP) on the compressive, flexural, tensile strengths, and rapid chloride migration test (RCMT) of concrete were assessed. In this study, samples with different binder content and water to binder ratios, including the MRP with the substitution levels of 0percent, 2.5percent and 5percent, and the GGBFS with the substitution ratios of 0percent, 20percent and 40percent by weight of Portland cement were made. According to the results, in the samples containing slag and rubber powder in the early ages, on average, a 12.2percent decrease in the mechanical characteristics of concrete was observed, nonetheless with raising the age of the samples, the impact of slag on reducing the porosity of concrete lowered the negative impact of rubber powder. Regarding durability characteristics, the RCMT results of the samples were enhanced by using rubber powder because of its insulation impact. Moreover, adding slag into the MRP-included mixtures results in a 23percent reduction in the migration rate of the chloride ion averagely. At last, four mathematical statements were derived for the mechanical and durability of concrete containing the MRP and GGBFS using the genetic programming method %K genetic algorithms, genetic programming, Micronized rubber powder, ground granulated blast furnace slag, waste materials, mechanical properties, durability %9 journal article %R 10.32604/jrm.2022.019726 %U https://www.sciencedirect.com/science/article/pii/S2164632522002323 %U http://dx.doi.org/10.32604/jrm.2022.019726 %P 2155-2177 %0 Journal Article %T Genetic programming application in predicting fluid loss severity %A Amish, Mohamed %A Etta-Agbor, Eta %J Results in Engineering %D 2023 %V 20 %@ 2590-1230 %F AMISH:2023:rineng %X Numerous wells worldwide encounter significant, costly, and time-consuming lost circulation issues during drilling or while deploying tubulars across naturally fractured or induced fractured formations. This can potentially lead to formation damage, wellbore instability, and even blowouts. Effectively addressing this problem and restoring fluid circulation becomes crucial to curbing non-productive time and overall operational expenses. Although numerous methods have been introduced, a universally accepted industry solution for predicting lost circulation remains absent due to the complex interplay of various factors influencing its severity. Anticipating the onset of circulation loss is imperative to mitigate its impacts, minimise costs, and reduce risks to personnel and the environment. In this study, an innovative machine learning approach employing multigene genetic algorithms is used to analyse a dataset of 16,970 drilling datasets from 61 wells within the Marun oil field, located in Iran, where severe loss of circulation occurred. Geological characteristics, operational drilling parameters, and the properties of the drilling fluid were all considered. The dataset encompasses 19 parameters, of which seven are chosen as inputs for predicting lost circulation incidents. These inputs are then employed to construct a predictive model, employing an 85:15 training-to-test data ratio. To assess the model’s performance, unseen datasets are used. The novelty of this study lies in the proposed model’s consideration of a concise set of relevant input parameters, particularly real-time surface drilling parameters that are easily accessible for every well. The model attains a remarkable level of prediction accuracy for fluid loss, as indicated by various performance indices. The results indicate a mean absolute error of 1.33, a root mean square error of 2.58, and a coefficient of determination of 0.968. The suggested prediction model is optimised not only for data reduction but also for universal prediction and compatibility with other existing platforms. Moreover, it aids drilling engineers in implementing suitable mitigation strategies and designing optimal values for key operational surface parameters, both prior to and during drilling operations %K genetic algorithms, genetic programming, Lost circulation, Machine learning, Multigene genetic algorithms, Drilling. non-productive time %9 journal article %R 10.1016/j.rineng.2023.101464 %U https://www.sciencedirect.com/science/article/pii/S2590123023005911 %U http://dx.doi.org/10.1016/j.rineng.2023.101464 %P 101464 %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 10.1016/j.neucom.2016.06.019 %U http://www.sciencedirect.com/science/article/pii/S0925231216306579 %U http://dx.doi.org/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 10.1007/s10710-013-9182-0 %U http://dx.doi.org/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 10.1109/ICESA.2015.7503454 %U http://dx.doi.org/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.github.io/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 10.1145/3194810.3194814 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/An_2018_GI.pdf %U http://dx.doi.org/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 10.1145/3302542.3302544 %U http://www.sigevolution.org/issues/SIGEVOlution1104.pdf %U http://dx.doi.org/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 10.1145/3338906.3341184 %U http://www.cs.ucl.ac.uk/staff/a.blot/files/an_2019_fse.pdf %U http://dx.doi.org/10.1145/3338906.3341184 %P 1100-1104 %0 Conference Proceedings %T "13th International Workshop on Genetic Improvement %F an:2024:GI %0 Journal Article %D 2024 %8 16 apr %I ACM %C Lisbon %F 2024"a %X The GI workshops continue to bring together researchers from across the world to exchange ideas about using optimisation techniques, particularly evolutionary computation, such as genetic programming, to improve existing software. Contents: \citeYoo:2024:GI \citeBlot:2024:GI \citeBaxter:2024:GI \citecallan:2024:GI \citeCraine:2024:GI \citelangdon:2024:GI \citeNemeth:2024:GI \citeSarmiento:2024:GI See also \citelangdon:2024:SEN Published: 08 August 2024 %K genetic algorithms, genetic programming, Genetic Improvement %9 journal article %R 10.1145/3643692 %U http://geneticimprovementofsoftware.com/events/icse2024 %U http://dx.doi.org/10.1145/3643692 %0 Thesis %T Synergizing Fault Localization and Continuous Integration to Streamline Bug Resolution in Large-Scale Software Systems %A An, Gabin %D 2024 %8 April %C Daejeon, Korea %C Korea Advanced Institute of Science and Technology %F An:thesis %X This thesis explores the synergistic interaction between Continuous Integration (CI) and Fault Localization (FL) within software development, aiming to enhance the efficiency and effectiveness of the bug resolution process. CI plays a critical role as developers frequently merge code changes into a central repository, followed by automated builds and tests to quickly detect bugs and maintain a unified code-base that supports effective collaboration for large-scale software systems. FL is an automated debugging technique designed to precisely detect the locations of bugs within the codebase, reducing the burden on developers. While CI and FL each aim to streamline software development and maintenance independently, their potential for interaction has not been fully explored. This research suggests that leveraging historical CI data can enable more effective application of FL, and that FL can improve the bug resolution process within CI systems. The thesis comprises three studies: identifying common root causes of test failures using diverse information sources available in the CI environment, efficiently identifying bug-inducing commits using FL and code change histories, and developing an explainable FL technique using large language models. Each study addresses specific challenges and provides novel solutions to simplify the debugging and maintenance stages of software development. The proposed solutions are empirically evaluated and thoroughly compared against their baselines using real-world open-source software and large-scale industry software %K SBSE, fault localization, SBFL, AutoFL, FONTE, Defects4J, BugsInPy, continuous integration, bug resolution, debugging, bug assignment, buginducing commit, large language model, LLN, ANN, GPT, SAP HANA, BIC %9 Ph.D. thesis %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 10.1109/ICCSIT.2010.5563737 %U http://dx.doi.org/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 GenClass: A parallel tool for data classification based on Grammatical Evolution %A Anastasopoulos, Nikolaos %A Tsoulos, Ioannis G. %A Tzallas, Alexandros %J SoftwareX %D 2021 %V 16 %@ 2352-7110 %F ANASTASOPOULOS:2021:SoftwareX %X A genetic programming tool is proposed here for data classification. The tool is based on Grammatical Evolution technique and it is designed to exploit multicore computing systems using the OpenMP library. The tool constructs classification programs in a C-like programming language in order to classify the input data, producing simple if-else rules and upon termination the tool produces the classification rules in C and Python files. Also, the user can use his own Backus Normal Form (BNF) grammar through a command line option. The tool is tested on a wide range of classification problems and the produced results are compared against traditional techniques for data classification, yielding very promising results %K genetic algorithms, genetic programming, Data classification, Grammatical evolution, Stochastic methods %9 journal article %R 10.1016/j.softx.2021.100830 %U https://www.sciencedirect.com/science/article/pii/S2352711021001199 %U http://dx.doi.org/10.1016/j.softx.2021.100830 %P 100830 %0 Journal Article %T Real-time prediction of river ice breakup phenomena: A jittered genetic programming model and wavelet analysis integrating remotely sensed imagery and machine learning %A Andaryani, Soghra %A Afkhaminia, Amin %J Journal of Hydrology %D 2024 %V 644 %@ 0022-1694 %F Andaryani:2024:jhydrol %X Forecasting the timing of breakup ice jams in rivers is crucial for early flood warning and effective management in cold regions where rising river flows can lead to significant damage. However, this task is hindered by insufficient data and the complex dynamics of river ice. These obstacles pose challenges in developing precise forecasting models. This study aimed to address the data scarcity issue by introducing innovative machine learning methods, focusing on classification and jittering (J), binary genetic programming (BGP), and wavelet transform (WT) for river ice forecasting (WT-JBGP) in the Tornio River, situated between Finland and Sweden. By considering time scales ranging from 2 to 32 days and time lags of 1 to 3 days, this method was applied to enhance the predictive capabilities of predictors. The findings reveal that certain predictors, with specific time scales and time lags, significantly influence the timing of breakup events. These include the 8-day temperature and 32-day discharge, both with a 2-day lag time, as well as the 4-day precipitation, approximation of albedo, and 16-day atmospheric pressure at ground level, all with a 1-day lag obtained from ERA5 and recorded data. Additionally, we conducted a quantitative evaluation of the effectiveness of the proposed model and contrasted its efficacy with that of BGP, WT-BGP, and advanced J-BGP techniques. The WT-JBGP model attains the highest overall classification accuracy of approximately 0.91, alongside a Heidke Skill Score exceeding 0.78, and a Positive Predictive Value surpassing 0.85, thereby demonstrating its superiority over competing methodologies. In summation, this study offers a promising approach to overcoming observed data scarcity in breakup date prediction, providing valuable insights into river ice dynamics %K genetic algorithms, genetic programming, Albedo & ERA5 data, Breakup date, Machin learning, River Ice, Tornio %9 journal article %R 10.1016/j.jhydrol.2024.132097 %U https://www.sciencedirect.com/science/article/pii/S0022169424014938 %U http://dx.doi.org/10.1016/j.jhydrol.2024.132097 %P 132097 %0 Generic %T Estimation of Gas Turbine Shaft Torque and Fuel Flow of a CODLAG Propulsion System Using Genetic Programming Algorithm %A Andelic, Nikola %A Baressi Segota, Sandi %A Lorencin, Ivan %A Car, Zlatan %D 2020 %8 July %I arXiv %F DBLP:journals/corr/abs-2012-03527 %X he publicly available dataset of condition based maintenance of combined diesel-electric and gas (CODLAG) propulsion system for ships has been used to obtain symbolic expressions which could estimate gas turbine shaft torque and fuel flow using genetic programming (GP) algorithm. The entire dataset consists of 11934 samples that was divided into training and testing portions of dataset in an 80:20 ratio. The training dataset used to train the GP algorithm to obtain symbolic expressions for gas turbine shaft torque and fuel flow estimation consisted of 9548 samples. The best symbolic expressions obtained for gas turbine shaft torque and fuel flow estimation were obtained based on their R2 score generated as a result of the application of the testing portion of the dataset on the aforementioned symbolic expressions. The testing portion of the dataset consisted of 2386 samples. The three best symbolic expressions obtained for gas turbine shaft torque estimation generated R2 scores of 0.999201, 0.999296, and 0.999374, respectively. The three best symbolic expressions obtained for fuel flow estimation generated R2 scores of 0.995495, 0.996465, and 0.996487, respectively. %K genetic algorithms, genetic programming, Artificial Intelligence, Combined Diesel-Electric and Gas Propulsion System, Genetic Programming Algorithm, Gas Turbine Shaft Torque Estimation, Fuel Flow Estimation %U https://arxiv.org/abs/2012.03527 %0 Journal Article %T Estimation of COVID-19 epidemic curves using genetic programming algorithm %A Andelic, Nikola %A Baressi Segota, Sandi %A Lorencin, Ivan %A Mrzljak, Vedran %A Car, Zlatan %J Health Informatics J. %D 2021 %V 27 %N 1 %F DBLP:journals/hij/AndelicSLMC21 %K genetic algorithms, genetic programming %9 journal article %R 10.1177/1460458220976728 %U https://doi.org/10.1177/1460458220976728 %U http://dx.doi.org/10.1177/1460458220976728 %P 146045822097672 %0 Journal Article %T Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm %A Andelic, Nikola %A Baressi Segota, Sandi %A Lorencin, Ivan %A Jurilj, Zdravko %A Sustersic, Tijana %A Blagojevic, Andela %A Protic, Alen %A Cabov, Tomislav %A Filipovic, Nenad %A Car, Zlatan %J International Journal of Environmental Research and Public Health %D 2021 %V 18 %N 3 %@ 1660-4601 %F andelic:2021:IJERPH %X Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is used to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is used on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22 January 2020–3 December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/ijerph18030959 %U https://www.mdpi.com/1660-4601/18/3/959 %U http://dx.doi.org/10.3390/ijerph18030959 %0 Journal Article %T Use of Genetic Programming for the Estimation of CODLAG Propulsion System Parameters %A Andelic, Nikola %A Baressi Segota, Sandi %A Lorencin, Ivan %A Poljak, Igor %A Mrzljak, Vedran %A Car, Zlatan %J Journal of Marine Science and Engineering %D 2021 %V 9 %N 6 %@ 2077-1312 %F andelic:2021:JMSE %X In this paper, the publicly available dataset for the Combined Diesel-Electric and Gas (CODLAG) propulsion system was used to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque using genetic programming (GP) algorithm. The dataset consists of 11,934 samples that were divided into training and testing portions in an 80:20 ratio. The training portion of the dataset which consisted of 9548 samples was used to train the GP algorithm to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller, port propeller, and total propeller torque, respectively. After the symbolic expressions were obtained the testing portion of the dataset which consisted of 2386 samples was used to measure estimation performance in terms of coefficient of correlation (R2) and Mean Absolute Error (MAE) metric, respectively. Based on the estimation performance in each case three best symbolic expressions were selected with and without decay state coefficients. From the conducted investigation, the highest R2 and lowest MAE values were achieved with symbolic expressions for the estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque without decay state coefficients while symbolic expressions with decay state coefficients have slightly lower estimation performance. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/jmse9060612 %U https://www.mdpi.com/2077-1312/9/6/612 %U http://dx.doi.org/10.3390/jmse9060612 %0 Conference Proceedings %T Utilization of Genetic Programming for Estimation of Molecular Structures Ground State Energies %A Andelic, Nikola %A Baressi Segota, Sandi %A Lorencin, Ivan %A Glucina, Matko %A Musulin, Jelena %A Stifanic, Daniel %A Car, Zlatan %S 1st Serbian International Conference on Applied Artificial Intelligence %D 2022 %8 may 19 20 %I Springer %C Kragujevac, Serbia %F Andelic:2022:SICAAI %X GP to predict ground-state energies of molecules made up of C, H, N, O, P, and S (CHONPS) atoms. The GP was trained and tested on a publicly available dataset which consist of 16242 molecules where ground state energies were computed using the density functional theory (DFT). The optimal parameters of GP were chosen using the random parameter search method. After multiple GP executions, the best symbolic expression was chosen using a coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE). The best symbolic expression achieved R, MAE, and RMSE of 0.9434, 0.48, and 0.86, respectively. %K genetic algorithms, genetic programming, CHNOPS dataset, ground state energies %U http://aai2022.kg.ac.rs/wp-content/uploads/upload/AAI_2022_Papers.zip %0 Journal Article %T Detection of Malicious Websites Using Symbolic Classifier %A Andelic, Nikola %A Baressi Segota, Sandi %A Lorencin, Ivan %A Glucina, Matko %J Future Internet %D 2022 %8 nov %V 14 %N 12 %I MDPI %@ 1999-5903 %F Andelic:2022:FI %X Malicious websites are web locations that attempt to install malware, which is the general term for anything that will cause problems in computer operation, gather confidential information, or gain total control over the computer. a novel approach is proposed which consists of the implementation of the genetic programming symbolic classifier (GPSC) algorithm on a publicly available dataset to obtain a simple symbolic expression (mathematical equation) which could detect malicious websites with high classification accuracy. Due to a large imbalance of classes in the initial dataset, several data sampling methods (random under-sampling/oversampling, ADASYN, SMOTE, BorderlineSMOTE, and KmeansSMOTE) were used to balance the dataset classes. For this investigation, the hyper-parameter search method was developed to find the combination of GPSC hyperparameters with which high classification accuracy could be achieved. The first investigation was conducted using GPSC with a random hyperparameter search method and each dataset variation was divided on a train and test dataset in a ratio of 70:30. To evaluate each symbolic expression, the performance of each symbolic expression was measured on the train and test dataset and the mean and standard deviation values of accuracy (ACC), AUC, precision, recall and f1-score were obtained. The second investigation was also conducted using GPSC with the random hyperparameter search method; however, 70percent, i.e., the train dataset, was used to perform 5-fold cross-validation. If the mean accuracy, AUC, precision, recall, and f1-score values were above 0.97 then final training and testing (train/test 70:30) were performed with GPSC with the same randomly chosen hyperparameters used in a 5-fold cross-validation process and the final mean and standard deviation values of the aforementioned evaluation methods were obtained. In both investigations, the best symbolic expression was obtained in the case where the dataset balanced with the KMeansSMOTE method was used for training and testing. The best symbolic expression obtained using GPSC with the random hyperparameter search method and classic train-test procedure (70:30) on a dataset balanced with the KMeansSMOTE method achieved values of %K genetic algorithms, genetic programming, malicious websites, oversampling methods, symbolic classifier, undersampling methods %9 journal article %R 10.3390/fi14120358 %U https://www.mdpi.com/1999-5903/14/12/358 %U http://dx.doi.org/10.3390/fi14120358 %P Articleno358 %0 Journal Article %T The Development of Symbolic Expressions for Fire Detection with Symbolic Classifier Using Sensor Fusion Data %A Andelic, Nikola %A Baressi Segota, Sandi %A Lorencin, Ivan %A Car, Zlatan %J Sensors %D 2022 %8 dec %V 23 %N 1 %I MDPI %@ 1424-8220 %F Andelic:2022:Sensors %X Fire is usually detected with fire detection systems that are used to sense one or more products resulting from the fire such as smoke, heat, infrared, ultraviolet light radiation, or gas. Smoke detectors are mostly used in residential areas while fire alarm systems (heat, smoke, flame, and fire gas detectors) are used in commercial, industrial and municipal areas. However, in addition to smoke, heat, infrared, ultraviolet light radiation, or gas, other parameters could indicate a fire, such as air temperature, air pressure, and humidity, among others. Collecting these parameters requires the development of a sensor fusion system. However, with such a system, it is necessary to develop a simple system based on artificial intelligence (AI) that will be able to detect fire with high accuracy using the information collected from the sensor fusion system. The novelty of this paper is to show the procedure of how a simple AI system can be created in form of symbolic expression obtained with a genetic programming symbolic classifier (GPSC) algorithm and can be used as an additional tool to detect fire with high classification accuracy. Since the investigation is based on an initially imbalanced and publicly available dataset (high number of samples classified as 1-Fire Alarm and small number of samples 0-No Fire Alarm), the idea is to implement various balancing methods such as random undersampling/oversampling, Near Miss-1, ADASYN, SMOTE, and Borderline SMOTE. The obtained balanced datasets were used in GPSC with random hyperparameter search combined with 5-fold cross-validation to obtain symbolic expressions that could detect fire with high classification accuracy. For this investigation, the random hyper-parameter search method and 5-fold cross-validation had to be developed. Each obtained symbolic expression was evaluated on train and test datasets to obtain mean and standard deviation values of accuracy (ACC ), area under the receiver operating characteristic curve (AUC , respectively. The symbolic expression using which best values of classification metrics were achieved is shown, and the final evaluation was performed on the original dataset. %K genetic algorithms, genetic programming, symbolic classifier, fire-alarm, oversampling methods, undersampling methods %9 journal article %R 10.3390/s23010169 %U https://www.mdpi.com/1424-8220/23/1/169 %U http://dx.doi.org/10.3390/s23010169 %P Articleno169 %0 Journal Article %T The Development of Symbolic Expressions for the Detection of Hepatitis C Patients and the Disease Progression from Blood Parameters Using Genetic Programming-Symbolic Classification Algorithm %A Andelic, Nikola %A Lorencin, Ivan %A Baressi Segota, Sandi %A Car, Zlatan %J Applied Sciences %D 2022 %8 dec %V 13 %N 1 %I MDPI %@ 2076-3417 %F Andelic:2022:applsci %X Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be used to detect HCV patients with high accuracy based on the enzymes, proteins, and biomarker values contained in a patient blood sample using genetic programming symbolic classification (GPSC) algorithm. Not only that, but the idea was also to obtain a mathematical equation that could detect the progress of the disease i.e., Hepatitis C, Fibrosis, and Cirrhosis using the GPSC algorithm. Since the original dataset was imbalanced (a large number of healthy patients versus a small number of Hepatitis C/Fibrosis/Cirrhosis patients) the dataset was balanced using random oversampling, SMOTE, ADSYN, and Borderline SMOTE methods. The symbolic expressions (mathematical equations) were obtained using the GPSC algorithm using a rigorous process of 5-fold cross-validation with a random hyperparameter search method which had to be developed for this problem. To evaluate each symbolic expression generated with GPSC the mean and standard deviation values of accuracy (ACC), the area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score were obtained. In a simple binary case (healthy vs. Hepatitis C patients) the best case was achieved with a dataset balanced with the Borderline SMOTE method. The results are ACC For the best binary and multi-class cases, the symbolic expressions are shown and evaluated on the original dataset. %K genetic algorithms, genetic programming, ADASYN, borderline SMOTE, genetic programming-symbolic classifier, Hepatitis C, fibrosis, cirrhosis, SMOTE %9 journal article %R 10.3390/app13010574 %U https://www.mdpi.com/2076-3417/13/1/574 %U http://dx.doi.org/10.3390/app13010574 %P Articleno574 %0 Journal Article %T Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier %A Andelic, Nikola %A Lorencin, Ivan %A Baressi Segota, Sandi %A Car, Zlatan %J Machines %D 2023 %8 jan %V 11 %N 1 %I MDPI %@ 2075-1702 %F Andelic:2023:Machines %X The navigation of mobile robots throughout the surrounding environment without collisions is one of the mandatory behaviors in the field of mobile robotics. The movement of the robot through its surrounding environment is achieved using sensors and a control system. The application of artificial intelligence could potentially predict the possible movement of a mobile robot if a robot encounters potential obstacles. The data used in this paper is obtained from a wall-following robot that navigates through the room following the wall in a clockwise direction with the use of 24 ultrasound sensors. The idea of this paper is to apply genetic programming symbolic classifier (GPSC) with random hyperparameter search and 5-fold cross-validation to investigate if these methods could classify the movement in the correct category (move forward, slight right turn, sharp right turn, and slight left turn) with high accuracy. Since the original dataset is imbalanced, oversampling methods (ADASYN, SMOTE, and BorderlineSMOTE) were applied to achieve the balance between class samples. These over-sampled dataset variations were used to train the GPSC algorithm with a random hyperparameter search and 5-fold cross-validation. The mean and standard deviation of accuracy (ACC), the area under the receiver operating characteristic (AUC), precision, recall, and F1−score values were used to measure the classification performance of the obtained symbolic expressions. The investigation showed that the best symbolic expressions were obtained on a dataset balanced with the BorderlineSMOTE method with ACC. respectively. The results of the investigation showed that this simple, non-linearly separable classification task could be solved using the GPSC algorithm with high accuracy. %K genetic algorithms, genetic programming, classification of robot movement, oversampling methods, symbolic classifier, ultrasound sensors %9 journal article %R 10.3390/machines11010105 %U https://www.mdpi.com/2075-1702/11/1/105 %U http://dx.doi.org/10.3390/machines11010105 %P Articleno105 %0 Journal Article %T Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier %A Andelic, Nikola %A Lorencin, Ivan %A Baressi Segota, Sandi %A Car, Zlatan %J Applied Sciences %D 2023 %8 feb %V 13 %N 3 %I MDPI %@ 2076-3417 %F Andelic:2023:applsci %X In autonomous manufacturing lines, it is very important to detect the faulty operation of robot manipulators to prevent potential damage. In this paper, the application of a genetic programming algorithm (symbolic classifier) with a random selection of hyperparameter values and trained using a 5-fold cross-validation process is proposed to determine expressions for fault detection during robotic manipulator operation, using a dataset that was made publicly available by the original researchers. The original dataset was reduced to a binary dataset (fault vs. normal operation); however, due to the class imbalance random oversampling, and SMOTE methods were applied. The quality of best symbolic expressions (SEs) was based on the highest mean values of accuracy (ACC...are equal to 0.9978, 0.998, 1.0, 0.997, and 0.998, respectively. The investigation showed that using the described procedure, symbolically expressed models of a high classification performance are obtained for the purpose of detecting faults in the operation of robotic manipulators. %K genetic algorithms, genetic programming, oversampling methods, robot fault operation, random oversampling, symbolic classifier, SMOTE %9 journal article %R 10.3390/app13031962 %U https://www.mdpi.com/2076-3417/13/3/1962 %U http://dx.doi.org/10.3390/app13031962 %P Articleno1962 %0 Journal Article %T Estimation of Interaction Locations in Super Cryogenic Dark Matter Search Detectors Using Genetic Programming-Symbolic Regression Method %A Andelic, Nikola %A Lorencin, Ivan %A Baressi Segota, Sandi %A Car, Zlatan %J Applied Sciences %D 2023 %8 feb %V 13 %N 4 %I MDPI %@ 2076-3417 %F Andelic:2023:applsci2 %X The Super Cryogenic Dark Matter Search (SuperCDMS) experiment is used to search for Weakly Interacting Massive Particles (WIMPs) candidates for dark matter particles. In this experiment, the WIMPs interact with nuclei in the detector; however, there are many other interactions (background interactions). To separate background interactions from the signal, it is necessary to measure the interaction energy and to reconstruct the location of the interaction between WIMPs and the nuclei. In recent years, some research papers have been investigating the reconstruction of interaction locations using artificial intelligence (AI) methods. In this paper, a genetic programming-symbolic regression (GPSR), with randomly tuned hyperparameters cross-validated via a five-fold procedure, was applied to the SuperCDMS experiment to estimate the interaction locations with high accuracy. To measure the estimation accuracy of obtaining the SEs, the mean and standard deviation (σ) values of R2, the root-mean-squared error (RMSE), and finally, the mean absolute error (MAE) were used. The investigation showed that using GPSR, SEs can be obtained that estimate the interaction locations with high accuracy. To improve the solution, the five best SEs were combined from the three best cases. The results demonstrated that a very high estimation accuracy can be achieved with the proposed methodology. %K genetic algorithms, genetic programming, cross-validation, interaction location, SuperCDMS, symbolic regression %9 journal article %R 10.3390/app13042059 %U https://www.mdpi.com/2076-3417/13/4/2059 %U http://dx.doi.org/10.3390/app13042059 %P Articleno2059 %0 Journal Article %T Development of Symbolic Expressions Ensemble for Breast Cancer Type Classification Using Genetic Programming Symbolic Classifier and Decision Tree Classifier %A Andelic, Nikola %A Baressi Segota, Sandi %J Cancers %D 2023 %8 29 jun %V 15 %N 13 %@ 2072-6694 %F Andelic:2023:Cancers %X Breast cancer is a type of cancer with several sub-types. It occurs when cells in breast tissue grow out of control. The accurate sub-type classification of a patient diagnosed with breast cancer is mandatory for the application of proper treatment. Breast cancer classification based on gene expression is challenging even for artificial intelligence (AI) due to the large number of gene expressions. The idea in this paper is to use genetic programming symbolic classifier (GPSC) on the publicly available dataset to obtain a set of symbolic expressions (SEs) that can classify the breast cancer sub-type using gene expressions with high classification accuracy. The initial problem with the used dataset is a large number of input variables (54676 gene expressions), a small number of dataset samples (151 samples), and six classes of breast cancer sub-types that are highly imbalanced. The large number of input variables is solved with principal component analysis (PCA), while the small number of samples and the large imbalance between class samples are solved with the application of different oversampling methods generating different dataset variations. On each oversampled dataset, the GPSC with random hyperparameter values search (RHVS) method is trained using 5-fold cross validation (5CV) to obtain a set of SEs. The best set of SEs is chosen based on mean values of accuracy (ACC), the area under the receiving operating characteristic curve (AUC), precision, recall, and F1-score values. In this case, the highest classification accuracy is equal to 0.992 across all evaluation metric methods. The best set of SEs is additionally combined with a decision tree classifier, which slightly improves ACC to 0.994. %K genetic algorithms, genetic programming, PCA, breast cancer, genetic programming symbolic classifier, 5-fold cross validation, random hyperparameter value search %9 journal article %R 10.3390/cancers15133411 %U https://www.mdpi.com/2072-6694/15/13/3411 %U http://dx.doi.org/10.3390/cancers15133411 %P articleno.3411 %0 Journal Article %T Improvement of Malicious Software Detection Accuracy through Genetic Programming Symbolic Classifier with Application of Dataset Oversampling Techniques %A Andelic, Nikola %A Baressi Segota, Sandi %A Car, Zlatan %J Computers %D 2023 %V 12 %N 12 %@ 2073-431X %F Andelic:2023:Computers %X Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automated pattern recognition, anomaly detection, and continuous learning, allowing security systems to adapt to evolving threats and identify complex, polymorphic malware that may exhibit varied behaviors. This synergy of hybrid features with AI empowers malware detection systems to efficiently and proactively identify and respond to sophisticated cyber threats in real time. In this paper, the genetic programming symbolic classifier (GPSC) algorithm was applied to the publicly available dataset to obtain symbolic expressions (SEs) that could detect the malware software with high classification performance. The initial problem with the dataset was a high imbalance between class samples, so various oversampling techniques were used to obtain balanced dataset variations on which GPSC was applied. To find the optimal combination of GPSC hyperparameter values, the random hyperparameter value search method (RHVS) was developed and applied to obtain SEs with high classification accuracy. The GPSC was trained with five-fold cross-validation (5FCV) to obtain a robust set of SEs on each dataset variation. To choose the best SEs, several evaluation metrics were used, i.e., the length and depth of SEs, accuracy score (ACC), area under receiver operating characteristic curve (AUC), precision, recall, f1-score, and confusion matrix. The best-obtained SEs are applied on the original imbalanced dataset to see if the classification performance is the same as it was on balanced dataset variations. The results of the investigation showed that the proposed method generated SEs with high classification accuracy (0.9962) in malware software detection. %K genetic algorithms, genetic programming, genetic programming symbolic classifier, 5-fold cross-validation, malware software detection, oversampling techniques, random hyperparameter value search method %9 journal article %R 10.3390/computers12120242 %U https://www.mdpi.com/2073-431X/12/12/242 %U http://dx.doi.org/10.3390/computers12120242 %P 242 %0 Journal Article %T Generating Mathematical Expressions for Estimation of Atomic Coordinates of Carbon Nanotubes Using Genetic Programming Symbolic Regression %A Andelic, Nikola %A Baressi Segota, Sandi %J Technologies %D 2023 %V 11 %N 6 %@ 2227-7080 %F andelic:2023:Technologies %X The study addresses the formidable challenge of calculating atomic coordinates for carbon nanotubes (CNTs) using density functional theory (DFT), a process that can endure for days. To tackle this issue, the research leverages the Genetic Programming Symbolic Regression (GPSR) method on a publicly available dataset. The primary aim is to assess if the resulting Mathematical Equations (MEs) from GPSR can accurately estimate calculated atomic coordinates obtained through DFT. Given the numerous hyperparameters in GPSR, a Random Hyperparameter Value Search (RHVS) method is devised to pinpoint the optimal combination of hyperparameter values, maximizing estimation accuracy. Two distinct approaches are considered. The first involves applying GPSR to estimate calculated coordinates (uc, vc, wc) using all input variables (initial atomic coordinates u, v, w, and integers n, m specifying the chiral vector). The second approach applies GPSR to estimate each calculated atomic coordinate using integers n and m alongside the corresponding initial atomic coordinates. This results in the creation of six different dataset variations. The GPSR algorithm undergoes training via a 5-fold cross-validation process. The evaluation metrics include the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and the depth and length of generated MEs. The findings from this approach demonstrate that GPSR can effectively estimate CNT atomic coordinates with high accuracy, as indicated by an impressive R2?1.0. This study not only contributes to the advancement of accurate estimation techniques for atomic coordinates but also introduces a systematic approach for optimising hyperparameters in GPSR, showcasing its potential for broader applications in materials science and computational chemistry. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/technologies11060185 %U https://www.mdpi.com/2227-7080/11/6/185 %U http://dx.doi.org/10.3390/technologies11060185 %P ArticleNo.185 %0 Journal Article %T On the application of symbolic regression in the energy sector: Estimation of combined cycle power plant electrical power output using genetic programming algorithm %A Anelic, Nikola %A Lorencin, Ivan %A Mrzljak, Vedran %A Car, Zlatan %J Engineering Applications of Artificial Intelligence %D 2024 %V 133 %@ 0952-1976 %F ANDELIC:2024:engappai %X This paper focuses on the estimation of electrical power output (Pe) in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming (GP) on a publicly available dataset to generate Symbolic Expressions (SEs) for high-accuracy Pe. To address the challenge of numerous GP hyperparameters, a random hyperparameter values search method (RHVS) is introduced to find optimal combinations, resulting in SEs with higher accuracy. SEs are created with varying input variables, and their performance is evaluated using multiple metrics (coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), Kling-Gupta Efficiency (KGE), and Bland-Altman (B-A) analysis). A key innovation involves combining the best SEs through an Averaging ensemble (AE), leading to a robust estimation accuracy. Notably, the AE YVE-2 achieves the highest (Pe) accuracy, including R2=0.9368, MAE=3.3378, MSE=18.4800, RMSE=4.2985, MAPE=0.7354percent, and KGE=0.9479. The investigation highlights AT as the most influential variable, underscoring the importance of choosing inputs aligned with physical processes. This paper’s outlined procedure, combining GP, hyperparameter optimization, and ensemble techniques, offers an efficient method for estimating Pe in CCPP. It promises simplicity and effectiveness in real-world applications. B-A analysis proves valuable for SE selection, enhancing the proposed methodology %K genetic algorithms, genetic programming, Averaging ensemble, Bland-Altman analysis, Combined cycle power plant, Random hyperparameter values search method %9 journal article %R 10.1016/j.engappai.2024.108213 %U https://www.sciencedirect.com/science/article/pii/S0952197624003713 %U http://dx.doi.org/10.1016/j.engappai.2024.108213 %P 108213 %0 Journal Article %T Improvement of pulsars detection using dataset balancing methods and symbolic classification ensemble %A Anelic, N. %J Astronomy and Computing %D 2024 %V 47 %@ 2213-1337 %F ANDELIC:2024:ascom %X Highly accurate detection of pulsars is mandatory. With the application of machine learning (ML) algorithms, the detection of pulsars can certainly be improved if the dataset is balanced. In this paper, the publicly available dataset (HTRU2) is highly imbalanced so various balancing methods were applied. The balanced dataset was used in genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect pulsars with high classification accuracy. To find the optimal combination of GPSC hyperparameters the random hyperparameter search (RHS) method was developed and applied. The GPSC was trained using 5-fold cross-validation so after each training a total of 5 SEs were obtained. The best set of SEs are selected based on their classification performance and all of them are applied on the original dataset. The best classification accuracy (ACC), the area under receiver operating characteristic (AUC), precision, recall, and f1-score were achieved in the case of the dataset balanced with the AllKNN method i.e. all mean evaluation metric values are equal to 0.995. The ensemble consisted of 25 SEs that achieved the ACC=0.978, AUC=0.9452 , Precision=0.905, Recall=0.9963, and F1-Score=0.94877, on the original dataset %K genetic algorithms, genetic programming, Dataset balancing methods, Genetic programming symbolic classifier, Pulsars detection %9 journal article %R 10.1016/j.ascom.2024.100801 %U https://www.sciencedirect.com/science/article/pii/S2213133724000167 %U http://dx.doi.org/10.1016/j.ascom.2024.100801 %P 100801 %0 Journal Article %T Enhancing Network Intrusion Detection: A Genetic Programming Symbolic Classifier Approach %A Andelic, Nikola %A Baressi Segota, Sandi %J Information %D 2024 %V 15 %N 3 %@ 2078-2489 %F andelic:2024:Information %X This investigation underscores the paramount imperative of discerning network intrusions as a pivotal measure to fortify digital systems and shield sensitive data from unauthorized access, manipulation, and potential compromise. The principal aim of this study is to leverage a publicly available dataset, employing a Genetic Programming Symbolic Classifier (GPSC) to derive symbolic expressions (SEs) endowed with the capacity for exceedingly precise network intrusion detection. In order to augment the classification precision of the SEs, a pioneering Random Hyperparameter Value Search (RHVS) methodology was conceptualized and implemented to discern the optimal combination of GPSC hyperparameter values. The GPSC underwent training via a robust five-fold cross-validation regimen, mitigating class imbalances within the initial dataset through the application of diverse oversampling techniques, thereby engendering balanced dataset iterations. Subsequent to the acquisition of SEs, the identification of the optimal set ensued, predicated upon metrics inclusive of accuracy, area under the receiver operating characteristics curve, precision, recall, and F1-score. The selected SEs were subsequently subjected to rigorous testing on the original imbalanced dataset. The empirical findings of this research underscore the efficacy of the proposed methodology, with the derived symbolic expressions attaining an impressive classification accuracy of 0.9945. If the accuracy achieved in this research is compared to the average state-of-the-art accuracy, the accuracy obtained in this research represents the improvement of approximately 3.78percent. In summation, this investigation contributes salient insights into the efficacious deployment of GPSC and RHVS for the meticulous detection of network intrusions, thereby accentuating the potential for the establishment of resilient cybersecurity defenses. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/info15030154 %U https://www.mdpi.com/2078-2489/15/3/154 %U http://dx.doi.org/10.3390/info15030154 %P ArticleNo.154 %0 Journal Article %T An Advanced Methodology for Crystal System Detection in Li-Ion Batteries %A Andelic, Nikola %A Baressi Segota, Sandi %J Electronics %D 2024 %8 jun %V 13 %N 12 %@ 2079-9292 %F Andelic:2024:Electronics %X Detecting the crystal system of lithium-ion batteries is crucial for optimising their performance and safety. Understanding the arrangement of atoms or ions within the battery electrodes and electrolyte allows for improvements in energy density, cycling stability, and safety features. This knowledge also guides material design and fabrication techniques, driving advancements in battery technology for various applications. In this paper, a publicly available dataset was used to develop mathematical equations (MEs) using a genetic programming symbolic classifier (GPSC) to determine the type of crystal structure in Li-ion batteries with a high classification performance. The dataset consists of three different classes transformed into three binary classification datasets using a one-versus-rest approach. Since the target variable of each dataset variation is imbalanced, several oversampling techniques were employed to achieve balanced dataset variations. The GPSC was trained on these balanced dataset variations using a five-fold cross-validation (5FCV) process, and the optimal GPSC hyperparameter values were searched for using a random hyperparameter value search (RHVS) method. The goal was to find the optimal combination of GPSC hyperparameter values to achieve the highest classification performance. After obtaining MEs using the GPSC with the highest classification performance, they were combined and tested on initial binary classification dataset variations. Based on the conducted investigation, the ensemble of MEs could detect the crystal system of Li-ion batteries with a high classification accuracy (1.0). %K genetic algorithms, genetic programming, crystal structure, genetic programming symbolic classifier, Lithium batteries, oversampling techniques, random hyperparameter value search method %9 journal article %R 10.3390/electronics13122278 %U https://www.mdpi.com/2079-9292/13/12/2278 %U http://dx.doi.org/10.3390/electronics13122278 %P articlenumber:2278 %0 Journal Article %T Robust password security: a genetic programming approach with imbalanced dataset handling %A Andelic, Nikola %A Baressi Segota, Sandi %A Car, Zlatan %J International Journal of Information Security %D 2024 %8 jun %V 23 %N 3 %@ 1615-5262 %F andelic:IJIS %K genetic algorithms, genetic programming, Genetic programming symbolic classifier, Random hyperparameter value search method, Fivefold cross-validation, Oversampling and undersampling techniques, Password strength classification %9 journal article %R 10.1007/s10207-024-00814-2 %U http://link.springer.com/article/10.1007/s10207-024-00814-2 %U http://dx.doi.org/10.1007/s10207-024-00814-2 %P 1761-1786 %0 Journal Article %T Achieving High Accuracy in Android Malware Detection through Genetic Programming Symbolic Classifier %A Andelic, Nikola %A Baressi Segota, Sandi %J Computers %D 2024 %8 aug %V 13 %N 8 %@ 2073-431X %F Andelic:2024:Computers %X The detection of Android malware is of paramount importance for safeguarding users personal and financial data from theft and misuse. It plays a critical role in ensuring the security and privacy of sensitive information on mobile devices, thereby preventing unauthorized access and potential damage. Moreover, effective malware detection is essential for maintaining device performance and reliability by mitigating the risks posed by malicious software. This paper introduces a novel approach to Android malware detection, leveraging a publicly available dataset in conjunction with a Genetic Programming Symbolic Classifier (GPSC). The primary objective is to generate symbolic expressions (SEs) that can accurately identify malware with high precision. To address the challenge of imbalanced class distribution within the dataset, various oversampling techniques are employed. Optimal hyperparameter configurations for GPSC are determined through a random hyperparameter values search (RHVS) method developed in this research. The GPSC model is trained using a 10-fold cross-validation (10FCV) technique, producing a set of 10 SEs for each dataset variation. Subsequently, the most effective SEs are integrated into a threshold-based voting ensemble (TBVE) system, which is then evaluated on the original dataset. The proposed methodology achieves a maximum accuracy of 0.956, thereby demonstrating its effectiveness for Android malware detection. %K genetic algorithms, genetic programming, Android malware detection, genetic programming symbolic classifier, oversampling techniques, random hyperparameters values method %9 journal article %R 10.3390/computers13080197 %U https://www.mdpi.com/2073-431X/13/8/197/pdf?version=1723704936 %U http://dx.doi.org/10.3390/computers13080197 %P articlenumber:197 %0 Journal Article %T A comprehensive study on symbolic expressions for fault detection-classification in photovoltaic farms %A Andelic, Nikola %A Baressi Segota, Sandi %A Mrzljak, Vedran %J Applied Energy %D 2025 %V 383 %@ 0306-2619 %F Andelic:2025:apenergy %X Large-scale photovoltaic (solar) farms play a crucial role in harnessing solar energy for electricity generation through photovoltaic (PV) technology. However, the control and management of such systems pose significant challenges, particularly in fault detection. This paper introduces the application of a genetic programming symbolic classifier (GPSC) to a publicly available dataset for fault detection in photovoltaic farms. Given the imbalanced nature of the original dataset, the study necessitated the application of oversampling techniques to achieve a balanced representation of class samples. Additionally, the impact of scaling and normalizing techniques on the performance of the GPSC was thoroughly investigated. The GPSC was systematically applied to each scaled or normalised balanced dataset variation, and its hyperparameters were fine-tuned using a random hyperparameter values search (RHVS) method. The algorithm underwent training, via a 5-fold cross-validation (5FCV) process, and the best symbolic expressions (SEs) were determined based on accuracy, area under the receiver operating characteristics curve, precision, recall, and F1-score. The research yielded many SEs, which were used to develop a threshold-based voting ensemble (TBVE). The TBVE for each class was tested on the initial dataset and the threshold was finely tuned to achieve even higher classification performance in photovoltaic detection/classification. Results demonstrated that this approach produced highly accurate TBVE for each class (accuracy in the majority of cases equal to 1.0), showcasing the effectiveness of the GPSC and TBVE in fault detection/classification for photovoltaic farms %K genetic algorithms, genetic programming, Data preprocessing and oversampling, Genetic programming symbolic classifier, Random hyperparameter value search method, Photovoltaic farms fault detection and classification, Threshold based voting ensemble %9 journal article %R 10.1016/j.apenergy.2025.125370 %U https://www.sciencedirect.com/science/article/pii/S030626192500100X %U http://dx.doi.org/10.1016/j.apenergy.2025.125370 %P 125370 %0 Journal Article %T Application of Symbolic Classifiers and Multi-Ensemble Threshold Techniques for Android Malware Detection %A Andelic, Nikola %A Baressi Segota, Sandi %A Mrzljak, Vedran %J Big Data and Cognitive Computing %D 2025 %V 9 %N 2 %@ 2504-2289 %F andelic:2025:BDCC %X Android malware detection using artificial intelligence today is a mandatory tool to prevent cyber attacks. To address this problem in this paper the proposed methodology consists of the application of genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect if the android is malware or not. To find the optimal combination of GPSC hyperparameter values the random hyperparameter values search method (RHVS) method and the GPSC were trained using 5-fold cross-validation (5FCV). It should be noted that the initial dataset is highly imbalanced (publicly available dataset). This problem was addressed by applying various preprocessing and oversampling techniques thus creating a huge number of balanced dataset variations and on each dataset variation the GPSC was trained. Since the dataset has many input variables three different approaches were considered: the initial investigation with all input variables, input variables with high feature importance, application of principal component analysis. After the SEs with the highest classification performance were obtained they were used in threshold-based voting ensembles and the threshold values were adjusted to improve classification performance. Multi-TBVE has been developed and using them the robust system for Android malware detection was achieved with the highest accuracy of 0.98 was obtained. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/bdcc9020027 %U https://www.mdpi.com/2504-2289/9/2/27 %U http://dx.doi.org/10.3390/bdcc9020027 %P ArticleNo.27 %0 Journal Article %T Fine-Tuning Network Slicing in 5G: Unveiling Mathematical Equations for Precision Classification %A Andelic, Nikola %A Baressi Segota, Sandi %A Mrzljak, Vedran %J Computers %D 2025 %V 14 %N 5 %@ 2073-431X %F andelic:2025:Computers %X Modern 5G network slicing centers on the precise design of virtual, independent networks operating over a shared physical infrastructure, each configured to meet specific service requirements. This approach plays a vital role in enabling highly customized and flexible service delivery within the 5G ecosystem. In this study, we present the application of a genetic programming symbolic classifier to a dedicated network slicing dataset, resulting in the generation of accurate symbolic expressions for classifying different network slice types. To address the issue of class imbalance, we employ oversampling strategies that produce balanced variations of the dataset. Furthermore, a random search strategy is used to explore the hyperparameter space comprehensively in pursuit of optimal classification performance. The derived symbolic models, refined through threshold tuning based on prediction correctness, are subsequently evaluated on the original imbalanced dataset. The proposed method demonstrates outstanding performance, achieving a perfect classification accuracy of 1.0. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/computers14050159 %U https://www.mdpi.com/2073-431X/14/5/159 %U http://dx.doi.org/10.3390/computers14050159 %P ArticleNo.159 %0 Journal Article %T Hybrid Explainable AI for Machine Predictive Maintenance: From Symbolic Expressions to Meta-Ensembles %A Andelic, Nikola %A Baressi Segota, Sandi %A Mrzljak, Vedran %J Processes %D 2025 %V 13 %N 7 %@ 2227-9717 %F andelic:2025:Processes %X Machine predictive maintenance plays a critical role in reducing unplanned downtime, lowering maintenance costs, and improving operational reliability by enabling the early detection and classification of potential failures. Artificial intelligence (AI) enhances these capabilities through advanced algorithms that can analyse complex sensor data with high accuracy and adaptability. This study introduces an explainable AI framework for failure detection and classification using symbolic expressions (SEs) derived from a genetic programming symbolic classifier (GPSC). Due to the imbalanced nature and wide variable ranges in the original dataset, we applied scaling/normalization and oversampling techniques to generate multiple balanced dataset variations. Each variation was used to train the GPSC with five-fold cross-validation, and optimal hyperparameters were selected using a Random Hyperparameter Value Search (RHVS) method. However, as the initial Threshold-Based Voting Ensembles (TBVEs) built from SEs did not achieve a satisfactory performance for all classes, a meta-dataset was developed from the outputs of the obtained SEs. For each class, a meta-dataset was preprocessed, balanced, and used to train a Random Forest Classifier (RFC) with hyperparameter tuning via RandomizedSearchCV. For each class, a TBVE was then constructed from the saved RFC models. The resulting ensemble demonstrated a near-perfect performance for failure detection and classification in most classes (0, 1, 3, and 5), although Classes 2 and 4 achieved a lower performance, which could be attributed to an extremely low number of samples and a hard-to-detect type of failure. Overall, the proposed method presents a robust and explainable AI solution for predictive maintenance, combining symbolic learning with ensemble-based meta-modelling. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/pr13072180 %U https://www.mdpi.com/2227-9717/13/7/2180 %U http://dx.doi.org/10.3390/pr13072180 %P ArticleNo.2180 %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 10.7717/peerj-cs.244 %U https://doi.org/10.7717/peerj-cs.244 %U http://dx.doi.org/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 10.7287/peerj.preprints.27731v1 %U https://doi.org/10.7287/peerj.preprints.27731v1 %U http://dx.doi.org/10.7287/peerj.preprints.27731v1 %P e27731 %0 Conference Proceedings %T Genetic Programming for Evolving Similarity Functions Tailored to Clustering Algorithms %A Andersen, Hayden %A Lensen, Andrew %A Xue, Bing %Y Ong, Yew-Soon %S 2021 IEEE Congress on Evolutionary Computation (CEC) %D 2021 %8 28 jun 1 jul %C Krakow, Poland %F Andersen:2021:CEC %X Clustering is the process of grouping related instances of unlabelled data into distinct subsets called clusters. While there are many different clustering methods available, almost all of them use simple distance-based (dis)similarity functions such as Euclidean Distance. However, these and most other predefined dissimilarity functions can be rather inflexible by considering each feature equally and not properly capturing feature interactions in the data. Genetic Programming is an evolutionary computation approach that evolves programs in an iterative process that naturally lends itself to the evolution of functions. This paper introduces a novel framework to automatically evolve dissimilarity measures for a provided clustering dataset and algorithm. The results show that the evolved functions create clusters exhibiting high measures of cluster quality. %K genetic algorithms, genetic programming, Measurement, Clustering methods, Clustering algorithms, Evolutionary computation, Euclidean distance, Iterative methods, Clustering, Similarity Function, Feature Selection %R 10.1109/CEC45853.2021.9504855 %U http://dx.doi.org/10.1109/CEC45853.2021.9504855 %P 688-695 %0 Conference Proceedings %T Intepretable Local Explanations Through Genetic Programming %A Andersen, Hayden %A Lensen, Andrew %A Browne, Will %A Mei, Yi %Y Mouret, Jean-Baptiste %Y Qin, Kai %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F andersen:2024:GECCOcomp %X As machine learning models become increasingly prevalent in everyday life, there is a growing demand for explanation of the predictions generated by these models. However, most models used by companies are black-boxes in nature, without the capacity to provide explanations to users. This reduces public trust in these models, and exists as a barrier to adoption of machine learning. Research into providing explanations to users has shown that local explanation techniques provide more acceptable explanations to users than attempting to explain an entire model, as a user often does not need to understand the entirety of a model.This work builds on prior work in the field to produce a competitive method for high-fidelity local explanations using genetic programming. Two different data representations targeted towards both users with and without machine learning experience are evaluated.The experimental results show comparable fidelity to the state-of-the art, while exhibiting more comprehensible explanations due to including fewer features in each explanation. The method enables decomposable explanations that are easy to interpret, while still capturing non-linear relationships in the original model. %K genetic algorithms, genetic programming, explainable AI, XAI, machine learning, Evolutionary Machine Learning: Poster %R 10.1145/3638530.3654370 %U http://dx.doi.org/10.1145/3638530.3654370 %P 247-250 %0 Conference Proceedings %T Towards evolution-based autonomy in large-scale systems %A Anderson, Damien %A Harvey, Paul %A Kaneta, Yusaku %A Papadopoulos, Petros %A Rodgers, Philip %A Roper, Marc %Y Bruce, Bobby R. %Y Nowack, Vesna %Y Blot, Aymeric %Y Winter, Emily %Y Langdon, W. B. %Y Petke, Justyna %S GI @ GECCO 2022 %D 2022 %8 September %I Association for Computing Machinery %C Boston, USA %F Anderson:2022:GI %X To achieve truly autonomous behaviour systems need to adapt to not only previously unseen circumstances but also previously unimagined ones. A hierarchical evolutionary system is proposed which is capable of displaying the emergent behaviour necessary to achieve this goal. we report the practical challenges encountered in implementing this proposed approach in a large-scale open-source system. %K genetic algorithms, genetic programming, genetic improvement, SBFL, Internet based content delivery network, CDN, 5G, grammar, Varnish Configuration Language, VCL %R 10.1145/3520304.3533975 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Anderson_2022_GI.pdf %U http://dx.doi.org/10.1145/3520304.3533975 %P 1924-1925 %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 10.1007/3-540-45712-7_66 %U http://dx.doi.org/10.1007/3-540-45712-7_66 %P 689-699 %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 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 10.1007/3-540-48885-5_13 %U http://dx.doi.org/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 10.1007/3-540-45561-2_31 %U http://dx.doi.org/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 10.1007/BFb0055935 %U http://dx.doi.org/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 10.1007/978-3-540-71805-5_63 %U http://dx.doi.org/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 10.1109/CEC.2007.4425027 %U 1814.pdf %U http://dx.doi.org/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 10.1109/ICSMC.2009.5346894 %U http://dx.doi.org/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 10.1109/CEC.2002.1006249 %U http://citeseer.ist.psu.edu/520794.html %U http://dx.doi.org/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 10.1016/S0020-0255(02)00235-9 %U http://www.sciencedirect.com/science/article/B6V0C-46WWB37-3/2/963172f8c0faa12d700376b07bfc96a5 %U http://dx.doi.org/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 10.1023/B:GENP.0000023685.83861.69 %U http://dx.doi.org/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 10.1007/978-3-642-33275-3_104 %U http://dx.doi.org/10.1007/978-3-642-33275-3_104 %P 845-853 %0 Conference Proceedings %T On the Use of Predation to Shape Evolutionary Computation %A Andrade, Felipe S. P. %A Aranha, Claus %A da Silva Torres, Ricardo %S 2020 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2020 %8 dec %F Andrade:2020:SSCI %X Classic Evolutionary Algorithms often use elitist approaches, such as fitness functions, to select individuals for new generations. In this work, we consider an alternative strategy to simulate the selection process that relies on exploiting ecological interactions between individuals instead of explicitly using a fitness based in the search progress. To demonstrate this strategy, we present an Artificial Life system which simulates an ecosystem where different species are different bio-inspired meta-heuristics, and the main ecological relationship is the predation. Specifically, individuals from a Particle Swarm Optimization (PSO), with movement rules defined by Genetic Programming, survive by predating on individuals from an Artificial Bee Colony (ABC) system that operates on traditional optimization rules. This ecology is investigated on optimization benchmarks, and we observed the development of interesting ecological dynamics between the two species. %K genetic algorithms, genetic programming, Predator prey systems, Statistics, Sociology, Biological system modeling, Ecosystems, Particle swarm optimization, Artificial Life, Evolutionary Computation, Ecological Relationship %R 10.1109/SSCI47803.2020.9308209 %U http://dx.doi.org/10.1109/SSCI47803.2020.9308209 %P 117-124 %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 %R 10.7551/mitpress/1108.003.0029 %U http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888 %U http://dx.doi.org/10.7551/mitpress/1108.003.0029 %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 10.1109/ICEC.1994.350007 %U http://dx.doi.org/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 10.1109/ICEC.1994.349906 %U http://citeseer.ist.psu.edu/31976.html %U http://dx.doi.org/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 %X ... in the C programming language ... migration rates between 1 percent and 8 percent ... more than linear speed up. %K genetic algorithms, genetic programming, even 5 parity, island model, demes, INMOS, 30MHz 32 bit 16 MByte TRAM T805 %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 percent. This level of accuracy exceeds that of the Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other rules produced by known previous automated approaches. Our genetically evolved rule is qualitatively different from other rules in that it uses a fine-grained internal representation of density information; it employs a large number of different domains and particles; and it uses an intricate set of signals for communicating information over large distances in time and space. %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 10.7551/mitpress/1109.003.0022 %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277532 %U http://dx.doi.org/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 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. %K genetic algorithms, genetic programming %9 journal article %R 10.1016/S0020-0255(97)10011-1 %U http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-21/2/22b9842f820b08883990bbae1d889c03 %U http://dx.doi.org/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 10.1109/ICEC.1998.699489 %U http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00699489 %U c023.pdf %U http://dx.doi.org/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 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/10.1007/3-540-48422-1_28 %P 346-351 %0 Generic %T GP considered Dangerous %A Andre, David %E Banzhaf, Wolfgang %E Trujillo, Leonardo %E Winkler, Stephan %E Worzel, Bill %D 2021 %8 19 21 may %I Springer %C East Lansing, USA %F Andre:2021:GPTP %O keynote %K genetic algorithms, genetic programming %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 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/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 %R 10.7551/mitpress/1108.003.0022 %U http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888 %U http://dx.doi.org/10.7551/mitpress/1108.003.0022 %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 10.1007/s10710-018-9335-2 %U https://rdcu.be/dR8cU %U http://dx.doi.org/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 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/10.1007/978-3-319-94030-4_11 %P 269-313 %0 Conference Proceedings %T Feature Selection for GPSR Based on Maximal Information Coefficient and Shapley Values %A Mohamad Anfar, Mohamad Rimas %A Chen, Qi %A Zhang, Mengjie %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F rimas:2024:CEC %X Feature selection is a critical aspect of improving the interpretability of machine learning models. Genetic Programming (GP) has a built-in feature selection mechanism that explores the search space to include informative features in models. However, this built-in mechanism is insufficient for identifying important features, when dealing with high-dimensional feature spaces. To overcome this limitation, the paper introduces a novel feature importance measurement based on the Maximal Infor-mation Coefficient and Shapley Values. The proposed algorithm operates in two stages. In the first stage, it identifies the best individuals from different populations. In the second stage, the best individuals from the first stage are used for the calculation of the novel individual feature importance measurement. The new feature importance measurement offers valuable insights into the significance and relevance of the selected features. Regression experiments were conducted on six datasets to assess the effectiveness of the proposed method. Furthermore, comparisons were made with two other algorithms to evaluate its performance. The results indicate that the proposed approach enhances GP performance for high dimensional datasets while maintaining GP trees of similar size compared to standard GP. %K genetic algorithms, genetic programming, Microwave integrated circuits, Sociology, Focusing, Machine learning, Feature extraction, Time measurement, Feature importance, MIC, symbolic regression, Shapley value %R 10.1109/CEC60901.2024.10611755 %U http://dx.doi.org/10.1109/CEC60901.2024.10611755 %0 Conference Proceedings %T Importance-Based Pruning for Genetic Programming Based Symbolic Regression %A Rimas, Mohamad %A Chen, Qi %A Zhang, Mengjie %S 37th Australasian Joint Conference on Artificial Intelligence, AI 2024 %D 2024 %8 nov 25 29 %I Springer %C Melbourne, Australia %F rimas:2024:AI %K genetic algorithms, genetic programming %R 10.1007/978-981-96-0351-0_14 %U https://link.springer.com/chapter/10.1007/978-981-96-0351-0_14 %U http://dx.doi.org/10.1007/978-981-96-0351-0_14 %0 Conference Proceedings %T Node Importance-Based Multi-Objective Genetic Programming for Enhanced Model Interpretability in Symbolic Regression %A Mohamad Anfar, Mohamad Rimas %A Chen, Qi %A Zhang, Mengjie %Y Jin, Yaochu %Y Baeck, Thomas %S 2025 IEEE Congress on Evolutionary Computation (CEC) %D 2025 %8 August 12 jun %I IEEE %C Hangzhou, China %F DBLP:conf/cec/AnfarCZ25 %X Interpretability is a critical requirement in high-stakes real-world applications. Genetic Programming (GP) is one of the most interpretable machine learning algorithms currently available. While certain datasets necessitate complex GP models to capture underlying patterns, others may not require such complexity. However, GP lacks a mechanism to dynamically assess the required model complexity during evolution. As a result, GP often evolves overly complex models that are challenging to analyse, thereby reducing interpretability. This paper introduces a novel algorithm designed to evolve compact multi-objective GP models without significantly compromising regression error, thereby enhancing interpretability. The proposed method employs NSGA-II to simultaneously minimise regression error and maximize a newly introduced per-node importance metric. This metric quantifies the contribution of each node in terms of error reduction. By leveraging this metric, the algorithm discards large GP models containing many low-importance nodes, effectively avoiding unnecessarily complex solutions. Experimental results on ten regression datasets demonstrate that the proposed method consistently evolves smaller GP models with better interpretability while maintaining competitive predictive performance, particularly on high-dimensional datasets. %K genetic algorithms, genetic programming, Measurement, Analytical models, Machine learning algorithms, Computational modeling, Heuristic algorithms, Evolutionary computation, Predictive models, Prediction algorithms, Complexity theory, Interpretability, NSGA-II, Per-node Importance, Regression, XAI %R 10.1109/CEC65147.2025.11042961 %U https://doi.org/10.1109/CEC65147.2025.11042961 %U http://dx.doi.org/10.1109/CEC65147.2025.11042961 %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 10.1109/CEC.2008.4631290 %U EC0777.pdf %U http://dx.doi.org/10.1109/CEC.2008.4631290 %P 3634-3641 %0 Journal Article %T A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities %A Angarita-Zapata, Juan S. %A Maestre-Gongora, Gina %A Calderin, Jenny Fajardo %J Sensors (Basel, Switzerland) %D 2021 %8 dec 16 %V 21 %N 24 %@ 1424-8220 %F angarita-zapata:2021:Sensors %X Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellin, Bogota, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas. %K genetic algorithms, genetic programming, TPOT, Bayes Theorem, Benchmarking, Bibliometrics, Cities, Colombia, Machine Learning, Internet of Things, automated machine learning, crash severity prediction, intelligent transportation systems, supervised learning %9 journal article %R 10.3390/s21248401 %U http://dx.doi.org/10.3390/s21248401 %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, memory, indexiing problem, search and intelligence, AI, schema theory, emergent intelligence, ANN, GNARL, Ant problem, FSM, FSA, GLIB, FUNC, modular programs, tower of hanoi, tic tac toe, Tomita, competeing conventions, tracker task %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 %R 10.7551/mitpress/1108.003.0009 %U http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap4.pdf %U http://dx.doi.org/10.7551/mitpress/1108.003.0009 %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 10.1016/0303-2647(94)90062-0 %U http://dx.doi.org/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 10.1109/MIS.1995.10027 %U http://dx.doi.org/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 10.7551/mitpress/2887.003.0037 %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6300850 %U http://dx.doi.org/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 %E Marks, II, Robert J. %E Fogel, David B. %E Fukuda, Toshio %B Computational Intelligence: A Dynamic Systems Perspective %D 1995 %I IEEE Press %@ 0-7803-1182-5 %G English %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 10.7551/mitpress/1109.001.0001 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp2.html %U http://dx.doi.org/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 10.7551/mitpress/1109.003.0004 %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277539 %U http://dx.doi.org/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 10.7551/mitpress/1109.003.0009 %U http://www.natural-selection.com/Library/1996/aigp2.ps.Z %U http://dx.doi.org/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, headless-chicken crossover %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 10.1007/BFb0014823 %U http://www.natural-selection.com/Library/1997/ep97b.pdf %U http://dx.doi.org/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 %S SPIE %D 1997 %8 April %V 3077 %C Orlando, FL, United States %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, multiple interacting programs, MIPs, evolutionary computation, evolutionary programming, system identification, dynamical systems, optimization, ANN, interconnection graph, XAI, dynamical systems %R 10.1117/12.271503 %U http://www.natural-selection.com/Library/1997/spie97.pdf %U http://dx.doi.org/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 10.1201/9781420050387.ptc %U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf %U http://dx.doi.org/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 10.1201/9781420050387.ptc %U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf %U http://dx.doi.org/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 10.1201/9781420050387.ptc %U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf %U http://dx.doi.org/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 %R 10.3233/FI-1998-35123410 %U http://www.natural-selection.com/Library/1998/gphist.pdf %U http://dx.doi.org/10.3233/FI-1998-35123410 %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, ANN %9 journal article %R 10.1080/019697298125407 %U http://www.natural-selection.com/Library/1998/mips3.pdf %U http://dx.doi.org/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 10.1117/12.304803 %U http://www.natural-selection.com/Library/1998/spie98.pdf %U http://dx.doi.org/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 Journal Article %T Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives %A Angelis, Dimitrios %A Sofos, Filippos %A Karakasidis, Theodoros E. %J Archives of Computational Methods in Engineering %D 2023 %8 jul %V 30 %F Angelis:2023:ACME %X Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalisable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed. %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s11831-023-09922-z %U https://rdcu.be/dmkPm %U http://dx.doi.org/10.1007/s11831-023-09922-z %P 3845-3865 %0 Journal Article %T Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods %A Angelis, Dimitrios %A Sofos, Filippos %A Papastamatiou, Konstantinos %A Karakasidis, Theodoros E. %J Micromachines %D 2023 %V 14 %N 7 %@ 2072-666X %F angelis:2023:Micromachines %X In this paper, we propose an alternative road to calculate the transport coefficients of fluids and the slip length inside nano-conduits in a Poiseuille-like geometry. These are all computationally demanding properties that depend on dynamic, thermal, and geometrical characteristics of the implied fluid and the wall material. By introducing the genetic programming-based method of symbolic regression, we are able to derive interpretable data-based mathematical expressions based on previous molecular dynamics simulation data. Emphasis is placed on the physical interpretability of the symbolic expressions. The outcome is a set of mathematical equations, with reduced complexity and increased accuracy, that adhere to existing domain knowledge and can be exploited in fluid property interpolation and extrapolation, bypassing timely simulations when possible. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/mi14071446 %U https://www.mdpi.com/2072-666X/14/7/1446 %U http://dx.doi.org/10.3390/mi14071446 %P ArticleNo.1446 %0 Journal Article %T Physically Consistent Self-Diffusion Coefficient Calculation with Molecular Dynamics and Symbolic Regression %A Angelis, Dimitrios %A Georgakopoulos, Chrysostomos %A Sofos, Filippos %A Karakasidis, Theodoros E. %J International Journal of Molecular Sciences %D 2025 %V 26 %N 14 %@ 1422-0067 %F angelis:2025:IJMS %X Machine Learning methods are exploited to extract a universal approach for self-diffusion coefficient calculation in molecular fluids. Analytical expressions are derived through symbolic regression for fluids both in bulk and confined nanochannels. The symbolic regression framework is trained on simulation data from molecular dynamics and correlates the values of the self-diffusion coefficients with macroscopic properties, such as density, temperature, and the width of confinement. New expressions are derived for nine different molecular fluids, while an all-fluid universal equation is extracted to capture molecular behaviour as well. In such a way, a highly computationally demanding property is predicted by easy-to-define macroscopic parameters, bypassing traditional numerical methods based on mean squared displacement and autocorrelation functions at the atomistic level. To achieve generalizability and interpretability, simple symbolic expressions are selected from a pool of genetic programming-derived equations. The obtained expressions present physical consistency, and they are discussed in terms of explainability. The accurate prediction of the self-diffusion coefficient both in bulk and confined systems is important for advancing the fundamental understanding of fluid behaviour and leading the design of nanoscale confinement devices containing real molecular fluids. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/ijms26146748 %U https://www.mdpi.com/1422-0067/26/14/6748 %U http://dx.doi.org/10.3390/ijms26146748 %P ArticleNo.6748 %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 10.1109/GEFS.2008.4484565 %U http://dx.doi.org/10.1109/GEFS.2008.4484565 %P 41-46 %0 Journal Article %T Interpretable machine learning models for displacement demand prediction in reinforced concrete buildings under pulse-like earthquakes %A Angelucci, Giulia %A Quaranta, Giuseppe %A Mollaioli, Fabrizio %A Kunnath, Sashi K. %J Journal of Building Engineering %D 2024 %V 95 %@ 2352-7102 %F Angelucci:2024:jobe %X This work proposes a novel procedure to guide the development of machine learning models for estimating the seismic demand in existing reinforced concrete (RC) buildings. The proposed approach is organized across two scales. A large-scale (nonparametric) machine learning model is first obtained by means of Gaussian Process Regression (GPR) using all candidate building attributes and intensity measures. SHapley Additive exPlanations (SHAP) values are used to facilitate its interpretation and to assist the rational selection of a small subset of intensity measures, which is finally employed to develop a (symbolic) reduced-scale machine learning model by means of Genetic Programming (GP). Simplified models of archetype buildings are adopted to develop machine learning techniques at both scales, in such a way to alleviate the simulation time for preparing large datasets. Refined models representative of actual buildings are instead considered for the unbiased final assessment. The proposed approach is applied to develop predictive machine learning models for the maximum inter-storey drift in bare frames, pilotis frames and frames with infills under pulse-like seismic ground motions. Consequently, the critical examination of the SHAP values revealed the most significant intensity measures and unfolded interesting patterns depending on the occupancy rate of the infills. Moreover, the final assessment demonstrates that this approach allows the management of a non-homogeneous building stock consisting of very diverse structural systems (i.e., spanning from existing buildings designed against gravity loads only to buildings that comply with outdated seismic codes) while providing satisfactory predictions of the seismic demand with minimum computational effort %K genetic algorithms, genetic programming, Engineering demand parameter, Gaussian process regression, Machine learning, Pulse-like earthquake, Reinforced concrete %9 journal article %R 10.1016/j.jobe.2024.110124 %U https://www.sciencedirect.com/science/article/pii/S2352710224016929 %U http://dx.doi.org/10.1016/j.jobe.2024.110124 %P 110124 %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 10.1016/j.optlaseng.2016.07.005 %U http://www.sciencedirect.com/science/article/pii/S0143816616301385 %U http://dx.doi.org/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, SBST %R 10.1007/978-3-030-16670-0_1 %U https://www.springer.com/us/book/9783030166694 %U http://dx.doi.org/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 10.1109/SSCI44817.2019.9003048 %U http://dx.doi.org/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 10.1007/978-3-030-44094-7_2 %U http://dx.doi.org/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 10.1145/3377930.3390167 %U https://doi.org/10.1145/3377930.3390167 %U http://dx.doi.org/10.1145/3377930.3390167 %P 1213-1221 %0 Journal Article %T Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing %A Anjum, Muhammad Sheraz %A Ryan, Conor %J SN Computer Science %D 2021 %V 2 %F Anjum:2021:SNCS %X Heuristic-based optimization techniques have been increasingly used to automate different types of code coverage analysis. Several studies suggest that interdependencies (in the form of comparisons) may exist between the condition constructs, of variables and constant values, in the branching conditions of real-world programs, e.g. ( i≤100 ) or ( i==j ), etc. In this work, by interdependencies we refer to the situations where, to satisfy a branching condition, there must be a certain relation-ship between the values of some specific condition constructs (which may or may not be a part of the respective condition predicates). For example, the values of variables i and j must be equal to satisfy the condition of ( i==j ), and the value of variable k must be equal to 100 for the satisfaction of the condition of ( k==100 ). To date, only the Ariadne, a Grammatical Evolution (GE)-based system, exploits these interdependencies between input variables (e.g. of the form ( i≤j ) or ( i==j ), etc.) to efficiently generate test data. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to evolve complex test data, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from interdependencies involving constants, e.g. ( i≤100 ) or ( j==500 ), etc., due to the difficulty in evolving precise values, and these are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. We propose to seed the grammar with constants extracted from the source code of the program under test to enhance and extend Ariadne capability to exploit richer types of dependencies (involving all combinations of both variables and constant values). We compared our results with the original system of Ariadne against a large set of benchmark problems which include 10 numeric programs in addition to the ones originally used for Ariadne. Our results demonstrate that the seeding strategy not only dramatically improves the generality of the system, as it improves the code coverage (effectiveness) by impressive margins, but it also reduces the search budgets (efficiency) often up to an order of magnitude. Moreover, we also performed a rigorous analysis to investigate the scalability of our improved Ariadne, showing that it stays highly scalable when compared to both the original system of Ariadne and GA-based test data generation approach %K genetic algorithms, genetic programming, Grammatical evolution, Automatic test data generation, Code coverage, Evolutionary testing %9 journal article %R 10.1007/s42979-021-00631-7 %U http://dx.doi.org/10.1007/s42979-021-00631-7 %P 280 %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 Using Heterogeneous Model Ensembles to Improve the Prediction of Yeast Contamination in Peppermint %A Anlauf, Stefan %A Haghofer, Andreas %A Dirnberger, Karl %A Winkler, Stephan M. %J Procedia Computer Science %D 2022 %V 200 %@ 1877-0509 %F ANLAUF:2022:procs %O 3rd International Conference on Industry 4.0 and Smart Manufacturing %X In this paper, we present an heterogeneous ensemble modeling approach to learn predictors for yeast contamination in freshly harvested peppermint batches. Our research is based on data about numerous parameters of the harvesting process, such as planting, tillage, fertilization, harvesting, drying, as well as information about microbial contamination. We use several different machine learning methods, namely random forests, gradient boosting trees, symbolic regression by genetic programming, and support vector machines to learn models that predict contamination on the basis of available harvesting parameters. Using those models we form model ensembles in order to improve the accuracy as well as to reduce the false negative rate, i.e., to oversee as few contaminations as possible. As we summarize in this paper, ensemble modeling indeed helps to increase the prediction accuracy for our application, especially when using only the best models. The final prediction accuracy as well as other statistical indicators such as false negative rate and false positive rate depend on the choice of the discrimination threshold; in the optimal case, model ensembles are able to predict yeast contamination with 65.91percent accuracy and only 19.15percent of the samples are false negative, i.e., overseen contaminations %K genetic algorithms, genetic programming, yeast contamination, herbs, machine learning, heterogeneous model ensembles %9 journal article %R 10.1016/j.procs.2022.01.319 %U https://www.sciencedirect.com/science/article/pii/S1877050922003283 %U http://dx.doi.org/10.1016/j.procs.2022.01.319 %P 1194-1200 %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 10.3233/ICA-2003-10202 %U http://content.iospress.com/articles/integrated-computer-aided-engineering/ica00140 %U http://dx.doi.org/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 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/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 10.1109/CEC.2019.8789920 %U http://dx.doi.org/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 10.1145/3319619.3321988 %U http://dx.doi.org/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 10.1145/2001576.2001805 %U http://dx.doi.org/10.1145/2001576.2001805 %P 1699-1706 %0 Conference Proceedings %T OpenTuner: an extensible framework for program autotuning %A Ansel, Jason %A Kamil, Shoaib %A Veeramachaneni, Kalyan %A Ragan-Kelley, Jonathan %A Bosboom, Jeffrey %A O’Reilly, Una-May %A Amarasinghe, Saman P. %Y Amaral, Jose Nelson %Y Torrellas, Josep %S International Conference on Parallel Architectures and Compilation, PACT ’14 %D 2014 %8 aug 24 27 %I ACM %C Edmonton, Canada %F DBLP:conf/IEEEpact/AnselKVRBOA14 %X Program autotuning has been shown to achieve better or more portable performance in a number of domains. However, autotuners themselves are rarely portable between projects, for a number of reasons: using a domain-informed search space representation is critical to achieving good results; search spaces can be intractably large and require advanced machine learning techniques; and the landscape of search spaces can vary greatly between different problems, sometimes requiring domain specific search techniques to explore efficiently. This paper introduces OpenTuner, a new open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully-customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the program to be autotuned. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously; techniques that perform well will dynamically be allocated a larger proportion of tests. We demonstrate the efficacy and generality of OpenTuner by building auto-tuners for 7 distinct projects and 16 total benchmarks, showing speedups over prior techniques of these projects of up to 2.8 fold with little programmer effort. %K genetic algorithms, genetic programming, genetic improvement, program autotuning, multi-armed bandit problem %R 10.1145/2628071.2628092 %U https://doi.org/10.1145/2628071.2628092 %U http://dx.doi.org/10.1145/2628071.2628092 %P 303-316 %0 Generic %T Local Search, Semantics, and Genetic Programming: a Global Analysis %A Anselmi, Fabio %A Castelli, Mauro %A d’Onofrio, Alberto %A Manzoni, Luca %A Mariot, Luca %A Saletta, Martina %D 2023 %I arXiv %F DBLP:journals/corr/abs-2305-16956 %K genetic algorithms, genetic programming %R 10.48550/ARXIV.2305.16956 %U https://doi.org/10.48550/arXiv.2305.16956 %U http://dx.doi.org/10.48550/ARXIV.2305.16956 %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 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/10.1145/1592761.1592768 %P 13-14 %0 Conference Proceedings %T Transformer Semantic Genetic Programming for Symbolic Regression %A Anthes, Philipp %A Sobania, Dominik %A Rothlauf, Franz %Y Ekart, Aniko %Y Pillay, Nelishia %S Proceedings of the 2025 Genetic and Evolutionary Computation Conference %S GECCO ’25 %D 2025 %8 14 18 jul %I Association for Computing Machinery %C Malaga, Spain %F anthes:2025:GECCO %X In standard genetic programming (stdGP), solutions are varied by modifying their syntax, with uncertain effects on their semantics. Geometric-semantic genetic programming (GSGP), a popular variant of GP, effectively searches the semantic solution space using variation operations based on linear combinations, although it results in significantly larger solutions. This paper presents Transformer Semantic Genetic Programming (TSGP), a novel and flexible semantic approach that uses a generative transformer model as search operator. The transformer is trained on synthetic test problems and learns semantic similarities between solutions. Once the model is trained, it can be used to create offspring solutions with high semantic similarity also for unseen and unknown problems. Experiments on several symbolic regression problems show that TSGP generates solutions with comparable or even significantly better prediction quality than stdGP, SLIM_GSGP, DSR, and DAE-GP. Like SLIM_GSGP, TSGP is able to create new solutions that are semantically similar without creating solutions of large size. An analysis of the search dynamic reveals that the solutions generated by TSGP are semantically more similar than the solutions generated by the benchmark approaches allowing a better exploration of the semantic solution space. %K genetic algorithms, genetic programming, transformer models, semantic operators, symbolic regression, ANN %R 10.1145/3712256.3726412 %U https://doi.org/10.1145/3712256.3726412 %U http://dx.doi.org/10.1145/3712256.3726412 %P 952-960 %0 Conference Proceedings %T Syntactic Flexibility Enables Compact Solutions in Transformer Semantic GP %A Anthes, Philipp %Y Manzoni, Luca %Y Cussat-Blanc, Sylvain %Y Chen, Qi %S European Conference on Genetic Programming, EuroGP 2026 %D 2026 %8 August 10 apr %I Springer Nature %C Toulouse %F anthes:2026:EuroGP %K genetic algorithms, genetic programming %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 10.1145/1068009.1068312 %U http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1789.pdf %U http://dx.doi.org/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 24 28 jun %C Edmonton, Canada %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 10.1109/IFSA-NAFIPS.2013.6608367 %U http://dx.doi.org/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 10.1007/978-3-642-12842-4 %U http://dx.doi.org/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 10.1007/978-3-642-16239-8_13 %U http://dx.doi.org/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 10.1016/B978-0-08-050684-5.50015-X %U http://dx.doi.org/10.1016/B978-0-08-050684-5.50015-X %P 193-204 %0 Conference Proceedings %T A Functional Analysis Approach to Symbolic Regression %A Antonov, Kirill %A Kalkreuth, Roman %A Yang, Kaifeng %A Baeck, Thomas %A Stein, Niki %A Kononova, Anna %Y Hu, Ting %Y Ekart, Aniko %Y Handl, Julia %Y Li, Xiaodong %Y Wagner, Markus %Y Garza-Fabre, Mario %Y Smith-Miles, Kate %Y Allmendinger, Richard %Y Bi, Ying %Y Dick, Grant %Y Gandomi, Amir H. %Y Martins, Marcella Scoczynski Ribeiro %Y Assimi, Hirad %Y Veerapen, Nadarajen %Y Sun, Yuan %Y Munyoz, Mario Andres %Y Kheiri, Ahmed %Y Su, Nguyen %Y Thiruvady, Dhananjay %Y Song, Andy %Y Neumann, Frank %Y Silva, Carla %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F antonov:2024:GECCO %X Symbolic regression (SR) poses a significant challenge for randomized search heuristics due to its reliance on the synthesis of expressions for input-output mappings. Although traditional genetic programming (GP) algorithms have achieved success in various domains, they exhibit limited performance when tree-based representations are used for SR. To address these limitations, we introduce a novel SR approach called Fourier Tree Growing (FTG) that draws insights from functional analysis. This new perspective enables us to perform optimization directly in a different space, thus avoiding intricate symbolic expressions. Our proposed algorithm exhibits significant performance improvements over traditional GP methods on a range of classical one-dimensional benchmarking problems. To identify and explain the limiting factors of GP and FTG, we perform experiments on a large-scale polynomials benchmark with high-order polynomials up to degree 100. To the best of the authors’ knowledge, this work represents the pioneering application of functional analysis in addressing SR problems. The superior performance of the proposed algorithm and insights into the limitations of GP open the way for further advancing GP for SR and related areas of explainable machine learning. %K genetic algorithms, genetic programming, symbolic regression, functional analysis, hilbert space optimization, XAI %R 10.1145/3638529.3654079 %U https://arxiv.org/abs/2402.06299 %U http://dx.doi.org/10.1145/3638529.3654079 %P 859-867 %0 Conference Proceedings %T Comparison of CNN and YOLOv5 For Melanoma Detection %A Antony, Divya %A C, Naseer %S 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) %D 2023 %8 jul %F Antony:2023:ICCCNT %X MELANOMA is one of the most dangerous forms of skin cancer that results from melanocytes, which produce the brown pigment that gives skin its colour. Sometimes it forms multi-colours based on its stage. The survival of melanoma patients depends on the early identification of the disease. But in the early stage, it becomes very small and does not meet the dermoscopic standards for cancer detection such as irregular shape, network, colour of pigments and also it is difficult to differentiate lesion to benign and melanoma. So early detection of melanoma is difficult. Therefore, we need an accurate melanoma classifier that classifies lesions at begin of the stage. In this paper, a comprehensive analysis of cnn, Alexnet and yolov5 for melanoma detection is performed using Accuracy, Precision, Recall and F1 Score %K genetic algorithms, genetic programming, Multi tree genetic programming, Location awareness, Shape, Melanoma, Colour, Pigments, Skin, Lesions, You only look once, Regional convolutional neural network %R 10.1109/ICCCNT56998.2023.10307675 %U http://dx.doi.org/10.1109/ICCCNT56998.2023.10307675 %0 Journal Article %T Mechanics guided data-driven model for seismic shear strength of exterior beam-column joints %A Anwar, Mohamed M. %A Ismail, Mohamed K. %A Hodhod, Hossam A. %A El-Dakhakhni, Wael %A Ibrahim, Hatem H. A. %J Structures %D 2024 %V 69 %@ 2352-0124 %F Anwar:2024:Structures %X This study presents an enhanced predictive model for the seismic shear strength of exterior beam-column joints (BCJs). Initially, the principles of strut-and-tie mechanism and variable selection procedures were first used to identify the most influential parameters. Subsequently, an evolutionary algorithm, specifically multigene genetic programming (MGGP), was used to search for the near-optimal predictive model. The dataset used to develop, train, and test the proposed model was compiled from previously published tests, focusing specifically on cyclically loaded exterior BCJs that encountered shear and flexure -shear failures. The prediction performance of the developed model was assessed through various statistical measures, and then compared with that of other existing models. Additionally, sensitivity analyses were also performed to identify the influence and importance of each design parameter. The results demonstrated that the methodology employed in this study yielded an elegant model that adheres to the underlying mechanics and provides higher prediction accuracy compared to existing models. Furthermore, the sensitivity analyses showed that BCJ shear strength positively correlates with concrete compressive strength, beam reinforcement, joint transverse reinforcement, column intermediate vertical reinforcement, and axial load ratio, while it negatively correlates with the joint aspect ratio. Among these design parameters, beam reinforcement has the greatest influence on the model response, followed by concrete compressive strength. Conversely, column intermediate vertical reinforcement and axial load ratio have the least impact on the model response. The notable prediction capabilities and robustness demonstrated by the developed model render it an efficient design tool with promising potentials for adoption by practicing engineers and for consideration in design guidelines %K genetic algorithms, genetic programming, Exterior beam-column joints, Seismic shear strength, Mechanics, Variables selection, Multi-gene genetic programming, Predictions, Sensitivity analyses %9 journal article %R 10.1016/j.istruc.2024.107320 %U https://www.sciencedirect.com/science/article/pii/S2352012424014723 %U http://dx.doi.org/10.1016/j.istruc.2024.107320 %P 107320 %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 10.1109/ICIP.1999.821685 %U http://dx.doi.org/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 10.1109/CEC.2013.6557949 %U http://dx.doi.org/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 10.1109/LA-CCI.2017.8285696 %U http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8275062 %U http://dx.doi.org/10.1109/LA-CCI.2017.8285696 %0 Conference Proceedings %T Prediction of Moisture Content of Chlorella vulgaris Microalgae Using Hybrid Evolutionary Computing and Neural Network Variants for Biofuel Production %A Aquino, Heinrick L. %A Concepcion, Ronnie S. %A Mayol, Andres Philip %A Bandala, Argel A. %A Culaba, Alvin %A Cuello, Joel %A Dadios, Elmer P. %A Ubando, Aristotle T. %A San Juan, Jayne Lois G. %S 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) %D 2021 %8 28 30 nov %C Manila, Philippines %F Aquino:2021:HNICEM %X Moisture content is an imperative indicator of biofuel lipid content in microalgae. This paper developed a reliable, computationally cost-effective combination of artificial neurons and an optimization tool for moisture content concentration prediction using computational intelligence. A total of 83 data of microalgae var. Chlorella vulgaris moisture content parameter factors were used. Using feed-forward, recurrent, and deep neural networks as prediction models, their MSE and R2 values were analyzed. Genetic programming GPTIPSv2, a multigene symbolic regression genetic programming (MSRGP) tool, was used to create objective functions of the ANNs. This convergence function was the main element in developing a genetic algorithm (GA)-optimized recurrent neural network model considered to suggest the optimal quantity of neurons in each of the hidden layers in neural network architecture. The feed-forward artificial neural network with 22 neurons in its layer was recommended using the Levenberg-Marquardt training tool. The MSE (5.27e-6) and R2 (0.9999) results of this model surpassed the other neural networks models. Hence, it implies that the developed optimized Levenberg-Marquardt-based feed-forward neural network is an effective moisture content predictor as it provided highly accurate and sensitive results at a low cost. %K genetic algorithms, genetic programming %R 10.1109/HNICEM54116.2021.9731926 %U http://dx.doi.org/10.1109/HNICEM54116.2021.9731926 %0 Journal Article %T Cautionary note on the use of genetic programming in statistical downscaling %A Arachchige, Sachindra Dhanapala %A Ahmed, Khandakar %A Shahid, S. %A Perera, B. J. C. %J International Journal of Climatology %D 2018 %8 jun %V 38 %N 8 %I Royal Meteorological Society %@ 1097-0088 %F vu37709 %O SHORT COMMUNICATION %X The selection of inputs (predictors) to downscaling models is an important task in any statistical downscaling exercise. The selection of an appropriate set of predictors to a downscaling model enhances its generalization skills as such set of predictors can reliably explain the catchment-scale hydroclimatic variable (predictand). Among the predictor selection procedures seen in the literature, the use of genetic programming (GP) can be regarded as a unique approach as it not only selects a set of predictors influential on the predictand but also simultaneously determines a linear or nonlinear regression relationship between the predictors and the predictand. In this short communication, the details of an investigation on the assessment of effectiveness of GP in identifying a unique optimum set of predictors influential on the predictand and its ability to generate a unique optimum predictor-predictand relationship are presented. In this investigation, downscaling models were evolved for relatively wet and dry precipitation stations pertaining to two study areas using two different sets of reanalysis data for each calendar month maintaining the same GP attributes. It was found that irrespective of the climate regime (i.e., wet and dry) and reanalysis data set used, the probability of identification of a unique optimum set of predictors influential on precipitation by GP is quite low. Therefore, it can be argued that the use of GP for the selection of a unique optimum set of predictors influential on a predictand is not effective. However, when run repetitively, GP algorithm selected certain predictors more frequently than others. Also, when run repetitively, the structure of the predictor-predictand relationships evolved by GP varied from one run to another, indicating that the physical interpretation of the predictor-predictand relationships evolved by GP in a downscaling exercise can be unreliable. %K genetic algorithms, genetic programming, GP algorithm, Victoria, Pakistan, downscaling models, climate, predictor–predictand relationships, atmospheric domain %9 journal article %R 10.1002/joc.5508 %U https://vuir.vu.edu.au/37709/ %U http://dx.doi.org/10.1002/joc.5508 %P 3449-3465 %0 Conference Proceedings %T Two-Level Software Obfuscation with Cooperative Co-Evolutionary Algorithms %A Aragon-Jurado, Jose Miguel %A Jareno, Javier %A de la Torre, Juan Carlos %A Ruiz, Patricia %A Dorronsoro, Bernabe %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F aragon-jurado:2024:CEC %X Computing devices are ubiquitous nowadays and because of the rise of new paradigms as the Internet of Things, their presence is continuously growing. Software (SW) is highly exposed, and SW companies are forced to protect their products from attacks to prevent plagiarism and the detection of security flaws. Obfuscation is a widespread technique to protect SW. It consists in making the code unintelligible, so that it is very hard to learn how it works. There are numerous obfuscation techniques, but they often require expert hands. Therefore, there is a clear need for fully automatic obfuscation tools that can offer high quality outputs independently of the specific features of the considered SW. we define a novel combinatorial optimisation problem for a two-level obfuscation method that makes use of typical obfuscation transformations, those provided by Tigress framework, as well as classical optimisation ones, those from LLVM compilation framework. The problem is solved with a cooperative co-evolutionary cellular genetic algorithm, providing a tool for automatic SW obfuscation. Three different obfuscation metrics are considered as fitness function. The results show that the proposed methodology offers outstanding obfuscation results, outperforming the original programs by up to 6,152,547percent. Moreover, compared to approaches from the literature, these results are as much as 405 times better. %K genetic algorithms, genetic programming, Measurement, Source coding, Plagiarism, Software algorithms, Evolutionary computation, Software, Security, Source code obfuscation, LLVM, Intermediate Representation, IR, Tigress, Cooperative coevolution %R 10.1109/CEC60901.2024.10612116 %U http://dx.doi.org/10.1109/CEC60901.2024.10612116 %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 10.1016/j.chemolab.2006.01.009 %U http://dx.doi.org/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. %K genetic algorithms, genetic programming %R 10.1109/SCIS-ISIS.2012.6505204 %U http://dx.doi.org/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 10.1007/978-3-319-01128-8_18 %U http://dx.doi.org/10.1007/978-3-319-01128-8_18 %P 277-291 %0 Journal Article %T Using Genetic Programming to Identify Characteristics of Brazilian Regions in Relation to Rural Credit Allocation %A Araujo, Adolfo Vicente %A Mota, Caroline %A Siraj, Sajid %J Agriculture %D 2023 %8 24 apr %V 13 %N 5 %@ 2077-0472 %F agriculture13050935 %X Rural credit policies have a strong impact on food production and food security. The attribution of credit policies to agricultural production is one of the main problems preventing the guarantee of agricultural expansion. In this work, we conduct family typology analysis applied to a set of research data to characterize different regions. Through genetic programming, a model was developed using user-defined terms to identify the importance and priority of each criterion used for each region. Access to credit results in economic growth and provides greater income for family farmers, as observed by the results obtained in the model for the Sul region. The Nordeste region indicates that the cost criterion is relevant, and according to previous studies, the Nordeste region has the highest number of family farming households and is also the region with the lowest economic growth. An important aspect discovered by this research is that the allocation of rural credit is not ideal. Another important aspect of the research is the challenge of capturing the degree of diversity across different regions, and the typology is limited in its ability to accurately represent all variations. Therefore, it was possible to characterize how credit is distributed across the country and the main factors that can influence access to credit. %K genetic algorithms, genetic programming, rural credit, criteria analysis, family farming, machine learning %9 journal article %R 10.3390/agriculture13050935 %U https://www.mdpi.com/2077-0472/13/5/935 %U http://dx.doi.org/10.3390/agriculture13050935 %P article-number935 %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 10.1007/3-540-36553-2_39 %U http://dx.doi.org/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 10.1007/978-3-540-24650-3_21 %U http://dx.doi.org/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 10.1007/11844297_44 %U http://ppsn2006.raunvis.hi.is/proceedings/055.pdf %U http://dx.doi.org/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 10.1109/CEC.2010.5586291 %U http://dx.doi.org/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 10.1145/2739482.2764629 %U http://doi.acm.org/10.1145/2739482.2764629 %U http://dx.doi.org/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 10.1007/s00500-017-2544-4 %U https://doi.org/10.1007/s00500-017-2544-4 %U http://dx.doi.org/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 10.1007/s10710-019-09361-5 %U http://dx.doi.org/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 10.1504/IJCISTUDIES.2014.058644 %U http://www.inderscience.com/link.php?id=58644 %U http://dx.doi.org/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 10.1145/2001576.2001746 %U http://dx.doi.org/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 10.1109/IRI.2011.6009572 %U http://dx.doi.org/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 10.1155/2012/893701 %U http://www.hindawi.com/journals/ase/2012/893701/ %U http://dx.doi.org/10.1155/2012/893701 %P ArticleID893701 %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 10.1145/1143997.1144042 %U http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p255.pdf %U http://dx.doi.org/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 10.1007/978-3-540-71783-6_2 %U http://dx.doi.org/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, SMILES %9 journal article %R 10.1007/s10710-007-9040-z %U https://rdcu.be/cYj4W %U http://dx.doi.org/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 10.1016/j.asoc.2009.06.013 %U http://dx.doi.org/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 10.1016/j.cor.2009.02.015 %U http://www.sciencedirect.com/science/article/B6VC5-4VS40CF-4/2/a55e5b35bc3d30ac9057d5fb8cdcd2d0 %U http://dx.doi.org/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 10.1145/1321631.1321693 %U http://dx.doi.org/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 10.1145/1370175.1370223 %U http://delivery.acm.org/10.1145/1380000/1370223/p1003-arcuri.pdf %U http://dx.doi.org/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 10.1109/CEC.2008.4630793 %U EC0063.pdf %U http://dx.doi.org/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 10.1007/978-3-540-89694-4_7 %U http://dx.doi.org/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 10.1109/SSBSE.2009.12 %U http://dx.doi.org/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 10.1016/j.ins.2009.12.019 %U http://www.sciencedirect.com/science/article/B6V0C-4Y34WFM-2/2/6700572128cf209a061759f28c5b7020 %U http://dx.doi.org/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 10.1016/j.asoc.2011.01.023 %U http://crest.cs.ucl.ac.uk/fileadmin/crest/sebasepaper/Arcurid09d.pdf %U http://dx.doi.org/10.1016/j.asoc.2011.01.023 %P 3494-3514 %0 Conference Proceedings %T A GPHH with Surrogate-assisted Knowledge Transfer for Uncertain Capacitated Arc Routing Problem %A Ansari Ardeh, Mazhar %A Mei, Yi %A Zhang, Mengjie %S 2020 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2020 %8 dec %F Ardeh:2020:SSCI %X The Uncertain Capacited Arc Routing Problem is an important and challenging problem that has many real-world applications. Genetic Programming is used to evolve routing policies for vehicles to make real-time decisions and handle uncertain environments efficiently. However, when the problem scenario changes (e.g. a new vehicle is bought or an existing vehicle breaks down), the previously trained routing policy becomes ineffective and a new routing policy needs to be retrained. The retraining process is time-consuming. On the other hand, by extraction and transfer of some knowledge learned from the previous similar problems, the efficiency and effectiveness of the retraining process can be improved. Previous studies have found that the lack of diversity in the transferred materials (e.g. sub-trees) could hurt the effectiveness of transfer learning. As a result, instead of using the genetic materials from a source domain directly, in this work, we use the knowledge from the source domain to create a surrogate model. This surrogate is used on a large number of randomly generated individuals by GP in the target domain to select the promising initial individuals. This way, the diversity of the initial population can be maintained by randomly generated individuals, but also guided by the transferred surrogate model. Our experiments demonstrate that the proposed surrogate-assisted transfer learning method is superior to existing methods and can improve training efficiency and final performance of GP in the target domain. %K genetic algorithms, genetic programming, Routing, Task analysis, Statistics, Sociology, Learning systems, Knowledge transfer, Training %R 10.1109/SSCI47803.2020.9308398 %U http://dx.doi.org/10.1109/SSCI47803.2020.9308398 %P 2786-2793 %0 Conference Proceedings %T Genetic Programming Hyper-Heuristics with Probabilistic Prototype Tree Knowledge Transfer for Uncertain Capacitated Arc Routing Problems %A Ansari Ardeh, Mazhar %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 10.1109/CEC48606.2020.9185714 %U http://dx.doi.org/10.1109/CEC48606.2020.9185714 %P paperid24067 %0 Conference Proceedings %T A Parametric Framework for Genetic Programming with Transfer Learning for Uncertain Capacitated Arc Routing Problem %A Ansari Ardeh, Mazhar %A Mei, Yi %A Zhang, Mengjie %S AI 2020: Advances in Artificial Intelligence %D 2020 %I Springer %F ardeh:2020:AI %K genetic algorithms, genetic programming %R 10.1007/978-3-030-64984-5_12 %U http://link.springer.com/chapter/10.1007/978-3-030-64984-5_12 %U http://dx.doi.org/10.1007/978-3-030-64984-5_12 %0 Conference Proceedings %T Surrogate-Assisted Genetic Programming with Diverse Transfer for the Uncertain Capacitated Arc Routing Problem %A Ansari Ardeh, Mazhar %A Mei, Yi %A Zhang, Mengjie %Y Ong, Yew-Soon %S 2021 IEEE Congress on Evolutionary Computation (CEC) %D 2021 %8 28 jun 1 jul %C Krakow, Poland %F Ardeh:2021:CEC %X The Uncertain Capacited Arc Routing Problem (UCARP) is an important routing problem that can model uncertainties of real-world scenarios. Genetic Programming (GP) is a powerful method for evolving routing policies for vehicles to enable them make real-time decisions and handle environmental uncertainties. When facing various problem domains, knowledge transfer can improve the effectiveness of the GP training. Previous studies have demonstrated that due to the existence of duplicated GP individuals in the source domain, the existing transfer learning methods do not perform satisfactorily for UCARP. To address this issue, in this work, we propose a method for detecting duplicates in the source domain and initialising the GP population in the target domain with phenotypically unique individuals. Additionally, since the presence of duplicates can limit the number of good GP individuals, we propose a surrogate-assisted initialisation approach that is able to generate much more diversely distributed initial individuals in the target domain. Our experiments demonstrate that our proposed transfer learning method can significantly improve the effectiveness of GP for training new UCARP routing policies. Compared with the state-of-the-art GP with knowledge transfer, the proposed approach can obtain significantly better solutions on a wide range of UCRP instances, in terms of both initial and final quality. %K genetic algorithms, genetic programming, Training, Adaptation models, Uncertainty, Transfer learning, Sociology, Routing %R 10.1109/CEC45853.2021.9504817 %U http://dx.doi.org/10.1109/CEC45853.2021.9504817 %P 628-635 %0 Conference Proceedings %T A Novel Multi-Task Genetic Programming Approach to Uncertain Capacitated Arc Routing Problem %A Ansari Ardeh, Mazhar %A Mei, Yi %A Zhang, Mengjie %Y Chicano, Francisco %Y Tonda, Alberto %Y Krawiec, Krzysztof %Y Helbig, Marde %Y Cleghorn, Christopher W. %Y Wilson, Dennis G. %Y Yannakakis, Georgios %Y Paquete, Luis %Y Ochoa, Gabriela %Y Bacardit, Jaume %Y Gagne, Christian %Y Mostaghim, Sanaz %Y Jourdan, Laetitia %Y Schuetze, Oliver %Y Posik, Petr %Y Segura, Carlos %Y Tinos, Renato %Y Cotta, Carlos %Y Heywood, Malcolm %Y Zhang, Mengjie %Y Trujillo, Leonardo %Y Miikkulainen, Risto %Y Xue, Bing %Y Neumann, Aneta %Y Allmendinger, Richard %Y Ishikawa, Fuyuki %Y Medina-Bulo, Inmaculada %Y Neumann, Frank %Y Sutton, Andrew M. %S Proceedings of the 2021 Genetic and Evolutionary Computation Conference %S GECCO ’21 %D 2021 %8 jul 10 14 %I Association for Computing Machinery %C internet %F Ardeh:2021:GECCO %X Uncertain Capacitated Arc Routing Problem (UCARP) is an NP-hard optimisation problem with many applications in logistics domains. Genetic Programming (GP) is capable of evolving routing policies to handle the uncertain environment of UCARP. There are many different but related UCARP domains in the real world to be solved (e.g. winter gritting and waste collection for different cities). Instead of training a routing policy for each of them, we can use the multi-task learning paradigm to improve the training effectiveness by sharing the common knowledge among the related UCARP domains. Previous studies showed that GP population for solving UCARP loses diversity during its evolution, which decreases the effectiveness of knowledge sharing. we propose a novel multi-task GP approach that takes the uniqueness of transferable knowledge, as well as its quality, into consideration. Additionally, the transferred knowledge in a manner that improves diversity. We investigated the performance of the proposed method with several experimental studies and demonstrated that the designed knowledge transfer mechanism can significantly improve the performance of GP for solving UCARP %K genetic algorithms, genetic programming, Uncertain Capacitated Arc Routing Problem, Hyper Heuristics, Multi-task Optimisation %R 10.1145/3449639.3459322 %U http://dx.doi.org/10.1145/3449639.3459322 %P 759-767 %0 Thesis %T Transfer Optimisation in Genetic Programming for Solving Uncertain Capacitated Arc Routing Problem %A Ansari Ardeh, Mazhar %D 2022 %8 15 jul %C New Zealand %C Computer Science, Victoria University of Wellington %F Ansari_Ardeh2022 %X Uncertain Capacitated Arc Routing Problem (UCARP) is a combinatorial optimisation problem with many important real-world applications. Genetic programming (GP) is a powerful machine learning technique that has been successfully used to automatically evolve routing policies for UCARP. Reusability is an open issue in the field of UCARP and in this direction, an open challenge is the case of scenario changes, e.g. change in the number of vehicles and probability distributions of random demands, which typically requires new training procedures to be initiated. Considering the expensive training cost of evolving routing policies for UCARP, a promising strategy is to learn and reuse knowledge from a previous problem-solving process to improve the effectiveness and efficiency of solving a new related problem, i.e. transfer learning. The overall goal of this thesis is to develop novel knowledge transfer algorithms for GP for solving UCARP to handle environment changes more effectively and efficiently. To fulfill this goal, a plethora of machine learning techniques, i.e. surrogate models, feature selection, searching and specialised genetic operators, are used in this thesis. First, this thesis explores the effectiveness of the existing transfer optimisation methods for solving UCARP. Accordingly, one of the main directions of this thesis is towards identifying the nature of transferable knowledge, which can impact the quality of knowledge transfer for GP to solve UCARP. For this purpose, a collection of the state-of-the-art transfer optimisation GP algorithms are evaluated for UCARP. After identifying some potential gaps in the literature, a number of preliminary transfer optimisation algorithms are proposed that supplement the literature. To evaluate the algorithms, a large set of knowledge transfer scenarios with various source and target problems were designed based on real-world datasets. According to the results, none of the methods showed significant improvement in the effectiveness of the trained UCARP routing policies. These results revealed the need for more effective transfer optimisation methods specifically designed for UCARP. Furthermore, our investigations revealed that the presence of duplicates in knowledge sources is one of the main challenges for effective transfer optimisation in solving UCARP. Second, we propose approaches to handling the presence of duplicates in the transferred knowledge. The first approach increases population diversity after knowledge transfer to counteract the loss of diversity that is introduced by the presence of duplicates in the transferred knowledge. In the second approach, the duplicates are removed from the transferred knowledge. Then, the transferred knowledge is used to create a diverse initial GP population of high-quality individuals. Both approaches are investigated through detailed experimental studies. The results indicate that, while the first approach did not perform better than GP with knowledge transfer, the second can improve the effectiveness of training routing policies with GP significantly. Third, this thesis proposes a novel algorithm that transfers the phenotypic characteristics of the routing policies for solving the source problem. In the new algorithm, the most fit and unique source routing policies are used for initialising GP for solving the target problem. Then, a tabu list is placed on the source routing policies and the GP process is prohibited from recreating any of the source routing policies. The motivation for this approach is that, due to the existence of similarity between the source and target problems, source routing policies are unlikely to have a good performance for the target problem. Our experimental studies confirmed that by prohibiting GP from recreating source policies, and the computational resources will be spent on searching and evaluating new regions of the search space, which can lead to discovering better solutions. Fourth, this thesis proposes a novel knowledge transfer algorithm based on the idea of maintaining the transferred knowledge as an auxiliary population. In this approach, first, the best individuals of the duplicate-free knowledge source are used to initialise GP. Additionally, these transferred individuals are also maintained as an auxiliary population and are evolved alongside the main population. To save the computational cost, the auxiliary population is evolved with a surrogate method. Additionally, an elaborate knowledge exchange mechanism between the two populations is devised that emphasises transferring high-quality and unique individuals, the transfer of which can improve the diversity of the receiving population. This allows GP to overcome the problem of losing its population diversity during the evolutionary process. Our detailed experimental results confirmed the superior performance of the proposed algorithm and confirmed that the proposed method improved the phenotypic diversity of GP population. %K genetic algorithms, genetic programming, UCARP, Evolutionary computation, Fuzzy computation, Transfer Optimisation, Uncertain Capacitated Arc Routing Problem %9 Ph.D. thesis %R 10.26686/wgtn.20311185 %U https://openaccess.wgtn.ac.nz/ndownloader/files/36279690 %U http://dx.doi.org/10.26686/wgtn.20311185 %0 Journal Article %T Genetic Programming With Knowledge Transfer and Guided Search for Uncertain Capacitated Arc Routing Problem %A Ansari Ardeh, Mazhar %A Mei, Yi %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2022 %8 aug %V 26 %N 4 %@ 1089-778X %F Ardeh:2022:ieeeTEC %X The uncertain capacitated arc routing problem has many real-world applications in logistics domains. Genetic programming (GP) is a promising approach to training routing policies to make real-time decisions and handle uncertain events effectively. In the real world, there are various problem domains and no single routing policy can work effectively in all of them. Instead of training in isolation, we can leverage the relatedness between the problems and transfer knowledge from previously solved source problems to solve the target problem. The existing transfer methods are not effective enough due to the loss of diversity during the knowledge transfer. To increase the diversity of the transferred knowledge, in this article, we propose a novel GP method that removes phenotypic duplicates from the source individuals to initialize the target individuals. Furthermore, assuming that the transferred knowledge used in initialization already includes all the important knowledge ex %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TEVC.2021.3129278 %U http://dx.doi.org/10.1109/TEVC.2021.3129278 %P 765-779 %0 Journal Article %T Knowledge Transfer Genetic Programming with Auxiliary Population for Solving Uncertain Capacitated Arc Routing Problem %A Ansari Ardeh, Mazhar %A Mei, Yi %A Zhang, Mengjie %A Yao, Xin %J IEEE Transactions on Evolutionary Computation %D 2023 %8 apr %V 27 %N 2 %@ 1089-778X %F Ardeh:ieeeTEC2 %X The uncertain capacitated arc routing problem is an NP-hard combinatorial optimisation problem with a wide range of applications in logistics domains. Genetic programming hyper-heuristic has been successfully applied to evolve routing policies to effectively handle the uncertain environment in this problem. The real world usually encounters different but related instances due to events like season change and vehicle breakdowns, and it is desirable to transfer knowledge gained from solving one instance to help solve another related one. However, the solutions found by the genetic programming process can lack diversity, and the existing methods use the transferred knowledge mainly during initialisation. Thus, they cannot sufficiently handle the change from the source to the target instance. To address this issue, we develop a novel knowledge transfer genetic programming with an auxiliary population. In addition to the main population for the target instance, we initialise a %K genetic algorithms, genetic programming, Arc routing, GP, hyper-heuristics, transfer optimization %9 journal article %R 10.1109/TEVC.2022.3169289 %U http://dx.doi.org/10.1109/TEVC.2022.3169289 %P 311-325 %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 Forward Kinematics for 2 DOF Planar Robot using Linear Genetic Programming %A Arellano, Humberto Velasco %A Rivera, Martin Montes %J Research in Computing Science %D 2019 %V 148 %N 6 %@ 1870-4069 %F DBLP:journals/rcs/ArellanoR19 %X In the field of robotics, forward kinematics is an activity that allows finding a mathematical model for the resulting position in the final effector based on the robot joints position, a popular alternative for determining this model is defined by the Denavit Hartenberg convention, nevertheless, this method requires knowledge about linear algebra and three-dimensional spatial kinematics. Machine learning uses specific computational methodologies to solving similar problems in several areas, so it could be a viable answer for automatic determining of forwarding kinematics. we propose the use of genetic programming as a machine learning algorithm for finding the forward kinematics of a 2 degrees of freedom robot, getting a satisfactory outcome obtaining a satisfactory result with blocks that describe the expected solution, validating the capacity of the genetic programming in order to validate this algorithm for later work with more complex robots. %K genetic algorithms, genetic programming, forward kinematics, automatic robot modeling, linear genetic programming %9 journal article %U https://www.rcs.cic.ipn.mx/2019_148_6/Forward%20Kinematics%20for%202%20DOF%20Planar%20Robot%20using%20Linear%20Genetic%20Programming.pdf %P 123-133 %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 10.1155/2009/353960 %U http://downloads.hindawi.com/journals/ace/2009/353960.pdf %U http://dx.doi.org/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 10.5772/50556 %U http://dx.doi.org/10.5772/50556 %P 239-254 %0 Journal Article %T Daily rainfall interpolation models obtained by means of genetic programming %A Arganis Juarez, Maritza Liliana %A Preciado-Jimenez, Margarita %A Rodriguez-Vazquez, Katya %J Revista Facultad de Ingenieria Universidad de Antioquia %D 2015 %8 jun %N 75 %I Universidad de Antioquia %@ 0120-6230 %F Arganis:2015:REDIN %X The evolutionary computing algorithm of genetic programming was applied to obtain mathematical daily rainfall interpolation models in one climatologic station, using the measured data in nearby stations in Cutzamala River basin in Mexico. The obtained models take into account both the geographical coordinates of the climatologic station and also its elevation; the answer of these models was compared against those obtained by means of multiple linear regression and a nonlinear model with parameters obtained with genetic algorithms; genetic programming models gave the best performance. Isohyets maps were then obtained to compare the spatial shapes between measured and calculated rainfall data in Cutzamala River Basin, for a maximum historic storm recorded in 2006 year, showing an adequate agreement of the results in case of rainfalls greater than 23 mm. Genetic programming represent a useful practical tool for approaching mathematical models of variables applied in engineering problems and new models could be obtained in several basins by applying these algorithms. %K genetic algorithms, genetic programming, daily rainfall, genetic programming, interpolation models, isohyet, geographic coordinates, Co Kriging, missing data, Matlab, 29 July 2006 %9 journal article %R 10.17533/udea.redin.n75a18 %U https://revistas.udea.edu.co/index.php/ingenieria/article/view/21564/18766 %U http://dx.doi.org/10.17533/udea.redin.n75a18 %P 189-201 %0 Journal Article %T Evaluation of the capacity of PET bottles, water aeration, and water recirculation to reduce evaporation in containers of water %A Arganis Juarez, Maritza Liliana %A Hernandez Ignacio, Maria Fernanda %A Rosales Silvestre, Sandra Lizbeth %A Osnaya Romero, Javier %A Carrizosa Elizondo, Eliseo %J Journal of King Saud University - Science %D 2022 %V 34 %N 4 %@ 1018-3647 %F ARGANISJUAREZ:2022:jksus %X Objective: To evaluate the effectiveness of an evaporation reduction method in which the greater part of the water surface was covered with PET-type plastic bottles. The capacity of this method to diminish natural evaporation was compared to water aeration, water recirculation, and the control (without intervention). With evolutionary computation, including genetic algorithms and genetic programming, equations for calculating evaporation were developed based on meteorological factors. Methods Four containers of water were placed on a flat roof in Mexico City (thus exposed to the factors of weather), and evaporation was measured daily with an evaporimeter. Each container was assigned to one of the evaporation reduction methods (PET bottles, water aeration, or water recirculation) or to the control (without intervention). Evaporation-related variables were selected according to previous reports and principal component analysis, and their values were acquired from a nearby meteorological station. The study was conducted from April of 2020 to February of 2021. Results Covering the water surface with PET bottles avoided 38.61percent (a total of 139mm) of natural evaporation, which is represented by the control. The water aeration and water recirculation methods diminished evaporation by 7.22percent (26mm) and 2.22percent (8mm), respectively. The best equations for estimating evaporation were obtained with genetic programming for the control container and a genetic algorithm for the container with PET bottles. Conclusions The PET bottle method of evaporation reduction was 7 and 17 times more effective than water aeration and water recirculation, respectively. The 38.61percent decrease in evaporation achieved by covering the water surface with PET bottles constitutes a substantial savings in water. Hence, the implementation of such a method should be considered to contribute to water conservation in reservoirs. The use of PET bottles is a practical and inexpensive method requiring only a few cleaning maneuvers to prevent the proliferation of unwanted aquatic fauna. %K genetic algorithms, genetic programming, Water evaporation, Evaporation reduction, PET plastic bottles, Principal component analysis %9 journal article %R 10.1016/j.jksus.2022.102046 %U https://www.sciencedirect.com/science/article/pii/S1018364722002270 %U http://dx.doi.org/10.1016/j.jksus.2022.102046 %P 102046 %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 10.1016/j.foodcont.2012.05.040 %U http://www.sciencedirect.com/science/article/pii/S0956713512002745 %U http://dx.doi.org/10.1016/j.foodcont.2012.05.040 %P 461-470 %0 Journal Article %T A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications %A Ari, Davut %A Alagoz, Baris Baykant %J Sakarya University Journal of Science %D 2021 %8 apr %V 25 %N 2 %I Sakarya University %@ 2147-835X %F Ari:2021:SAUJS %X Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers. When GP first appeared, it has become a popular computational intelligence method because of its successful applications and its potentials to find effective solutions for difficult practical problems of many different disciplines. With the use of GP in a wide variety of areas, numerous variants of GP methods have emerged to provide more effective solutions for computation problems of diverse application fields. Therefore, GP has a very rich literature that is progressively growing. Many GP software tools developed along with process of GP algorithms. There is a need for an inclusive survey of GP literature from the beginning to today of GP in order to reveal the role of GP in the computational intelligence field. This survey study aims to provide an overview of the growing GP literature in a systematic way. The researchers, who need to implement GP methods, can gain insight of potentials in GP methods, their essential drawbacks and prevalent superiorities. Accordingly, taxonomy of GP methods is given by a systematic review of popular GP methods. In this manner, GP methods are analyzed according to two main categories, which consider the discrepancies in their program (chromosome) representation styles and their methodologies. Besides, GP applications in diverse problems are summarized. This literature survey is especially useful for new researchers to gain the required broad perspective before implementing a GP method in their problems. %K genetic algorithms, genetic programming, gp types, gp applications, gp software %9 journal article %R 10.16984/saufenbilder.793333 %U https://abakus.inonu.edu.tr/items/21ec5e12-72fd-42e0-98e2-baf039ece53a %U http://dx.doi.org/10.16984/saufenbilder.793333 %P 397-416 %0 Conference Proceedings %T A Genetic Programming Based Pollutant Concentration Predictor Design for Urban Pollution Monitoring Based on Multi-Sensor Electronic Nose %A Ari, Davut %A Alagoz, Baris Baykant %S 2021 International Conference on Information Technology (ICIT) %D 2021 %8 jul %F Ari:2021:ICIT %X An important part of air pollution control is the pollution monitoring. Since industrial spectrometers are expensive equipment, the number of observation points to monitor air pollution over an urban area can be limited. The low-cost multi-sensors network can spread over areas and form a wide-area electronic nose to estimate pollutant concentration distributions. However, the collected multisensor data should be analysed to correctly estimate pollutant concentrations. This study demonstrates implementation of genetic programming (GP) to obtain prediction models that can estimate CO and NO2 concentrations from multisensor electronic nose data. For this purpose, to function as an electronic nose, a regression model from a training data set is obtained by using a tree-based GP algorithm. In order to improve performance of the GP based prediction models, data normalization is performed and prediction performance enhancements are demonstrated via statistical performance analyses on a test data set. %K genetic algorithms, genetic programming %R 10.1109/ICIT52682.2021.9491122 %U http://dx.doi.org/10.1109/ICIT52682.2021.9491122 %P 168-172 %0 Conference Proceedings %T Modeling Daily Financial Market Data by Using Tree-Based Genetic Programming %A Ari, Davut %A Alagoz, Baris Baykant %S 2021 International Conference on Information Technology, ICIT %D 2021 %8 14 15 jul %I IEEE %C Amman, Jordan %F conf/cit/AriA21a %X A behavioural modelling of financial markets based on daily data is not an easy problem for machine learning algorithms. The social and physiological factors can take effect on market data and result in significant uncertainty in data. This study demonstrates an implementation of tree-based genetic programming (GP) to develop a mathematical model of stock market from the daily stock data of other stock markets to observe relations between global market trends and to consider this effect in market prediction problems. To obtain a prediction model of Istanbul Stock Exchange 100 Index (ISE100), numerical data from ISE100 and seven other international stock market indices are used to produce GP models that can estimate daily price changes in ISE100 according to daily change in other international stock market indices. To reduce negative effects of the data uncertainty on the GP modelling, ensemble average GP modelling performances are investigated and the results are reported for future research direction suggestions. %K genetic algorithms, genetic programming %R 10.1109/ICIT52682.2021.9491652 %U http://dx.doi.org/10.1109/ICIT52682.2021.9491652 %P 382-386 %0 Journal Article %T An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application %A Ari, Davut %A Alagoz, Baris Baykant %J Neural Computing and Applications %D 2022 %V 34 %N 15 %F ari:2022:NCaA %K genetic algorithms, genetic programming, ANN %9 journal article %R 10.1007/s00521-022-07129-0 %U http://link.springer.com/article/10.1007/s00521-022-07129-0 %U http://dx.doi.org/10.1007/s00521-022-07129-0 %0 Thesis %T The genetic programming and its applications in engineering %A Ari, Davut %D 2023 %8 jan %C İnönü University, Turkey %F DBLP:phd/tr/Ari23 %X Genetic Programming (GP) is an evolutionary computational method that can generate symbolic and mathematical models. In addition to being a type of evolutionary computing, the GP is also frequently used in solving symbolic regression problems in machine learning applications. Since its first appearance, it has become one of the popular evolutionary calculation methods as a result of being successfully applied for solution of modeling problems appeared in many different disciplines. Within the scope of this thesis, research studies have been carried out for development of data driven prediction models and their engineering applications by using the classical GP and its a variant, Gene Expression Programming (GEP). In order to increase the effectiveness of these GP methods in practice, data normalization, ensemble learning, hybrid model development and hyperparameter optimization techniques are studied. In addition, the chromosome structure of the GEP method has been modified and an optimal solution to the constant value determination problem has been proposed. Then, the modified GEP method was combined with popular metaheuristic optimization methods, and thus a metaheuristic optimization based GEP (MetaSezGEP) approach was developed. Contributions of these improvements to some engineering applications have been investigated. %K genetic algorithms, genetic programming, Gene Expression Programming, Hava kirliligi = Air pollution, Veri analizi = Data analysis %9 Ph.D. thesis %U https://tez.yok.gov.tr/UlusalTezMerkezi/tezDetay.jsp?id=5cvay5p_jJQNEtvmnO5fww&no=HNOMlxzz-uJmirw8rmMHEQ %0 Journal Article %T DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction %A Ari, Davut %A Alagoz, Baris Baykant %J Soft Computing %D 2023 %8 mar %V 27 %N 5 %@ 1432-7643 %F ari:2023:SC %X Stock markets are a popular kind of financial markets because of the possibility of bringing high revenues to their investors. To reduce risk factors for investors, intelligent and automated stock market forecast tools are developed by using computational intelligence techniques. This study presents a hyperparameter optimal genetic programming-based forecast model generation algorithm for a-day-ahead prediction of stock market index trends. To obtain an optimal forecast model from the modeling dataset, a differential evolution (DE) algorithm is employed to optimize hyperparameters of the genetic programming orthogonal least square (GpOls) algorithm. Thus, evolution of GpOls agents within the hyperparameter search space enables adaptation of the GpOls algorithm for the modeling dataset. This evolutionary hyperparameter optimization technique can enhance the data-driven modeling performance of the GpOls algorithm and allow the optimal autotuning of user-defined parameters. In the current study, the proposed DE-based hyper-GpOls (DEHypGpOls) algorithm is used to generate forecaster models for prediction of a-day-ahead trend prediction for the Istanbul Stock Exchange 100 (ISE100) and the Borsa Istanbul 100 (BIST100) indexes. In this experimental study, daily trend data from ISE100 and BIST100 and seven other international stock markets are used to generate a-day-ahead trend forecaster models. Experimental studies on 4 different time slots of stock market index datasets demonstrated that the forecast models of the DEHypGpOls algorithm could provide 57.87 percent average accuracy in buy-sell recommendations. The market investment simulations with these datasets showed that daily investments to the ISE100 and BIST100 indexes according to buy or sell signals of the forecast model of DEHypGpOls could provide 4.8 percent more average income compared to the average income of a long-term investment strategy. %K genetic algorithms, genetic programming, Stock market prediction, Stock price, Hyperparameter optimization, Trend prediction %9 journal article %R 10.1007/s00500-022-07571-1 %U https://rdcu.be/daFKI %U http://dx.doi.org/10.1007/s00500-022-07571-1 %P 2553-2574 %0 Journal Article %T A differential evolutionary chromosomal gene expression programming technique for electronic nose applications %A Ari, Davut %A Alagoz, Baris Baykant %J Applied Soft Computing %D 2023 %8 mar %V 136 %@ 1568-4946 %F Ari:2023:ASOC %X The intelligent system applications require automated data-driven modeling tools. The performance consistency of modeling tools is very essential to reduce the need for human intervention. Classical Gene Expression Programmings (GEPs) employ predefined genetic rules for the node-based evolution of expression trees in the absence of optimal numerical values of constant terminals, and these shortcomings can limit the search efficiency of expression trees. To alleviate negative impacts of these limitations on the data-driven GEP modeling performance, a Differential Evolutionary Chromosomal GEP (DEC-GEP) algorithm is suggested. The DEC-GEP uses the Differential Evolution (DE) algorithm for the optimization of a complete genotype of expression trees. For this purpose, a modifier gene container, which stores numerical values of constant terminals, is appended to the frame of GEP chromosome, and this modified chromosome structure enables simultaneous optimization of expression tree genotypes together with numerical values of constant terminals. Besides, the DEC-GEP algorithm can benefit from exploration and exploitation capabilities of the DE algorithm for more efficient evolution of GEP expression trees. To investigate consistency of the DEC-GEP algorithm in a data-driven modeling application, an experimental study was conducted for soft calibration of the low-cost, solid-state sensor array measurements, and results indicated that the DEC-GEP could yield dependable CO concentration estimation models for electronic nose applications. %K genetic algorithms, genetic programming, Gene expression programming, Air quality electronic nose, Differential evolution, Sensor calibration %9 journal article %R 10.1016/j.asoc.2023.110093 %U https://www.sciencedirect.com/science/article/pii/S1568494623001114 %U http://dx.doi.org/10.1016/j.asoc.2023.110093 %P 110093 %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 10.1109/ICTAI.2017.00080 %U http://dx.doi.org/10.1109/ICTAI.2017.00080 %P 485-492 %0 Conference Proceedings %T Vertical Electrical Sounding Inversion Models Trained from Dataset using Synthetic Data and Genetic Programming %A Aristotle De Leon, Joseph %A Louie Enriquez, Mike %A Concepcion, Ronnie %A Valenzuela, Ira %A Rhay Vicerra, Ryan %A Co, Homer %A Bandala, Argel %A Dadios, Elmer %S 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) %D 2022 %8 dec %F Aristotle-De-Leon:2022:HNICEM %X The inversion process of Vertical Electrical Sounding (VES) is an important step in 1-D subsurface surface surveys to determine the true resistivities and heights of different layers of soil or rocks underground which is beneficial in geological and hydrological applications like locating potential areas for aquifers. Machine learning based algorithms is currently a trend in the inversion of vertical electrical sounding (VES) data to address the issues of the conventional methods. However, most models trained are being limited to one electrode half spacing configuration, and not being able to explain the underlying relationships of the model. Hence, the present study seeks to address these by obtaining VES inversion models for four-layer earth models through genetic programming and a synthetic dataset. The synthetic dataset covering different electrode half spacing configurations and VES curve types was generated and used to train the genetic programming model through GPTIPS software. By testing the best models on the synthetic dataset, it offered good metrics on the true resistivities of each layer, but performed poorly on estimating the layers’ heights. Regardless, the models obtained can be symbolically expressed and be interpreted which has not been done in other machine learning inversion models for VES. While this study’s implementation of genetic programming is not yet satisfactory, obtaining the symbolic expressions can allow future works to systematically improve the worst performing models. %K genetic algorithms, genetic programming, Measurement, Electrodes, Earth, Machine learning, Conductivity, Soil, Vertical Electrical Sounding, Inversion, Underground Imaging, Resistivity Imaging %R 10.1109/HNICEM57413.2022.10109565 %U http://dx.doi.org/10.1109/HNICEM57413.2022.10109565 %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 10.1016/S1367-5788(00)90015-4 %U http://dx.doi.org/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 10.1007/s00366-017-0526-x %U http://dx.doi.org/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 10.1007/s00521-016-2618-8 %U http://link.springer.com/article/10.1007/s00521-016-2618-8 %U http://dx.doi.org/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 10.4203/ccp.97.43 %U http://www.ctresources.info/ccp/paper.html?id=6631 %U http://dx.doi.org/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 10.4203/ccp.99.216 %U http://webapp.tudelft.nl/proceedings/cst2012/html/summary/armani.htm %U http://dx.doi.org/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 10.1007/978-3-662-44303-3_2 %U http://dx.doi.org/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, MRGP, Multiple Regression, Artificial intelligence, Automatic Programming %R 10.1145/2576768.2598291 %U https://www.cs.put.poznan.pl/kkrawiec/wiki/uploads/Site/2015GeccoMRGP.pdf %U http://dx.doi.org/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 10.1145/2739480.2754693 %U http://doi.acm.org/10.1145/2739480.2754693 %U http://dx.doi.org/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 %R 10.1145/2598394 %U http://dl.acm.org/citation.cfm?id=2598394 %U http://dx.doi.org/10.1145/2598394 %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 10.5120/8021-0505 %U http://research.ijcaonline.org/volume51/number3/pxc3880505.pdf %U http://dx.doi.org/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 10.1109/IMTC.2009.5168651 %U http://dx.doi.org/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 10.1007/s10723-014-9313-8 %U http://eprints.ucm.es/30960/ %U http://dx.doi.org/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 10.1016/j.asoc.2016.11.044 %U http://www.sciencedirect.com/science/article/pii/S1568494616306135 %U http://dx.doi.org/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 10.1109/FIT.2014.55 %U http://dx.doi.org/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 10.1109/IJCNN.2014.6889727 %U http://dx.doi.org/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 10.1109/BIYOMUT.2017.8478885 %U http://dx.doi.org/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 10.1016/j.asoc.2019.03.014 %U http://www.sciencedirect.com/science/article/pii/S1568494619301322 %U http://dx.doi.org/10.1016/j.asoc.2019.03.014 %P 515-527 %0 Journal Article %T Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification %A Arslan, Sibel %A Ozturk, Celal %J Applied Sciences %D 2019 %V 9 %N 9 %@ 2076-3417 %F arslan:2019:AS %O Special Issue Machine Learning and Compressed Sensing in Image Reconstruction %X Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behaviour of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor. %K genetic algorithms, genetic programming, Texture classification, artificial bee colony programming-descriptor, image descriptor, local binary pattern, genetic programming-descriptor %9 journal article %R 10.3390/app9091930 %U https://www.mdpi.com/2076-3417/9/9/1930 %U http://dx.doi.org/10.3390/app9091930 %0 Conference Proceedings %T Titan Yellow Biosorption of Hemp Waste in Acidic Medium and Modeling of Experimental Conditions by Multi Gene Genetic Programming %A Arslan, Sibel %A Kutuk, Nursah %S 2022 Innovations in Intelligent Systems and Applications Conference (ASYU) %D 2022 %8 sep %F Arslan:2022:ASYU %X In recent years, pollutants such as dyes, drugs and heavy metals in wastewater have caused serious environmental pollution. In this study, biosorption of titan yellow (TY) using hemp waste was studied. In the biosorption of TY dye to hemp waste, 88percent biosorption was achieved with an initial dye concentration of 10 mg/L and a biosorbent ratio of 2 g/L in acidic medium. When Langmuir and Freundlich isotherms were examined, R-squared values were obtained as 0.92 and 0.95, respectively. Its maximum biosorption capacity has been calculated as 51.8 mg/g. It has also been observed that the biosorption process adapts to the pseudo second order reaction R-squared = 0.99) kinetics. We have also developed more accurate and reliable correlation models using Multi-Gene Genetic Programming (MGGP), a powerful method based on evolutionary computation. The performance of the developed models was examined using three statistical criteria. A comparison of the criteria reveals that MGGP is effective in simulating the biosorption process in the real world. %K genetic algorithms, genetic programming %R 10.1109/ASYU56188.2022.9925394 %U http://dx.doi.org/10.1109/ASYU56188.2022.9925394 %0 Journal Article %T Immune Plasma Programming: A new evolutionary computation-based automatic programming method %A Arslan, Sibel %J Applied Soft Computing %D 2024 %V 152 %@ 1568-4946 %F ARSLAN:2024:asoc %X Immune plasma therapy, one of the treatment modalities, has proven effective in combating the now rapidly spreading COVID-19 and many other pandemics. The immune plasma algorithm (IPA), inspired by the application phases of this treatment modality, is a recently proposed metaheuristic algorithm. Since its introduction, it has achieved promising results in engineering applications. In this paper, we propose for the first time immune plasma programming (IPP) based on the structure of IPA as a new evolutionary computation-based automatic programming (AP) method. It is compared with well-known AP methods such as artificial bee colony programming, genetic programming, and cartesian ant programming using symbolic regression test problems. It is also compared with baseline methods, many of which are based on recurrent neural networks and a real-word problem is solved. The control parameters of IPP are also tuned separately. The results of the experiments and statistical tests have shown that the prediction accuracy and convergence speed of the models produced by IPP are high. Therefore, IPP has been proposed as a method that can be used to solve various problems %K genetic algorithms, genetic programming, Automatic programming, Immune plasma programming, Immune plasma algorithm, Symbolic regression %9 journal article %R 10.1016/j.asoc.2023.111204 %U https://www.sciencedirect.com/science/article/pii/S156849462301222X %U http://dx.doi.org/10.1016/j.asoc.2023.111204 %P 111204 %0 Journal Article %T A comprehensive review of automatic programming methods %A Arslan, Sibel %A Ozturk, Celal %J Applied Soft Computing %D 2023 %V 143 %@ 1568-4946 %F ARSLAN:2023:asoc %X Automatic programming (AP) is one of the most attractive branches of artificial intelligence because it provides effective solutions to problems with limited knowledge in many different application areas. AP methods can be used to determine the effects of a system’s inputs on its outputs. Although there is increasing interest in solving many problems using these methods for a variety of applications, there is a lack of reviews that address the methods. Therefore, the goal of this paper is to provide a comprehensive literature review of AP methods. At the same time, we mention the main characteristics of the methods by grouping them according to how they represent solutions. We also try to give an outlook on the future of the field by highlighting possible bottlenecks and perspectives for the benefit of the researchers involved %K genetic algorithms, genetic programming, Artificial intelligence, Evolutionary computation, Automatic programming %9 journal article %R 10.1016/j.asoc.2023.110427 %U https://www.sciencedirect.com/science/article/pii/S1568494623004453 %U http://dx.doi.org/10.1016/j.asoc.2023.110427 %P 110427 %0 Journal Article %T Symbolic regression with feature selection of dye biosorption from an aqueous solution using pumpkin seed husk using evolutionary computation-based automatic programming methods %A Arslan, Sibel %A Kutuk, Nursah %J Expert Systems with Applications %D 2023 %V 231 %@ 0957-4174 %F ARSLAN:2023:eswa %X Industrial waste pollution is a serious and systematic problem that harms the environment and people. The development of cheap, simple, and efficient techniques to solve this problem is important for sustainability. In this study, both experimental and evolutionary computation (EC)-based automatic programming (AP) methods were used to investigate the biosorption process for water treatment. In the experiments, titan yellow (TY), an anionic dye, was biosorbed from an aqueous solution containing pumpkin seed husk (PSH). The structure of PSH was examined using a Fourier transform infrared spectroscopy (FTIR) and a scanning electron microscope (SEM). The result of the experimental studies was that TY biosorption of PSH reached a biosorption efficiency of 95percent after 120 min of contact time. The maximum biosorption capacity (qmax) was calculated to be 181.8 mg/g. It was found that the biosorption of TY better followed the Dubinin-Radushkevich isotherm (R2=0.98) and pseudo second-order reaction kinetics (R2=0.99). Based on the thermodynamic data, the biosorption process was exothermic and spontaneous. After the experiments, the process was modeled using pH, biosorbent concentration, initial dye concentration, contact time, and temperature as inputs and biosorption efficiency (percent) as output for the methods. Moreover, the success of these AP methods was compared with a newly proposed evolutionary method. The simulation results indicate that AP methods generate best models (Rtrain2=0.99 and Rtest2=0.97). At the same time, the most important parameter of the process in the feature analysis is contact time. This study shows that EC-based AP methods can effectively model applications such as the biosorption process %K genetic algorithms, genetic programming, Pumpkin seed husk, Biosorption, Titan yellow, System modeling, Artificial bee colony programming %9 journal article %R 10.1016/j.eswa.2023.120676 %U https://www.sciencedirect.com/science/article/pii/S0957417423011788 %U http://dx.doi.org/10.1016/j.eswa.2023.120676 %P 120676 %0 Journal Article %T Investigating the best automatic programming method in predicting the aerodynamic characteristics of wind turbine blade %A Arslan, Sibel %A Koca, Kemal %J Engineering Applications of Artificial Intelligence %D 2023 %V 123 %@ 0952-1976 %F ARSLAN:2023:engappai %X Automatic programming (AP) is a subfield of artificial intelligence (AI) that can automatically generate computer programs and solve complex engineering problems. This paper presents the accuracy of four different AP methods in predicting the aerodynamic coefficients and power efficiency of the AH 93-W-145 wind turbine blade at different Reynolds numbers and angles of attack. For the first time in the literature, Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods are used for such predictions. In addition, Airfoil Tools and JavaFoil are used for airfoil selection and dataset generation. The Reynolds number and angle of attack of the wind turbine airfoil are input parameters, while the coefficients CL, CD and power efficiency are output parameters. The results show that while all four methods tested in the study accurately predict the aerodynamic coefficients, Multi Gene GP (MGGP) method achieves the highest accuracy for RTrain2 and RTest2 (R2 values in CD Train: 0.997-Test: 0.994, in CL Train: 0.991-Test: 0.990, in PE Train: 0.990-Test: 0.970). By providing the most precise model for properly predicting the aerodynamic performance of higher cambered wind turbine airfoils, this innovative and comprehensive study will close a research gap. This will make a significant contribution to the field of AI and aerodynamics research without experimental cost, labor, and additional time %K genetic algorithms, genetic programming, Automatic programming, Artificial bee colony programming, Aerodynamic coefficients, Power efficiency, Wind turbine blade %9 journal article %R 10.1016/j.engappai.2023.106210 %U https://www.sciencedirect.com/science/article/pii/S0952197623003949 %U http://dx.doi.org/10.1016/j.engappai.2023.106210 %P 106210 %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 10.1007/s10710-005-3718-x %U https://rdcu.be/dR8dB %U http://dx.doi.org/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, ANN, electrocardiography, neural nets %R 10.1109/ICBPE.2009.5384063 %U http://dx.doi.org/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 10.1007/s12665-019-8092-8 %U http://link.springer.com/article/10.1007/s12665-019-8092-8 %U http://dx.doi.org/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 10.1016/j.asoc.2010.06.009 %U http://www.sciencedirect.com/science/article/B6W86-50CVPW4-2/2/863c13a5a1c7be6da7b1ea6592b11bd3 %U http://dx.doi.org/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 10.1016/j.calphad.2015.07.005 %U http://www.sciencedirect.com/science/article/pii/S0364591615300080 %U http://dx.doi.org/10.1016/j.calphad.2015.07.005 %P 35-41 %0 Journal Article %T Symbolic regression based hybrid semiparametric modelling of processes: An example case of a bending process %A Asadzadeh, Mohammad Zhian %A Ganser, Hans-Peter %A Mucke, Manfred %J Applications in Engineering Science %D 2021 %V 6 %@ 2666-4968 %F ASADZADEH:2021:AES %X Hybrid semiparametric models integrate physics-based (’white-box’, parametric) and data-driven (’black-box’, non-parametric) submodels. Black-box models are often implemented using artificial neural networks (ANNs). In this work, we investigate the fitness of symbolic regression (SR) for black-box modelling. The main advantage of this approach is that a trained hybrid model can be expressed in closed form as an algebraic equation. We examine and test the idea on a simple example, namely the v-shape bending of a metal sheet, where an analytical solution for the stamping force is readily available. We explore unconstrained and hybrid symbolic regression modelling to show that hybrid SR models, where the regression tree is partly fixed according to a-priori knowledge, perform much better than purely data-driven SR models based on unconstrained regression trees. Furthermore, the generation of algebraic equations by this method is much more repeatable, which makes the approach applicable to process knowledge discovery %K genetic algorithms, genetic programming, Hybrid modelling, Symbolic regression, Knowledge discovery, Metal sheet bending %9 journal article %R 10.1016/j.apples.2021.100049 %U https://www.sciencedirect.com/science/article/pii/S2666496821000157 %U http://dx.doi.org/10.1016/j.apples.2021.100049 %P 100049 %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 10.1016/S0141-9331(98)00054-4 %U http://www.sciencedirect.com/science/article/B6V0X-3TB0788-6/2/445577f1e7cd0c0d531457835edf327e %U http://dx.doi.org/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 %X We present a family of related test problems for genetic programming. These test problems form a very simple test environment that nevertheless possesses some degree of algorithmic subtlety. We term this genetic programming environment plus-one-recall-store (PORS). This genetic programming environment has only a pair of terminals, 1 and recall, and a pair of operations, plus and store, together with a single memory location. We present an extensive mathematical characterization of the PORS environment and report experiments testing the benefits of incorporating expert knowledge into the initial population and into the operation of crossover. The experiments indicate that, in the test environment, expert knowledge is best incorporated only in the initial population. This is a welcome result as this is the computationally inexpensive choice of the two methods of incorporating expert knowledge tested. %K genetic algorithms, genetic programming %R 10.1007/BFb0040753 %U https://rdcu.be/cTHTU %U http://dx.doi.org/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 10.1109/CEC.2003.1299824 %U http://dx.doi.org/10.1109/CEC.2003.1299824 %P 1340-1347 %0 Conference Proceedings %T On Taxonomy of Evolutionary Computation Problems %A Ashlock, Daniel %A Bryden, Kenneth M. %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 10.1109/CEC.2004.1331102 %U https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1072.3780&rep=rep1&type=pdf %U http://dx.doi.org/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 10.1109/CEC.2004.1331089 %U http://orion.math.iastate.edu/danwell/eprints/TartarusCE.pdf %U http://dx.doi.org/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 10.1109/CEC.2005.1554957 %U http://dx.doi.org/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 16 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 10.1109/CEC.2006.1688295 %U http://dx.doi.org/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 10.1007/0-387-31909-3 %U http://dx.doi.org/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 10.1115/1.802566.paper22 %U http://dx.doi.org/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 10.1115/1.802566.paper18 %U http://dx.doi.org/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 10.1109/CEC.2008.4630865 %U EC0169.pdf %U http://dx.doi.org/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 320320 fitness cases for the standard Tartarus task of which 297040 are valid according to the original statement of the problem. This paper studies different schemes for allocating fitness trials for Tartarus using an agent-based metric on the fitness cases to aid in the design process. This agent-based metric is a tool that permits exploration of the geometry of the space of fitness cases. The information gained from this exploration demonstrates why a scheme designed to yield a superior set of training cases in fact yielded an inferior one. The information gained also suggests a new scheme for allocating fitness trials that decreases the number of trials required to achieve a given fitness of the best agent. This scheme achieves similar fitness to a standard evolutionary algorithm using fewer fitness cases. The space of fitness cases for Tartarus is found, relative to the agent-based metric, to form a hollow sphere with a nonuniform distribution of the fitness cases within the space. The tools developed in this study include a generalisable technique for placing an agent-based metric space structure on the fitness cases of any problem that has multiple fitness cases. This metric space structure can be used to better understand the distribution of fitness cases and so design more effective evolutionary algorithms. %K genetic algorithms, genetic programming %R 10.1109/CEC.2008.4630965 %U EC0339.pdf %U http://dx.doi.org/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 10.1109/CEC.2008.4631152 %U EC0599.pdf %U http://dx.doi.org/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 10.1115/1.802953.paper4 %U http://dx.doi.org/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 10.1115/1.802953.paper24 %U http://dx.doi.org/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 10.1109/CEC.2010.5586239 %U http://dx.doi.org/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 10.1109/CEC.2015.7257148 %U http://eldar.mathstat.uoguelph.ca/dashlock/eprints/RFSfrac.pdf %U http://dx.doi.org/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 10.1109/CEC.2016.7743963 %U http://dx.doi.org/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 10.1109/CEC.2016.7743814 %U http://dx.doi.org/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 10.1109/CEC.2005.1554823 %U http://dx.doi.org/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 10.1109/CEC.2006.1688325 %U http://dx.doi.org/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 10.1115/1.802566.paper2 %U http://dx.doi.org/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 10.1109/CIBCB.2011.5948463 %U http://dx.doi.org/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 10.1109/CIBCB.2019.8791496 %U https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8791496 %U http://dx.doi.org/10.1109/CIBCB.2019.8791496 %0 Journal Article %T Logical genetic programming (LGP) application to water resources management %A Ashofteh, Parisa-Sadat %A Bozorg-Haddad, Omid %A Loaiciga, Hugo A. %J Environmental Monitoring and Assessment %D 2019 %V 192 %N 1 %F ashofteh:2019:EMaA %X Genetic programming (GP) is a variant of evolutionary algorithms (EA). EAs are general-purpose search algorithms. Yet, GP does not solve multi-conditional problems satisfactorily. This study improves the GP predictive skill by development and integration of mathematical logical operators and functions to it. The proposed improvement is herein named logical genetic programming (LGP) whose performance is compared with that of GP using examples from the fields of mathematics and water resources. The results of the examples show the LGP superior performance in both examples, with LGP producing improvements of 74 and 42 percent in the objective functions of the mathematical and water resources examples, respectively, when compared with the GP results. The objective functions minimize the mean absolute error (MAE). The comparison of the LGP and GP results with alternative performance criteria demonstrate a better capability of the former algorithm in solving multi-conditional problems. %K genetic algorithms, genetic programming, GP algorithm, LGP approach, Standard operating procedure (SOP) rule, Logical operators, Logical functions, Multi-conditional mathematical problem %9 journal article %R 10.1007/s10661-019-8014-y %U http://link.springer.com/article/10.1007/s10661-019-8014-y %U http://dx.doi.org/10.1007/s10661-019-8014-y %P Articlenumber:34 %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 10.1016/S0045-7949(02)00437-6 %U http://dx.doi.org/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 10.1016/j.measurement.2019.107309 %U http://www.sciencedirect.com/science/article/pii/S026322411931173X %U http://dx.doi.org/10.1016/j.measurement.2019.107309 %P 107309 %0 Journal Article %T Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners %A Asif, Usama %A Javed, Muhammad Faisal %A Abuhussain, Maher %A Ali, Mujahid %A Khan, Waseem Akhtar %A Mohamed, Abdullah %J Case Studies in Construction Materials %D 2024 %V 20 %@ 2214-5095 %F ASIF:2024:cscm %X This study presents a comparative analysis of individual and ensemble learning algorithms (ELAs) to predict the compressive strength (CS) and flexural strength (FS) of plastic concrete. Multilayer perceptron neuron network (MLPNN), Support vector machine (SVM), random forest (RF), and decision tree (DT) were used as base learners, which were then combined with bagging and Adaboost methods to improve the predictive performance. In addition, gene expression programming (GEP) was used to develop computational equations that can be used to predict the CS and FS of plastic concrete. An extensive database containing 357 and 125 data points was obtained from the literature, and the eight most impactful ingredients were used in the model’s development. The accuracy of all models was assessed using several statistical measures, including an error matrix, Akaike information criterion (AIC), K-fold cross-validation, and other external validation equations. Furthermore, sensitivity and SHAP analysis were performed to evaluate input variables’ relative significance and impact on the anticipated CS and FS. Based on statistical measures and other validation criteria, GEP outpaces all other individual models, whereas, in ELAs, the SVR ensemble with Adaboost and RF modified with the Bagging technique demonstrated superior performance. SHapley Additive exPlanations (SHAP) and sensitivity analysis reveal that plastic, cement, water, and the age of the specimens have the highest influence, while superplasticizer has the lowest impact, which is consistent with experimental studies. Moreover, GUI and GEP-based simple mathematical correlation can enhance the practical scope of this study and be an effective tool for the pre-mix design of plastic concrete %K genetic algorithms, genetic programming, Plastic concrete, Machine learning, Compressive strength, Flexural strength, Sustainability, Ensemble learning algorithms, Gene expression programming %9 journal article %R 10.1016/j.cscm.2024.e03135 %U https://www.sciencedirect.com/science/article/pii/S2214509524002869 %U http://dx.doi.org/10.1016/j.cscm.2024.e03135 %P e03135 %0 Journal Article %T Enhancing the predictive accuracy of marshall design tests using generative adversarial networks and advanced machine learning techniques %A Asif, Usama %A Khan, Waseem Akhtar %A Naseem, Khawaja Atif %A Rizvi, Syed Abdul Sami %J Materials Today Communications %D 2025 %V 45 %@ 2352-4928 %F Asif:2025:mtcomm %X Experimentally determining the Marshall design test results for Air voids (Va), Marshall Stability (MS), and Marshall Flow (MF) in hot mixed asphalt (HMA) is often expensive, time-consuming, and requires skilled personnel. To address these challenges, various traditional machine learning (ML) models have been employed to optimise the mix design of HMA. However, their performance is significantly limited by the size and quality of the training dataset. To address these limitations, this study employed Generative Adversarial Networks (GANs) to augment the dataset, which consisted of 184 samples gathered from four construction projects in Pakistan. The augmented dataset was then used to train two advanced ML models: Gene Expression Programming (GEP) and ensemble learning with stacking (ELS). A thorough comparison of the models trained on both original and GAN-augmented datasets was conducted using a range of statistical metrics to evaluate their predictive performance. Additionally, sensitivity and parametric analysis were performed to assess the impact of input variables on the outputs. The results demonstrate that GAN-augmented data significantly improved model accuracy, with GEP and ELS achieving Rsquared values exceeding 0.93 in all cases. Furthermore, GEP models provided interpretable equations for HMA predictions. Sensitivity analysis identified binder content (Pbpercent) as the most influential variable, contributing over 55percent to the variance in Va and MF predictions and 61.56percent in MS. In contrast, other inputs had minimal influence, which was consistent with the experimental findings. This study highlights the potential of advanced ML techniques and data augmentation in developing reliable predictive models for Marshall design test results, advancing efficient HMA design practices %K genetic algorithms, genetic programming, Hot mixed asphalt, Generative adversarial networks, Ensemble learning with stacking, Marshall stability, Marshall flow, gene expression programming %9 journal article %R 10.1016/j.mtcomm.2025.112379 %U https://www.sciencedirect.com/science/article/pii/S2352492825008918 %U http://dx.doi.org/10.1016/j.mtcomm.2025.112379 %P 112379 %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 10.1016/j.soildyn.2018.04.020 %U http://www.sciencedirect.com/science/article/pii/S0267726118301349 %U http://dx.doi.org/10.1016/j.soildyn.2018.04.020 %P 1-7 %0 Journal Article %T A parametric assessing and intelligent forecasting of the energy and exergy performances of a dish concentrating photovoltaic/thermal collector considering six different nanofluids and applying two meticulous soft computing paradigms %A Askari, Ighball Baniasad %A Shahsavar, Amin %A Jamei, Mehdi %A Calise, Francesco %A Karbasi, Masoud %J Renewable Energy %D 2022 %V 193 %@ 0960-1481 %F ASKARI:2022:renene %X In the present study, the application of six engine oil-based Nano fluids (NFs) in a solar concentrating photovoltaic thermal (CPVT) collector is investigated. The calculations were performed for different values of nanoparticle volume concentration, receiver tube diameter, concentrator surface area, receiver length, receiver actual to the maximum number of channels ratio, beam radiation, and a constant volumetric flow rate. Besides, two novel soft computing paradigms namely, the cascaded forward neural network (CFNN) and Multi-gene genetic programming (MGGP) were adopted to predict the first law efficiency (?I) and second law efficiency (?II) of the system based on the influential parameters, as the input features. It was found that the increase of nanoparticle concentration leads to an increase in ?I and a decrease in ?II. Moreover, the rise of both the concentrator surface area (from 5 m2 to 20 m2) and beam irradiance (from 150 W/m2 to 1000 W/m2) entails an increase in both the ?I (by 39percent and 261percent) and ?II (by 55percent and 438percent). Furthermore, it was reported that the pattern of changes in both ?I and ?II with serpentine tube diameter, receiver plate length, and absorber tube length is increasing-decreasing. The results of modeling demonstrated that the CFNN had superior performance than the MGGP model %K genetic algorithms, genetic programming, Dish concentrating photovoltaic thermal system, Exergy, Multi-gene genetic optimization, Nanofluid, Thermodynamic analysis %9 journal article %R 10.1016/j.renene.2022.04.155 %U https://www.sciencedirect.com/science/article/pii/S0960148122006231 %U http://dx.doi.org/10.1016/j.renene.2022.04.155 %P 149-166 %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 10.1109/MILCOM.2010.5680232 %U http://dx.doi.org/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 10.1109/TWC.2012.060412.110460 %U http://dx.doi.org/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 10.1016/j.eswa.2013.04.003 %U http://www.sciencedirect.com/science/article/pii/S0957417413002406 %U http://dx.doi.org/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 10.1109/MLSP.2013.6661901 %U http://dx.doi.org/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 10.1109/SAI.2015.7237187 %U http://dx.doi.org/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 10.1016/j.asoc.2018.03.035 %U http://www.sciencedirect.com/science/article/pii/S1568494618301571 %U http://dx.doi.org/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 10.1007/978-3-030-16692-2_28 %U https://web.cs.hacettepe.edu.tr/~ssen/files/papers/EvoStar19-1.pdf %U http://dx.doi.org/10.1007/978-3-030-16692-2_28 %P 413-429 %0 Journal Article %T A dynamic trust management model for vehicular ad hoc networks %A Aslan, Mehmet %A Sen, Sevil %J Vehicular Communications %D 2023 %V 41 %@ 2214-2096 %F ASLAN:2023:vehcom %X Trust management in vehicular ad hoc networks (VANETs) is a challenging dynamic optimization problem due to their decentralized, infrastructureless, and dynamically changing topology. Evolutionary computation (EC) algorithms are good candidates for solving dynamic optimization problems (DOPs), since they are inspired from the biological evolution that is occurred as a result of changes in the environment. In this study, we explore the use of genetic programming (GP) algorithm and evolutionary dynamic optimization (EDO) techniques to build a dynamic trust management model for VANETs. The proposed dynamic trust management model properly evaluates the trustworthiness of vehicles and their messages in the simulation of experimental scenarios including bogus information attacks. The simulation results show that the evolved trust calculation formula prevents the propagation of bogus messages over VANETs successfully and the dynamic trust management model detects changes in the problem and reacts to them in a timely manner. The best evolved formula achieves 89.38percent Matthews Correlation Coefficient (MCC), 91.81percent detection rate (DR), and 1.01percent false positive rate (FPR), when approx 5percent of the network traffic is malicious. The formula obtains 87.33percent MCC, 92.01percent DR, and 4.8percent FPR when approx 40percent of the network traffic is malicious, demonstrating its robustness to increasing malicious messages. The proposed model is also run on a real-world traffic model and obtains high MCC and low FPR values. To the best of our knowledge, this is the first application of EC and EDO techniques that generate a trust formula automatically for dynamic trust management in VANETs %K genetic algorithms, genetic programming, Vehicular ad hoc networks, Security, Trust management, Evolutionary computation, Evolutionary dynamic optimization %9 journal article %R 10.1016/j.vehcom.2023.100608 %U https://www.sciencedirect.com/science/article/pii/S2214209623000384 %U http://dx.doi.org/10.1016/j.vehcom.2023.100608 %P 100608 %0 Journal Article %T SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound %A Asonitis, Tasos %A Allmendinger, Richard %A Benatan, Matt %A Climent, Ricardo %J Genetic Programming and Evolvable Machines %D 2023 %V 24 %@ 1389-2576 %F Asonitis:2023:GPEM %O Special Issue: Evolutionary Computation in Art, Music and Design %X We present an extension of SonOpt, the first ever openly available tool for the sonification of bi-objective population-based optimisation algorithms. SonOpt has already introduced benefits on the understanding of algorithmic behaviour by proposing the use of sound as a medium for the process monitoring of bi-objective optimisation algorithms. The first edition of SonOpt used two different sonification paths to provide information on convergence, population diversity, recurrence of objective values across consecutive generations and the shape of the approximation set. The present extension provides further insight through the introduction of a third sonification path, which involves hypervolume contributions to facilitate the understanding of the relative importance of non-dominated solutions. Using a different sound generation approach than the existing ones, this newly proposed sonification path %K genetic algorithms, genetic programming, Sonification, Multi-objective optimisation, Population-based optimisation algorithms, Algorithm behaviour, Hypervolume, Sound %9 journal article %R 10.1007/s10710-023-09451-5 %U https://rdcu.be/c7KTf %U http://dx.doi.org/10.1007/s10710-023-09451-5 %P articleno.3 %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 10.1007/s10710-009-9090-5 %U http://dx.doi.org/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 10.1016/j.swevo.2017.05.009 %U http://dx.doi.org/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 10.1016/j.eswa.2017.11.035 %U http://dx.doi.org/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 10.1007/s00521-018-3401-9 %U http://link.springer.com/article/10.1007/s00521-018-3401-9 %U http://dx.doi.org/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 10.1109/CICYBS.2014.7013373 %U http://dx.doi.org/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 10.1109/CEC.2017.7969488 %U http://dx.doi.org/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 10.1145/3071178.3071286 %U http://doi.acm.org/10.1145/3071178.3071286 %U http://dx.doi.org/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 10.1007/978-3-319-77553-1_1 %U http://dx.doi.org/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 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/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. 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, ANN %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 10.1007/978-3-030-16670-0_13 %U https://www.springer.com/us/book/9783030166694 %U http://dx.doi.org/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, sets a new state-of-the-art on methods that seek to automatically design CNNs. %K genetic algorithms, genetic programming, Grammatical Evolution, ANN %9 journal article %R 10.1007/s10710-018-9339-y %U https://arxiv.org/abs/1801.01563 %U http://dx.doi.org/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 10.1007/978-3-030-44094-7_3 %U http://dx.doi.org/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 10.1007/978-3-030-43722-0_34 %U http://dx.doi.org/10.1007/978-3-030-43722-0_34 %P 530-545 %0 Journal Article %T Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests %A Asteris, Panagiotis G. %A Skentou, Athanasia D. %A Bardhan, Abidhan %A Samui, Pijush %A Lourenco, Paulo B. %J Construction and Building Materials %D 2021 %V 303 %@ 0950-0618 %F ASTERIS:2021:CBM %X This study presents a comparative assessment of conventional soft computing techniques in estimating the compressive strength (CS) of concrete using two non-destructive tests, namely ultrasonic pulse velocity and rebound hammer test. In specific, six conventional soft computing models namely back-propagation neural network (BPNN), relevance vector machine, minimax probability machine regression, genetic programming, Gaussian process regression, and multivariate adaptive regression spline, were used. To construct and validate these models, a total of 629 datasets were collected from the literature. Experimental results show that the BPNN attained the most accurate prediction of concrete CS based on both ultrasonic pulse velocity and rebound number values. The results of the employed MARS and BPNN models are significantly better than those obtained in earlier studies. Thus, these two models are very potential to assist engineers in the design phase of civil engineering projects to estimate the concrete CS with a greater accuracy level %K genetic algorithms, genetic programming, Artificial neural networks, Compressive strength of Concrete, Non-destructive testing methods, Soft computing, Artificial Intelligence %9 journal article %R 10.1016/j.conbuildmat.2021.124450 %U https://www.sciencedirect.com/science/article/pii/S0950061821022078 %U http://dx.doi.org/10.1016/j.conbuildmat.2021.124450 %P 124450 %0 Journal Article %T Soft computing-based models for the prediction of masonry compressive strength %A Asteris, Panagiotis G. %A Lourenco, Paulo B. %A Hajihassani, Mohsen %A Adami, Chrissy-Elpida N. %A Lemonis, Minas E. %A Skentou, Athanasia D. %A Marques, Rui %A Nguyen, Hoang %A Rodrigues, Hugo %A Varum, Humberto %J Engineering Structures %D 2021 %V 248 %@ 0141-0296 %F ASTERIS:2021:ES %X Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constituents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature %K genetic algorithms, genetic programming, Artificial neural networks, Machine learning, Masonry, Metaheuristic algorithms, Compressive strength %9 journal article %R 10.1016/j.engstruct.2021.113276 %U https://www.sciencedirect.com/science/article/pii/S0141029621013997 %U http://dx.doi.org/10.1016/j.engstruct.2021.113276 %P 113276 %0 Journal Article %T Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks %A Asteris, Panagiotis G. %A Mamou, Anna %A Hajihassani, Mohsen %A Hasanipanah, Mahdi %A Koopialipoor, Mohammadreza %A Le, Tien-Thinh %A Kardani, Navid %A Armaghani, Danial J. %J Transportation Geotechnics %D 2021 %8 jul %V 29 %@ 2214-3912 %F ASTERIS:2021:TG %X This paper reports the results of soft computing-based models correlating L and N-type Schmidt hammer rebound numbers of rock. A data-independent database was compiled from available measurements reported in the literature, which was used to train and develop back propagating neural networks, genetic programming and least square method models for the prediction of L-type Schmidt hammer rebound numbers. The results show that the highest predictive accuracy was obtained for the neural network model, which predicts the L type Schmidt hammer rebound number, with less than plus-minus20percent deviation from the experimental data for 97.27percent of the samples. The optimum neural network is presented as a closed form equation and is also incorporated into an Excel-based graphical user interface, which directly calculates the Rn(L) number for any input Rn(N) = 12.40-75.97 and which is made available as supplementary material %K genetic algorithms, genetic programming, Artificial neural networks, Machine learning, Metaheuristic algorithms, Non-destructive testing, Rocks, Schmidt hammer rebound number %9 journal article %R 10.1016/j.trgeo.2021.100588 %U https://www.sciencedirect.com/science/article/pii/S2214391221000787 %U http://dx.doi.org/10.1016/j.trgeo.2021.100588 %P 100588 %0 Journal Article %T Model Predictive Evolutionary Temperature Control via Neural-Network-Based Digital Twins %A Ates, Cihan %A Bicat, Dogan %A Yankov, Radoslav %A Arweiler, Joel %A Koch, Rainer %A Bauer, Hans-Jorg %J Algorithms %D 2023 %V 16 %N 8 %@ 1999-4893 %F ates:2023:Algorithms %X In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is used to virtually test alternative control actions for a multi-objective optimisation task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by using an evolutionary algorithm on measured data, a population of control laws can be effectively learnt in the virtual space. This empowers the system to deliver robust performance. Significantly, the digital twin-assisted, population-based model predictive control (MPC) pipeline emerges as a superior approach compared to individual control models, especially when facing sudden and random changes in local heat loads. Leveraging the digital twin to virtually test alternative control policies leads to substantial improvements in the controller’s performance, even with limited training data. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/a16080387 %U https://www.mdpi.com/1999-4893/16/8/387 %U http://dx.doi.org/10.3390/a16080387 %P ArticleNo.387 %0 Conference Proceedings %T Supply and Demand Planning for Water: A Sustainable Water Management System %A Athapaththu, A. M. H. N. %A Illeperumarachchi, D. U. S. %A Herath, H. M. K. U. %A Jayasinghe, H. K. %A Rankothge, W. H. %A Gamage, Narmadha %S 2020 2nd International Conference on Advancements in Computing (ICAC) %D 2020 %8 dec %V 1 %F Athapaththu:2020:ICAC %X Sustainable water management requires maintaining the balance between the demand and supply, specifically addressing water demand in urban, agricultural, and natural systems. Having an insight on water supply forecasting and water consumption forecasting, will be useful to generate an optimal water distribution plan. A platform that targets the sustainable water management concepts for domestic usage and paddy cultivation is proposed in this paper, with the following components: (1) forecasting water levels of reservoirs, (2) forecasting water consumption patterns, and (3) optimizing the water distribution. We have used Recurrent Neural Network (RNN) and, Long Short-Term Memory (LSTM) for forecasting modules and, Genetic Programming (GP) for optimizing water distribution. Our results show that, using our proposed modules, sustainable water management related services can be automated efficiently and effectively. %K genetic algorithms, genetic programming %R 10.1109/ICAC51239.2020.9357256 %U http://dx.doi.org/10.1109/ICAC51239.2020.9357256 %P 305-310 %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 10.1080/08839514.2017.1378140 %U http://dx.doi.org/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 10.1109/ICIEA.2012.6360718 %U http://dx.doi.org/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 10.1504/IJHST.2016.075560 %U http://www.inderscience.com/link.php?id=75560 %U http://dx.doi.org/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 robustness and performance of ELM were studied using the data from two different catchments located in two different climatic conditions. The robustness of ELM was evaluated by varying number of lagged input variables the number of hidden nodes and input parameter (regularization coefficient). Higher lead days prediction and extrapolation capability were also investigated. The results show that (1) ELM yields reasonable results with two or higher lagged input variables (flows) for 1-day lead prediction; (2) ELM produced satisfactory results very rapidly when the number of hidden nodes was greater than or equal to 1000; (3) ELM showed improved results when regularization coefficient was fine-tuned; (4) ELM was able to extrapolate extreme values well; (5) ELM generated reasonable results for higher number of lead days (second and third) predictions; (6) ELM was computationally much faster and capable of producing better results compared to other leading AI methods for prediction of flow series from the same catchment. ELM has the potential for forecasting real-time hydrological flow series %K genetic algorithms, genetic programming, Catchment, Flow series, Prediction, Hydrology, Modeling, Extreme learning machine %9 journal article %R 10.1016/j.cageo.2018.08.003 %U http://www.sciencedirect.com/science/article/pii/S0098300417304867 %U http://dx.doi.org/10.1016/j.cageo.2018.08.003 %P 105-114 %0 Conference Proceedings %T Genetic programming to learn an agent’s monitoring strategy %A Atkin, M. %A Cohen, P. R. %Y Shen, Wei-Min %S Proceedings of the AAAI-93 Workshop on Learning Action Models %D 1993 %I AAAI Press %F Atkin:1993:GPLAMS %X Many tasks require an agent to monitor its environment, but little is known about appropriate monitoring strategies to use in particular situations. Our approach is to learn good monitoring strategies with a genetic programming algorithm. To this end, we have developed a simple agent programming language in which we represent monitoring strategies as programs that control a simulated robot, and a simulator in which the programs can be evaluated. The effect of different environments and tasks is determined experimentally; changing features of the environment will change which strategies are learnt. The correspondence can then be analysed. %K genetic algorithms, genetic programming %U http://www.aaai.org/Papers/Workshops/1993/WS-93-06/WS93-06-009.pdf %P 36-41 %0 Report %T Genetic programming to learn an agent’s monitoring strategy %A Atkin, M. %A Cohen, P. R. %D 1993 %N TR-93-26 %I Computer Science Department, University of Massachusetts %C Amherst, MA, USA %F Atkin:1993:GPLAMSa %K genetic algorithms, genetic programming %U http://www-eksl.cs.umass.edu/papers/93-26.ps %0 Conference Proceedings %T Learning monitoring strategies: A difficult genetic programming application %A Atkin, Marc S. %A Cohen, Paul R. %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 Atkin:1994:LMSDGP %X Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed results. Since the agent control language was kept purposefully general, the set of monitoring strategies constitutes only a small part of the overall space of possible behaviours. Because of this, it was often difficult for the genetic algorithm to evolve them, even though their performance was superior. These results raise questions as to how easy it will be for genetic programming to scale up as the areas it is applied to become more complex. %K genetic algorithms, genetic programming, cupcake problem, agent control language, genetic programming application, monitoring strategy learning, optimal strategies, possible behaviour, learning (artificial intelligence), monitoring %K optimisation %R 10.1109/ICEC.1994.349931 %U http://www-eksl.cs.umass.edu/papers/AtkinIEEE.pdf %U http://dx.doi.org/10.1109/ICEC.1994.349931 %P 328-332a %0 Report %T Monitoring in Embedded Agents %A Atkin, Marc S. %A Cohen, Paul R. %D 1995 %N 95-66 %I Experimental Knowledge Systems Laboratory, Computer Science Department, University of Massachusetts %C Box 34610, Lederle Graduate Research Center, Amherst. MA 01003-4610, USA %F atkin:1995:mea %X Finding good monitoring strategies is an important process in the design of any embedded agent. We describe the nature of the monitoring problem, point out what makes it difficult, and show that while periodic monitoring strategies are often the easiest to derive, they are not always the most appropriate. We demonstrate mathematically and empirically that for a wide class of problems, the so-called ’cupcake problems’, there exists a simple strategy, interval reduction, that outperforms periodic monitoring. We also show how features of the environment may influence the choice of the optimal strategy. The paper concludes with some thoughts about a monitoring strategy taxonomy, and what its defining features might be. %K genetic algorithms, genetic programming %9 Computer Science Technical Report %U http://www-eksl.cs.umass.edu/papers/ijcai95-msa_95-66.pdf %0 Journal Article %T Monitoring Strategies for Embedded Agents: Experiments and Analysis %A Atkin, Marc S. %A Cohen, Paul R. %J Adaptive Behavior %D 1995 %8 Fall %V 4 %N 2 %F atkin:1995:AB %X Monitoring is an important activity for any embedded agent. To operate effectively, agents must gather information about their environment. The policy by which they do this is called a monitoring strategy. Our work has focused on classifying different types of monitoring strategies and understanding how strategies depend on features of the task and environment. We have discovered only a few general monitoring strategies, in particular periodic and interval reduction, and speculate that there are no more. The relative advantages and generality of each strategy will be discussed in detail. The wide applicability of interval reduction will be demonstrated both empirically and analytically. We conclude with a number of general laws that state when a strategy is most appropriate. %K genetic algorithms, genetic programming, Monitoring, embedded agents, planning %9 journal article %U http://www-eksl.cs.umass.edu/papers/atkin96.pdf %P 125-172 %0 Conference Proceedings %T Evolution of aesthetically pleasing images without human-in-the-loop %A Atkins, Daniel L. %A Klapaukh, Roman %A Browne, Will N. %A Zhang, Mengjie %S IEEE Congress on Evolutionary Computation (CEC 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Atkins:2010:cec %X Evolutionary Art is a sub-field of Evolutionary Computing that involves creating interesting images using Evolutionary Techniques. Previously Genetic Programming has been used to create such images autonomously -that is, without a human in the loop. However, this work did not explore alternative fitness measures, consider colour in fitness or provide independent validation of results. Four fitness functions based on the concept that the pleasingness of an image is based on the ratio of image complexity to processing complexity are explored. We introduce the use of Shannon Entropy as a measure of image complexity to compare with Jpeg Compression. Similarly, we introduce Run Length Encoding to compare with Fractal Compression as a measure of processing complexity. A survey of 100 participants showed that it is possible to generate aesthetically pleasing graphics using each fitness function. Importantly, it was the introduction of colour that separated the aesthetic effects of the fitness measures. %K genetic algorithms, genetic programming %R 10.1109/CEC.2010.5586283 %U http://dx.doi.org/10.1109/CEC.2010.5586283 %0 Conference Proceedings %T A Domain Independent Genetic Programming Approach to Automatic Feature Extraction for Image Classification %A Atkins, Daniel %A Neshatian, Kourosh %A Zhang, Mengjie %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 Atkins:2011:ADIGPAtAFEfIC %X In this paper we explore the application of Genetic Programming (GP) to the problem of domain-independent image feature extraction and classification. We propose a new GP-based image classification system that extracts image features autonomously, and compare its performance against a baseline GP-based classifier system that uses human-extracted features. We found that the proposed system has a similar performance to the baseline system, and that GP is capable of evolving a single program that can both extract useful features and use those features to classify an image. %K genetic algorithms, genetic programming, automatic image feature extraction, baseline system, classifier system, domain independent genetic programming, human-extracted features, image classification, feature extraction, image classification %R 10.1109/CEC.2011.5949624 %U http://dx.doi.org/10.1109/CEC.2011.5949624 %P 238-245 %0 Report %T Applying Space Technology to Enhance Control of an Artificial Arm for Children and Adults with Amputations %A Atkins, Diane J. %D 1998 %8 30 jun %N IN-63 006665? %I The Institute for Rehabilitation and Research (TIRR) %C USA %F Atkins:1998:space %K genetic algorithms, genetic programming, myoelectric, MRI %U http://hdl.handle.net/2060/19990025668 %0 Conference Proceedings %T Evolving Graphs by Graph Programming %A Atkinson, Timothy %A Plump, Detlef %A Stepney, Susan %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 Atkinson:2018:EuroGP %X Rule-based graph programming is a deep and rich topic. We present an approach to exploiting the power of graph programming as a representation and as an execution medium in an evolutionary algorithm (EGGP). We demonstrate this power in comparison with Cartesian Genetic Programming (CGP), showing that it is significantly more efficient in terms of fitness evaluations on some classic benchmark problems. We hypothesise that this is due to its ability to exploit the full graph structure, leading to a richer mutation set, and outline future work to test this hypothesis, and to exploit further the power of graph programming within an EA. %K genetic algorithms, genetic programming, Cartesian Genetic Programming %R 10.1007/978-3-319-77553-1_3 %U http://eprints.whiterose.ac.uk/126500/1/AtkinsonPlumpStepney.EuroGP.18.pdf %U http://dx.doi.org/10.1007/978-3-319-77553-1_3 %P 35-51 %0 Generic %T Semantic Neutral Drift %A Atkinson, Timothy %A Plump, Detlef %A Stepney, Susan %D 2018 %8 24 oct %I arXiv %F DBLP:journals/corr/abs-1810-10453 %X We introduce the concept of Semantic Neutral Drift (SND) for evolutionary algorithms, where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals fitness scores. A number of digital circuit benchmark problems have been implemented with rule-based graph programs and empirically evaluated, demonstrating quantitative improvements in evolutionary performance. Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for an evolutionary algorithm if that facilitates the inclusion of SND. %K genetic algorithms, genetic programming Evolutionary Algorithms, Neutral Drift, Semantic Equivalence, Mutation Operators, Graph Programming %U http://arxiv.org/abs/1810.10453 %0 Conference Proceedings %T Quantum Program Synthesis: Swarm Algorithms and Benchmarks %A Atkinson, Timothy %A Drake, John %A Karsa, Athena %A Swan, Jerry %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 Atkinson:2019:EuroGP %X In the two decades since Shor celebrated quantum algorithm for integer factorisation, manual design has failed to produce the anticipated growth in the number of quantum algorithms. Hence, there is a great deal of interest in the automatic synthesis of quantum circuits and algorithms. Here we present a set of experiments which use Ant Programming to automatically synthesise quantum circuits. In the proposed approach, ants choosing paths in high-dimensional Cartesian space are analogous to transformation of qubits in Hilbert space. In addition to the proposed algorithm, we introduce new evaluation criteria for searching the space of quantum circuits, both for classical simulation and simulation on a quantum computer. We demonstrate that the proposed approach significantly outperforms random search on a suite of benchmark problems based on these new measures. %K genetic algorithms, genetic programming %R 10.1007/978-3-030-16670-0_2 %U https://www.springer.com/us/book/9783030166694 %U http://dx.doi.org/10.1007/978-3-030-16670-0_2 %P 19-34 %0 Conference Proceedings %T Evolving graphs with horizontal gene transfer %A Atkinson, Timothy %A Plump, Detlef %A Stepney, Susan %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 Atkinson:2019:GECCO %X We introduce a form of neutral Horizontal Gene Transfer (HGT) to Evolving Graphs by Graph Programming (EGGP). We introduce the mu x lambda evolutionary algorithm, where u parents each produce l children who compete with only their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging genetic information without reproduction. Experimental results from 14 symbolic regression benchmark problems show that the introduction of the u x l EA and HGT events improve the performance of EGGP. Comparisons with Genetic Programming and Cartesian Genetic Programming strongly favour our proposed approach. %K genetic algorithms, genetic programming, Evolving Graphs, Horizontal Gene Transfer, Neutrality %R 10.1145/3321707.3321788 %U http://dx.doi.org/10.1145/3321707.3321788 %P 968-976 %0 Journal Article %T Horizontal gene transfer for recombining graphs %A Atkinson, Timothy %A Plump, Detlef %A Stepney, Susan %J Genetic Programming and Evolvable Machines %D 2020 %8 sep %V 21 %N 3 %@ 1389-2576 %F Atkinson:GPEM:H2019 %O Special Issue: Highlights of Genetic Programming 2019 Events %X We introduce a form of neutral horizontal gene transfer (HGT) to evolving graphs by graph programming (EGGP). We introduce the mu x lambda evolutionary algorithm (EA), where mu parents each produce lambda children who compete only with their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging genetic information without reproduction. Experimental results from symbolic regression problems show that the introduction of the mu x lambda EA and HGT events improve the performance of EGGP. Comparisons with genetic programming and Cartesian genetic programming strongly favour our proposed approach. We also investigate the effect of using HGT events in neuroevolution tasks. We again find that the introduction of HGT improves the performance of EGGP, demonstrating that HGT is an effective cross-domain mechanism for recombining graphs. %K genetic algorithms, genetic programming, Graph-based genetic programming, Neuroevolution, Horizontal gene transfer, HGT, EGGP %9 journal article %R 10.1007/s10710-020-09378-1 %U http://dx.doi.org/10.1007/s10710-020-09378-1 %P 321-347 %0 Thesis %T Evolving Graphs by Graph Programming %A Atkinson, Timothy %D 2019 %C UK %C University of York %F Atkinson:thesis %X Graphs are a ubiquitous data structure in computer science and can be used to represent solutions to difficult problems in many distinct domains. This motivates the use of Evolutionary Algorithms to search over graphs and efficiently find approximate solutions. However, existing techniques often represent and manipulate graphs in an ad-hoc manner. In contrast, rule-based graph programming offers a formal mechanism for describing relations over graphs. This thesis proposes the use of rule-based graph programming for representing and implementing genetic operators over graphs. We present the Evolutionary Algorithm Evolving Graphs by Graph Programming and a number of its extensions which are capable of learning stateful and stateless digital circuits, symbolic expressions and Artificial Neural Networks. We demonstrate that rule-based graph programming may be used to implement new and effective constraint-respecting mutation operators and show that these operators may strictly generalise others found in the literature. Through our proposal of Semantic Neutral Drift, we accelerate the search process by building plateaus into the fitness landscape using domain knowledge of equivalence. We also present Horizontal Gene Transfer, a mechanism whereby graphs may be passively recombined without disrupting their fitness. Through rigorous evaluation and analysis of over 20,000 independent executions of Evolutionary Algorithms, we establish numerous benefits of our approach. We find that on many problems, Evolving Graphs by Graph Programming and its variants may significantly outperform other approaches from the literature. Additionally, our empirical results provide further evidence that neutral drift aids the efficiency of evolutionary search. %K genetic algorithms, genetic programming, EGGP %9 Ph.D. thesis %U https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.803685 %0 Conference Proceedings %T An evolutionary model for dynamically controlling a behavior-based autonomous agent %A Atkinson-Abutridy, John A. %A Carrasco-Leon, Julio R. %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 atkinson-abutridy:1999:A %P 16-24 %0 Conference Proceedings %T Learning Distributed Reactive Strategies by Genetic Programming for the General Job Shop Problem %A Atlan, Laurent %A Bonnet, Jerome %A Naillon, Martine %S Proceedings of the 7th annual Florida Artificial Intelligence Research Symposium %D 1994 %8 may %I IEEE Press %C Pensacola, Florida, USA %F atlan:1994:gpjss %X proposed is a general system to infer symbolic policy functions for distributed reactive scheduling in non-stationary environments. The job shop problem is only used as a validating case study. Our system is based both on an original distributed scheduling model and on genetic programming for the inference of symbolic policy functions. The purpose is to determine heuristic policies that are local in time, long term near-optimal, and robust with respect to perturbations. Furthermore, the policies are local in state space: the global decision problem is split into as many decision problems as there are agents, i.e. machines in the job shop problem. If desired, the genetic algorithm can use expert knowledge as a priori knowledge, via implementation of the symbolic representation of the policy functions. %K genetic algorithms, genetic programming %U ftp://ftp.ens.fr/pub/reports/biologie/disgajsp.ps.Z %0 Conference Proceedings %T The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications %A Atmosukarto, Indriyati %A Shapiro, Linda G. %A Heike, Carrie %Y Ercil, Aytul %S Proceedings of the 2010 20th International Conference on Pattern Recognition %D 2010 %8 23 26 aug %I IEEE %C Istanbul, Turkey %F Atmosukarto:2010:UGP:1904935.1906046 %X Craniofacial disorders commonly result in various head shape dysmorphologies. The goal of this work is to quantify the various 3D shape variations that manifest in the different facial abnormalities in individuals with a craniofacial disorder called 22q11.2 Deletion Syndrome. Genetic programming (GP) is used to learn the different 3D shape quantifications. Experimental results show that the GP method achieves a higher classification rate than those of human experts and existing computer algorithms [1], [2]. %K genetic algorithms, genetic programming, 3D Shape quantification %R 10.1109/ICPR.2010.598 %U http://www.cs.washington.edu/research/VACE/Multimedia/icpr10_Atmosukarto.pdf %U http://dx.doi.org/10.1109/ICPR.2010.598 %P 2444-2447 %0 Thesis %T 3D Shape Analysis for Quantification, Classification, and Retrieval %A Atmosukarto, Indriyati %D 2010 %C USA %C Computer Science and Engineering, University of Washington %F AtmosukartoPhd %X Three-dimensional objects are now commonly used in a large number of applications including games, mechanical engineering, archaeology, culture, and even medicine. As a result, researchers have started to investigate the use of 3D shape descriptors that aim to encapsulate the important shape properties of the 3D objects. This thesis presents new 3D shape representation methodologies for quantification, classification and retrieval tasks that are flexible enough to be used in general applications, yet detailed enough to be useful in medical craniofacial dysmorphology studies. The methodologies begin by computing low-level features at each point of the 3D mesh and aggregating the features into histograms over mesh neighbourhoods. Two different methodologies are defined. The first methodology begins by learning the characteristics of salient point histograms for each particular application, and represents the points in a 2D spatial map based on longitude-latitude transformation. The second methodology represents the 3D objects by using the global 2D histogram of the azimuth-elevation angles of the surface normals of the points on the 3D objects. Four datasets, two craniofacial datasets and two general 3D object datasets, were obtained to develop and test the different shape analysis methods developed in this thesis. Each dataset has different shape characteristics that help explore the different properties of the methodologies. Experimental results on classifying the craniofacial datasets show that our methodologies achieve higher classification accuracy than medical experts and existing state-of-the-art 3D descriptors. Retrieval and classification results using the general 3D objects show that our methodologies are comparable to existing view-based and feature-based descriptors and outperform these descriptors in some cases. Our methodology can also be used to speed up the most powerful general 3D object descriptor to date. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://grail.cs.washington.edu/theses/AtmosukartoPhd.pdf %0 Journal Article %T GPLAB: software review %A Atmosukarto, Indriyati %J Genetic Programming and Evolvable Machines %D 2012 %8 dec %V 12 %N 4 %@ 1389-2576 %F Atmosukarto:2011:GPEM %O Software Review %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-011-9142-5 %U https://rdcu.be/dR8d6 %U http://dx.doi.org/10.1007/s10710-011-9142-5 %P 457-459 %0 Journal Article %T Distribution function of relaxation times: An alternative to classical methods for evaluating the reaction kinetics of oxygen evolution reaction %A Attias, Rinat %A Bhowmick, Sourav %A Tsur, Yoed %J Chemical Engineering Journal %D 2023 %V 476 %@ 1385-8947 %F ATTIAS:2023:cej %X Reaction kinetics of RuO2 is precisely evaluated by distribution function of relaxation times (DFRT) model using impedance spectroscopy analysis by genetic programming (ISGP). Effective resistances of the Faradaic processes, measured using Electrochemical impedance spectroscopy (EIS) at various overpotentials, were determined using DFRT, by separating and associating three electrochemical phenomena occurring during the reaction. The effective resistances are used to generate a Tafel plot of potential as a function of log1Reff. The classical method, based on linear sweep voltammetry (LSV), to evaluate the Tafel slope is associated with some considerations for accurate results. For RuO2, a reference catalyst, LSV illustrates an average Tafel slope of 182 pm 8 mV/dec while the effective resistance method, estimated using DFRT, shows an average of 181 pm 3 mV/dec. A relative error of 0.3 percent between the two methodologies, and a lower standard deviation for DFRT demonstrate the higher precision and effectiveness of ISGP in determining the reaction kinetics via Tafel slope analysis. Therefore, using DFRT with the ability to separate Faradaic from non-Faradaic processes to evaluate the relevant part of the effective resistance, reaction kinetics can be estimated, avoiding shortcomings of the classical Tafel method %K genetic algorithms, genetic programming, Tafel slope, DFRT, DRT, ISGP, EIS, Reaction kinetics %9 journal article %R 10.1016/j.cej.2023.146708 %U https://www.sciencedirect.com/science/article/pii/S1385894723054396 %U http://dx.doi.org/10.1016/j.cej.2023.146708 %P 146708 %0 Conference Proceedings %T GP under streaming data constraints: a case for pareto archiving? %A Atwater, Aaron %A Heywood, Malcolm I. %A Zincir-Heywood, Nur %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 Atwater:2012:GECCO %X Classification as applied to streaming data implies that only a small number of new training instances appear at each generation and are never explicitly reintroduced by the stream. Pareto competitive coevolution provides a potential framework for archiving useful training instances between generations under an archive of finite size. Such a coevolutionary framework is defined for the online evolution of classifiers under genetic programming. Benchmarking is performed under multi-class data sets with class imbalance and training partitions with between 1,000’s to 100,000’s of instances. The impact of enforcing different constraints for accessing the stream are investigated. The role of online adaptation is explicitly documented and tests made on the relative impact of label error on the quality of streaming classifier results. %K genetic algorithms, genetic programming %R 10.1145/2330163.2330262 %U http://dx.doi.org/10.1145/2330163.2330262 %P 703-710 %0 Conference Proceedings %T Benchmarking Pareto archiving heuristics in the presence of concept drift: diversity versus age %A Atwater, Aaron %A Heywood, Malcolm I. %Y Blum, Christian %Y Alba, Enrique %Y Auger, Anne %Y Bacardit, Jaume %Y Bongard, Josh %Y Branke, Juergen %Y Bredeche, Nicolas %Y Brockhoff, Dimo %Y Chicano, Francisco %Y Dorin, Alan %Y Doursat, Rene %Y Ekart, Aniko %Y Friedrich, Tobias %Y Giacobini, Mario %Y Harman, Mark %Y Iba, Hitoshi %Y Igel, Christian %Y Jansen, Thomas %Y Kovacs, Tim %Y Kowaliw, Taras %Y Lopez-Ibanez, Manuel %Y Lozano, Jose A. %Y Luque, Gabriel %Y McCall, John %Y Moraglio, Alberto %Y Motsinger-Reif, Alison %Y Neumann, Frank %Y Ochoa, Gabriela %Y Olague, Gustavo %Y Ong, Yew-Soon %Y Palmer, Michael E. %Y Pappa, Gisele Lobo %Y Parsopoulos, Konstantinos E. %Y Schmickl, Thomas %Y Smith, Stephen L. %Y Solnon, Christine %Y Stuetzle, Thomas %Y Talbi, El-Ghazali %Y Tauritz, Daniel %Y Vanneschi, Leonardo %S GECCO ’13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference %D 2013 %8 June 10 jul %I ACM %C Amsterdam, The Netherlands %F Atwater:2013:GECCO %X A framework for coevolving genetic programming teams with Pareto archiving is benchmarked under two representative tasks for non-stationary streaming environments. The specific interest lies in determining the relative contribution of diversity and aging heuristics to the maintenance of the Pareto archive. Pareto archiving, in turn, is responsible for targeting data (and therefore champion individuals) as appropriate for retention beyond the limiting scope of the sliding window interface to the data stream. Fitness sharing alone is considered most effective under a non-stationary stream characterised by continuous (incremental) changes. Fitness sharing with an aging heuristic acts as the preferred heuristic when the stream is characterised by non-stationary stepwise changes. %K genetic algorithms, genetic programming %R 10.1145/2463372.2463489 %U http://dx.doi.org/10.1145/2463372.2463489 %P 885-892 %0 Conference Proceedings %T RoboGen: Robot Generation through Artificial Evolution %A Auerbach, Joshua E. %A Aydin, Deniz %A Maesani, Andrea %A Kornatowski, Przemyslaw M. %A Cieslewski, Titus %A Heitz, Gregoire %A Fernando, Pradeep R. %A Loshchilov, Ilya %A Daler, Ludovic %A Floreano, Dario %Y Sayama, Hiroki %Y Rieffel, John %Y Risi, Sebastian %Y Doursat, Rene %Y Lipson, Hod %S Proceedings of the Fourteenth International Conference of the Synthesis and Simulation of Living Systems, ALIFE 14 %S Complex Adaptive Systems %D 2014 %8 30 jul 2 aug %I MIT Press %C New York %F Auerbach:2014:ALIFE %X Science instructors from a wide range of disciplines agree that hands-on laboratory components of courses are pedagogically necessary (Freedman, 1997). However, certain shortcomings of current laboratory exercises have been pointed out by several authors (Mataric, 2004; Hofstein and Lunetta, 2004). The overarching theme of these analyses is that hands-on components of courses tend to be formulaic, closed-ended, and at times outdated. To address these issues, we envision a novel platform that is not only a didactic tool but is also an experimental testbed for users to play with different ideas in evolutionary robotics (Nolfi and Floreano, 2000), neural networks, physical simulation, 3D printing, mechanical assembly, and embedded processing. Here, we introduce RoboGen an open-source software and hardware platform designed for the joint evolution of robot morphologies and controllers a la Sims (1994); Lipson and Pollack (2000); Bongard and Pfeifer (2003). Robo-Gen has been designed specifically to allow evolved robots to be easily manufactured via widely available desktop 3D-printers, and the use of simple, open-source, low-cost, off-the-shelf electronic components. RoboGen features an evolution engine complete with a physics simulator, as well as utilities both for generating design files of body components for 3D printing, and for compiling neural-network controllers to run on an Arduino microcontroller board. In this paper, we describe the RoboGen platform, and provide some metrics to assess the success of using it as the hands-on component of a masters-level bio-inspired artificial intelligence course. %K genetic algorithms, genetic programming, RoboGen %R 10.7551/978-0-262-32621-6-ch022 %U http://mitpress.mit.edu/sites/default/files/titles/content/alife14/ch022.html %U http://dx.doi.org/10.7551/978-0-262-32621-6-ch022 %P 136-137 %0 Conference Proceedings %T Symbolic Regression via Genetic Programming %A Augusto, Douglas A. %A Barbosa, Helio J. C. %S VI Brazilian Symposium on Neural Networks (SBRN’00) %D 2000 %8 jan 22 25 %I IEEE %C Rio de Janeiro, RJ, Brazil %@ 0-7695-0856-1 %G eng %F sbrn2000meta029 %O VI Simposio Brasileiro de Redes Neurais %X In this work, we present an implementation of symbolic regression, which is based on genetic programming (GP). Unfortunately, standard implementations of GP in compiled languages are not usually the most efficient ones. The present approach employs a simple representation for tree-like structures by making use of Read’s linear code, leading to more simplicity and better performance when compared with traditional GP implementations. Creation, crossover and mutation of individuals are formalized. An extension allowing for the creation of random coefficients is presented. The efficiency of the proposed implementation was confirmed in computational experiments, which are summarized in this paper. %K genetic algorithms, genetic programming %R 10.1109/SBRN.2000.889734 %U http://dx.doi.org/10.1109/SBRN.2000.889734 %P 173 %0 Conference Proceedings %T Coevolution of data samples and classifiers integrated with grammatically-based genetic programming for data classification %A Augusto, Douglas A. %A Barbosa, Helio J. C. %A Ebecken, Nelson F. F. %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 Augusto:2008:gecco %X The present work treats the data classification task by means of evolutionary computation techniques using three ingredients: genetic programming, competitive coevolution, and context-free grammar. The robustness and symbolic/interpretative qualities of the genetic programming are employed to construct classification trees via Darwinian evolution. The flexible formal structure of the context-free grammar replaces the standard genetic programming representation and describes a language which encodes trees of varying complexity. Finally, competitive coevolution is used to promote competitions between data samples and classification trees in order to create and sustain an evolutionary arms-race for improved solutions %K genetic algorithms, genetic programming, competitive coevolution, context-free grammar, data classification %R 10.1145/1389095.1389328 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1171.pdf %U http://dx.doi.org/10.1145/1389095.1389328 %P 1171-1178 %0 Conference Proceedings %T Coevolutionary multi-population genetic programming for data classification %A Augusto, Douglas Adriano %A Barbosa, Helio Jose Correa %A Ebecken, Nelson Francisco Favilla %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 Augusto:2010:gecco %X This work presents a new evolutionary ensemble method for data classification, which is inspired by the concepts of bagging and boosting, and aims at combining their good features while avoiding their weaknesses. The approach is based on a distributed multiple-population genetic programming (GP) algorithm which exploits the technique of coevolution at two levels. On the inter-population level the populations cooperate in a semi-isolated fashion, whereas on the intrapopulation level the candidate classifiers coevolve competitively with the training data samples. The final classifier is a voting committee composed by the best members of all the populations. The experiments performed in a varying number of populations show that our approach outperforms both bagging and boosting for a number of benchmark problems. %K genetic algorithms, genetic programming, distributed genetic programming %R 10.1145/1830483.1830650 %U http://dx.doi.org/10.1145/1830483.1830650 %P 933-940 %0 Conference Proceedings %T A new approach for generating numerical constants in grammatical evolution %A Augusto, Douglas A. %A Barbosa, Helio J. C. %A Barreto, Andre M. S. %A Bernardino, Heder S. %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 Augusto:2011:GECCOcomp %X A new approach for numerical-constant generation in Grammatical Evolution is presented. Experiments comparing our method with the three most popular methods for constant creation are performed. By varying the number of bits to represent a constant, we can increase our methods precision to the desired level of accuracy, overcoming by a large margin the other approaches. %K genetic algorithms, genetic programming, grammatical evolution: Poster %R 10.1145/2001858.2001966 %U http://dx.doi.org/10.1145/2001858.2001966 %P 193-194 %0 Conference Proceedings %T Evolving Numerical Constants in Grammatical Evolution with the Ephemeral Constant Method %A Augusto, Douglas Adriano %A Barbosa, Helio J. C. %A da Motta Salles Barreto, Andre %A Bernardino, Heder S. %Y Antunes, Luis %Y Pinto, Helena Sofia %S Proceedings 15th Portuguese Conference on Artificial Intelligence, EPIA 2011 %S Lecture Notes in Computer Science %D 2011 %8 oct 10 13 %V 7026 %C Lisbon, Portugal %F Augusto:2011:EPIA %K genetic algorithms, genetic programming, grammatical evolution, constant creation %R 10.1007/978-3-642-24769-9_9 %U http://dx.doi.org/10.1007/978-3-642-24769-9_9 %P 110-124 %0 Journal Article %T Accelerated parallel genetic programming tree evaluation with OpenCL %A Augusto, Douglas A. %A Barbosa, Helio J. C. %J Journal of Parallel and Distributed Computing %D 2013 %V 73 %N 1 %@ 0743-7315 %F Augusto2012 %O Metaheuristics on GPUs %X Inspired by the process of natural selection, genetic programming (GP) aims at automatically building arbitrarily complex computer programs. Being classified as an embarrassingly parallel technique, GP can theoretically scale up to tackle very diverse problems by increasingly adding computational power to its arsenal. With today’s availability of many powerful parallel architectures, a challenge is to take advantage of all those heterogeneous compute devices in a portable and uniform way. This work proposes both (i) a transcription of existing GP parallelisation strategies into the OpenCL programming platform; and (ii) a freely available implementation to evaluate its suitability for GP, by assessing the performance of parallel strategies on the CPU and GPU processors from different vendors. Benchmarks on the symbolic regression and data classification domains were performed. On the GPU we could achieve 13 billion node evaluations per second, delivering almost 10 times the throughput of a twelve-core CPU. %K genetic algorithms, genetic programming, GPU, OpenCL, GP-GPU, Accelerated tree evaluation %9 journal article %R 10.1016/j.jpdc.2012.01.012 %U http://www.sciencedirect.com/science/article/pii/S074373151200024X %U http://dx.doi.org/10.1016/j.jpdc.2012.01.012 %P 86-100 %0 Book Section %T Parallel Genetic Programming on Graphics Processing Units %A Augusto, Douglas A. %A Bernardino, Heder S. %A Barbosa, Helio J. C. %E Ventura, Sebastian %B Genetic Programming - New Approaches and Successful Applications %D 2012 %I InTech %G en %F Augusto:2012:GPnew %K genetic algorithms, genetic programming, GPU, OpenCL, stack-based interpreter %R 10.5772/48364 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.374.745 %U http://dx.doi.org/10.5772/48364 %P 95-114 %0 Conference Proceedings %T Improving recruitment effectiveness using genetic programming techniques %A Augusto, Douglas A. %A Bernardino, Heder S. %A Barbosa, Helio J. C. %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 Augusto:2013:GECCOcomp %X A real-world problem, namely to improve the recruitment effectiveness of a certain company, is tackled here by evolving accurate and human-readable classifiers by means of grammar-based genetic programming techniques. %K genetic algorithms, genetic programming %R 10.1145/2464576.2464673 %U http://dx.doi.org/10.1145/2464576.2464673 %P 177-178 %0 Conference Proceedings %T Predicting the Performance of Job Applicants by Means of Genetic Programming %A Augusto, D. A. %A Bernardino, H. S. %A Barbosa, H. J. C. %S BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC 2013) %D 2013 %8 sep %F Augusto:2013:CCI.CBIC %X Since their early development, genetic programming-based algorithms have been showing to be successful at challenging problems, attaining several human-competitive results and other awards. This paper will present another achievement of such algorithms by describing how our team has won an international machine-learning competition. We have solved, by means of grammar-based genetic programming techniques, a real-world problem of meritocracy in jobs by evolving classifiers that were both accurate and human-readable. %K genetic algorithms, genetic programming %R 10.1109/BRICS-CCI-CBIC.2013.27 %U http://dx.doi.org/10.1109/BRICS-CCI-CBIC.2013.27 %P 98-103 %0 Book Section %T Programação Genética %A Augusto, Douglas Adriano %A Bernardino, Heder Soares %A Barbosa, Helio Jose Correa %E Lopes, Heitor Silvrio %E de Abreu Rodrigues, Luiz Carlos %E Steiner, Maria Teresinha Arns %B Meta-Heursticas em Pesquisa Operacional %D 2013 %7 1 %I Omnipax %C Curitiba, PR %F Augustoetal2013 %X Genetic programming is an evolutionary metaheuristic designed to automatically generate programs by means of an iterative process inspired by the theory of natural selection. In operational research, genetic programming techniques are normally used to infer heuristics for decision-making problems. In this way, genetic programming is a hyper-heuristic creating new search methods which are more efficient that those traditionally considered. This chapter describes genetic programming and presents its applications in the operations research field. %K genetic algorithms, genetic programming, operations research, Optimization %R 10.7436/2013.mhpo.05 %U http://omnipax.com.br/site/?page_id=387 %U http://dx.doi.org/10.7436/2013.mhpo.05 %P 69-86 %0 Conference Proceedings %T Creation Of A Learning, Flying Robot By Means Of Evolution %A Augustsson, Peter %A Wolff, Krister %A Nordin, Peter %Y Langdon, W. B. %Y Cantú-Paz, E. %Y Mathias, K. %Y Roy, R. %Y Davis, D. %Y Poli, R. %Y Balakrishnan, K. %Y Honavar, V. %Y Rudolph, G. %Y Wegener, J. %Y Bull, L. %Y Potter, M. A. %Y Schultz, A. C. %Y Miller, J. F. %Y Burke, E. %Y Jonoska, N. %S GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %D 2002 %8 September 13 jul %I Morgan Kaufmann Publishers %C New York %@ 1-55860-878-8 %F Augustsson:2002:gecco %X We demonstrate the first instance of a real on-line robot learning to develop feasible flying (flapping) behavior, using evolution. Here we present the experiments and results of the first use of evolutionary methods for a flying robot. With nature’s own method, evolution, we address the highly non-linear fluid dynamics of flying. The flying robot is constrained in a test bench where timing and movement of wing flapping is evolved to give maximal lifting force. The robot is assembled with standard off-the-shelf R/C servomotors as actuators. The implementation is a conventional steady-state linear evolutionary algorithm. %K genetic algorithms, genetic programming, evolutionary robotics, evolutionary algorithm, flying %U http://fy.chalmers.se/~wolff/Papers/ANW_gecco02.pdf %P 1279-1285 %0 Conference Proceedings %T Evolving Texture Features by Genetic Programming %A Aurnhammer, Melanie %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 aurnhammer:evows07 %X Feature extraction is a crucial step for Computer Vision applications. Finding appropriate features for an application often means hand-crafting task specific features with many parameters to tune. A generalisation to other applications or scenarios is in many cases not possible. Instead of engineering features, we describe an approach which uses Genetic Programming to generate features automatically. In addition, we do not predefine the dimension of the feature vector but pursue an iterative approach to generate an appropriate number of features. We present this approach on the problem of texture classification based on co-occurrence matrices. Our results are compared to those obtained by using seven Haralick texture features, as well as results reported in the literature on the same database. Our approach yielded a classification performance of up to 87percent which is an improvement of 30percent over the Haralick features. We achieved an improvement of 12percent over previously reported results while reducing the dimension of the feature vector from 78 to four. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-71805-5_38 %U http://dx.doi.org/10.1007/978-3-540-71805-5_38 %P 351-358 %0 Report %T Adaptive systems for foreign exchange trading %A Austin, M. P. %A Bates, R. G. %A Dempster, M. A. H. %A Williams, S. N. %D 2003 %N WP 15/2003 %I Judge Institute of Management, University of Cambridge %C UK %F austin:2003:WP %K genetic algorithms, genetic programming %9 Working paper %U http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/WP1503.pdf %0 Journal Article %T Adaptive systems for foreign exchange trading %A Austin, Mark %A Bates, Graham %A Dempster, Michael %A Williams, Stacy %J Eclectic %D 2004 %8 Autumn %V 18 %F Austin:2004:E %X A joint project between academics and bankers has shown how banks can improve the forecasting performance of their technical trading systems in foreign exchange markets. Professor Michael Dempster and Graham Bates, both of the Centre for Financial Research, Cambridge, and Dr Mark Austin and Dr Stacy Williams, both of HSBC Global Markets, outline the results of their research. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest it can be done. Automated trading systems are being used successfully to predict intraday and daily exchange rates. Trading systems using only publicly available technical indicators can be profitable ? but those that also use proprietary information can be more accurate and therefore more profitable. A joint project by the Centre for Financial Research (at the Judge Institute of Management, Cambridge University) and HSBC used the bank’s customer order information to show that using proprietary information in trading systems can improve their forecasting performance and profitability. The research findings also intuitively make sense. Successful traders in the FX markets apply human judgement to a range of information and techniques. In this project the researchers mimicked these traders by combining the techniques of technical analysis with the stream of public and non-public information available to them. %K genetic algorithms, genetic programming %9 journal article %U http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/adaptive.pdf %P 21-26 %0 Journal Article %T Adaptive systems for foreign exchange trading %A Austin, Mark P. %A Bates, Graham %A Dempster, Michael A. H. %A Leemans, Vasco %A Williams, Stacy N. %J Quantitative Finance %D 2004 %8 aug %V 4 %N 4 %I Routledge, part of the Taylor and Francis Group %@ 1469-7688 %F Austin:2004:QF %X Foreign exchange markets are notoriously difficult to predict. For many years academics and practitioners alike have tried to build trading models, but history has not been kind to their efforts. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest otherwise. With newly developed computational techniques and newly available data, the development of successful trading models is looking possible. The Centre for Financial Research (CFR) at Cambridge University’s Judge Institute of Management has been researching trading techniques in foreign exchange markets for a number of years. Over the last 18 months a joint project with HSBC Global Markets has looked at how the bank’s proprietary information on customer order flow and on the customer limit order book can be used to enhance the profitability of technical trading systems in FX markets. Here we give an overview of that research and report our results. %K genetic algorithms, genetic programming, fx trading %9 journal article %R 10.1080/14697680400008593 %U http://www-cfr.jbs.cam.ac.uk/archive/PRESENTATIONS/seminars/2006/dempster2.pdf %U http://dx.doi.org/10.1080/14697680400008593 %P 37-45 %0 Conference Proceedings %T Evaluation of chess position by modular neural network generated by genetic algorithm %A Autones, Mathieu %A Beck, Aryel %A Camacho, Phillippe %A Lassabe, Nicolas %A Luga, Herve %A Scharffe, Franccois %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 autones:2004:eurogp %X Chess engine Tempo. One of the major difficulties for this type of program lies in the function for evaluating game positions. This function is composed of a large number of parameters which have to be determined and then adjusted. We propose an alternative which consists in replacing this function by an artificial neuron network (ANN). Without topological knowledge of this complex network, we use the evolutionist methods for its inception, thus enabling us to obtain, among other things, a modular network. Finally, we present our results: (i) reproduction of the XOR function which validates the method used and (ii) generation of an evaluation function %K genetic algorithms, genetic programming %R 10.1007/978-3-540-24650-3_1 %U http://dx.doi.org/10.1007/978-3-540-24650-3_1 %P 1-10 %0 Conference Proceedings %T A genetic programming approach to support the design of service compositions %A Aversano, Lerina %A Di Penta, Massimiliano %A Taneja, Kunal %Y Zirpins, Christian %Y Ortiz, Guadalupe %Y Lamersdorf, Winfried %Y Emmerich, Wolfgang %S Proceedings of the first International Workshop of Engineering Service Compositions, WESC’05 %S IBM Research Reports %D 2005 %8 dec %N RC23821 (W0512-008) %C Amsterdam, The Netherlands %F Aversano:2005:WSEC %K genetic algorithms, genetic programming %U http://domino.research.ibm.com/library/cyberdig.nsf/papers/DE71563B7B69D362852570D000548D0D/$File/rc23821.pdf %P 17-24 %0 Journal Article %T A genetic programming approach to support the design of service compositions %A Aversano, Lerina %A Di Penta, Massimiliano %A Taneja, Kunal %J International Journal of Computer Systems Science & Engineering %D 2006 %8 jul %V 21 %N 4 %I CRL Publishing, admin@crlpublishing.co.uk %@ 0267 6192 %F Aversano:2006:IJCSSE %X The design of service composition is one of the most challenging research problems in service-oriented software engineering. Building composite services is concerned with identifying a suitable set of services that orchestrated in some way is able to solve a business goal which cannot be resolved using a single service amongst those available. Despite the literature reports several approaches for (semi) automatic service composition, several problems, such as the capability of determining the composition’s topology, still remain open. This paper proposes a search-based approach to semi-automatically support the design of service compositions. In particular, the approach uses genetic programming to automatically generate workflows that accomplish a business goal and exhibit a given QoS level, with the aim of supporting the service integrator activities in the finalization of the workflow. %K genetic algorithms, genetic programming, SBSE, service compositions, distributed software, workflow %9 journal article %U http://www.rcost.unisannio.it/mdipenta/papers/csse06.pdf %P 247-254 %0 Conference Proceedings %T Multi-label Classification with Gene Expression Programming %A Avila, J. L. %A Gibaja Galindo, Eva Lucrecia %A Ventura, Sebastian %Y Corchado, Emilio %Y Wu, Xindong %Y Oja, Erkki %Y Herrero, Alvaro %Y Baruque, Bruno %S Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009 %S Lecture Notes in Computer Science %D 2009 %8 jun 10 12 %V 5572 %I Springer %C Salamanca, Spain %F conf/hais/AvilaGV09 %X In this paper, we introduce a Gene Expression Programming algorithm for multi label classification. This algorithm encodes each individual into a discriminant function that shows whether a pattern belongs to a given class or not. The algorithm also applies a niching technique to guarantee that the population includes functions for each existing class. In order to evaluate the quality of our algorithm, its performance is compared to that of four recently published algorithms. The results show that our proposal is the best in terms of accuracy, precision and recall %K genetic algorithms, genetic programming, gene expression programming %R 10.1007/978-3-642-02319-4_76 %U http://dx.doi.org/10.1007/978-3-642-02319-4 %U http://dx.doi.org/10.1007/978-3-642-02319-4_76 %P 629-637 %0 Book Section %T Evolving Multi-label Classification Rules with Gene Expression Programming: A Preliminary Study %A Avila-Jimenez, Jose Luis %A Gibaja, Eva %A Ventura, Sebastian %E Corchado, Emilio %E Grana Romay, Manuel %E Manhaes Savio, Alexandre %B Hybrid Artificial Intelligence Systems %S Lecture Notes in Computer Science %D 2010 %8 jun 23 25 %V 6077 %I Springer %C San Sebastian, Spain %F Avila-Jimenez:2010:HAIS %X The present work expounds a preliminary work of a genetic programming algorithm to deal with multi-label classification problems. The algorithm uses Gene Expression Programming and codifies a classification rule into each individual. A niching technique assures diversity in the population. The final classifier is made up by a set of rules for each label that determines if a pattern belongs or not to the label. The proposal have been tested over several domains and compared with other multi-label algorithms and the results shows that it is specially suitable to handle with nominal data sets. %K genetic algorithms, genetic programming, gene expression programming %R 10.1007/978-3-642-13803-4_2 %U http://dx.doi.org/10.1007/978-3-642-13803-4_2 %P 9-16 %0 Journal Article %T A Gene Expression Programming Algorithm for Multi-Label Classification %A Avila-Jimenez, Jose Luis %A Gibaja Galindo, Eva Lucrecia %A Zafra, Amelia %A Ventura, Sebastian %J Journal of Multiple-Valued Logic and Soft Computing %D 2011 %V 17 %N 2-3 %@ 1542-3980 %F journals/mvl/Avila-JimenezGZV11 %X This paper presents a Gene Expression Programming algorithm for multilabel classification which encodes a discriminant function into each individual to show whether a pattern belongs to a given class or not. The algorithm also applies a niching technique to guarantee that the population includes functions for each existing class. The algorithm has been compared to other recently published algorithms. The results found on several datasets demonstrate the feasibility of this approach in the tackling of multi-label problems. %K genetic algorithms, genetic programming, gene expression programming, multi-label classification, discriminant functions, machine learning %9 journal article %U http://www.oldcitypublishing.com/journals/mvlsc-home/mvlsc-issue-contents/mvlsc-volume-17-number-2-3-2011/mvlsc-17-2-3-p-183-206/ %P 183-206 %0 Thesis %T Genetic Programing for multi-label classification %A Avila-Jimenez, Jose Luis %D 2013 %8 jun %C Spain %C Department of Computers Science and Numerical Analysis, University of Cordoba %F Avila-Jimenez:thesis %X El problema de clasificacion consiste en asociar una serie de etiquetas a una serie de ejemplos o patrones. En la clasificacion clasica a cada patron de entrenamiento solamente se le puede asociar una sola etiqueta de un conjunto de etiquetas. Por tanto se consideran que los conjuntos de clases objetivo en los que se agruparan los patrones son por definicion conjuntos disjuntos. En el caso de la clasificacion multi-etiqueta, los conjuntos objetivos no son disjuntos, pudiendo haber patrones a los que se les asocie mas de una etiqueta. Por tanto, los ejemplos se asocian a un conjunto de etiquetas y el resultado puede tomar varios valores dentro del conjunto de etiquetas[1]. El objetivo que se plantea en esta tesis es el desarrollo de una serie de modelos de programacion genetica para resolver problemas de clasificacion multi-etiqueta. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://www.uco.es/grupos/kdis/docs/thesis/2013-JLAvila.pdf %0 Conference Proceedings %T Designing Stream Cipher Systems Using Genetic Programming %A Awad, Wasan Shaker %Y Coello Coello, Carlos A. %S Selected papers from the 5th International Conference on Learning and Intelligent Optimization (LION 5) 2011 %S Lecture Notes in Computer Science %D 2011 %8 jan 17 21 %V 6683 %C Rome, Italy %F conf/lion/Awad11 %O Selected Papers %X Genetic programming is a good technique for finding near-global optimal solutions for complex problems, by finding the program used to solve the problems. One of these complex problems is designing stream cipher systems automatically. Steam cipher is an important encryption technique used to protect private information from an unauthorised access, and it plays an important role in the communication and storage systems. In this work, we propose a new approach for designing stream cipher systems of good properties, such as high degree of security and efficiency. The proposed approach is based on the genetic programming. Three algorithms are presented here, which are simple genetic programming, simulated annealing programming, and adaptive genetic programming. Experiments were performed to study the effectiveness of these algorithms in solving the underlying problem. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-25566-3_23 %U http://dx.doi.org/10.1007/978-3-642-25566-3_23 %P 308-320 %0 Book Section %T Symbolic Regression %A Awange, Joseph L. %A Palancz, Bela %B Geospatial Algebraic Computations: Theory and Applications %D 2016 %I Springer %F Awange2016 %X Symbolic regression (SR) is the process of determining the symbolic function, which describes a data set-effectively developing an analytic model, which summarizes the data and is useful for predicting response behaviours as well as facilitating human insight and understanding. The symbolic regression approach adopted herein is based upon genetic programming wherein a population of functions are allowed to breed and mutate with the genetic propagation into subsequent generations based upon a survival-of-the-fittest criteria. Amazingly, this works and, although computationally intensive, summary solutions may be reasonably discovered using current laptop and desktop computers. %K genetic algorithms, genetic programming, Support Vector Machine, SVM, Pareto Front, Binary Tree, Thin Plate Spline %R 10.1007/978-3-319-25465-4_11 %U http://dx.doi.org/10.1007/978-3-319-25465-4_11 %P 203-216 %0 Journal Article %T Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques %A Awoyera, Paul O. %A Kirgiz, Mehmet S. %A Viloria, A. %A Ovallos-Gazabon, D. %J Journal of Materials Research and Technology %D 2020 %8 jul – aug %V 9 %N 4 %@ 2238-7854 %F AWOYERA:2020:JMRT %X There has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium silicate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to the conventional material (river sand), fly ash was partially replaced with silica fume and granulated blast furnace slag. Various properties of the concrete, filler ability and passing ability of fresh mixtures, and compressive, split-tensile and flexural strength of hardened concrete were determined. The model development involved using raw materials and fresh mix properties as predictors, and strength properties as response. Results shows that the use of the admixtures enhanced both the fresh and hardened properties of the concrete. Both GEP and ANN methods exhibited good prediction of the experimental data, with minimal errors. However, GEP models can be preferred as simple equations are developed from the process, while ANN is only a predictor %K genetic algorithms, genetic programming, Gene expression programming, Artificial neural networks, ANN, Predictor, Response, Self-Compacting concrete, Geopolymers %9 journal article %R 10.1016/j.jmrt.2020.06.008 %U http://www.sciencedirect.com/science/article/pii/S2238785420314095 %U http://dx.doi.org/10.1016/j.jmrt.2020.06.008 %P 9016-9028 %0 Conference Proceedings %T Feature Selection and Classification Using Age Layered Population Structure Genetic Programming %A Awuley, Anthony %A Ross, Brian J. %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 Awuley:2016:CEC %X This paper presents a new algorithm called Feature Selection Age Layered Population Structure (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS is a modification of Hornby’s ALPS algorithm - an evolutionary algorithm renown for avoiding pre-mature convergence on difficult problems. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal-symbol selection for the construction of GP trees/sub-trees. The FSALPS meta-heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non-converging evolutionary process that favours selection of features with high discrimination of class labels. We compared the performance of canonical GP, ALPS and FSALPS on some high-dimensional benchmark classification datasets, including a hyperspectral vision problem. Although all algorithms had similar classification accuracy, ALPS and FSALPS usually dominated canonical GP in terms of smaller and efficient trees. Furthermore, FSALPS significantly outperformed canonical GP, ALPS, and other feature selection strategies in the literature in its ability to perform dimensionality reduction %K genetic algorithms, genetic programming %R 10.1109/CEC.2016.7744088 %U http://dx.doi.org/10.1109/CEC.2016.7744088 %P 2417-2426 %0 Thesis %T Automatic Generation of Mobile Malwares Using Genetic Programming %A Aydogan, Emre %D 2014 %8 aug %C Ankara, Turkey %C Hacettepe Universitesi %F Aydogan:mastersthesis %X The number of mobile devices has increased dramatically in the past few years. These smart devices provide many useful functionalities accessible from anywhere at any time, such as reading and writing e-mails, surfing on the Internet, showing facilities nearby, and the like. Hence, they become an inevitable part of our daily lives. However the popularity and adoption of mobile devices also attract virus writers in order to harm our devices. So, many security companies have already proposed new solutions in order to protect our mobile devices from such malicious attempts. However developing methodologies that detect unknown malwares is a research challenge, especially on devices with limited resources. This study presents a method that evolves automatically variants of malwares from the ones in the wild by using genetic programming. We aim to evaluate existing security solutions based on static analysis techniques against these evolved unknown malwares. The experimental results show the weaknesses of the static analysis tools available in the market, and the need of new detection techniques suitable for mobile devices. %K genetic algorithms, genetic programming, mobile malware, static analysis, obfuscation, evolutionary computation, %9 Masters thesis %U https://web.cs.hacettepe.edu.tr/~ssen/files/thesis/EmreTez.pdf %0 Conference Proceedings %T Automatic Generation of Mobile Malwares Using Genetic Programming %A Aydogan, Emre %A Sen, Sevil %Y Mora, Antonio M. %Y Squillero, Giovanni %S 18th European Conference on the Applications of Evolutionary Computation %S LNCS %D 2015 %8 August 10 apr %V 9028 %I Springer %C Copenhagen %F Aydogan:2015:evoApplications %X The number of mobile devices has increased dramatically in the past few years. These smart devices provide many useful functionalities accessible from anywhere at anytime, such as reading and writing e-mails, surfing on the Internet, showing facilities nearby, and the like. Hence, they become an inevitable part of our daily lives. However the popularity and adoption of mobile devices also attract virus writers in order to harm our devices. So, many security companies have already proposed new solutions in order to protect our mobile devices from such malicious attempts. However developing methodologies that detect unknown malwares is a research challenge, especially on devices with limited resources. This study presents a method that evolves automatically variants of malwares from the ones in the wild by using genetic programming (GP). We aim to evaluate the efficacy of current anti-virus products, using static analysis techniques, in the market. The experimental results show the weaknesses of the static analysis tools available in the market, and the need of new detection techniques suitable for mobile devices. %K genetic algorithms, genetic programming, Mobile malware, Static analysis, Obfuscation, Evolutionary computation %R 10.1007/978-3-319-16549-3_60 %U http://web.cs.hacettepe.edu.tr/~ssen/files/papers/EvoStar15.pdf %U http://dx.doi.org/10.1007/978-3-319-16549-3_60 %P 745-756 %0 Conference Proceedings %T A Central Intrusion Detection System for RPL-Based Industrial Internet of Things %A Aydogan, E. %A Yilmaz, S. %A Sen, S. %A Butun, I. %A Forsstroem, S. %A Gidlund, M. %S 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS) %D 2019 %8 may %F Aydogan:2019:WFCS %X Although Internet-of-Things (IoT) is revolutionizing the IT sector, it is not mature yet as several technologies are still being offered to be candidates for supporting the backbone of this system. IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is one of those promising candidate technologies to be adopted by IoT and Industrial IoT (IIoT). Attacks against RPL have shown to be possible, as the attackers use the unauthorized parent selection system of the RLP protocol. In this work, we are proposing a methodology and architecture to detect intrusions against IIoT. Especially, we are targeting to detect attacks against RPL by using genetic programming. Our results indicate that the developed framework can successfully (with high accuracy, along with high true positive and low false positive rates) detect routing attacks in RPL-based Industrial IoT networks. %K genetic algorithms, genetic programming %R 10.1109/WFCS.2019.8758024 %U http://dx.doi.org/10.1109/WFCS.2019.8758024 %0 Journal Article %T Prediction of moment redistribution capacity in reinforced concrete beams using gene expression programming %A Aydogan, Mehmet Safa %A Alacali, Sema %A Arslan, Guray %J Structures %D 2023 %V 47 %@ 2352-0124 %F AYDOGAN:2023:istruc %X Moment redistribution can play an important role in making the design of reinforced concrete (RC) structures more realistic and economical. In this paper, a new comprehensive formula has been proposed that considers four input parameters that are thought to influence moment redistribution the most in statically indeterminate RC beams using gene expression programming (GEP). For this reason, an experimental database of 108 data points was collected from experimental studies in the literature to predict the moment redistribution of the RC beams using genetic programming. All of these collected data points belong to two-span RC continuous beams. The results of the GEP formulation were statistically compared with the experimental results obtained from the literature and the results from the equations provided by the current design code provisions. The results of the comparison revealed that the proposed GEP-based formulation has the best performance and accuracy among the proposed models. Moreover, the sensitivity analysis and parametric study were also carried out to evaluate the most critical parameters affecting on moment redistribution of RC continuous beams %K genetic algorithms, genetic programming, Moment redistribution, RC continuous beams, Prediction, Design codes, Gene expression programming %9 journal article %R 10.1016/j.istruc.2022.12.054 %U https://www.sciencedirect.com/science/article/pii/S2352012422012425 %U http://dx.doi.org/10.1016/j.istruc.2022.12.054 %P 2209-2219 %0 Conference Proceedings %T Generating Metamorphic Relations for Cyber-Physical Systems with Genetic Programming: An Industrial Case Study %A Ayerdi, Jon %A Terragni, Valerio %A Arrieta, Aitor %A Tonella, Paolo %A Sagardui, Goiuria %A Arratibel, Maite %Y Allamanis, Miltiadis %Y Beller, Moritz %S ESEC/FSE 2021 %D 2021 %8 23 28 aug %I ACM %C Athens, Greece %F Ayerdi:2021:FSE-IND %X One of the major challenges in the verification of complex industrial Cyber-Physical Systems is the difficulty of determining whether a particular system output or behaviour is correct or not, the so-called test oracle problem. Metamorphic testing alleviates the oracle problem by reasoning on the relations that are expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. In this paper, we present a case study aiming at automating this process. To this end, we implemented GAssertMRs, a tool to automatically generate MRs with genetic programming. We assess the cost-effectiveness of this tool in the context of an industrial case study from the elevation domain. Our experimental results show that in most cases GAssertMRs outperforms the other baselines, including manually generated MRs developed with the help of domain experts. We then describe the lessons learned from our experiments and we outline the future work for the adoption of this technique by industrial practitioners. %K genetic algorithms, genetic programming, SBSE, CPS, mutation testing, metamorphic testing, evolutionary algorithm, cyber physical systems, quality of service, oracle generation, oracle improvement %R 10.1145/3468264.3473920 %U https://www.conference-publishing.com/list.php?Event=FSE21&Full=noabs#fse21ind-p28-p-title %U http://dx.doi.org/10.1145/3468264.3473920 %P 1264-1274 %0 Conference Proceedings %T Evolutionary Generation of Metamorphic Relations for Cyber-Physical Systems %A Ayerdi, Jon %A Terragni, Valerio %A Arrieta, Aitor %A Tonella, Paolo %A Sagardui, Goiuria %A Arratibel, Maite %Y Gallagher, Marcus %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F ayerdi:2022:GECCOhop %X A problem when testing Cyber-Physical Systems (CPS) is the difficulty of determining whether a particular system output or behaviour is correct or not. Metamorphic testing alleviates such a problem by reasoning on the relations expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. This paper summarizes our recent publication: ’Generating Metamorphic Relations for Cyber-Physical Systems with Genetic Programming: An Industrial Case Study’, presented at ESEC/FSE 2021. In that publication we presented GAssertMRs, the first technique to automatically generate MRs for CPS, leveraging GP to explore the space of candidate solutions. We evaluated GAssertMRs in an industrial case study, outperforming other baselines. %K genetic algorithms, genetic programming, quality of service, cyber physical systems, metamorphic testing, oracle improvement, oracle generation, genetic programming, evolutionary algorithm, mutation testing, metamorphic testing %R 10.1145/3520304.3534077 %U https://valerio-terragni.github.io/assets/pdf/ayerdi-gecco-2022.pdf %U http://dx.doi.org/10.1145/3520304.3534077 %P 15-16 %0 Generic %T Automatically Generating Metamorphic Relations via Genetic Programming %A Ayerdi, Jon %A Terragni, Valerio %A Jahangirova, Gunel %A Arrieta, Aitor %A Tonella, Paolo %D 2023 %I arXiv %F DBLP:journals/corr/abs-2312-15302 %K genetic algorithms, genetic programming %R 10.48550/ARXIV.2312.15302 %U https://doi.org/10.48550/arXiv.2312.15302 %U http://dx.doi.org/10.48550/ARXIV.2312.15302 %0 Journal Article %T GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming %A Ayerdi, Jon %A Terragni, Valerio %A Jahangirova, Gunel %A Arrieta, Aitor %A Tonella, Paolo %J IEEE Transactions on Software Engineering %D 2024 %8 jul %V 50 %N 7 %@ 0098-5589 %G English %F Genmorph_TSE_2024 %X Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding outputs. Deriving MRs is mostly a manual activity, since their automated generation is a challenging and largely unexplored problem. GENMORPH, a technique to automatically generate MRs for Java methods that involve inputs and outputs that are Boolean, numerical, or ordered sequences. GENMORPH uses an evolutionary algorithm to search for effective test oracles, i.e., oracles that trigger no false alarms and expose software faults in the method under test. The proposed search algorithm is guided by two fitness functions that measure the number of false alarms and the number of missed faults for the generated MRs. Our results show that GENMORPH generates effective MRs for 18 out of 23 methods (mutation score >20 percent). Furthermore, it can increase RANDOOP fault detection capability in 7 out of 23 methods, and EVOSUITE in 14 out of 23 methods. %K genetic algorithms, genetic programming, Testing, Java, Generators, Space exploration, Manuals, metamorphic testing, oracle problem, metamorphic relations, mutation analysis, mutation testing, Filters, GenMorph, EvoSuite %9 journal article %R 10.1109/TSE.2024.3407840 %U https://kclpure.kcl.ac.uk/portal/en/publications/genmorph-automatically-generating-metamorphic-relations-via-genet %U http://dx.doi.org/10.1109/TSE.2024.3407840 %P 1888-1900 %0 Conference Proceedings %T GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming %A Ayerdi, Jon %A Terragni, Valerio %A Jahangirova, Gunel %A Arrieta, Aitor %A Tonella, Paolo %Y Sturm, Arnon %Y Cai, Haipeng %S ICSE 2025 Journal-first Papers %D 2025 %8 30 apr %C Ottawa %F Ayerdi:2025:ICSE %O Presented \citeGenmorph_TSE_2024 %X Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding outputs. Deriving MRs is mostly a manual activity, since their automated generation is a challenging and largely unexplored problem. We present GenMorph, a technique to automatically generate MRs for Java methods that involve inputs and outputs that are Boolean, numerical, or ordered sequences. GenMorph uses an evolutionary algorithm to search for effective test oracles, i.e., oracles that trigger no false alarms and expose software faults in the method under test. The proposed search algorithm is guided by two fitness functions that measure the number of false alarms and the number of missed faults for the generated MRs. Our results show that GenMorph generates effective MRs for 18 out of 23 methods (mutation score > 20 percent). Furthermore, it can increase Randoop fault detection capability in 7 out of 23 methods, and Evosuite in 14 out of 23 methods. When compared with AutoMR, a state-of-the-art MR generator, GenMorph also outperformed its fault detection capability in 9 out of 10 methods. %K genetic algorithms, genetic programming, SBSE, AutoMR %U https://conf.researchr.org/details/icse-2025/icse-2025-journal-first-papers/55/GenMorph-Automatically-Generating-Metamorphic-Relations-via-Genetic-Programming %0 Generic %T Replication package for “GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming” (Version 1) %A Ayerdi, Jon %A Terragni, Valerio %A Jahangirova, Gunel %A Arrieta, Arrieta %A Tonella, Paolo %D 2023 %8 nov %I Zenodo %F DBLP:data/10/AyerdiTJAT23 %K genetic algorithms, genetic programming %R 10.5281/ZENODO.10067096 %U https://doi.org/10.5281/zenodo.10067096 %U http://dx.doi.org/10.5281/ZENODO.10067096 %0 Journal Article %T Parallel and in-process compilation of individuals for genetic programming on GPU %A Ayral, Hakan %A Albayrak, Songul %J PeerJ PrePrints %D 2017 %V 5 %F journals/peerjpre/AyralA17 %X Three approaches to implement genetic programming on GPU hardware are compilation, interpretation and direct generation of machine code. The compiled approach is known to have a prohibitive overhead compared to other two. %K genetic algorithms, genetic programming, GPU %9 journal article %R 10.7287/peerj.preprints.2936v1 %U http://dx.doi.org/10.7287/peerj.preprints.2936v1 %P e2936 %0 Journal Article %T Effects of Population, Generation and Test Case Count on Grammatical Genetic Programming for Integer Lists %A Ayral, Hakan %A Albayrak, Songul %J Journal of Software %D 2017 %8 jun %V 12 %N 6 %@ 1796-217X %F journals/jsw/AyralA17 %X This paper investigates how grammatical genetic programming performs for evolving simple integer list manipulation functions. We propose three sub-problems which are related to, or component of integer sorting problem as defined by genetic programming literature. We further investigate the effects of modifying evolutionary parameters, such as the number of generations allowed, number of populations, and number of test cases, on the number and distribution of successful solutions. Finally, we propose an AST based dead-code removal for the intron induced non-functional codes on evolved individuals. %K genetic algorithms, genetic programming %9 journal article %R 10.17706/jsw.12.6.483-492 %U http://dx.doi.org/10.17706/jsw.12.6.483-492 %P 483-492 %0 Journal Article %T A genetic programming approach to suspended sediment modelling %A Aytek, Ali %A Kisi, Ozgur %J Journal of Hydrology %D 2008 %8 15 apr %V 351 %N 3-4 %F Aytek:2008:JH %X This study proposes genetic programming (GP) as a new approach for the explicit formulation of daily suspended sediment-discharge relationship. Empirical relations such as sediment rating curves are often applied to determine the average relationship between discharge and suspended sediment load. This type of models generally underestimates or overestimates the amount of sediment. During recent decades, some black box models based on artificial neural networks have been developed to overcome this problem. But these type of models are implicit that can not be simply used by other investigators. Therefore it is still necessary to develop an explicit model for the discharge-sediment relationship. It is aimed in this study, to develop an explicit model based on genetic programming. Explicit models obtained using the GP are compared with rating curves and multi-linear regression techniques in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The results indicate that the proposed GP formulation performs quite well compared to sediment rating curves and multi-linear regression models and is quite practical for use. %K genetic algorithms, genetic programming, Suspended sediment load, Rating curves, Soft computing %9 journal article %R 10.1016/j.jhydrol.2007.12.005 %U http://dx.doi.org/10.1016/j.jhydrol.2007.12.005 %P 288-298 %0 Journal Article %T An application of artificial intelligence for rainfall-runoff modeling %A Aytek, Ali %A Asce, M. %A Alp, Murat %J Journal of Earth System Science %D 2008 %8 apr %V 117 %N 2 %F Aytek:2008:JESS %X This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modelling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalised regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R2) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models. %K genetic algorithms, genetic programming, Gene Expression Programming %9 journal article %U http://www.ias.ac.in/jess/apr2008/d093.pdf %P 145-155 %0 Book Section %T An application of genetic programming to the 4-OP problem using map-trees %A Aytekin, Tevfik %A Korkmaz, Emin Erkan %A Güvennir, Halil Altay %E Yao, Xin %B Progress in Evolutionary Computation %S Lecture Notes in Artificial Intelligence %D 1995 %V 956 %I Springer-Verlag %C Heidelberg, Germany %F aytekin:1995:4-OPmap %X In Genetic programming (GP) applications the programs are expressed as parse trees. A node of a parse tree is an element either from the function-set or terminal-set, and an element of a terminal set can be used in a parse tree more than once. However, when we attempt to use the elements in the terminal set at most once, we encounter problems in creating the initial random population and in crossover and mutation operations. 4-Op problem is an example for such a situation. We developed a technique called map-trees to overcome these anomalies. Experimental results on 4-Op using map-trees are presented. %K genetic algorithms, genetic programming %R 10.1007/3-540-60154-6_45 %U http://www.cs.bilkent.edu.tr/tech-reports/1994/BU-CEIS-9441.ps.z %U http://dx.doi.org/10.1007/3-540-60154-6_45 %P 28-40 %0 Conference Proceedings %T A Re-examination Of The Cart Centering Problem Using The Chorus System %A Azad, R. Muhammad Atif %A Ryan, Conor %A Burke, Mark E. %A Ansari, Ali R. %Y Langdon, W. B. %Y Cantú-Paz, E. %Y Mathias, K. %Y Roy, R. %Y Davis, D. %Y Poli, R. %Y Balakrishnan, K. %Y Honavar, V. %Y Rudolph, G. %Y Wegener, J. %Y Bull, L. %Y Potter, M. A. %Y Schultz, A. C. %Y Miller, J. F. %Y Burke, E. %Y Jonoska, N. %S GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %D 2002 %8 September 13 jul %I Morgan Kaufmann Publishers %C New York %@ 1-55860-878-8 %F azad:2002:gecco %X The cart centering problem is well known in the field of evolutionary algorithms and has often been used as a proof of concept problem for techniques such as Genetic Programming. This paper describes the application of a grammar based, position independent encoding scheme, Chorus, to the problem. It is shown that using the traditional experimental set up employed to solve the problem, Chorus is able to come up with the solutions which appear to beat the theoretically optimal solution, known and accepted for decades in the field of control theory. However, further investigation into the literature of the relevant area reveals that there is an inherent error in the standard E.C. experimental approach to this problem, leaving room for a multitude of solutions to out perform the apparent best. This argument is validated by the performance of Chorus, producing better solutions at a number of occasions. %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2002/GP144.ps %P 707-715 %0 Conference Proceedings %T A Position Independent Evolutionary Automatic Programming Algorithm - The Chorus System %A Azad, R. Muhammad Atif %Y Luke, Sean %Y Ryan, Conor %Y O’Reilly, Una-May %S Graduate Student Workshop %D 2002 %8 August %I AAAI %C New York %F azad:2002:gecco:workshop %K genetic algorithms, genetic programming, grammatical evolution %P 260-263 %0 Conference Proceedings %T Structural Emergence with Order Independent Representations %A Azad, R. Muhammad Atif %A Ryan, Conor %Y Cantú-Paz, E. %Y Foster, J. A. %Y Deb, K. %Y Davis, D. %Y Roy, R. %Y O’Reilly, U.-M. %Y Beyer, H.-G. %Y Standish, R. %Y Kendall, G. %Y Wilson, S. %Y Harman, M. %Y Wegener, J. %Y Dasgupta, D. %Y Potter, M. A. %Y Schultz, A. C. %Y Dowsland, K. %Y Jonoska, N. %Y Miller, J. %S Genetic and Evolutionary Computation – GECCO-2003 %S LNCS %D 2003 %8 December 16 jul %V 2724 %I Springer-Verlag %C Chicago %@ 3-540-40603-4 %F azad:2003:gecco %X This paper compares two grammar based Evolutionary Automatic Programming methods, Grammatical Evolution (GE) and Chorus. Both systems evolve sequences of derivation rules which can be used to produce computer programs, however, Chorus employs a position independent representation, while GE uses polymorphic codons, the meaning of which depends on the context in which they are used. We consider issues such as the order in which rules appear in individuals, and demonstrate that an order always emerges with Chorus, which is similar to that of GE, but more flexible. The paper also examines the final step of evolution, that is, how perfect individuals are produced, and how they differ from their immediate neighbours. We demonstrate that, although Chorus appears to be more flexible structure-wise, GE tends to produce individuals with a higher neutrality, suggesting that its representation can, in some cases, make finding the perfect solution easier. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1007/3-540-45110-2_57 %U http://dx.doi.org/10.1007/3-540-45110-2_57 %P 1626-1638 %0 Thesis %T A Position Independent Representation for Evolutionary Automatic Programming Algorithms - The Chorus System %A Azad, Raja Muhammad Atif %D 2003 %8 dec %C Ireland %C University of Limerick %F Azad:thesis %X We describe a new position independent encoding system, Chorus, for grammar based Evolutionary Algorithms. This scheme is coarsely based on the manner in nature in which genes produce proteins that regulate the metabolic pathways of the cell. The phenotype is the behaviour of the cells metabolism, which corresponds to the development of the computer program in our case. In this procedure, the actual protein encoded by a gene is the same regardless of the position of the gene within the genome. We show that the Chorus system has a very convenient Regular Expression type schema notation that can be used to describe the presence of various phenotypic traits. This notation is used to demonstrate that massive areas of neutrality can exist in the search landscape, and the system is also shown to be able to dispense with large areas of the search space that are unlikely to contain useful solutions. The searching capability of the system is exemplified by its application on a number of proof of concept problems, where the system has shown comparable performance to Genetic Programming and Grammatical Evolution and, in certain cases, it has produced superior results. We also analyse the role of the crossover in the Chorus System and conclude by showing its application on a real world problem from the blood flow domain. %K genetic algorithms, genetic programming, Chorus System, Grammatical Evolution %9 Ph.D. thesis %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/azad_thesis.ps.gz %0 Journal Article %T An evolutionary approach to Wall Sheer Stress prediction in a grafted artery %A Azad, R. Muhammad Atif %A Ansari, Ali R. %A Ryan, Conor %A Walsh, Michael %A McGloughlin, Tim %J Applied Soft Computing %D 2004 %8 may %V 4 %N 2 %I Elsevier %@ 1568-4946 %F Azad:2004:ASC %X Restoring the blood supply to a diseased artery is achieved by using a vascular bypass graft. The surgical procedure is a well documented and successful technique. The most commonly cited hemodynamic factor implicated in the disease initiation and proliferation processes at graft/artery junctions is Wall Shear Stress (WSS). WSS distributions are predicted using numerical simulations as they can provide quick and precise results to assess the effects that alternative graft/artery junction geometries have on the WSS distributions in bypass grafts. Validation of the numerical model is required and in vitro studies, using laser Doppler anemometry (LDA), have been employed to achieve this. Numerically, the Wall Shear Stress is predicted using velocity values stored in the computational cell near the wall and assuming zero velocity at the wall. Experimentally obtained velocities require a mathematical model to describe their behavior. This study employs a grammar based evolutionary algorithm termed Chorus for this purpose and demonstrates that Chorus successfully attains this objective. It is shown that even with the lack of domain knowledge, the results produced by this automated system are comparable to the results in the literature. %K genetic algorithms, genetic programming, grammatical evolution, chorus system, Wall Shear Stress, Laser Doppler anemometry, Mathematical modeling, Computational Fluid Dynamics %9 journal article %R 10.1016/j.asoc.2003.11.001 %U http://dx.doi.org/10.1016/j.asoc.2003.11.001 %P 139-148 %0 Book Section %T An Examination of Simultaneous Evolution of Grammars and Solutions %A Azad, R. Muhammad Atif %A Ryan, Conor %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 Kluwer %C Ann Arbor %@ 0-387-28110-X %F azad:2005:GPTP %X This chapter examines the notion of co-evolving grammars with a population of individuals. This idea has great promise because it is possible to dynamically reshape the solution space while evolving individuals. We compare such a system with a more standard system with fixed grammars and demonstrate that, on a selection of benchmark problems, the standard approach appears to be better. Several different context free grammars, including one inspired by Koza’s GPPS system are examined, and a number of surprising results appear, which indicate that several representative GP benchmark problems are best tackled by a standard GP approach. %K genetic algorithms, genetic programming, Grammatical Evolution, Evolving Grammars, Grammatical ADFs, Generative Representations %R 10.1007/0-387-28111-8_10 %U http://dx.doi.org/10.1007/0-387-28111-8_10 %P 141-158 %0 Conference Proceedings %T Gecco 2008 grammatical evolution tutorial %A Azad, R. Muhammad Atif %A Ryan, Conor %Y Ebner, Marc %Y Cattolico, Mike %Y van Hemert, Jano %Y Gustafson, Steven %Y Merkle, Laurence D. %Y Moore, Frank W. %Y Congdon, Clare Bates %Y Clack, Christopher D. %Y Rand, William %Y Ficici, Sevan G. %Y Riolo, Rick %Y Bacardit, Jaume %Y Bernado-Mansilla, Ester %Y Butz, Martin V. %Y Smith, Stephen L. %Y Cagnoni, Stefano %Y Hauschild, Mark %Y Pelikan, Martin %Y Sastry, Kumara %S GECCO-2008 tutorials %D 2008 %8 December 16 jul %I ACM %C Atlanta, GA, USA %F Azad:2008:geccocomp %K genetic algorithms, genetic programming, grammatical evolution, chorus, GAuGE, grammars, linear strings %R 10.1145/1388969.1389058 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p2339.pdf %U http://dx.doi.org/10.1145/1388969.1389058 %P 2339-2366 %0 Conference Proceedings %T Abstract functions and lifetime learning in genetic programming for symbolic regression %A Azad, R. Muhammad Atif %A Ryan, Conor %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 Azad:2010:gecco %X Typically, an individual in Genetic Programming (GP) can not make the most of its genetic inheritance. Once it is mapped, its fitness is immediately evaluated and it survives only until the genetic operators and its competitors eliminate it. Thus, the key to survival is to be born strong. This paper proposes a simple alternative to this powerlessness by allowing an individual to tune its internal nodes and going through several evaluations before it has to compete with other individuals. We demonstrate that this system, Chameleon, outperforms standard GP over a selection of symbolic regression type problems on both training and test sets; that the system works harmoniously with two other well known extensions to GP, that is, linear scaling and a diversity promoting tournament selection method; that it can benefit dramatically from a simple cache; that adding to functions set does not always add to the tuning expense; and that tuning alone can be enough to promote smaller trees in the population. Finally, we touch upon the consequences of ignoring the effects of complexity when focusing on just the tree sizes to induce parsimony pressure in GP populations. %K genetic algorithms, genetic programming %R 10.1145/1830483.1830645 %U http://dx.doi.org/10.1145/1830483.1830645 %P 893-900 %0 Conference Proceedings %T Variance based selection to improve test set performance in genetic programming %A Azad, R. Muhammad Atif %A Ryan, Conor %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 Azad:2011:GECCO %X This paper proposes to improve the performance of Genetic Programming (GP) over unseen data by minimizing the variance of the output values of evolving models alongwith reducing error on the training data. Variance is a well understood, simple and inexpensive statistical measure; it is easy to integrate into a GP implementation and can be computed over arbitrary input values even when the target output is not known. Moreover, we propose a simple variance based selection scheme to decide between two models (individuals). The scheme is simple because, although it uses bi-objective criteria to differentiate between two competing models, it does not rely on a multi-objective optimisation algorithm. In fact, standard multi-objective algorithms can also employ this scheme to identify good trade-offs such as those located around the knee of the Pareto Front. The results indicate that, despite some limitations, these proposals significantly improve the performance of GP over a selection of high dimensional (multi-variate) problems from the domain of symbolic regression. This improvement is manifested by superior results over test sets in three out of four problems, and by the fact that performance over the test sets does not degrade as often witnessed with standard GP; neither is this performance ever inferior to that on the training set. As with some earlier studies, these results do not find a link between expressions of small sizes and their ability to generalise to unseen data. %K genetic algorithms, genetic programming %R 10.1145/2001576.2001754 %U http://dx.doi.org/10.1145/2001576.2001754 %P 1315-1322 %0 Conference Proceedings %T The Best Things Don’t Always Come in Small Packages: Constant Creation in Grammatical Evolution %A Azad, R. Muhammad Atif %A Ryan, Conor %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 azad:2014:EuroGP %X This paper evaluates the performance of various methods to constant creation in Grammatical Evolution (GE), and validates the results against those from Genetic Programming (GP). Constant creation in GE is an important issue due to the disruptive nature of ripple crossover, which can radically remap multiple terminals in an individual, and we investigate if more compact methods, which are more similar to the GP style of constant creation (Ephemeral Random Constants (ERCs), perform better. The results are surprising. The GE methods all perform significantly better than GP on unseen test data, and we demonstrate that the standard GE approach of digit concatenation does not produce individuals that are any larger than those from methods which are designed to use less genetic material. %K genetic algorithms, genetic programming, Grammatical Evolution :poster %R 10.1007/978-3-662-44303-3_16 %U http://dx.doi.org/10.1007/978-3-662-44303-3_16 %P 186-197 %0 Journal Article %T A Simple Approach to Lifetime Learning in Genetic Programming based Symbolic Regression %A Azad, Raja Muhammad Atif %A Ryan, Conor %J Evolutionary Computation %D 2014 %8 Summer %V 22 %N 2 %@ 1063-6560 %F Azad:2014:EC %X Genetic Programming (GP) coarsely models natural evolution to evolve computer programs. Unlike in nature, where individuals can often improve their fitness through lifetime experience, the fitness of GP individuals generally does not change during their lifetime, and there is usually no opportunity to pass on acquired knowledge. This paper introduces the Chameleon system to address this discrepancy and augment GP with lifetime learning by adding a simple local search that operates by tuning the internal nodes of individuals. Although not the first attempt to combine local search with GP, its simplicity means that it is easy to understand and cheap to implement. A simple cache is added which leverages the local search to reduce the tuning cost to a small fraction of the expected cost, and we provide a theoretical upper limit on the maximum tuning expense given the average tree size of the population and show that this limit grows very conservatively as the average tree size of the population increases. We show that Chameleon uses available genetic material more efficiently by exploring more actively than with standard GP, and demonstrate that not only does Chameleon outperform standard GP (on both training and test data) over a number of symbolic regression type problems, it does so by producing smaller individuals and that it works harmoniously with two other well known extensions to GP, namely, linear scaling and a diversity-promoting tournament selection method. %K genetic algorithms, genetic programming, hill climbing, Lamarckian, genetic repair, Memetic Algorithms, lifetime learning, local search, hybrid genetic algorithms %9 journal article %R 10.1162/EVCO_a_00111 %U http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00111 %U http://dx.doi.org/10.1162/EVCO_a_00111 %P 287-317 %0 Conference Proceedings %T Efficient Approaches to Interleaved Sampling of training data for Symbolic Regression %A Azad, R. Muhammad Atif %A Medernach, David %A Ryan, Conor %Y Madureira, Ana Maria %Y Abraham, Ajith %Y Corchado, Emilio %Y Varela, Leonilde %Y Muda, Azah Kamilah %Y yun Huoy, Choo %S Sixth World Congress on Nature and Biologically Inspired Computing %D 2014 %8 30 jul 1 jul %I IEEE %C Porto, Portugal %F Azad:2014:NaBIC %X The ability to generalise beyond the training set is paramount for any machine learning algorithm and Genetic Programming (GP) is no exception. This paper investigates a recently proposed technique to improve generalisation in GP, termed Interleaved Sampling where GP alternates between using the entire data set and only a single data point in alternate generations. This paper proposes two alternatives to using a single data point: the use of random search instead of a single data point, and simply minimising the tree size. Both the approaches are more efficient than the original Interleaved Sampling because they simply do not evaluate the fitness in half the number of generations. The results show that in terms of generalisation, random search and size minimisation are as effective as the original Interleaved Sampling; however, they are computationally more efficient in terms of data processing. Size minimisation is particularly interesting because it completely prevents bloat while still being competitive in terms of training results as well as generalisation. The tree sizes with size minimisation are substantially smaller reducing the computational expense substantially. %K genetic algorithms, genetic programming %R 10.1109/NaBIC.2014.6921874 %U http://dx.doi.org/10.1109/NaBIC.2014.6921874 %P 176-183 %0 Conference Proceedings %T Efficient interleaved sampling of training data in genetic programming %A Azad, R. Muhammad Atif %A Medernach, David %A Ryan, Conor %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 Azad:2014:GECCOcomp %X The ability to generalise beyond the training set is important for Genetic Programming (GP). Interleaved Sampling is a recently proposed approach to improve generalisation in GP. In this technique, GP alternates between using the entire data set and only a single data point. Initial results showed that the technique not only produces solutions that generalise well, but that it so happens at a reduced computational expense as half the number of generations only evaluate a single data point. This paper further investigates the merit of interleaving the use of training set with two alternatives approaches. These are: the use of random search instead of a single data point, and simply minimising the tree size. Both of these alternatives are computationally even cheaper than the original setup as they simply do not invoke the fitness function half the time. We test the utility of these new methods on four, well cited, and high dimensional problems from the symbolic regression domain. The results show that the new approaches continue to produce general solutions despite taking only half the fitness evaluations. Size minimisation also prevents bloat while producing competitive results on both training and test data sets. The tree sizes with size ionisation are substantially smaller than the rest of the setups, which further brings down the training costs. %K genetic algorithms, genetic programming: Poster %R 10.1145/2598394.2598480 %U http://doi.acm.org/10.1145/2598394.2598480 %U http://dx.doi.org/10.1145/2598394.2598480 %P 127-128 %0 Journal Article %T Krzysztof Krawiec: Behavioral program synthesis with genetic programming %A Azad, Raja Muhammad Atif %J Genetic Programming and Evolvable Machines %D 2017 %8 mar %V 18 %N 1 %@ 1389-2576 %F Azad:2017:GPEM %O Book review %X Review of \citeKrawiecBPS2016 %K genetic algorithms, genetic programming, program synthesis, machine learning %9 journal article %R 10.1007/s10710-016-9283-7 %U https://rdcu.be/dR8eg %U http://dx.doi.org/10.1007/s10710-016-9283-7 %P 111-113 %0 Book Section %T Comparing Methods to Creating Constants in Grammatical Evolution %A Azad, R. Muhammad Atif %A Ryan, Conor %E Ryan, Conor %E O’Neill, Michael %E Collins, J. J. %B Handbook of Grammatical Evolution %D 2018 %I Springer %F Azad:2018:hbge %X This chapter evaluates the performance of various methods to constant creation in Grammatical Evolution (GE), and validates the results by comparing against those from a reasonably standard Genetic Programming (GP) setup. Specifically, the chapter compares a standard GE method to constant creation termed digit concatenation with what this chapter calls compact methods to constant creation. Constant creation in GE is an important issue due to the disruptive nature of ripple crossover, which can radically remap multiple terminals in an individual, and we investigate if more compact methods, which are more similar to the GP style of constant creation (Ephemeral Random Constants (ERCs), perform better. The results are surprising. Against common wisdom, a standard GE approach of digit concatenation does not produce individuals that are any larger than those from methods which are designed to use less genetic material. In fact, while GP characteristically evolves increasingly larger individuals, GE (after an initial growth or drop in sizes) tends to keep individual sizes stable despite no explicit mechanisms to control size growth. Furthermore, various GE setups perform acceptably well on unseen test data and typically outperform GP. Overall, these results encourage a belief that standard GE methods to symbolic regression are relatively resistant to pathogenic evolutionary tendencies of code bloat and overfitting. %K genetic algorithms, genetic programming, Grammatical Evolution %R 10.1007/978-3-319-78717-6_10 %U http://dx.doi.org/10.1007/978-3-319-78717-6_10 %P 245-262 %0 Conference Proceedings %T Dynamic Systems Identification: A Comparitive Study %A Azam, Farooq %A VanLandingham, H. F. %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 azam:1998:dsi:cs %K genetic algorithms, genetic programming %P 2-5 %0 Conference Proceedings %T Dynamic Systems Identification using Genetic Programming %A Azam, Farooq %A VanLandingham, H. F. %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 azam:1998:dsiGP %K genetic algorithms, genetic programming %P 6 %0 Journal Article %T Genetic programming to predict ski-jump bucket spill-way scour %A Azamathulla, H. Md %A Ab. Ghani, A. %A Zakaria, N. A. %A Lai, S. H. %A Chang, C. K. %A Leow, C. S. %A Abuhasan, Z. %J Journal of Hydrodynamics, Ser. B %D 2008 %8 aug %V 20 %N 4 %@ 1001-6058 %F MDAZAMATHULLA2008477 %X Researchers in the past had noticed that application of Artificial Neural Networks (ANN) in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming (GP) has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model. %K genetic algorithms, genetic programming, neural networks, spillway scour, ski-jump bucket %9 journal article %R 10.1016/S1001-6058(08)60083-9 %U http://www.sciencedirect.com/science/article/B8CX5-4TCY8GV-B/2/f3004ab0cd7ed153a22b7f5d637afc89 %U http://dx.doi.org/10.1016/S1001-6058(08)60083-9 %P 477-484 %0 Journal Article %T Genetic Programming to Predict Bridge Pier Scour %A Azamathulla, H. Md. %A Ab Ghani, Aminuddin %A Zakaria, Nor Azazi %A Guven, Aytac %J Journal of Hydraulic Engineering %D 2010 %V 136 %N 3 %F Azamathulla:2010:JHE %X Bridge pier scouring is a significant problem for the safety of bridges. Extensive laboratory and field studies have been conducted examining the effect of relevant variables. This note presents an alternative to the conventional regression-based equations (HEC-18 and regression equation developed by authors), in the form of artificial neural networks (ANNs) and genetic programming (GP). 398 data sets of field measurements were collected from published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The performance of GP was found more effective when compared to regression equations and ANNs in predicting the scour depth of bridge piers. %K genetic algorithms, genetic programming, Local scour, Bridge pier, Artificial neural networks, Radial basis function %9 journal article %R 10.1061/(ASCE)HY.1943-7900.0000133 %U http://dx.doi.org/10.1061/(ASCE)HY.1943-7900.0000133 %P 165-169 %0 Journal Article %T Linear genetic programming to scour below submerged pipeline %A Azamathulla, H. Md. %A Guven, Aytac %A Demir, Yusuf Kagan %J Ocean Engineering %D 2011 %8 jun %V 38 %N 8-9 %@ 0029-8018 %F Azamathulla2011 %X Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents Linear Genetic Programming (LGP), which is an extension to GP, as an alternative tool in the prediction of scour depth below a pipeline. The data sets of laboratory measurements were collected from published literature and were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at submerged pipeline. %K genetic algorithms, genetic programming, Local scour, Neuro-fuzzy, Pipelines %9 journal article %R 10.1016/j.oceaneng.2011.03.005 %U http://www.sciencedirect.com/science/article/B6V4F-52M3TGW-1/2/279184e6554e6b6977d8b9f0180c9f53 %U http://dx.doi.org/10.1016/j.oceaneng.2011.03.005 %P 995-1000 %0 Journal Article %T Genetic Programming for Predicting Longitudinal Dispersion Coefficients in Streams %A Azamathulla, Hazi Mohammad %A Ghani, Aminuddin Ab. %J Water Resources Management %D 2011 %V 25 %N 6 %F azamathulla:2011:WRM %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s11269-010-9759-9 %U http://link.springer.com/article/10.1007/s11269-010-9759-9 %U http://dx.doi.org/10.1007/s11269-010-9759-9 %0 Journal Article %T GP approach for critical submergence of intakes in open channel flows %A Azamathulla, H. Md. %A Ahmad, Z. %J Journal of Hydroinformatics %D 2012 %8 oct %V 14 %N 4 %@ 1464-7141 %F Azamathulla:2012:JH %X This technical note presents the genetic programming (GP) approach to predict the critical submergence for horizontal intakes in open channel flow for different bottom clearances. Laboratory data from the literature for the critical submergence for a wide range of flow conditions were used for the development and testing of the proposed method. Froude number, Reynolds number, Weber number and ratio of intake velocity and channel velocity were considered dominant parameters affecting the critical submergence. The proposed GP approach produced satisfactory results compared to the existing predictors. %K genetic algorithms, genetic programming, critical submergence, intakes, open channel %9 journal article %R 10.2166/hydro.2012.089 %U http://www.iwaponline.com/jh/up/pdf/jh2012089.pdf %U http://dx.doi.org/10.2166/hydro.2012.089 %P 937-943 %0 Journal Article %T Gene-expression programming to predict scour at a bridge abutment %A Azamathulla, H. Md. %J Journal of Hydroinformatics %D 2012 %V 14 %N 2 %@ 1464-7141 %F Azamathulla:2012a:JH %X The process involved in the local scour at an abutment is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This study presents the use of gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to estimate the scour depth. The datasets of laboratory measurements were collected from the published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The proposed GEP approach gives satisfactory results compared with existing predictors and artificial neural network (ANN) modelling in predicting the scour depth at an abutment. %K genetic algorithms, genetic programming, gene expression programming, artificial neural networks, bridge abutments, local scour, radial basis function %9 journal article %R 10.2166/hydro.2011.135 %U http://www.iwaponline.com/jh/014/0324/0140324.pdf %U http://dx.doi.org/10.2166/hydro.2011.135 %P 324-331 %0 Journal Article %T Gene-expression programming for transverse mixing coefficient %A Azamathulla, H. Md. %A Ahmad, Z. %J Journal of Hydrology %D 2012 %8 apr %V 434-435 %@ 0022-1694 %F Azamathulla2012142 %X This study presents gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to predict the transverse mixing coefficient in open channel flows. Laboratory data were collected in the present study and also from the literature for the transverse mixing coefficient covering wide range of flow conditions. These data were used for the development and testing of the proposed method. A functional relation for the estimation of transverse mixing coefficient has been developed using GEP. The proposed GEP approach produced satisfactory results compared to the existing predictors for the transverse mixing coefficient. %K genetic algorithms, genetic programming, Transverse mixing, Open channel flow, gene expression programming, River systems %9 journal article %R 10.1016/j.jhydrol.2012.02.018 %U http://www.sciencedirect.com/science/article/pii/S0022169412001187 %U http://dx.doi.org/10.1016/j.jhydrol.2012.02.018 %P 142-148 %0 Journal Article %T Flow discharge prediction in compound channels using linear genetic programming %A Azamathulla, H. Md. %A Zahiri, A. %J Journal of Hydrology %D 2012 %8 June %V 454-455 %@ 0022-1694 %F Azamathulla2012203 %X Flow discharge determination in rivers is one of the key elements in mathematical modelling in the design of river engineering projects. Because of the inundation of floodplains and sudden changes in river geometry, flow resistance equations are not applicable for compound channels. Therefore, many approaches have been developed for modification of flow discharge computations. Most of these methods have satisfactory results only in laboratory flumes. Due to the ability to model complex phenomena, the artificial intelligence methods have recently been employed for wide applications in various fields of water engineering. Linear genetic programming (LGP), a branch of artificial intelligence methods, is able to optimise the model structure and its components and to derive an explicit equation based on the variables of the phenomena. In this paper, a precise dimensionless equation has been derived for prediction of flood discharge using LGP. The proposed model was developed using published data compiled for stage-discharge data sets for 394 laboratories, and field of 30 compound channels. The results indicate that the LGP model has a better performance than the existing models. %K genetic algorithms, genetic programming, Stage-discharge curve, Flooded rivers, Floodplains %9 journal article %R 10.1016/j.jhydrol.2012.05.065 %U http://www.sciencedirect.com/science/article/pii/S0022169412004684 %U http://dx.doi.org/10.1016/j.jhydrol.2012.05.065 %P 203-207 %0 Journal Article %T Gene expression programming for prediction of scour depth downstream of sills %A Azamathulla, H. Md. %J Journal of Hydrology %D 2012 %8 16 aug %V 460-461 %@ 0022-1694 %F Azamathulla:2012b:JH %X Local scour is an important issue in environmental and water resources engineering in order to prevent degradation of river bed and safe the stability of grade control structures, stilling basins, aprons, ski-jump bucket spillways, bed sills, weirs, check dams, etc. This short communication presents Gene-Expression Programming (GEP), which is an extension to Genetic Programming (GP), as an alternative approach to predict scour depth downstream of sills. Published data were compiled from the literature for the scour depth downstream of sills. The proposed GEP approach gives satisfactory results (R2=0.967 and RMSE =0.088) compared to existing predictors [Chinnarasri and Kositgittiwong, ICE Water Management, 161(5), 267-275, 2008] with R2 =0.87 and RMSE= 2.452 for relative scour depth. %K genetic algorithms, genetic programming, gene expression programming, Grade control structures, local scour, water resources engineering %9 journal article %R 10.1016/j.jhydrol.2012.06.034 %U http://www.sciencedirect.com/science/article/pii/S0022169412005197?v=s5 %U http://dx.doi.org/10.1016/j.jhydrol.2012.06.034 %P 156-159 %0 Book Section %T A Review on Application of Soft Computing Methods in Water Resources Engineering %A Azamathulla, H. Md %E Yang, Xin-She %E Gandomi, Amir Hossein %E Alavi, Amir Hossein %B Metaheuristics in Water, Geotechnical and Transport Engineering %D 2013 %I Elsevier %C Oxford %F Azamathulla:2013:MWGTE %X This chapter reviews the application of soft computing techniques, namely radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS), gene-expression programming (GEP), and linear genetic programming (LGP) in water resources engineering. The capabilities of these techniques have been illustated by applying them to the prediction of scour downstream of flip spillway/bridge pier and abutment scour/pipeline scour/culvert scour/sediment load in hydraulics, and the river stage-discharge curve in hydrology. The accuracy of the results obtained by the soft computing techniques supports their further use for the prediction of hydraulic and hydrologic variables. Availability of free and easy-to-apply software for a specified method can invite a huge number of its applications by enthusiastic investigators. %K genetic algorithms, genetic programming, gene expression programming, Water resources engineering, applied soft computing, artificial neural network, adaptive neuro-fuzzy inference system, scour, river stage %R 10.1016/B978-0-12-398296-4.00002-7 %U http://www.sciencedirect.com/science/article/pii/B9780123982964000027 %U http://dx.doi.org/10.1016/B978-0-12-398296-4.00002-7 %P 27-41 %0 Journal Article %T Gene-expression programming to predict friction factor for Southern Italian rivers %A Azamathulla, H. Md. %J Neural Computing and Applications %D 2013 %V 23 %N 5 %F journals/nca/Azamathulla13 %X This briefing article presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to predict friction factor for Southern Italian rivers. Published data were compiled for the friction for 43 gravel-bed rivers of Calabria. The proposed GEP approach produces satisfactory results (R-squared = 0.958 and RMSE = 0.079) compared with existing predictors. %K genetic algorithms, genetic programming, gene expression programming, GEP, Rivers, Friction factor, Streams, Gravel-bed %9 journal article %U http://dx.doi.org/10.1007/s00521-012-1091-2 %P 1421-1426 %0 Journal Article %T An expert system for predicting Manning’s roughness coefficient in open channels by using gene expression programming %A Azamathulla, H. Md. %A Ahmad, Zulfequar %A Ab. Ghani, Aminuddin %J Neural Computing and Applications %D 2013 %V 23 %N 5 %F journals/nca/AzamathullaAG13 %X Manning’s roughness coefficient (n) has been widely used in the estimation of flood discharges or depths of flow in natural channels. Accurate estimation of Manning’s roughness coefficient is essential for the computation of flow rate, velocity. Conventional formulae that are greatly based on empirical methods lack in providing high accuracy for the prediction of Manning’s roughness coefficient. Consequently, new and accurate techniques are still highly demanded. In this study, gene expression programming (GEP) is used to estimate the Manning roughness coefficient. The estimated value of the roughness coefficient is used in Mannings equation to compute the flow parameters in open-channel flows in order to carry out a comparison between the proposed GEP-based approach and the conventional ones. Results show that computed discharge using estimated value of roughness coefficient by GEP is in good agreement (10percent) with the experimental results compared to the conventional formulae (R-squared = 0.97 and RMSE = 0.0034 for the training data and Rsquared = 0.94 and RMSE = 0.086 for the testing data). %K genetic algorithms, genetic programming, gene expression programming, GEP %9 journal article %U http://dx.doi.org/10.1007/s00521-012-1078-z %P 1343-1349 %0 Journal Article %T Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia %A Azamathulla, H. Md. %A Rathnayake, Upaka %A Shatnawi, Ahmad %J Applied Water Science %D 2018 %V 8 %N 6 %F azamathulla:2018:AWS %K genetic algorithms, genetic programming, Gene expression programming %9 journal article %R 10.1007/s13201-018-0831-6 %U http://link.springer.com/article/10.1007/s13201-018-0831-6 %U http://dx.doi.org/10.1007/s13201-018-0831-6 %0 Conference Proceedings %T Evolutionary architecture design for approximate DCT %A Azaraien, Abbas %A Djalaei, Babak %A Salehi, Mostafa E. %S 2017 19th International Symposium on Computer Architecture and Digital Systems (CADS) %D 2017 %8 dec %F Azaraien:2017:CADS %X Discrete Cosine Transform (DCT) which has a major role in image and video compression has also a major role in power consumption. Approximate Computing let us trade precision to save power in error resilient applications such as multimedia. Therefore, DCT is a potential candidate for approximation. In this paper, we propose a method for evolutionary design of DCT architecture exploiting the inherent behaviour of DCT. Unlike the prior works on DCT approximation, which concentrated mostly on optimizing, replacing, or removing less effective building blocks of DCT, in our proposed method we use the evolutionary method to find new structures for DCT. According to the results, the evolution methods lead to architectures with less area and acceptable accuracy. %K genetic algorithms, genetic programming %R 10.1109/CADS.2017.8310731 %U http://dx.doi.org/10.1109/CADS.2017.8310731 %0 Journal Article %T Nonlinear genetic-base models for prediction of fatigue life of modified asphalt mixtures by precipitated calcium carbonate %A Azarhoosh, A. R. %A Zojaji, Z. %A Moghadas Nejad, F. %J Road Materials and Pavement Design %D 2020 %V 21 %N 3 %I Taylor & Francis %F Azarhoosh:2020:RMPD %X Fatigue cracking is the most important structural failure in flexible pavements. The results of a laboratory study evaluating the fatigue properties of mixtures containing precipitated calcium carbonate (PCC) using indirect tensile fatigue (ITF) test were investigated in this paper. The hot mix asphalt (HMA) samples were made with four PCC contents (0percent, 5percent, 10percent, and 15percent), and tested at three different testing temperatures (2degrees Celcius, 10degrees Celcius and 20degrees Celcius) and stress levels (100, 300, and 500 kPa). Due to the complex behaviour of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting the fatigue life of asphalt pavement is difficult. In this study, genetic programming (GP) is used to predict the fatigue life of HMA. Based on the results of the ITF test, PCC improved the fatigue behaviour of studied mixes at different temperatures. But, the considerable negative effect of the increase of the temperature on the fatigue life of HMA is evident. On the other hand, the results indicate The GP-based formulas are simple, straightforward, and particularly valuable for providing an analysis tool accessible to practicing engineers. %K genetic algorithms, genetic programming, fatigue life, indirect tensile fatigue test, ITF test, precipitated calcium carbonate, PCC, CaCO3 %9 journal article %R 10.1080/14680629.2018.1513372 %U http://dx.doi.org/10.1080/14680629.2018.1513372 %P 850-866 %0 Journal Article %T Performance analysis of ultrasound-assisted synthesized nano-hierarchical SAPO-34 catalyst in the methanol-to-lights-olefins process via artificial intelligence methods %A Azarhoosh, Mohammad Javad %A Halladj, Rouein %A Askari, Sima %A Aghaeinejad-Meybodi, Abbas %J Ultrasonics Sonochemistry %D 2019 %V 58 %@ 1350-4177 %F AZARHOOSH:2019:US %X The present study has focused on performance analysis of ultrasound-assisted synthesized nano-hierarchical silico-alumino-phosphate-34 (SAPO-34) catalyst during methanol-to-light-olefins (MTO) process. A classical method, i.e., multiple linear regression (MLR) and two intelligent methods, i.e., genetic programming (GP) and artificial neural networks (ANN) were used for modeling of the performance of nano-hierarchical SAPO-34 catalyst. We studied the influence of basic parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst such as crystallization time, ultrasonic irradiation time, ultrasonic intensity, amount of organic template (diethylamine (DEA) and carbon nanotube (CNT)) on its performance (methanol conversion and light olefins selectivity) in MTO process. The results revealed that the models achieved using the GP method had the highest accuracy for training and test data. Therefore, GP models were used in the following to predict the effect of main parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst. Finally, an optimal catalyst with the highest yield into light olefins was predicted using the genetic optimization algorithm. The genetic models were employed as an evaluation function in the genetic algorithm (GA). A good agreement between the outputs of GP models for the optimal catalyst and experimental results were obtained. The optimal ultrasound-assisted synthesized nano-hierarchical SAPO-34 was accompanied by light olefins selectivity of 77percent and methanol conversion of 94percent from the onset of the process after %K genetic algorithms, genetic programming, Ultrasound-assisted synthesis, Nano-hierarchical SAPO-34, MTO process, Multi-linear regression, Artificial neural network %9 journal article %R 10.1016/j.ultsonch.2019.104646 %U http://www.sciencedirect.com/science/article/pii/S1350417719305103 %U http://dx.doi.org/10.1016/j.ultsonch.2019.104646 %P 104646 %0 Journal Article %T Prediction of Marshall Mix Design Parameters in Flexible Pavements Using Genetic Programming %A Azarhoosh, Alireza %A Pouresmaeil, Salman %J Arabian Journal for Science and Engineering %D 2020 %V 45 %N 10 %F azarhoosh:2020:AJSE %X The mix design of asphalt concrete is usually accomplished in the Iranian ministry of road and transportation according to the Marshall method. Marshall mix design parameters are a function of grading and properties of aggregates, amount and type of bitumen in asphalt mixtures. Therefore, in order to determine these parameters and the optimum bitumen content, many samples with different compounds and conditions must be manufactured and tested in the laboratory, a process that requires considerable time and cost. Accordingly, the necessity of using new and advanced methods for the design and quality control of asphalt mixtures is becoming more and more evident. Therefore, in this study, a genetic programming simulation method was employed to predict the Marshall mix design parameters of asphalt mixtures. Also, multiple linear regression models were adopted as the base model to evaluate the models presented by the genetic programming method. The models proposed here predict the Marshall mix design parameters based on parameters such as the index of aggregate particle shape and texture, the amount and viscosity of the bitumen. The results demonstrated that the proposed methods are more efficient than the costly laboratory method, and genetic programming models with minimal error (identified in this study with RMSE and MAE parameters) and correlation coefficients > 0.9 can predict relatively accurate Marshall mix design parameters. %K genetic algorithms, genetic programming, Hot-mix asphalt, Marshall mix design, Index of aggregate particle shape and texture (particle index), Viscosity of bitumen, Genetic programming method %9 journal article %R 10.1007/s13369-020-04776-0 %U http://link.springer.com/article/10.1007/s13369-020-04776-0 %U http://dx.doi.org/10.1007/s13369-020-04776-0 %P 8427-8441 %0 Journal Article %T Predictive model of algal biofuel production based on experimental data %A Azari, Aryandokht %A Tavakoli, Hossein %A Barkdoll, Brian D. %A Haddad, Omid Bozorg %J Algal Research %D 2020 %V 47 %@ 2211-9264 %F AZARI:2020:AR %X Algal biofuels are of growing interest in the quest to reduce carbon emissions in the atmosphere but the sensitivity of the fuel production to various factors is not well understood. Therefore, the effects of temperature, light intensity, carbon concentration, aeration rate, pH, and time on the CO2 biofixation rate of Chlorella vulgaris (ISC-23) were investigated using experimental, and Genetic Programming (GP) modeling techniques. The impacts of applying the cement industrial flue gas as a source of carbon, useful for the growth of microalgae, were also studied. Chlorella vulgaris (ISC-23) was cultivated in a laboratory photobioreactor on a BG-11 medium. The developed GP model was used to optimize the CO2 biofixation based on the studied variables and produce a predictive equation. By using statistical measurements and error analysis, the predictive equation was shown to agree with the experimentally obtained values. It was found that the optimum conditions occur at 26o C, and 3200 lx of light, in the existence of CO2. Applying 6percent CO2 as the input with the aeration rate of 0.5 vvm in 11 days was also reported as the optimum scenario for algae production with keeping the pH close to 7.5. The results indicate that the predictions determined with the proposed equation can be of practical worth for researchers and experts in the biofuel industry %K genetic algorithms, genetic programming, Bioenergy, Algae, Optimization, CO biofixation rate, Photobioreactors %9 journal article %R 10.1016/j.algal.2020.101843 %U http://www.sciencedirect.com/science/article/pii/S2211926419309087 %U http://dx.doi.org/10.1016/j.algal.2020.101843 %P 101843 %0 Conference Proceedings %T Genetic Programming for Preprocessing Tandem Mass Spectra to Improve the Reliability of Peptide Identification %A Azari, Samaneh %A Zhang, Mengjie %A Xue, Bing %A Peng, Lifeng %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 Azari:2018:CEC %X Tandem mass spectrometry (MS/MS) is currently the most commonly used technology in proteomics for identifying proteins in complex biological samples. Mass spectrometers can produce a large number of MS/MS spectra each of which has hundreds of peaks. These peaks normally contain background noise, therefore a preprocessing step to filter the noise peaks can improve the accuracy and reliability of peptide identification. This paper proposes to preprocess the data by classifying peaks as noise peaks or signal peaks, i.e., a highly-imbalanced binary classification task, and uses genetic programming (GP) to address this task. The expectation is to increase the peptide identification reliability. Meanwhile, six different types of classification algorithms in addition to GP are used on various imbalance ratios and evaluated in terms of the average accuracy and recall. The GP method appears to be the best in the retention of more signal peaks as examined on a benchmark dataset containing 1, 674 MS/MS spectra. To further evaluate the effectiveness of the GP method, the preprocessed spectral data is submitted to a benchmark de novo sequencing software, PEAKS, to identify the peptides. The results show that the proposed method improves the reliability of peptide identification compared to the original un-preprocessed data and the intensity-based thresholding methods. %K genetic algorithms, genetic programming %R 10.1109/CEC.2018.8477810 %U http://dx.doi.org/10.1109/CEC.2018.8477810 %0 Conference Proceedings %T A Decomposition Based Multi-objective Genetic Programming Algorithm for Classification of Highly Imbalanced Tandem Mass Spectrometry %A Azari, Samaneh %A Xue, Bing %A Zhang, Mengjie %A Peng, Lifeng %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/AzariXZP19 %K genetic algorithms, genetic programming %R 10.1007/978-3-030-41299-9_35 %U https://doi.org/10.1007/978-3-030-41299-9_35 %U http://dx.doi.org/10.1007/978-3-030-41299-9_35 %P 449-463 %0 Conference Proceedings %T Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-Based Scoring Function for Re-ranking Peptide-Spectrum Matches %A Azari, Samaneh %A Xue, Bing %A Zhang, Mengjie %A Peng, Lifeng %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 III %S Lecture Notes in Computer Science %D 2019 %V 11672 %I Springer %F DBLP:conf/pricai/Azari0ZP19 %K genetic algorithms, genetic programming %R 10.1007/978-3-030-29894-4_38 %U https://doi.org/10.1007/978-3-030-29894-4_38 %U http://dx.doi.org/10.1007/978-3-030-29894-4_38 %P 474-487 %0 Conference Proceedings %T Learning to Rank Peptide-Spectrum Matches Using Genetic Programming %A Azari, Samaneh %A Xue, Bing %A Zhang, Mengjie %A Peng, Lifeng %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 Azari:2019:CEC %X The analysis of tandem mass spectrometry (MS/MS) proteomics data relies on automated methods that assign peptides to observed MS/MS spectra. Typically these methods return a list of candidate peptide-spectrum matches (PSMs), ranked according to a scoring function. Normally the highest-scoring candidate peptide is considered as the best match for each spectrum. However, these best matches do not necessary always indicate the true matches. Identifying a full-length correct peptide by peptide identification tools is crucial, and we do not want to assign a spectrum to the peptide which is not expressed in the given biological sample. Therefore in this paper, we present a new approach to improving the previous ordering/ranking of the PSMs, aiming at bringing the correct PSM for spectrum ahead of all the incorrect ones for the same spectrum. We develop a new method called GP-PSM-rank, which employs genetic programming (GP) to learn a ranking function by combining different feature functions %K genetic algorithms, genetic programming, ranking function, peptide-spectrum match, tandem mass spectrometry %R 10.1109/CEC.2019.8790049 %U http://dx.doi.org/10.1109/CEC.2019.8790049 %P 3244-3251 %0 Journal Article %T Preprocessing Tandem Mass Spectra Using Genetic Programming for Peptide Identification %A Azari, Samaneh %A Xue, Bing %A Zhang, Mengjie %A Peng, Lifeng %J Journal of The American Society for Mass Spectrometry %D 2019 %V 30 %N 7 %F azari:2019:JASMS %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s13361-019-02196-5 %U http://link.springer.com/article/10.1007/s13361-019-02196-5 %U http://dx.doi.org/10.1007/s13361-019-02196-5 %0 Thesis %T Evolutionary Algorithms for Improving De Novo Peptide Sequencing %A Azari, Samaneh %D 2020 %C New Zealand %C Victoria University of Wellington %F Azari:thesis %X De novo peptide sequencing algorithms have been developed for peptide identification in proteomics from tandem mass spectra (MS/MS), which can be used to identify and discover novel peptides and proteins that do not have a database available. Despite improvements in MS instrumentation and de novo sequencing methods, a significant number of CID MS/MS spectra still remain unassigned with the current algorithms, often leading to low confidence of peptide assignments to the spectra. Moreover, current algorithms often fail to construct the completely matched sequences, and produce partial matches. Therefore, identification of full-length peptides remains challenging. Another major challenge is the existence of noise in MS/MS spectra which makes the data highly imbalanced. Also missing peaks, caused by incomplete MS fragmentation makes it more difficult to infer a full-length peptide sequence. In addition, the large search space of all possible amino acid sequences for each spectrum leads to a high false discovery rate. This thesis focuses on improving the performance of current methods by developing new algorithms corresponding to three steps of preprocessing, sequence optimisation and post-processing using machine learning for more comprehensive interrogation of MS/MS datasets. From the machine learning point of view, the three steps can be addressed by solving different tasks such as classification, optimisation, and symbolic regression. Since Evolutionary Algorithms (EAs), as effective global search techniques, have shown promising results in solving these problems, this thesis investigates the capability of EAs in improving the de novo peptide sequencing. In the preprocessing step, this thesis proposes an effective GP-based method for classification of signal and noise peaks in highly imbalanced MS/MS spectra with the purpose of having a positive influence on the reliability of the peptide identification. The results show that the proposed algorithm is the most stable classification method across various noise ratios, outperforming six other benchmark classification algorithms. The experimental results show a significant improvement in high confidence peptide assignments to MS/MS spectra when the data is preprocessed by the proposed GP method. Moreover, the first multi-objective GP approach for classification of peaks in MS/MS data, aiming at maximising the accuracy of the minority class (signal peaks) and the accuracy of the majority class (noise peaks) is also proposed in this thesis. The results show that the multi-objective GP method outperforms the single objective GP algorithm and a popular multi-objective approach in terms of retaining more signal peaks and removing more noise peaks. The multi-objective GP approach significantly improved the reliability of peptide identification. This thesis proposes a GA-based method to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full-length sequences. The proposed GA method benefits the GA capability of searching a large search space of potential amino acid sequences to find the most likely full-length sequence. The experimental results show that the proposed method outperforms the most commonly used de novo sequencing method at both amino acid level and peptide level. This thesis also proposes a novel method for re-scoring and re-ranking the peptide spectrum matches (PSMs) from the result of de novo peptide sequencing, aiming at minimising the false discovery rate as a post-processing approach. The proposed GP method evolves the computer programs to perform regression and classification simultaneously in order to generate an effective scoring function for finding the correct PSMs from many incorrect ones. The results show that the new GP-based PSM scoring function significantly improves the identification of full-length peptides when it is used to post-process the de novo sequencing results. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://hdl.handle.net/10063/8898 %0 Conference Proceedings %T Evolving Artificial General Intelligence for Video Controllers %A Azaria, Itay %A Elyasaf, Achiya %A Sipper, Moshe %Y Riolo, Rick %Y Worzel, Bill %Y Goldman, Brian %Y Tozier, Bill %S Genetic Programming Theory and Practice XIV %D 2016 %8 19 21 may %I Springer %C Ann Arbor, USA %F Azaria:2016:GPTP %X The General Video Game Playing Competition (GVGAI) defines a challenge of creating controllers for general video game playing, a testbed, as it were-for examining the issue of artificial general intelligence. We develop herein a game controller that mimics human-learning behaviour, focusing on the ability to generalize from experience and diminish learning time as new games present themselves. We use genetic programming to evolve hyper heuristic-based general players, our results showing the effectiveness of evolution in meeting the generality challenge. %K genetic algorithms, genetic programming, Hyper-Heuristic %R 10.1007/978-3-319-97088-2_4 %U https://www.cs.bgu.ac.il/~sipper/publications/Evolving%20Artificial%20General%20Intelligence.pdf %U http://dx.doi.org/10.1007/978-3-319-97088-2_4 %P 53-63 %0 Conference Proceedings %T GP-Gammon: Using Genetic Programming to Evolve Backgammon Players %A Azaria, Yaniv %A Sipper, Moshe %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:AzariaS05 %X We apply genetic programming to the evolution of strategies for playing the game of backgammon. Pitted in a 1000-game tournament against a standard benchmark player—Pubeval—our best evolved program wins 58% of the games, the highest verifiable result to date. Moreover, several other evolved programs attain win percentages not far behind the champion, evidencing the repeatability of our approach. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-31989-4_12 %U http://dx.doi.org/10.1007/978-3-540-31989-4_12 %P 132-142 %0 Journal Article %T GP-Gammon: Genetically Programming Backgammon Players %A Azaria, Yaniv %A Sipper, Moshe %J Genetic Programming and Evolvable Machines %D 2005 %8 sep %V 6 %N 3 %@ 1389-2576 %F azaria:2005:GPEM %O Published online: 12 August 2005 %X We apply genetic programming to the evolution of strategies for playing the game of backgammon. We explore two different strategies of learning: using a fixed external opponent as teacher, and letting the individuals play against each other. We conclude that the second approach is better and leads to excellent results: Pitted in a 1000-game tournament against a standard benchmark player Pubeval our best evolved program wins 62.4 percent of the games, the highest result to date. Moreover, several other evolved programs attain win percentages not far behind the champion, evidencing the repeatability of our approach. %K genetic algorithms, genetic programming, backgammon, self-learning, STGP, demes, coevolution %9 journal article %R 10.1007/s10710-005-2990-0 %U http://www.cs.bgu.ac.il/~sipper/papabs/gpgammon.pdf %U http://dx.doi.org/10.1007/s10710-005-2990-0 %P 283-300 %0 Conference Proceedings %T Genetic Programming in Geostatistical Reservoir Geophysics %A Azevedo, Leonardo %A Nunes, Ruben %A Soares, Amilcar %S 2016 International Conference on Computational Science and Computational Intelligence (CSCI) %D 2016 %8 dec %F Azevedo:2016:CSCI %X Hydrocarbon reservoir modelling and characterisation is a critical step for the success of oil and/or gas exploration and production projects. Reservoir modelling is frequently based on the results provided by geostatistical seismic inversion techniques. These procedures are computationally heavy and expensive even for small-to-medium size fields due to the use of stochastic sequential simulation as the model perturbation technique. This work proposes the use of machine learning techniques, specifically symbolic regression, a category from the group of genetic programming methodologies, as a proxy to surpass the need of stochastic sequential simulation without compromising the advantage of using these simulation methodologies, for example uncertainty assessment of the property of interest. The proposed methodology is illustrated with an application example to a real case study and the results compared with the traditional geostatistical seismic inversion approach. %K genetic algorithms, genetic programming, Computational modelling, Correlation coefficient, Data models, Iterative methods, Mathematical model, Reflection, Stochastic processes, genetic programming geostatistical seismic inversion seismic reservoir characterisation %R 10.1109/CSCI.2016.0228 %U http://dx.doi.org/10.1109/CSCI.2016.0228 %P 1208-1213 %0 Conference Proceedings %T Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models %A Azimlu, Fateme %A Rahnamayan, Shahryar %A Makrehchi, Masoud %A Kalra, Naveen %S 2019 14th International Conference on Computer Science Education (ICCSE) %D 2019 %8 aug %F Azimlu:2019:ICCSE %X Prediction is one of the most important tasks in the machine learning field. Data scientists employ various learning methods to find the most appropriate and accurate model for each family of applications or dataset. This study compares the symbolic regression using genetic programming (GP), with conventional machine learning techniques. In cases it is required to model an unknown, poorly understood, and/or complicated system. In these cases, we use genetic programming to generate a symbolic model without using any pre-known model. In this paper, the GP is studied as a tool for prediction in different types of datasets and conducted experiments to verify the superiority of GP over conventional models in certain conditions and datasets. The accuracy of GP-based regression results are compared with other machine learning techniques, and are found to be more accurate in certain conditions. %K genetic algorithms, genetic programming %R 10.1109/ICCSE.2019.8845381 %U http://dx.doi.org/10.1109/ICCSE.2019.8845381 %P 785-791 %0 Conference Proceedings %T House Price Prediction Using Clustering and Genetic Programming along with Conducting a Comparative Study %A Azimlu, Fateme %A Rahnamayan, Shahryar %A Makrehchi, Masoud %Y Chiba, Kazuhisa %Y Oyama, Akira %Y Palar, Pramudita Satria %Y Shimoyama, Koji %Y Singh, Hemant K. %S Real-World Applications of Continuous and Mixed-integer Optimization %S GECCO ’21 %D 2021 %8 jul 10 14 %I Association for Computing Machinery %C internet %F Azimlu:2021:RWACMO %X One of the most important tasks in machine learning is prediction. Data scientists use different regression methods to find the most appropriate and accurate model for each type of datasets. This study proposes a method to improve accuracy in regression and prediction. In common methods, different models are applied to the whole data to find the best model with higher accuracy. In our proposed approach, first, we cluster data using different methods such as K-means, DBSCAN, and agglomerative hierarchical clustering algorithms. Then, for each clustering method and for each generated cluster we apply various regression models including linear and polynomial regressions, SVR, neural network, and symbolic regression in order to find the most accurate model and study the genetic programming potential in improving the prediction accuracy. This model is a combination of clustering and regression. After clustering, the number of samples in each created cluster, compared to the number of samples in the whole dataset is reduced, and consequently by decreasing the number of samples in each group, we lose accuracy. On the other hand, specifying data and setting similar samples in one group enhances the accuracy and decreases the computational cost. As a case study, we used real estate data with 20 features to improve house price estimation; however, this approach is applicable to other large datasets. %K genetic algorithms, genetic programming, symbolic Regression, Regression, Machine Learning, Clustering, Multi-level-model, House Price Prediction %R 10.1145/3449726.3463141 %U http://dx.doi.org/10.1145/3449726.3463141 %P 1809-1816 %0 Conference Proceedings %T Search-Based SQL Injection Attacks Testing using Genetic Programming %A Aziz, Benjamin %A Bader, Mohamed %A Hippolyte, Cerana %Y Heywood, Malcolm I. %Y McDermott, James %Y Castelli, Mauro %Y Costa, Ernesto %Y Sim, Kevin %S EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming %S LNCS %D 2016 %8 30 mar –1 apr %V 9594 %I Springer Verlag %C Porto, Portugal %F Aziz:2016:EuroGP %X Software testing is a key phase of many development methodologies as it provides a natural opportunity for integrating security early in the software development lifecycle. However despite the known importance of software testing, this phase is often overlooked as it is quite difficult and labour-intensive to obtain test datasets to effectively test an application. This lack of adequate automatic software testing renders software applications vulnerable to malicious attacks after they are deployed as detected software vulnerabilities start having an impact during the production phase. Among such attacks are SQL injection attacks. Exploitation of SQL injection vulnerabilities by malicious programs could result in severe consequences such as breaches of confidentiality and false authentication. We present in this paper a search-based software testing technique to detect SQL injection vulnerabilities in software applications. This approach uses genetic programming as a means of generating our test datasets, which are then used to test applications for SQL injection-based vulnerabilities. %K genetic algorithms, genetic programming, Search-Based Testing, SBFT, SQL Injections, Database Vulnerabilities %R 10.1007/978-3-319-30668-1_12 %U https://pure.port.ac.uk/ws/portalfiles/portal/3598717/Aziz.pdf %U http://dx.doi.org/10.1007/978-3-319-30668-1_12 %P 183-198 %0 Journal Article %T A two-objective memetic approach for the node localization problem in wireless sensor networks %A Aziz, Mahdi %A Tayarani-N, Mohammad-H %A Meybodi, Mohammad R. %J Genetic Programming and Evolvable Machines %D 2016 %8 dec %V 17 %N 4 %@ 1389-2576 %F Aziz:2016:GPEM %X Wireless sensor networks (WSNs) are emerging as an efficient way to sense the physical phenomenon without the need of wired links and spending huge money on sensor devices. In WSNs, finding the accurate locations of sensor nodes is essential since the location inaccuracy makes the collected data fruitless. In this paper, we propose a two-objective memetic approach called the Three Phase Memetic Approach that finds the locations of sensor nodes with high accuracy. The proposed algorithm is composed of three operators (phases). The first phase, which is a combination of three node-estimating approaches, is used to provide good starting locations for sensor nodes. The second and third phases are then used for mitigating the localization errors in the first operator. To test the proposed algorithm, we compare it with the simulated annealing-based localization algorithm, genetic algorithm-based localization, Particle Swarm Optimization-based Localization algorithm, trilateration-based simulated annealing algorithm, imperialist competitive algorithm and Pareto Archived Evolution Strategy on ten randomly created and four specific network topologies with four different values of transmission ranges. The comparisons indicate that the proposed algorithm outperforms the other algorithms in terms of the coordinate estimations of sensor nodes. %9 journal article %R 10.1007/s10710-016-9274-8 %U http://dx.doi.org/10.1007/s10710-016-9274-8 %P 321-358 %0 Conference Proceedings %T Generate knowledge base from very high spatial resolution satellite image using robust classification rules and genetic programming %A Azmi, Rida %A Amar, Hicham %A Norelyaqine, Abderrahim %S 2020 IEEE International conference of Moroccan Geomatics (Morgeo) %D 2020 %8 may %F Azmi:2020:Morgeo %X Object based image analysis techniques give accurate results when a good knowledge base is extracted from remote sensing imagery. Data mining algorithms and especially evolutionary process can extract useful knowledge that can be used in different fields. In this paper, object-oriented classification was used, more particularly object-based image analysis approach (OOIA) to classify a large feature space composed of a very high spatial resolution satellite image (VHR). Genetic programming (GP) concept was applied to extract classification rules with an induction form. Comparison of the performance of three GP algorithms (Bojarczuc_GP, Falco_GP and Tan_GP) was mad using JCLEC Framework. Results showed two main conclusions. 1) testing and evaluation of the generated rules allow us to discover that GP algorithms can classify and extract useful knowledge from VHR satellite data. 2) evaluation of the performance of the three Genetic programming models demonstrates that the Bojarczuk model is efficient on accuracy classification than the Falco and Tan models. %K genetic algorithms, genetic programming, Remote sensing, High resolution, Data Mining %R 10.1109/Morgeo49228.2020.9121914 %U http://dx.doi.org/10.1109/Morgeo49228.2020.9121914 %0 Conference Proceedings %T A Vectorial Approach to Genetic Programming %A Azzali, Irene %A Vanneschi, Leonardo %A Silva, Sara %A Bakurov, Illya %A Giacobini, Mario %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 Azzali:2019:EuroGP %X Among the various typologies of problems to which Genetic Programming (GP) has been applied since its origins, symbolic regression is one of the most popular. A common situation consists in the prediction of a target time series based on scalar features and other time series variables collected from multiple subjects. To manage this problem with GP data needs a panel representation where each observation corresponds to a collection on a subject at a precise time instant. However, representing data in this form may imply a loss of information: for instance, the algorithm may not be able to recognize observations belonging to the same subject and their recording order. To maintain the source of knowledge supplied by ordered sequences as time series, we propose a new approach to GP that keeps instances of the same observation together in a vector, introducing vectorial variables as terminals. This new representation allows aggregate functions in the primitive GP set, included with the purpose of describing the behaviour of vectorial variables. In this work, we perform a comparative analysis of vectorial GP (VE-GP) against standard GP (ST-GP). Experiments are conducted on different benchmark problems to highlight the advantages of this new approach. %K genetic algorithms, genetic programming, Vector-based representation, Panel Data regression: Poster %R 10.1007/978-3-030-16670-0_14 %U https://hdl.handle.net/2318/1725688 %U http://dx.doi.org/10.1007/978-3-030-16670-0_14 %P 213-227 %0 Journal Article %T Towards the use of genetic programming in the ecological modelling of mosquito population dynamics %A Azzali, Irene %A Vanneschi, Leonardo %A Mosca, Andrea %A Bertolotti, Luigi %A Giacobini, Mario %J Genetic Programming and Evolvable Machines %D 2020 %8 dec %V 21 %N 4 %@ 1389-2576 %F Azzali:GPEM %X Predictive algorithms are powerful tools to support infection surveillance plans based on the monitoring of vector abundance. In this article, we explore the use of genetic programming (GP) to build a predictive model of mosquito abundance based on environmental and climatic variables. We claim, in fact, that the heterogeneity and complexity of this kind of dataset demands algorithms capable of discovering complex relationships among variables. For this reason, we benchmarked GP performance with state of the art machine learning predictive algorithms. In order to provide a real exploitable model of mosquito abundance, we trained GP and the other algorithms on mosquito collections from 2002 to 2005 and we tested the predictive ability in 2006 collections. Results reveal that, among the studied methods, GP has the best performance in terms of accuracy and generalization ability. Moreover, the intrinsic feature selection and readability of the solution provided by GP offer the possibility of a biological interpretation of the model which highlights known or new behaviours responsible for mosquito abundance. GP, therefore, reveals to be a promising tool in the field of ecological modelling, opening the way to the use of a vector based GP approach (VE-GP) which may be more appropriate and beneficial for the problems in analysis. %K genetic algorithms, genetic programming, West Nile Virus, WNV, Ecological modelling, Machine learning, Regression %9 journal article %R 10.1007/s10710-019-09374-0 %U https://iris.unito.it/retrieve/handle/2318/1722575/562795/Manuscript.pdf %U http://dx.doi.org/10.1007/s10710-019-09374-0 %P 629-642 %0 Conference Proceedings %T Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming %A Azzali, Irene %A Vanneschi, Leonardo %A Giacobini, Mario %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 Azzali:2020:EuroGP %X Vectorial Genetic Programming (VE_GP) is a new GP approach for panel data forecasting. Besides permitting the use of vectors as terminal symbols to represent time series and including aggregation functions to extract time series features, it introduces the possibility of evolving the window of aggregation. The local aggregation of data allows the identification of meaningful patterns overcoming the drawback of considering always the previous history of a series of data. In this work, we investigate the use of geometric semantic operators (GSOs) in VE_GP, comparing its performance with traditional GP with GSOs. Experiments are conducted on two real panel data forecasting problems, one allowing the aggregation on moving windows, one not. Results show that classical VE_GP is the best approach in both cases in terms of predictive accuracy, suggesting that GSOs are not able to evolve efficiently individuals when time series are involved. We discuss the possible reasons of this behaviour, to understand how we could design valuable GSOs for time series in the future. %K genetic algorithms, genetic programming, Vector-based genetic programming, Time series, Sliding windows, Geometric semantic operators %R 10.1007/978-3-030-44094-7_4 %U http://dx.doi.org/10.1007/978-3-030-44094-7_4 %P 52-67 %0 Journal Article %T Towards the use of vector based GP to predict physiological time series %A Azzali, Irene %A Vanneschi, Leonardo %A Bakurov, Illya %A Silva, Sara %A Ivaldi, Marco %A Giacobini, Mario %J Applied Soft Computing %D 2020 %V 89 %@ 1568-4946 %F AZZALI:2020:ASC %X Prediction of physiological time series is frequently approached by means of machine learning (ML) algorithms. However, most ML techniques are not able to directly manage time series, thus they do not exploit all the useful information such as patterns, peaks and regularities provided by the time dimension. Besides advanced ML methods such as recurrent neural network that preserve the ordered nature of time series, a recently developed approach of genetic programming, VE-GP, looks promising on the problem in analysis. VE-GP allows time series as terminals in the form of a vector, including new strategies to exploit this representation. In this paper we compare different ML techniques on the real problem of predicting ventilation flow from physiological variables with the aim of highlighting the potential of VE-GP. Experimental results show the advantage of applying this technique in the problem and we ascribe the good performances to the ability of properly catching meaningful information from time series %K genetic algorithms, genetic programming, Ventilation, Physiological data, Machine learning, Time series %9 journal article %R 10.1016/j.asoc.2020.106097 %U http://www.sciencedirect.com/science/article/pii/S1568494620300375 %U http://dx.doi.org/10.1016/j.asoc.2020.106097 %P 106097 %0 Conference Proceedings %T Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis %A Azzali, Irene %A Cilia, Nicole Dalia %A De Stefano, Claudio %A Fontanella, Francesco %A Giacobini, Mario %A Vanneschi, Leonardo %Y Laredo, Juan Luis Jimenez %Y Hidalgo, J. Ignacio %Y Babaagba, Kehinde Oluwatoyin %S 25th International Conference, EvoApplications 2022 %S LNCS %D 2022 %8 20 22 apr %V 13224 %I Springer %C Madrid %F Azzali:2022:evoapplications %X Alzheimer Disease (AD) is a neurodegenerative disease which causes a continuous cognitive decline. This decline has a strong impact on daily life of the people affected and on that of their relatives. Unfortunately, to date there is no cure for this disease. However, its early diagnosis helps to better manage the course of the disease with the treatments currently available. In recent years, AI researchers have become increasingly interested in developing tools for early diagnosis of AD based on handwriting analysis. In most cases, they use a feature engineering approach: domain knowledge by clinicians is used to define the set of features to extract from the raw data. In this paper, we present a novel approach based on vectorial genetic programming (VE_GP) to recognize the handwriting of AD patients. VE_GP is a recently defined method that enhances Genetic Programming (GP) and is able to directly manage time series in such a way to automatically extract informative features, without any need of human intervention. We applied VE_GP to handwriting data in the form of time series consisting of spatial coordinates and pressure. These time series represent pen movements collected from people while performing handwriting tasks. The presented experimental results indicate that the proposed approach is effective for this type of application. Furthermore, VE_GP is also able to generate rather small and simple models, that can be read and possibly interpreted. These models are reported and discussed in the Last part of the paper. %K genetic algorithms, genetic programming, Alzheimer disease, Artificial intelligence, Handwriting analysis, Vectorial genetic programming %R 10.1007/978-3-031-02462-7_33 %U http://dx.doi.org/10.1007/978-3-031-02462-7_33 %P 517-530 %0 Journal Article %T Automatic feature extraction with Vectorial Genetic Programming for Alzheimer’s Disease prediction through handwriting analysis %A Azzali, Irene %A Cilia, Nicole D. %A De Stefano, Claudio %A Fontanella, Francesco %A Giacobini, Mario %A Vanneschi, Leonardo %J Swarm and Evolutionary Computation %D 2024 %V 87 %@ 2210-6502 %F AZZALI:2024:swevo %X Alzheimer’s Disease (AD) is an incurable neurodegenerative disease that strongly impacts the lives of the people affected. Even if, to date, there is no cure for this disease, its early diagnosis helps to manage the course of the disease better with the treatments currently available. Even more importantly, an early diagnosis will also be necessary for the new treatments available in the future. Recently, machine learning (ML) based tools have demonstrated their effectiveness in recognizing people’s handwriting in the early stages of AD. In most cases, they use features defined by using the domain knowledge provided by clinicians. In this paper, we present a novel approach based on vectorial genetic programming (VE_GP) to recognize the handwriting of AD patients. VE_GP is an enhanced version of GP that can manage time series directly. We applied VE_GP to data collected using an experimental protocol, which was defined to collect handwriting data to support the development of ML tools for the early diagnosis of AD based on handwriting analysis. The experimental results confirmed the effectiveness of the proposed approach in terms of classification performance, size, and simplicity %K genetic algorithms, genetic programming, Vectorial Genetic Programming, Alzheimer’s Disease, Machine learning, Healthcare applications %9 journal article %R 10.1016/j.swevo.2024.101571 %U https://www.sciencedirect.com/science/article/pii/S2210650224001093 %U http://dx.doi.org/10.1016/j.swevo.2024.101571 %P 101571 %0 Conference Proceedings %T Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets %A Azzawi, Hasseeb %A Hou, Jingyu %A Alanni, Russul %A Xiang, Yong %A Abdu-Aljabar, Rana %A Azzawi, Ali %Y Cong, Gao %Y Peng, Wen-Chih %Y Zhang, Wei Emma %Y Li, Chengliang %Y Sun, Aixin %S Advanced Data Mining and Applications - 13th International Conference, ADMA 2017, Singapore, November 5-6, 2017, Proceedings %S Lecture Notes in Computer Science %D 2017 %V 10604 %I Springer %F conf/adma/AzzawiHAXAA17 %K genetic algorithms, genetic programming, gene expression programming %R 10.1007/978-3-319-69179-4_38 %U http://dx.doi.org/10.1007/978-3-319-69179-4_38 %P 541-553 %0 Journal Article %T Evolutionary ANNs: A state of the art survey %A Azzini, Antonia %A Tettamanzi, Andrea G. B. %J Intelligenza Artificiale %D 2011 %V 5 %N 1 %@ 1724-8035 %F Azzini:2011:IA %X Neuro-genetic systems have become a very important topic of study in evolutionary computation in recent years. They are models that use evolutionary algorithms to optimise neural network design. This article is a survey of the state of art of evolutionary ANN systems, with a focus on the most recent developments, presented in the literature during the last decade. The main purpose of this work is to provide an update and extension of Yao’s milestone survey, published back in 1999, by taking the most recent literature into account. %K genetic algorithms, genetic programming, ANN, Neural Networks, Classification %9 journal article %R 10.3233/IA-2011-0002 %U http://dx.doi.org/10.3233/IA-2011-0002 %P 19-35 %0 Journal Article %T Learning answer set programs with aggregates via sampling and genetic programming %A Azzolini, Damiano %J Machine Learning %D 2025 %@ 0885-6125 %F azzolini:2025:ML %X we introduce GENTIANS, a tool based on a genetic algorithm to learn answer set programs possibly with aggregates, arithmetic, and comparison operators, from examples %K genetic algorithms, genetic programming, Answer set programming, ASP, Inductive logic programming, ILP %9 journal article %R 10.1007/s10994-025-06780-7 %U https://rdcu.be/exefl %U http://dx.doi.org/10.1007/s10994-025-06780-7 %P articlenumber148 %0 Conference Proceedings %T Comparison of Conventional and Automated Machine Learning approaches for Breast Cancer Prediction %A B, Akaramuthalvi J. %A Palaniswamy, Suja %S 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) %D 2021 %8 sep %F B:2021:ICIRCA %X Breast cancer is a type of cancer in which the breast cells grow out of control. It is one of the leading cause for the high pace of death in women. Breast cancer classification is mainly done with the help of Machine Learning (ML) algorithms. In this work, we did a comparative analysis by creating a framework using ML and Auto ML algorithms (genetic programming) to accurately classify the cells in the breast as cancerous or non-cancerous. The work focused on automating and optimizing the algorithms for better prediction of cancerous cells. In Auto ML, Tree- based Pipeline Optimization Tool (TPOT), a genetic programming approach is used for finding the suitable classifiers and to automatically select the significant features and parameter values associated with the classifiers. Wisconsin Breast cancer diagnostic dataset, which comprises of digitized images taken from fine needle aspirate of breast mass has been used in this work. Evaluation based on recall, precision and accuracy have showed good results. %K genetic algorithms, genetic programming, TPOT %R 10.1109/ICIRCA51532.2021.9544863 %U http://dx.doi.org/10.1109/ICIRCA51532.2021.9544863 %P 1533-1537 %0 Journal Article %T Evolutionary Design of a Carbon Dioxide Emission Prediction Model using Genetic Programming %A Baareh, Abdel Karim %J International Journal of Advanced Computer Science and Applications %D 2018 %V 9 %N 3 %I The Science and Information (SAI) Organization %G eng %F Baareh:2018:IJACSA %X Weather pollution is considered as one of the most important, dangerous problem that affects our life and the society security from the different sides. The global warming problem affecting the atmosphere is related to the carbon dioxide emission (CO2) from the different fossil fuels along with temperature. In this paper, this phenomenon is studied to find a solution for preventing and reducing the poison CO2 gas emerged from affecting the society and reducing the smoke pollution. The developed model consists of four input attributes: the global oil, natural gas, coal, and primary energy consumption and one output the CO2 gas. The stochastic search algorithm Genetic Programming (GP) was used as an effective and robust tool in building the forecasting model. The model data for both training and testing cases were taken from the years of 1982 to 2000 and 2003 to 2010, respectively. According to the results obtained from the different evaluation criteria, it is nearly obvious that the performance of the GP in carbon gas emission estimation was very good and efficient in solving and dealing with the climate pollution problems. %K genetic algorithms, genetic programming, fossil fuels, carbon emission, forecasting %9 journal article %R 10.14569/IJACSA.2018.090341 %U http://thesai.org/Downloads/Volume9No3/Paper_41-Evolutionary_Design_of_a_Carbon_Dioxide_Emission.pdf %U http://dx.doi.org/10.14569/IJACSA.2018.090341 %P 298-303 %0 Conference Proceedings %T Search-based testing, the underlying engine of Future Internet testing %A Baars, Arthur I. %A Lakhotia, Kiran %A Vos, Tanja E. J. %A Wegener, Joachim %S Federated Conference on Computer Science and Information Systems (FedCSIS 2011) %D 2011 %8 18 21 sep %I IEEE %C Szczecin %F Baars:2011:FedCSIS %X The Future Internet will be a complex interconnection of services, applications, content and media, on which our society will become increasingly dependent. Time to market is crucial in Internet applications and hence release cycles grow ever shorter. This, coupled with the highly dynamic nature of the Future Internet will place new demands on software testing. Search-Based Testing is ideally placed to address these emerging challenges. Its techniques are highly flexible and robust to only partially observable systems. This paper presents an overview of Search-Based Testing and discusses some of the open challenges remaining to make search-based techniques applicable to the Future Internet. %K genetic algorithms, genetic programming, SBSE, Evolutionary computation, Internet, Optimisation, Search problems, Software, Testing, future Internet testing, search-based testing, software testing, time to market, evolutionary testing %U http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6078178 %P 917-923 %0 Journal Article %T Analytical closed-form model for predicting the power and efficiency of Stirling engines based on a comprehensive numerical model and the genetic programming %A Babaelahi, Mojtaba %A Sayyaadi, Hoseyn %J Energy %D 2016 %V 98 %@ 0360-5442 %F Babaelahi:2016:Energy %X High accuracy and simplicity in use are two important required features of thermal models of Stirling engines. A new numerical second-order thermal model was presented through the improvement of our previous modified-PSVL model in order to have an elevated accuracy. The modified-PSVL model was modified by considering a non-isothermal model for heater and cooler. Then, the model called as CPMS-Comprehensive Polytropic Model of Stirling engine, was used to simulate the GPU-3 Stirling engine, and the obtained results were compared with those of the previous thermal models as well as the experimental data. For the sack of the simplicity, the combination of the CPMS model and genetic programming was employed to generate analytical closed-form correlation. In this regards, a comprehensive data bank of results of the CPMS was constructed and exported to the GP tool and analytical expressions of the power, efficiency, and polytropic indexes were obtained. It was shown that the analytical correlations not only had the same accuracy as the CPMS model, but also, it can be simply used without difficulties of numerical models. The CPMS and its out coming analytical expressions, predicted the power and efficiency of the GPU-3 Stirling with +1.13percent and +0.45 (as difference), respectively. %K genetic algorithms, genetic programming, Closed-form model, Comprehensive numerical model of Stirling engines, CPMS model, Non-isothermal heat exchangers, Polytropic model %9 journal article %R 10.1016/j.energy.2016.01.031 %U http://www.sciencedirect.com/science/article/pii/S0360544216000505 %U http://dx.doi.org/10.1016/j.energy.2016.01.031 %P 324-339 %0 Journal Article %T Optimum analytical design of medical heat sink with convex parabolic fin including variable thermal conductivity and mass transfer %A Babaelahi, Mojtaba %A Eshraghi, Hamed %J Extreme Mechanics Letters %D 2017 %V 15 %@ 2352-4316 %F BABAELAHI:2017:EML %X Electronic medical devices have become more powerful in recent years. These medical devices contain arrays of electronic components, which required high-performance heat sinks to prevent from overheating and damaging. For the design of high-performance medical heat sinks, the temperature distribution should be evaluated. Thus, in this paper, the Generalized Differential Transformation Method (GDTM) is applied to the medical heat sink with a convex parabolic convective fin with variable thermal conductivity and mass transfer. In the first section of the current paper, the general heat balance equation related to the medical heat sink with convex parabolic fins is derived. Because of the fractional type of derivative, the concept of GDTM is employed to derive analytical solutions. The major aim of this study, which is exclusive for this article, is to find the closed-form analytical solution for the fractional differential equation in considered heat sink for the first time. In the next step, multiobjective optimization of the considerable fin is performed for minimum volume and maximum thermal efficiency. For evaluation of optimum design at various environmental conditions, the multiobjective optimizations are performed for a wide range of environmental conditions. In the final step, the results of multiobjective optimization in various environmental conditions are applied to the genetic programming tool and suitable analytical correlations are created for optimum geometrical design %K genetic algorithms, genetic programming, Medical heat sink, Convex parabolic, Variable thermal conductivity, Fractional, Generalizes differential transformation method (GDTM) %9 journal article %R 10.1016/j.eml.2017.06.005 %U http://www.sciencedirect.com/science/article/pii/S2352431616302826 %U http://dx.doi.org/10.1016/j.eml.2017.06.005 %P 83-90 %0 Journal Article %T Analytical design and optimization of a new hybrid solar-driven micro gas turbine/stirling engine, based on exergo-enviro-economic concept %A Babaelahi, Mojtaba. %A Jafari, Hamed. %J Sustainable Energy Technologies and Assessments %D 2020 %V 42 %@ 2213-1388 %F BABAELAHI:2020:SETA %X One of the crucial problems in the power systems is the selection of energy-efficient systems with suitable efficiency, cost, and environmental performance. Accordingly, this paper introduces a new power generation system that supplies a significant part of the required energy from solar energy and uses liquefied natural gas (LNG) fuel as an auxiliary source. To evaluation of the system, exergo-enviro-economic analysis and thermohydraulic design of are performed using Matlab code. A comparison of the governed results with the base cycle (ThermoFlex simulation) shows good improvement in exergy efficiency fuel consumption. Since the preparation of an analytical model has a practical effect on the selection of optimum configuration, an analytical model for objective functions is provided based on the exergoeconomic and environmental numerical model. For this analytical model, A large data bank from the numerical simulation results is obtained, and the artificial intelligence tool known as Genetic Programming is used for multivariate fitting. Finally, to find the optimal configuration, various optimizations (using the particle swarm optimization) have been made, and the final optimal design has been selected. The results indicated that the thermal and exergetic efficiencies in the ultimate optimum point increased about 6.252 and 8.842 percent, respectively %K genetic algorithms, genetic programming, Solar, Micro gas turbine, Exergoeconomic, Environmental, Particle swarm optimization %9 journal article %R 10.1016/j.seta.2020.100845 %U https://www.sciencedirect.com/science/article/pii/S2213138820312728 %U http://dx.doi.org/10.1016/j.seta.2020.100845 %P 100845 %0 Journal Article %T Numerical-based multi-objective optimization and regression analysis using genetic programming for novel microfabricated thermoresistive calorimetric flow sensor in precise low-velocity biomedical applications %A Babaelahi, Mojtaba %A Kazemi, Mohammad %J Journal of Thermal Analysis and Calorimetry %D 2025 %F babaelahi:2025:JTAC %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10973-025-14208-6 %U https://link.springer.com/article/10.1007/s10973-025-14208-6 %U http://dx.doi.org/10.1007/s10973-025-14208-6 %0 Journal Article %T Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming %A Babanajad, Saeed K. %A Gandomi, Amir H. %A Mohammadzadeh S., Danial %A Alavi, Amir H. %J Automation in Construction %D 2013 %8 dec %V 36 %@ 0926-5805 %F Babanajad:2013:AiC %X New numerical models are developed to predict the strength of concrete under multi-axial compression using linear genetic programming (LGP). The models are established based on a comprehensive database obtained from the literature. To verify the applicability of the derived models, they are employed to estimate the strength of parts of the test results that are not included in the modelling process. The external validation of the model is further verified using several statistical criteria. The results obtained by the proposed models are much better than those provided by several models found in the literature. The LGP-based equations are remarkably straightforward and useful for pre-design applications. %K genetic algorithms, genetic programming, Discipulus, Multiaxial compression, Compressive strength, Ultimate strength, Linear genetic programming %9 journal article %R 10.1016/j.autcon.2013.08.016 %U http://www.sciencedirect.com/science/article/pii/S0926580513001301 %U http://dx.doi.org/10.1016/j.autcon.2013.08.016 %P 136-144 %0 Book Section %T Application of Genetic Programming for Uniaxial and Multiaxial Modeling of Concrete %A Babanajad, Saeed K. %E Gandomi, Amir H. %E Alavi, Amir H. %E Ryan, Conor %B Handbook of Genetic Programming Applications %D 2015 %I Springer %F Babanajad:2015:hbgpa %X In current chapter, an overview of recently established genetic programming based techniques for strength modelling of concrete has been presented. The comprehensive uniaxial and multiaxial strengths modelling of hardened concrete have been concentrated in this chapter as one of the main area of interests in concrete modeling for structural engineers. For this engineering case the literature has been reviewed and the most applied numerical/analytical/experimental models and national building codes have been introduced. After reviewing the artificial intelligence/machine learning based models, genetic programming based models are presented, with accent on the applicability and efficiency of each model and its suitability. The advantages and weaknesses of the aforementioned models are summarized and compared with existing numerical/analytical/experimental models and national building codes, and a few illustrative examples briefly are presented. The genetic programming based techniques are remarkably straightforward and have enabled reliable, stable, and robust tools for pre-design and design applications. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-20883-1_16 %U http://dx.doi.org/10.1007/978-3-319-20883-1_16 %P 399-430 %0 Thesis %T Methods for Sensing, Analysis and Computation of Loads and Distributed Damage in Bridges %A Mamaghani, Saeed Karim Baba Najad %D 2016 %C USA %C College of Engineering, University of Illinois at Chicago %F Babanajad:thesis %X The worldwide ageing of the infrastructure and the development of new technologies in the construction industry provided a need for structural health monitoring (SHM). SHM provides a tool for owners and researchers to assess the condition of a structure and monitor its behaviour under real life conditions. Road transport and the related infrastructures are clearly an integral part of the economic, political, and social development of the western world. As an example, highway bridges as a major part of infrastructures can be greatly damaged by excessively heavy vehicles or severe environmental conditions. It is therefore, important to assure that such facilities are well maintained and function properly in order to avoid major failures or the need for costly repairs. In current thesis, it is attempted to innovate techniques in order to measure the vehicles loads affecting the bridge elements as well as damage detection methods to monitor the defects along the in-service bridge structural components. Bridge Weigh-in-Motion (BWIM) is using an existing bridge to weigh trucks while they are moving at full highway speeds. A new method of BWIM has been established in order to weigh the passing trucks relying on the shear strain measurements near the bridge abutments which differs from the flexural strain based traditional systems. The shear strain are measured using the rosettes sensors installed on the webs of bridge girders to directly measure the individual axle weights of trucks passing over the bridge abutments. Two concrete slab on steel girder bridges, and a box girder prestressed concrete with different structural types, span lengths, and different sizes were instrumented for the performance verification of the proposed BWIM system. A series of truck runs were implemented in the field to calibrate and evaluate the proposed BWIM system’s efficiency. In addition, current research formulated a reference-free distributed damage detection method in order to locate the defects that occur in structures under in-service operating conditions. The sensing method is developed on the basis of Brillouin scattering phenomena. It employs the dynamic distributed strain measurement data in order to sense the structural perturbations under in-service operations, i.e. bridges subjected to traffic loadings, or aircrafts during flights. The advantage of the method developed in this study is that it enables the structure to be monitored at any stage during its service life without the need for prior reference data. An experimental program was designed to investigate the feasibility of the proposed approach in detecting the locations of very small defects. Laboratory experiments were designed in order to simulate the effect of ambient conditions in bridges, especially in terms of realistic displacements, i.e. deflections occurring in highway bridges. In a following effort, a theoretical model was also investigated to analysis the strain transfer mechanism from the structure surface to the distributed optical fibre components in the presence of local defects. The main objective pertained to the accurate quantification of local defects sizes based on distributed monitoring of strains in large structural systems. The theoretical formulation simulated the strain distribution within the components of an optical fiber crossing over a single crack opening. The proposed model was formulated in a manner to quantify defects in the presence of structural vibration. Both linear and nonlinear mechanical characteristics of optical fibre components were also assumed in the formulation. The spatial resolution effect was further numerically implemented within the formulation in order to simulate the measurement configurations. An experimental program was designed for calibration as well as the validation of theoretical formulation. The experiments involved dynamic tests of a 15 meter long steel I beam with two fabricated defects with small opening displacements ranging between 50 and 550 microns. %K genetic algorithms, genetic programming, Bridge Weigh in Motion, Damage Detection, Discrete Sensing, Fibre Optic Sensors, Distributed Sensing %9 Ph.D. thesis %U https://dspace-prod.lib.uic.edu/bitstream/handle/10027/20220/Karimbabanajadmamaghani_Saeed.pdf %0 Journal Article %T New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach %A Babanajad, Saeed K. %A Gandomi, Amir H. %A Alavi, Amir H. %J Advances in Engineering Software %D 2017 %@ 0965-9978 %F Babanajad:2017:AES %X The complexity associated with the in-homogeneous nature of concrete suggests the necessity of conducting more in-depth behavioral analysis of this material in terms of different loading configurations. Distinctive feature of Gene Expression Programming (GEP) has been employed to derive computer-aided prediction models for the multiaxial strength of concrete under true-triaxial loading. The proposed models correlate the concrete true-triaxial strength (sigma 1) to mix design parameters and principal stresses (sigma 2, sigma 3), needless of conducting any time-consuming laboratory experiments. A comprehensive true-triaxial database is obtained from the literature to build the proposed models, subsequently implemented for the verification purposes. External validations as well as sensitivity analysis are further carried out using several statistical criteria recommended by researchers. More, they demonstrate superior performance to the other existing empirical and analytical models. The proposed design equations can readily be used for pre-design purposes or may be used as a fast check on deterministic solutions. %K genetic algorithms, genetic programming, Gene expression programming, Artificial intelligence, Triaxial, Machine learning, Computer-aided, Strength model %9 journal article %R 10.1016/j.advengsoft.2017.03.011 %U http://www.sciencedirect.com/science/article/pii/S096599781630566X %U http://dx.doi.org/10.1016/j.advengsoft.2017.03.011 %0 Conference Proceedings %T Genetic Programming Based Degree Constrained Spanning Tree Extraction %A Babar, Zaheer %A Waqas, Muhammad %A Halim, Zahid %A Islam, Muhammad Arshad %S Eighth International Conference on Digital Information Management (ICDIM 2013) %D 2013 %8 sep %F Babar:2013:ICDIM %X The problem of extracting a degree constraint spanning tree deals with extraction of a spanning tree from a given graph. Where, the degree of each node is greater than or equal to a thread hold value. Genetic Programming is an evolution based strategy appropriate for optimisation problems. In this paper we propose a genetic programming based solution to the degree constraint spanning tree extraction. The individuals of the population are represented as a tree and mutation is applied as the only genetic operator for evaluation to occur. We have further tested our proposed solution on different graph and found it to be suitable for degree constraint spanning tree extraction problem. %K genetic algorithms, genetic programming, Degree Constraint Spanning Tree, Spanning Tree, Minimum Spanning Tree, Cyclic Interchange %R 10.1109/ICDIM.2013.6693966 %U http://dx.doi.org/10.1109/ICDIM.2013.6693966 %P 241-246 %0 Conference Proceedings %T Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work Analysis and Social Network Analysis %A Baber, C. %A Stanton, N. %A Howard, D. %A Houghton, Robert J. %Y Ruiz, J. %S NATO RTO Modelling and Simulation Group Symposium %D 2009 %8 15 16 oct %N RTO-MP-MSG-069 AC/323(MSG-069)TP/297 %C Brussels, Belgium %F Baber:2009:MSG %X A significant challenge in intelligence analysis involves knowing when a social network description is complete, i.e., when sufficient connections have been found to render the network complete. In this paper, a combination of methods is used to predict covert network structures for specific missions. The intention is to support hypothesis-generation in the Social Network Analysis of covert organisations. The project employs a four phase approach to modelling social networks, working from task descriptions rather than from contacts between individual: phase one involves the collation of intelligence covering types of mission, in terms of actors and goals; phase two involves the building of task models, based on Cognitive Work Analysis, to provide both a process model of the operation and an indication of the constraints under which the operation will be performed; phase three involves the generation of alternative networks using Genetic Programming; phase four involves the analysis of the resulting networks using social network analysis. Subsequent analysis explores the resilience of the networks, in terms of their resistance to losses of agents or tasks. The project demonstrates that it is possible to define a set of structures that can be tackled using different intervention strategies, demonstrates how patterns of social network structures can be predicted on the basis of task knowledge, and how these structures can be used to guide the gathering of intelligence and to define plausible Covert Networks. %K genetic algorithms, genetic programming %U http://ftp.rta.nato.int/public//PubFullText/RTO/MP/RTO-MP-MSG-069///MP-MSG-069-15.pdf %P Paper15 %0 Conference Proceedings %T Evolutionary Algorithm Approach to Bilateral Negotiations %A Baber, Vinaysheel %A Ananthanarayanan, Rema %A Kummamuru, Krishna %Y Foster, James A. %Y Lutton, Evelyne %Y Miller, Julian %Y Ryan, Conor %Y Tettamanzi, Andrea G. B. %S Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002 %S LNCS %D 2002 %8 March 5 apr %V 2278 %I Springer-Verlag %C Kinsale, Ireland %@ 3-540-43378-3 %F baber:2002:EuroGP %X The Internet is quickly changing the way business-to-consumer and business-to-business commerce is conducted. The technology has created an opportunity to get beyond single-issue negotiation by determining sellers’ and buyers’ preferences across multiple issues, thereby creating possible joint gains for all parties. We develop simple multiple issue algorithms and heuristics that could be used in electronic auctions and electronic markets. In this study, we show how a genetic algorithm based technique, coupled with a simple heuristic can achieve good results in business negotiations. The negotiations’ outcomes are evaluated on two dimensions: joint utility and number of ex-changes of offers to reach a deal. The results are promising and indicate possible use of such approaches in actual electronic commerce systems. %K genetic algorithms, genetic programming %R 10.1007/3-540-45984-7_20 %U http://dx.doi.org/10.1007/3-540-45984-7_20 %P 202-211 %0 Thesis %T Analiza kaljenih materialov s pomocjo fraktalne geometrije %A Babic, Matej %D 2014 %C Maribor %C Racunalnistvo in Informatiko, Fakulteta za Elektrotehniko, Univerza v Mariboru %F Babic:thesis %X In this dissertation we study intelligent systems and the search for knowledge, computing paradigms that are useful and beneficial for the heat treatment of materials. To identify the complexity of different heat-treated samples, we used the fractal geometry method. We designed an intelligent system through which we announced topographical properties of the material after heat treatment. We have also developed a new algorithm for 3D graph visibility. With the help of the topological properties density of 3D graphs, we have built an intelligent system which can predict the topographic characteristics of the samples after heat treatment. Fractal geometry can be used to analyse complex structures that occur in the heat treatment of materials. Thus, the use of fractal geometry demonstrates the advantages of laser heat treatment techniques over the inductive, classical and the hardening furnace. Fractal geometry is a new approach, based on the characterisation of irregular microstructures, and serves as an assessment tool for determining structural properties. It can be used in the analysis of different heattreated materials. Fractal geometry is based on the idea of invariant magnification, which means that the observed image is not the same regardless of how strong the microscope is. It should be noted that the fractal dimension does not fully characterise the geometry, but is rather an indication of irregularities. Fractal geometry was used here to determine the topographical properties of hardened materials . We have introduced a new method for calculating the fractal dimension of a 3D object. With the development of laser technology in the field of heat treatment of materials there is an increased need to develop new methods with which to determine (set) better resistance of material, lower friction and better heat resistance of material. We therefore aim to build intelligent systems to increase productivity in the field of heat treatment of materials. With the help of the intelligent system we intend to show which technique of heat treatment is best. In this dissertation we present four new composite hybrid methods: * composite hybrid genetic algorithms - multiple regression - neural network-multiple regression (we call it a hybrid loop). * composite hybrid genetic algorithm - neural network - multiple regression- neural network (we call it the optimal hybrid loop). * composite hybrid genetic algorithm - neural network - multiple regression-neural network - multiple regression (we call it the cyclic hybrid). * composite hybrid genetic algorithm - multiple regression - neural network -multiple regression - neural network (we call it the optimal linear hybrid). Composite hybrid performances were slightly worse than expected, because of the shortcomings of the individual basic methods. The multiple regression method is the worst method and adversely affected the composite hybrid. The new composite hybrids give better results than existing composite hybrid systems, however. We want to improofe results of new hybrid system, thus we built new composite hybrib, hyiper hybrid. At the end of the dissertation further comments are made and a two new hybrid systems proposed which we call the spiral hybrid and optimal spiral hybrid. This method are useful when a large number of basic methods are employed. We also propose combining (pooling) the six new hybrid methods presented in the new hyper hybrids. %K genetic algorithms, genetic programming, inteligentni sistemi, algoritmi, hibridni sistemi, strojno ucenje, fraktalna geometrija, teorija grafov, topografija materiala po toplotni obdelavi, intelligent system, algorithms, hybrid system, machine learning, ANN, fractal geometry, graph theory, topography of materials after heat treatment %9 Ph.D. thesis %U https://dk.um.si/IzpisGradiva.php?id=46366&lang=eng&prip=rul:10960417:d1 %0 Journal Article %T Prediction of the hardness of hardened specimens with a neural network %A Babic, Matej %A Kokol, Peter %A Belic, Igor %A Panjan, Peter %A Kovacic, Miha %A Balic, Joze %A Verbovsek, Timotej %J Materiali in tehnologije/Materials and Technology %D 2014 %8 may jun %V 48 %N 3 %@ 1580-2949 %F babic:2014:MT %X In this article we describe the methods of intelligent systems to predict the hardness of hardened specimens. We use the mathematical method of fractal geometry in laser techniques. To optimise the structure and properties of tool steel, it is necessary to take into account the effect of the self-organisation of a dissipative structure with fractal properties at a load. Fractal material science researches the relation between the parameters of fractal structures and the dissipative properties of tool steel. This paper describes an application of the fractal dimension in the robot laser hardening of specimens. By using fractal dimensions, the changes in the structure can be determined because the fractal dimension is an indicator of the complexity of the sample forms. The tool steel was hardened with different speeds and at different temperatures. The effect of the parameters of robot cells on the material was better understood by researching the fractal dimensions of the microstructures of hardened specimens. With an intelligent system the productivity of the process of laser hardening was increased because the time of the process was decreased and the topographical property of the material was increased. %K ANN, fractal dimension, laser, hardening, neural network %9 journal article %U http://mit.imt.si/Revija/ %P 409-414 %0 Journal Article %T Using of genetic programming in engineering %A Babic, Matej %A Kokol, Peter %A Belic, Igor %A Panjan, Peter %A Kovacic, Miha %A Balic, Joze %J Elektrotehniski vestnik %D 2014 %8 jul %V 81 %N 3 %@ 0013-5852 %F babic:2014:etv %X Intelligent systems are process coupled with robotics in industrial usually settings, though they may be used as diagnostic systems connected only to passive sensors. In this paper we use a new method which combines an intelligent genetic algorithm and multiple regression to predict the hardness of hardened specimens. The hardness of a material is an important mechanical property affecting mechanical properties of materials. The Microstructures of the hardened specimens are very complex and cannot be described them with the classical Euclidian geometry. Thus, we use a new method, i.e. fractal geometry. By using the method intelligent-system, genetic programming and multiple regression, improved production the process laser-hardening increases because of the decreased time of the process and, the improved increased topographical property of the used materials. The genetic-programming modelling results show a good agreement with the measured hardness of the hardened specimens. %K genetic algorithms, genetic programming, engineering, complex geometry structure %9 journal article %U http://ev.fe.uni-lj.si/3-2014/Babic.pdf %P 143-147 %0 Journal Article %T A new hybrid-system method of Machine Learning using a new method of fractal geometry and a new method of graph theory %A Babic, Matej %J Elektrotehniski vestnik %D 2016 %V 83 %N 1-2 %@ 0013-5852 %F babic:2016:EV %X a hybrid system method to predict the volume of the robot-laser-hardened specimens when one of the parameters in the existing model cannot be measured or calculated the intelligent-system is presented. Also, we have a model of the intelligent system to predict the volume of hardened specimens developed by someone, but we can not calculate one parameter in it. Thus, we develop a new method of the hybrid intelligent system to solve this problem. We develop a hybrid of genetic programming and multiple regression. To predict the volume of hardened specimens, we use teh neural network, genetic algorithm and multiple regression. The genetic programming modelling results show a good agreement with the measured volume of hardened specimens. We analyse the SEM picture of the microstructure of robot-laser-hardened specimens with a mathematical method. In this open problem we use the graph theory and fractal geometry. Fractal dimensions are calculated using image processing of a SEM micrographs in combination with a box-counting algorithm using ImageJ software. %K genetic algorithms, genetic programming, image processing, intelligent system, visibility graphs, fractal dimension %9 journal article %U https://ev.fe.uni-lj.si/1-2-2016/Babic.pdf %P 42-46 %0 Journal Article %T New Method for Constructing a Visibility Graph-Network in 3D Space and a New Hybrid System of Modeling %A Babic, Matej %A Hluchy, Ladislav %A Krammer, Peter %A Matovic, Branko %A Kumar, Ravi %A Kovac, Pavel %J Computing and Informatics %D 2017 %8 19 dec %V 36 %N 5 %@ 1335-9150 %F babic:2017:CAI %X This paper describes a new method for constructing a visibility graph in 3D space. We use a method for predicting porosity of hardened specimens... %K genetic algorithms, genetic programming, Artificial intelligence, visibility graphs, pattern recognition, modeling, hybrid system %9 journal article %R 10.4149/cai_2017_5_1107 %U https://www.cai.sk/ojs/index.php/cai/issue/view/179 %U http://dx.doi.org/10.4149/cai_2017_5_1107 %P 1107-1126 %0 Journal Article %T Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans %A Babic, Matej %A Petrovic, Dusan %A Sodnik, Jost %A Soldo, Bozo %A Komac, Marko %A Chernieva, Olena %A Kovacic, Miha %A Mikos, Matjaz %A Cali, Michele %J Remote Sensing %D 2021 %8 28 apr %V 13 %N 9 %I MDPI %@ 2072-4292 %F Babic:2021:Remote_Sensing %X Alluvial (torrential) fans, especially those created from debris-flow activity, often endanger built environments and human life. It is well known that these kinds of territories where human activities are favored are characterized by increasing instability and related hydrological risk; therefore, treating the problem of its assessment and management is becoming strongly relevant. The aim of this study was to analyse and model the geomorphological aspects and the physical processes of alluvial fans in relation to the environmental characteristics of the territory for classification and prediction purposes. The main geomorphometric parameters capable of describing complex properties, such as relative fan position depending on the neighborhood, which can affect their formation or shape, or properties delineating specific parts of fans, were identified and evaluated through digital elevation model (DEM) data. Five machine learning (ML) methods, including a hybrid Euler graph ML method, were compared to analyze the geomorphometric parameters and physical characteristics of alluvial fans. The results obtained in 14 case studies of Slovenian torrential fans, validated with data of the empirical model proposed by Bertrand et al. (2013), confirm the validity of the developed method and the possibility to identify alluvial fans that can be considered as debris-flow prone. %K genetic algorithms, genetic programming, random forest, RF, support vector machine, SVM, neural network, ANN, digital elevation model, torrential fan surfaces, geomorphometric parameters, graph method, debris flows %9 journal article %R 10.3390/rs13091711 %U https://repozitorij.uni-lj.si/IzpisGradiva.php?id=127268 %U http://dx.doi.org/10.3390/rs13091711 %0 Conference Proceedings %T A New Composite Method of Modeling Bicycle Traffic using Convolutional Neural Networks and Genetic programming %A Babic, Matej %A Ster, Branko %A Povh, Janez %A Rodrigues, Joel J. P. C. %S 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech) %D 2021 %8 sep %F Babic:2021:SpliTech %X This study proposes a new composite method of modelling bicycle traffic in the town of Novo mesto, Slovenia, using Convolutional neural networks and Genetic programming. Public passenger transport (PPT) is important for every municipality, as the current transport system faces well-known problems such as congestion, environmental impact, lack of parking areas, increased safety risks and high energy consumption. The wider Novo mesto region, with about 30000 inhabitants, is an important industrial center and is heavily dependent on urban traffic. The aim of the research is to analyze and model bicycle rentals. Convolutional neural networks and genetic programming are used to predict bicycle traffic over 35 weeks. %K genetic algorithms, genetic programming, ANN, GoNM %R 10.23919/SpliTech52315.2021.9566405 %U http://dx.doi.org/10.23919/SpliTech52315.2021.9566405 %0 Journal Article %T Evaluation of microstructural complex geometry of robot laser hardened materials through a genetic programming model %A Babic, M. %A Lesiuk, G. %A Marinkovic, D. %A Cali, M. %J Procedia Manufacturing %D 2021 %V 55 %@ 2351-9789 %F BABIC:2021:PM %O FAIM 2021 %X Surface-hardening process of steel materials by robot laser technologies can involve the challenge of modeling the determining process parameters through non-conventional tools in order to evaluate the quality of the heat treatment. In the current study a new method based on fractal geometry, used to determine the microstructural properties of laser hardened steels manufactured by anthropomorphic robots, is presented. The assumptions were that the microstructure of laser hardened steel can be studied as a complex structural geometry and the modeling of the analyzed complex geometries can be made through genetic programming for prediction purposes. The effect of process parameters and their joint combination on the final microstructures geometry of the heat treated steel was investigated. In particular, the influence of temperature, laser beam velocity, and impact angle were studied since they were showed in a preliminary study to be the process parameters that most significantly influenced the quality of the heat treated steel. The developed model reached a precision of the prediction equal to 98.59 percent %K genetic algorithms, genetic programming, Microstructure geometry, Fractal geometry, Laser beam process parameters, Forecast model, Hardened steels %9 journal article %R 10.1016/j.promfg.2021.10.036 %U https://www.sciencedirect.com/science/article/pii/S235197892100233X %U http://dx.doi.org/10.1016/j.promfg.2021.10.036 %P 253-259 %0 Journal Article %T Machine Learning Tools in the Analyze of a Bike Sharing %A Babic, Matej %A Fragassa, Cristiano %A Marinkovic, Dragan %A Povh, Janez %J International Journal for Quality Research %D 2022 %V 16 %N 2 %@ 1800-6450 %F babic:2022:IJQR %X Advanced models, based on artificial intelligence and machine learning, are used here to analyze a bike-sharing system. The specific target was to predict the number of rented bikes in the Nova Mesto, Slovenia public bike share scheme. For this purpose, the topological properties of the transport network were determined and related to the weather conditions. Pajek software was used and the system behavior during a 30-week period was investigated. Open questions were, for instance: how many bikes are shared in different weather conditions? How the network topology impacts the bike sharing system? By providing a reasonable answer to these and similar questions, several accurate ways of modeling the bike sharing system which account for both topological properties and weather conditions, were developed and used for its optimization. %K genetic algorithms, genetic programming, GoNM, Transportation Systems Engineering, bicycle, Cycles, Bike-Sharing System (PBS), Artificial Intelligence (AI), Machine Learning (ML), Hybrid Intelligent Systems, Weather Conditions %9 journal article %R 10.24874/IJQR16.02-04 %U http://ijqr.net/paper.php?id=989 %U http://dx.doi.org/10.24874/IJQR16.02-04 %P 375-394 %0 Journal Article %T Complexity Modeling of Steel-Laser-Hardened Surface Microstructures %A Babic, Matej %A Marinkovic, Dragan %A Bonfanti, Marco %A Cali, Michele %J Applied Sciences %D 2022 %V 12 %N 5 %@ 2076-3417 %F babic:2022:AS %X Nowadays, laser hardening is a consolidated process in many industrial sectors. One of the most interesting aspects to be considered when treating the surface-hardening process in steel materials by means of laser devices is undoubtedly the evaluation of the heat treatment quality and surface finish. In the present study, an innovative method based on fractal geometry was proposed to evaluate the quality of surface-steel-laser-hardened treatment. A suitable genetic programming study of SEM images (1280 × 950 pixels) was developed in order to predict the effect of the main laser process parameters on the microstructural geometry, assuming the microstructure of laser-hardened steel to be of a structurally complex geometrical nature. Specimens hardened by anthropomorphic laser robots were studied to determine an accurate measure of the process parameters investigated (surface temperature, laser beam velocity, laser beam impact angle). In the range of variation studied for these parameters, the genetic programming model obtained was in line with the complexity index calculated following the fractal theory. In particular, a percentage error less than 1percent was calculated. Finally, a preliminary study of the surface roughness was carried out, resulting in its strong correlation with complex surface microstructures. Three-dimensional voxel maps that reproduce the surface roughness were developed by automating a routine in Python virtual environment. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/app12052458 %U https://www.mdpi.com/2076-3417/12/5/2458 %U http://dx.doi.org/10.3390/app12052458 %0 Journal Article %T A New Method of Quantifying the Complexity of Fractal Networks %A Babic, Matej %A Marinkovic, Dragan %A Kovacic, Miha %A Ster, Branko %A Cali, Michele %J Fractal and Fractional %D 2022 %V 6 %N 6 %@ 2504-3110 %F Babic:2022:FF %X There is a large body of research devoted to identifying the complexity of structures in networks. In the context of network theory, a complex network is a graph with nontrivial topological features; features that do not occur in simple networks, such as lattices or random graphs, but often occur in graphs modeling real systems. The study of complex networks is a young and active area of scientific research inspired largely by the empirical study of real-world networks, such as computer networks and logistic transport networks. Transport is of great importance for the economic and cultural cooperation of any country with other countries, the strengthening and development of the economic management system, and in solving social and economic problems. Provision of the territory with a well-developed transport system is one of the factors for attracting population and production, serving as an important advantage for locating productive forces and providing an integration effect. we introduce a new method for quantifying the complexity of a network based on presenting the nodes of the network in Cartesian coordinates, converting to polar coordinates, and calculating the fractal dimension using the ReScaled ranged (R/S) method. Our results suggest that this approach can be used to determine complexity for any type of network that has fixed nodes, and it presents an application of this method in the public transport system. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/fractalfract6060282 %U https://www.mdpi.com/2504-3110/6/6/282 %U http://dx.doi.org/10.3390/fractalfract6060282 %P articlenumber282 %0 Journal Article %T A New Approach to Determining the Network Fractality with Application to Robot-Laser-Hardened Surfaces of Materials %A Babic, Matej %A Marinkovic, Dragan %J Fractal and Fractional %D 2023 %V 7 %N 10 %@ 2504-3110 %F babic:2023:FaF %X A new method to determine a fractal network in chaotic systems is presented together with its application to the microstructure recognition of robot-laser-hardened (RLH) steels under various angles of a laser beam. The method is based on fractal geometry. An experimental investigation was conducted by investigating the effect of several process parameters on the final microstructures of material that has been heat-treated. The influences of the surface temperature, laser speed, and different orientation angles of the laser beam on the microstructural geometry of the treated surfaces were considered. The fractal network of the microstructures of robot-laser-hardened specimens was used to describe how the geometry was changed during the heat treatment of materials. In order to predict the fractal network of robot-laser-hardened specimens, we used a method based on intelligent systems, namely genetic programming (GP) and a convolutional neural network (CNN). The proposed GP model achieved a prediction accuracy of 98.4percent, while the proposed CNN model reached 96.5percent. The performed analyses demonstrate that the angles of the robot laser cell have a noticeable effect on the final microstructures. The specimen laser-hardened under the conditions of 4 mm/s, 1000 ?C, and an impact angle of the laser beam equal to 75? presented the maximum fractal network. The minimum fractal network was observed for the specimen before the robot-laser-hardening process. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/fractalfract7100710 %U https://www.mdpi.com/2504-3110/7/10/710 %U http://dx.doi.org/10.3390/fractalfract7100710 %P ArticleNo.710 %0 Journal Article %T Selective Laser Melting: A Novel Method for Surface Roughness Analysis %A Babic, Matej %A Kovacic, Miha %A Fragassa, Cristiano %A Sturm, Roman %J Strojniski vestnik - Journal of Mechanical Engineering %D 2024 %V 70 %N 7-8 %@ 0039-2480 %F Babic:2024:sv-jme %X The present study introduces a novel approach to analyse the surface roughness of metal parts made by 3D selective laser melting (SLM). This technology, known for its ability to efficiently produce functional prototypes and limited-run series, is particularly effective when surface conditions directly meet usage requirements. Thus, the suitability of surfaces is a critical factor, emphasising the importance of new methods for predicting their quality. Here fractal geometry and network theory are integrated to delve into the complexities of SLM-produced surfaces, while machine learning and pattern recognition concepts are employed to evaluate the surface roughness. Specifically, genetic programming, artificial neural networks, support vector machine, random forest, k-nearest neighbors are compared in terms of accuracy demonstrating that only the first method provided valid estimation due to the presence of very little training data. Experimental work with EOS Maraging Steel MS1 and an EOS M 290 3D printer validates the method’s practicality and effectiveness. Then, the research offers a fresh perspective in surface analysis and has significant implications for quality control in additive manufacturing, potentially enhancing the precision and efficiency of 3D metal printing. %K genetic algorithms, genetic programming, additive manufacturing, selective laser melting, surface roughness, fractal geometry, network theory, ANN, kNN, SVM %9 journal article %R 10.5545/sv-jme.2024.1009 %U https://www.sv-jme.eu/?ns_articles_pdf=/ns_articles/files/ojs30/1009/66cc2d6717e58.pdf&id=7068 %U http://dx.doi.org/10.5545/sv-jme.2024.1009 %P 313-324 %0 Journal Article %T Modeling Porosity Surface of 3D Selective Laser Melting Metal Materials %A Babic, Matej %A Sturm, Roman %A Galatanu, Teofil-Florin %A Szava, Ildiko-Renata %A Szava, Ioan %J Fractal and Fractional %D 2025 %V 9 %N 6 %@ 2504-3110 %F babic:2025:FaF %X The most popular method for additively printing metal components is selective laser melting (SLM), which works well for creating working models and prototypes. A fine metal powder, often (stainless) steel or aluminum, serves as the initial material. A very accurate laser is used to melt this layer by layer. The most important factor here is the short throughput time in comparison to milling. Selective laser melting becomes increasingly valuable as geometry becomes more complex. Presented study models the porosity of 3D SLM of metal materials using genetic programming and network theory. We used fractal dimensions to determine the complexity of the microstructure of selective laser melting specimens. The method’s usefulness and efficiency were confirmed by experimental work using an EOS M 290 3D printer and EOS Maraging Steel MS1. This study then presented a novel viewpoint on porosity and has important ramifications for additive manufacturing quality control, which could improve the accuracy and effectiveness of 3D metal printing. The goal was to present a modelling porosity of 3D SLM of metal materials by using a method of intelligent system. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/fractalfract9060331 %U https://www.mdpi.com/2504-3110/9/6/331 %U http://dx.doi.org/10.3390/fractalfract9060331 %P ArticleNo.331 %0 Conference Proceedings %T Development of an Intelligent GUI with ChatGPT for Efficient and Accurate Transformer Fault Diagnosis Using DGA, Duval Triangle Method, and Machine Learning Automation %A Babiker, Safia %A Jadalhaq, Jouri %A Jadalhaq, Jana %A Barkat, Enfel %S 2025 22nd International Learning and Technology Conference (L&T) %D 2025 %8 jan %V 22 %F Babiker:2025:LT %X This study presents an intelligent graphical user interface (GUI) for transformer fault diagnosis, using Dissolved Gas Analysis (DGA), the Duval Triangle method, and automated machine learning model selection. Key gases-Methane (CH4), Ethylene (C2 H4), and Acetylene (C2 H2)-are analysed to classify fault types, including partial discharges and thermal/electrical faults, through gas concentration ratios. The system leverages TPOT’s model selection capabilities, with the Multi-Layer Perceptron (MLP) emerging as the optimal classifier, achieving over 95percent accuracy. The GUI, developed using Gradio, enables CSV uploads for batch processing and real-time fault classification, complemented by ChatGPT-generated explanations and maintenance suggestions. A case study on mineral oil-based transformers demonstrates the tool’s high diagnostic accuracy, user-friendly design, and potential for expanded applications in predictive maintenance. %K genetic algorithms, genetic programming, Gases, Accuracy, Oil insulation, Predictive models, Transformers, Chatbots, Minerals, Maintenance, Graphical user interfaces, Predictive maintenance, Transformer fault diagnosis, Dissolved Gas Analysis, Duval Triangle, Machine Learning, TPOTClassifier, Gradio, Artificial Intelligence, GUI, ChatGPT Integration %R 10.1109/LT64002.2025.10941322 %U http://dx.doi.org/10.1109/LT64002.2025.10941322 %P 263-268 %0 Conference Proceedings %T Use of computational adaptive methodologies in hydroinformatics %A Babovic, Vladan %A Minns, A. W. %Y Verwey, A. %Y Minns, A. W. %Y Babovic, V. %Y Maksimovic, C. %S Proceedings of the first international conference on hydroinformatics, Delft, Netherlands %D 1994 %8 19–23 sep %I A. A. Balkema %@ 90-5410-512-7 %F babovic:1994:camh %X Summaries a study of the performance of artificial neural networks and GP compared to an empirically-based method using a problem of salt intrusion as an example. %K genetic algorithms, genetic programming %U http://www.amazon.co.uk/Hydroinformatics-Proceedings-International-Conference-Netherlands/dp/9054105127 %P 201-210 %0 Conference Proceedings %T Genetic Model Induction Based on Experimental Data %A Babovic, Vladan %Y Gardiner, J. %S Proceedings of the XXVIth Congress of International Association for Hydraulics Research %D 1995 %8 November %I Thomas Telford Ltd %C London, UK %@ 0-7277-2059-7 %F babovic:1995:gmibed %X GP used to perform an analysis of sediment transport data and to induce relationshop between bed concentration of suspended sediment and the hydraulic conditions. GP results similar accuracy to traditional techniques. IHE-Delft, The Netherlands %K genetic algorithms, genetic programming %U http://www.amazon.co.uk/Hydra-2000-Development-Proceedings-International/dp/0727720597/ref=sr_1_4?s=books&ie=UTF8&qid=1324144161&sr=1-4 %0 Thesis %T Emergence, Evolution, Intelligence: Hydroinformatics %A Babovic, Vladan %D 1996 %8 20 mar %C The Netherlands %C International Institute for Infrastructural, Hydraulic and Environmental Engineering and Technical University Delft %F babovic:thesis %O Published by A. A. Balkema Publishers %X The computer-controlled operating environments of such facilities as automated factories, nuclear power plants, telecommunication centres and space stations are continually becoming more complex.The situation is similar, if not even more apparent and urgent, in the case of water. Water is not only mankind’s most valuable natural resource, but one which is in increasingly limited supply. The fresh water is the vita! natural resource which supports all environmental activities, that is, natura! economy, and all human socio-economic activities, that is, the artificial economy. The pressure for a sustainable control and exploration of water and thus for the peaceful co-existence of human- and hydro-economies, is not only a human, socio-economic pressure, but it is the question of life and death! Hydroinformatics - the nascent technology concerned with the flow of information related to the flow of fluids and all that they convey - is probably the best possible answer yet proposed to the problem of the control of the waters, the very arteries and veins of the biosphere. This work addresses some of the central issues within hydroinformatics paradigm. It focuses on ttie analysis of decentralised and distributed computation, as well as the issues of design of individual computatiorial agents using evolutionary algorithms. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://repository.tudelft.nl/view/ir/uuid%3A58c50efe-4a6a-40b4-8c60-2b81d629b49c/ %0 Book %T Emergence, evolution, intelligence; Hydroinformatics - A study of distributed and decentralised computing using intelligent agents %A Babovic, Vladan %D 1996 %I A. A. Balkema Publishers %C Rotterdam, Holland %@ 90-5410-404-X %F babovic:book %X The computer controlled operating environments of such facilities as automated factories, nuclear power plants, telecommunication centres and space stations are continually becoming more complex. The situation is similar, if not even more apparent and urgent, in the case of water. Water is not only mankind’s most valuable natural resource, but one which is in increasingly limited supply. The fresh water is the vital natural resource which supports all environmental activities, that is, natural economy, and all human socio-economic activities, that is, the artificial economy. The pressure for a sustainable control and exploration of water and thus for the peaceful co-existence of human- & hydro-economies is not only a human, socio-economic pressure, but it is the question of life and death. Hydroinformatics - the nascent technology concerned with the flow of information related to the flow of fluids and all that they convey - is probably the best possible answer yet proposed to the problem of the control of the waters, the very arteries and veins of the biosphere. %K genetic algorithms, genetic programming %U https://www.amazon.com/Hydroinformatics-Emergence-Evolution-Intelligence-Thesis/dp/905410404X/ref=sr_1_1/165-1740647-7487049?s=books&ie=UTF8&qid=1477940894&sr=1-1&keywords=9789054104049 %0 Book Section %T Can water resources management benefit from artificial intelligence? %A Babovic, Vladan %E Kongeter, J. %B Computation Fluid Dynamics: Bunte Bilder in der Praxis %D 1996 %I Meinz Verlag %C Aachen, Germany %F babovic:1996:wmbAI %K genetic algorithms, genetic programming %U http://www.dwa.de/dwa/sitemapping.nsf/literaturvorschau?openform&bestandsnr=36547 %P 337-358 %0 Journal Article %T The evolution of equation from hydraulic data, Part I: Theory %A Babovic, Vladan %A Abbott, Michael B. %J Journal of Hydraulic Research %D 1997 %V 35 %N 3 %F babovic:1997:eehd1 %X Even as hydroinformatics continues to elaborate more advanced operational tools, languages and environments for engineering and management practice, it necessarily also promotes a number of concepts and methodologies that are eminently applicable within the more traditional areas of hydraulic research. Among the many new possibilities thereby introduced, that of evolving equations from hydraulic data using evolutionary algorithms has a particularly wide range of applications. The present paper is in two parts, the first of which introduces the subject and outlines its theory, while the second is given over to four representative applications and to some of the most immediate lessons that may be drawn from these. The first of the applications is derived from a hydrologie model but provides equations with purely hydraulic interpretations. The second, taken from sediment transport studies, raises the question of ambiguity in the identification of ’thresholds’ in physical processes. It also provides a means for analysing the significance of variables and indicates the need, or otherwise, for introducing further variables. A third example, based upon physical observations of salt water intrusion in estuaries, introduces the application of the present methods to accelerating prediction processes, while the fourth example extends this kind of application to cover numerically generated data, in this case appertaining to the case of flow resistance in the presence of vegetation. %K genetic algorithms, genetic programming %9 journal article %R 10.1080/00221689709498420 %U http://dx.doi.org/10.1080/00221689709498420 %P 397-410 %0 Journal Article %T The evolution of equation from hydraulic data, Part II: Applications %A Babovic, Vladan %A Abbott, Michael B. %J Journal of Hydraulic Research %D 1997 %V 35 %N 3 %F babovic:1997:eehd2 %X This second part of the paper \citebabovic:1997:eehd1 is given over to describing four representative applications and to some of the most immediate lessons that may be drawn from these. The first of the applications is derived from a hydrologic model but provides equations with purely hydraulic interpretations. The second, taken from sediment transport studies, raises the question of ambiguity in the identification of ’thresholds’ in physical processes. It also provides a means for analysing the significance of variables and indicates the need, or otherwise, for introducing further variables. A third example, based upon physical observations of salt water intrusion in estuaries, introduces the application of the present methods to accelerating prediction processes, while the fourth example extends this kind of application to cover numerically-generated data, in this case appertaining to the case of flow resistance in the presence of vegetation. In conclusion, this work is set within the context of other developments, such as those of data mining and knowledge discovery generally %K genetic algorithms, genetic programming %9 journal article %R 10.1080/00221689709498421 %U http://dx.doi.org/10.1080/00221689709498421 %P 411-430 %0 Book Section %T On the Modelling and Forecasting of Non-linear Systems %A Babovic, Vladan %E Refsgaard, J. C. %E Karalis, E. A. %B Operational Water Management: Proceedings of the European Water Resources Association Conference, Copenhagen, Denmark, 3-6 September 1997 %D 1997 %I Balkema %C Rotterdam %@ 90-5410-897-5 %F babovic:1997:mfnls %K genetic algorithms, genetic programming %U http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=9054108975 %P 195-202 %0 Conference Proceedings %T Sediment transport data - Large knowledge mine %A Babovic, V. %S Proceedings of the Third International Conference on Hydroscience and Engineering %D 1998 %C Cottbus, Germany %F babovic:1998:stdlkm %K genetic algorithms, genetic programming %0 Conference Proceedings %T A data mining approach to time series modelling and forecasting %A Babovic, V. %E Babovic %E Larsen %S Proceeding of the Third International Conference on Hydroinformatics %D 1998 %I Balkema %C Copenhagen, Denmark %@ 90-5410-983-1 %F babovic:1998:dmtsmf %K genetic algorithms, genetic programming, Vltava River system, flood control and protection of Prague, artificial neural networks %P 847-856 %0 Conference Proceedings %T Mining sediment transport data with genetic programming %A Babovic, Vladan %S Proceedings of the First International Conference on New Information Technologies for Decision Making in Civil Engineering %D 1998 %8 November 13 oct %C Montreal, Canada %F babovic:1998:mstGP %K genetic algorithms, genetic programming %P 875-886 %0 Conference Proceedings %T Computer supported knowledge discovery - A case study in flow resistance induced by vegetation %A Babovic, Vladan %A Keijzer, Maarten %S Proceedings of the XXVIII Congress of International Association for Hydraulic Research %D 1999 %8 22 27 aug %C Graz, Austria %F babovic:1999:cskd-veg %X Data Mining and Knowledge Discovery aims at providing tools to facilitate the conversion of data to a better understanding of processes that generated or produced those data. We call this the mining of data for knowledge. Data mining extracts patterns from data. It creates models from data, by using for example, genetic programming, polynomial or artificial neural networks, or even support vector machines. These new models, combined with the understanding of the physical processes - the theory - can result in an improved understanding and novel formulations of physical laws and an improved predictive capability. The present paper describes some of the very first efforts under the D2K (Data to Knowledge) Research Project currently conducted at Danish Hydraulic Institute with a support from the Danish Technical Research Council (STVF). The paper firstly outlines elementary data mining principles, particularly when applied to analysis of scientific data. In the second half of the contribution, results obtained through analysis of the data related to the additional resistance to the flow induced by flexible vegetation are presented. The data are analysed by the means of genetic programming (GP). Induced formulations and discussed in terms of accuracy and physical interpretability. %K genetic algorithms, genetic programming %U http://www.iahr.org/membersonly/grazproceedings99/pdf/C021.pdf %0 Conference Proceedings %T Data to knowledge - The new scientific paradigm %A Babovic, V. %A Keijzer, M. %Y Savic, Dragan %Y Walters, Godfrey %S Water Industry Systems %D 1999 %8 13 15 sep %I Research Studies Pr Ltd %C Exeter, United Kingdom %F babovic:1999:d2k %K genetic algorithms, genetic programming %U http://bookweb.kinokuniya.co.jp/htmy/0863802486.html %P 3-14 %0 Conference Proceedings %T Evolutionary algorithms approach to induction of differential equations %A Babovic, Vladan %A Keijzer, Maarten %S Proceedings of the Fourth International Conference on Hydroinformatics %D 2000 %8 jul 23 27 %I International Association for Hydro-Environment Engineering and Research %C Iowa City, USA %F me15 %K genetic algorithms, genetic programming %U http://members.iahr.org/core/orders/product.aspx?catid=3&prodid=47 %0 Journal Article %T Data Mining and Knowledge Discovery in Sediment Transport %A Babovic, Vladan %J Computer-Aided Civil and Infrastructure Engineering %D 2000 %8 sep %V 15 %N 5 %@ 1093-9687 %F babovic:1999:td2ksed %X The means for data collection have never been as advanced as they are today. Moreover, the numerical models we use today have never been so advanced. Feeding and calibrating models against collected measurements, however, represents only a one-way flow: from measurements to the model. The observations of the system can be analyzed further in the search for the information they encode. Such automated search for models accurately describing data constitutes a new direction that can be identified as that of data mining. It can be expected that in the years to come we shall concentrate our efforts more and more on the analysis of the data we acquire from natural or artificial sources and that we shall mine for knowledge from the data so acquired. Data mining and knowledge discovery aim at providing tools to facilitate the conversion of data into a number of forms, such as equations, that provide a better understanding of the process generating or producing these data. These new models combined with the already available understanding of the physical processes – the theory – result in an improved understanding and novel formulations of physical laws and improved predictive capability. This article describes the data mining process in general, as well as an application of a data mining technique in the domain of sediment transport. Data related to the concentration of suspended sediment near a bed are analyzed by the means of genetic programming. Machine-induced relationships are compared against formulations proposed by human experts and are discussed in terms of accuracy and physical interpretability. %K genetic algorithms, genetic programming %9 journal article %R 10.1111/0885-9507.00202 %U http://dx.doi.org/10.1111/0885-9507.00202 %P 383-389 %0 Journal Article %T Genetic programming as a model induction engine %A Babovic, Vladan %A Keijzer, Maarten %J Journal of Hydroinformatics %D 2000 %8 jan %V 1 %N 1 %@ 1464-7141 %F babovic:1999:GPmie %X Present day instrumentation networks already provide immense quantities of data, very little of which provides any insights into the basic physical processes that are occurring in the measured medium. This is to say that the data by itself contributes little to the knowledge of such processes. Data mining and knowledge discovery aim to change this situation by providing technologies that will greatly facilitate the mining of data for knowledge. In this new setting the role of a human expert is to provide domain knowledge, interpret models suggested by the computer and devise further experiments that will provide even better data coverage. Clearly, there is an enormous amount of knowledge and understanding of physical processes that should not be just thrown away. Consequently, we strongly believe that the most appropriate way forward is to combine the best of the two approaches: theory-driven, understanding-rich with data-driven discovery process. This paper describes a particular knowledge discovery algorithm Genetic Programming (GP). Additionally, an augmented version of GP - dimensionally aware GP - which is arguably more useful in the process of scientific discovery is described in great detail. Finally, the paper concludes with an application of dimensionally aware GP to a problem of induction of an empirical relationship describing the additional resistance to flow induced by flexible vegetation. %K genetic algorithms, genetic programming, data mining, knowledge discovery %9 journal article %R 10.2166/hydro.2000.0004 %U http://jh.iwaponline.com/content/2/1/35 %U http://dx.doi.org/10.2166/hydro.2000.0004 %P 35-60 %0 Book Section %T On Computer-Aided Discovery of Knowledge in Hydraulic Engineering %A Babovic, Vladan %A Bergmann, H. %E Bergmann, H. %B Advances in Hydraulic Research and Engineering %D 2000 %I Technical University Graz %C Graz %F Babovic:2000:IAHR %K genetic algorithms, genetic programming %0 Book Section %T On the introduction of declarative bias in knowledge discovery computer systems %A Babovic, Vladan %A Keijzer, Maarten %E Goodwin, Peter %B New paradigms in river and estuarine management %D 2001 %I Kluwer %F me25 %K genetic algorithms, genetic programming %0 Conference Proceedings %T An evolutionary approach to knowledge induction: Genetic Programming in Hydraulic Engineering %A Babovic, Vladan %A Keijzer, Maarten %A Rodriguez Aguilera, David %A Harrington, Joe %Y Phelps, Don %Y Sehlke, Gerald %S Proceedings of the World Water and Environmental Resources Congress %D 2001 %8 20 24 may %V 111 %I ASCE %C Orlando, Florida, USA %F me27 %X The process of scientific discovery has long been viewed as the pinnacle of creative thought. Thus, to many people, including some scientists themselves is seems unlikely candidate for automation by computer. However, over the past two decades researchers in AI have repeatedly questioned this attitude. The paper describes a specific evolutionary algorithm technique, genetic programming, within a scientific discovery framework, as well as its application on real world data. %K genetic algorithms, genetic programming %R 10.1061/40569(2001)64 %U http://www.cs.vu.nl/~mkeijzer/publications/ASCE_paper.pdf %U http://dx.doi.org/10.1061/40569(2001)64 %P 64-64 %0 Journal Article %T Modelling of water supply assets: a data mining approach %A Babovic, Vladan %A Drecourt, Jean-Philippe %A Keijzer, Maarten %A Hansen, Peter Friis %J Urban Water %D 2002 %V 4 %N 4 %I Elsevier %F me24 %K genetic algorithms, genetic programming %9 journal article %U http://www.sciencedirect.com/science/article/B6VR2-4718F0J-1/2/e361659261f99d438f8f2207f67eedf8 %P 401-414 %0 Journal Article %T Rainfall Runoff Modelling based on Genetic Programming %A Babovic, Vladan %A Keijzer, Maarten %J Nordic Hydrology %D 2002 %8 oct %V 33 %N 5 %@ 0029-1277 %F NordicHy %X The runoff formation process is believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases it is difficult to collect all the data necessary for such a model. By using data driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses use of genetic programming for creating rainfall-runoff models on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain). %K genetic algorithms, genetic programming %9 journal article %R 10.2166/nh.2002.0012 %U http://www.iwaponline.com/nh/033/0331/0330331.pdf %U http://dx.doi.org/10.2166/nh.2002.0012 %P 331-346 %0 Journal Article %T Data mining in hydrology %A Babovic, Vladan %J Hydrological Processes %D 2005 %8 30 apr %V 19 %N 7 %@ 1099-1085 %F Babovic:2005:HP %X Present-day instrumentation networks already provide immense quantities of data, very little of which provide any insight into the basic physical phenomena that are occurring in the medium measured. In order to exploit fully the information contained in the data, scientists are developing a suite of techniques to ’mine the knowledge’ from data. %K genetic algorithms, genetic programming %9 journal article %R 10.1002/hyp.5862 %U http://dx.doi.org/10.1002/hyp.5862 %P 1511-1515 %0 Book Section %T Rainfall-Runoff Modeling Based on Genetic Programming %A Babovic, Vladan %A Keijzer, Maarten %E Anderson, Malcolm G. %E Beven, Keith %E et al. %B Encyclopedia of Hydrological Sciences %D 2006 %8 15 apr %I Wiley %F Babovic:2006: %X The runoff formation process is believed to be highly nonlinear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases, it is difficult to collect all the data necessary for such a model. By using data-driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses the use of GP for creating rainfall-runoff (R-R) models both on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain). %K genetic algorithms, genetic programming, Hydroinformatics, symbolic regression, empirical equations, rainfall-runoff %R 10.1002/0470848944.hsa017 %U http://onlinelibrary.wiley.com/doi/10.1002/0470848944.hsa017/abstract %U http://dx.doi.org/10.1002/0470848944.hsa017 %0 Conference Proceedings %T Data-Driven Knowledge Discovery: Four Roads to Vegetation-Induced Roughness Formulae %A Babovic, Vladan %Y Navarro, Pilar Garcia %Y Playan, Enrique %S Numerical Modelling of Hydrodynamics for Water Resources: Proceedings of the International Workshop on Numerical Modelling of Hydrodynamic Systems %D 2007 %8 18 21 jun %I Taylor & Franics, Balkema %C Zaragoza, Spain %@ 0-415-44056-4 %F Babovic:2007:NMHS %K genetic algorithms, genetic programming %U http://www.amazon.com/Numerical-Modelling-Hydrodynamics-Water-Resources/dp/0415440564/ref=cm_cr_pr_pb_t %P 67-76 %0 Journal Article %T Introducing knowledge into learning based on genetic programming %A Babovic, Vladan %J Journal of Hydroinformatics %D 2009 %V 11 %N 3-4 %@ 1464-7141 %F Babovic:2009:JH %X This work examines various methods for creating empirical equations on the basis of data while taking advantage of knowledge about the problem domain. It is demonstrated that the use of high level concepts aid in evolving equations that are easier to interpret by domain specialists. The application of the approach to real-world problems reveals that the use of such concepts results in equations with performance equal or superior to that of human experts. Finally, it is argued that the algorithm is best used as a hypothesis generator assisting scientists in the discovery process. %K genetic algorithms, genetic programming, empirical equations, hydraulics, sediment transport, strong typing, symbolic regression, units of measurement %9 journal article %R 10.2166/hydro.2009.041 %U http://www.iwaponline.com/jh/011/0181/0110181.pdf %U http://dx.doi.org/10.2166/hydro.2009.041 %P 181-193 %0 Book Section %T Evolutionary Computing in Hydrology %A Babovic, Vladan %A Rao, Raghuraj %E Sivakumar, Bellie %E Berndtsson, Ronny %B Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting %D 2010 %I World Scientific Publishing Co. %C Singapore %@ 981-4307-97-1 %F Babovic:2010:ECinH %X Many hydrologic processes are believed to be highly complex, nonlinear, time-varying, and spatially distributed. Hence, the governing mechanisms are not easily described by simple models. With unprecedented growth in instrumentation technology, recent investigations in hydrology are supported with immense quantities of data. In order to take full advantage of the information contained in such data, scientists are increasingly relying on a suite of data-driven techniques to understand the complex hydrologic processes. Evolutionary computing (EC) techniques, with a host of optimisation and modelling tools, can contribute significantly to achieve the objectives of this knowledge-discovery exercise in hydrology. This chapter discusses the utility of these EC techniques in attempting data analysis and modeling problems associated with hydrologic systems. It introduces the concept and working principle of EC techniques in general and reviews their applications to different domains of hydrology. The study also illustrates different case studies of genetic programming (GP) technique as a modelling, data assimilation, and model emulation tool %K genetic algorithms, genetic programming %R 10.1142/9789814307987_0007 %U http://ebooks.worldscinet.com/ISBN/9789814307987/9789814307987_0007.html %U http://dx.doi.org/10.1142/9789814307987_0007 %P 347-369 %0 Journal Article %T Genetic Programming for Symbolic Regression of Chemical Process Systems %A Babu, B. V. %A Karthik, S. %J Engineering Letters %D 2007 %8 jun %V 14 %N 2 %I International Association of Engineers %@ 1816-0948 %F Babu:2007:EL %X The novel evolutionary artificial intelligence formalism namely, genetic programming (GP) a branch of genetic algorithms is used to develop mathematical models based on input-output data, instead of conventional regression and neural network modeling techniques which are commonly used for this purpose. This paper summarizes the available MATLAB toolboxes and their features. Glucose to gluconic acid batch bioprocess has been modeled using both GPLAB and hybrid approach of GP and Orthogonal Least Square method (GP OLS). GP OLS which is capable of pruning of trees has generated parsimonious expressions simpler to GPLAB, with high fitness values and low mean square error which is an indicative of the good prediction accuracy. The capability of GP OLS to generate non-linear input-output dynamic systems has been tested using an example of fed-batch bioreactor. The simulation and GP model prediction results indicate GP OLS is an efficient and fast method for predicting the order and structure for non-linear input and output model. %K genetic algorithms, genetic programming, GPLAB %9 journal article %U https://www.engineeringletters.com/issues_v14/issue_2/index.html %P 42-55 %0 Conference Proceedings %T Approximation of digital circuits using cartesian genetic programming %A Babu, Kagana.Sarath %A Balaji, N. %S 2016 International Conference on Communication and Electronics Systems (ICCES) %D 2016 %8 oct %F Babu:2016:ICCES %X Digital circuits can be approximated in which the exact functionality can be relaxed. Approximate circuits are constructed such that the logic given by the user is not implemented completely and hence their functionality can be traded for area, delay and power consumption. An evolutionary approach like Cartesian Genetic programming (CGP) is used in this paper to make automatic design process of digital circuits. The quality of approximate circuits can be improved along with the reduction of evolution time by using a heuristic population seeding method which is embedded into CGP. In particular, digital circuits like full adder, 2 bit multiplier and 2 bit adder are addressed in this paper. Experimental results are given where random seeding mechanism is compared with heuristic seeding methods. %K genetic algorithms, genetic programming, cartesian genetic programming %R 10.1109/CESYS.2016.7889978 %U http://dx.doi.org/10.1109/CESYS.2016.7889978 %0 Conference Proceedings %T OCR-Based Multi-class Classification of Hate Speech in Images %A M, Nithish Babu %A P, Preethi %S 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI) %D 2023 %8 19 20 oct %C Tiruchengode, India %F Babu:2023:ICAEECI %X The depersonalization and anonymity afforded by ubiquitous social media platforms facilitate open discourse, yet also create a potential avenue for hate speech dissemination. The rising incidence of hate speech on these platforms necessitates vigilant monitoring and intervention. However, the sheer volume of user-generated content renders manual oversight infeasible. Technological solutions must be developed to efficiently identify and mitigate hate speech, striking a balance between maintaining open expression and safeguarding against harmful content. Additionally, when using traditional machine learning methodologies as prediction methods, the language being used and the length of the messages provide a barrier. In this study, a Genetic Programming (GP) model for identifying hate speech is presented, where each chromosome acts as a classifier with a universal sentence encoder feature. The performance of the GP model was enhanced by enriching the offspring pool with alternative solutions using a unique mutation strategy that only modifies the feature values in addition to the conventional one-point mutation technique. For the six categories of hate-type hate speech text datasets, the suggested GP model beat all cutting-edge solutions. In contrast to the machine learning models such as Random Forest, Decision Tree, and Naive Bayes which gave the following accuracy of 79.25percent, 77.88percent and 77.33percent whereas GP model outperformed with an accuracy of 97percent. The following evaluation matrices are considered as precision, recall training, and testing accuracy. %K genetic algorithms, genetic programming, Training, Social networking (online), Hate speech, User-generated content, Speech recognition, Prediction methods, Decision trees, Random Forest, Naive Bayes, Optical Character Recognition, Multiclass classification, Binary classification %R 10.1109/ICAEECI58247.2023.10370942 %U http://dx.doi.org/10.1109/ICAEECI58247.2023.10370942 %0 Conference Proceedings %T Genetic programming methods for reinforcement learning %A Babuska, Robert %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 Babuska:2019:GECCO %O Invited keynote %X Reinforcement Learning (RL) algorithms can be used to optimally solve dynamic decision-making and control problems. With continuous valued state and input variables, RL algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learnt, and they require significant trial and error tuning of their meta-parameters. In addition, results obtained with deep neural networks suffer from the lack of reproducibility. In this talk, we discuss a family of new approaches to constructing smooth approximators for RL by means of genetic programming and more specifically by symbolic regression. We show how to construct process models and value functions represented by parsimonious analytic expressions using state-of-the-art algorithms, such as Single Node Genetic Programming and Multi-Gene Genetic Programming. We will include examples of non-linear control problems that can be successfully solved by reinforcement learning with symbolic regression and illustrate some of the challenges this exciting field of research is currently facing. %K genetic algorithms, genetic programming, Reinforcement learning, Symbolic regression %R 10.1145/3321707.3326935 %U http://dx.doi.org/10.1145/3321707.3326935 %P 2-2 %0 Conference Proceedings %T GECCO ’11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation %E Bacardit, Jaume %E Tanev, Ivan %E Mehnen, Joern %E Bartz-Beielstein, Thomas %E Davis, David %E Coello, Carlos Artemio Coello %E Curran, Dara %E Jansen, Thomas %E Loiacono, Daniele %E Orriols-Puig, Albert %E Urbanowicz, Ryan %E Harding, Simon %E Langdon, W. B. %E Wong, Man Leung %E Wilson, Garnett %E Lewis, Tony %E Smith, Stephen L. %E Cagnoni, Stefano %E Patton, Robert %E Rand, William %E Stonedahl, Forrest %E Pappa, Gisele L. %E Freitas, Alex A. %E Swan, Jerry %E Woodward, John %E Blesa, Maria J. %E Blum, Christian %E Gustafson, Steven %E Vladislavleva, Ekaterina %E Hauschild, Mark %E Pelikan, Martin %E Ozcan, Ender %E Parkes, Andrew J. %E Rowe, Jonathan %E Bouvry, Pascal %E Khan, Samee U. %E Danoy, Gregoire %E Tantar, Alexandru-Adrian %E Tantar, Emilia %E Dorronsoro, Bernabe %E Nicolau, Miguel %E Whitley, Darrell %D 2011 %8 December 16 jul %C Dublin, Ireland %F Bacardit:2011:GECCOcomp %K genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Bioinformatics, computational, systems, and synthetic biology, Digital entertainment technologies and arts, Evolutionary combinatorial optimization and metaheuristics, Estimation of distribution algorithms, Evolutionary multiobjective optimization, Evolution strategies and evolutionary programming, Genetics based machine learning, Generative and developmental systems, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory, Evolutionary computation in practice, Evolutionary computation techniques for constraint handling, Fourteenth international workshop on learning classifier systems, Computational intelligence on consumer games and graphics hardware (CIGPU), Medical applications of genetic and evolutionary computation (MedGEC), Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop, 1st workshop on evolutionary computation for designing generic algorithms, Bio-inspired solutions for wireless sensor networks (GECCO BIS-WSN 2011), 3rd symbolic regression and modeling workshop for GECCO 2011, Optimization by building and using probabilistic models (OBUPM-2011), Scaling behaviours of landscapes, parameters and algorithms, GreenIT evolutionary computation, Graduate students workshop, Late breaking abstracts, Specialized techniques and applications, Tutorials %U http://dl.acm.org/citation.cfm?id=2001858 %0 Conference Proceedings %T Evolutionary Computation and Explainable AI: a year in review %A Bacardit, Jaume %A Brownlee, Alexander E. I. %A Cagnoni, Stefano %A Iacca, Giovanni %A McCall, John %A Walker, David %Y Mora, Antonio M. %Y Esparcia-Alcazar, Anna I. %S Evostar 2023 Late breaking abstracts %D 2023 %8 December 14 apr %C Brno %F Bacardit:2023:evostarLBA %X In 2022, we organized the first Workshop on Evolutionary Computation and Explainable AI (ECXAI). With no pretence at completeness, this paper briefly comments on its outcome, what has happened since then in the field, and our expectations for the near future. %K genetic algorithms, genetic programming, Explainable Artificial Intelligence, XAI, Evolutionary Computation and Optimisation, Machine Learning %U https://arxiv.org/abs/2403.13950 %P 38-41 %0 Conference Proceedings %T Evolved Design of Microstrip Patch Antenna by Genetic Programming %A Bach, Thuan Bui %A Manh, Linh Ho %A Khac, Kiem Nguyen %A Beccaria, Michele %A Massaccesi, Andrea %A Zich, Riccardo %S 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA) %D 2019 %8 sep %F Bach:2019:ICEAA %X An evolved antenna is an antenna designed fully or substantially by an automatic computer design program that uses an evolutionary algorithm. In this article, we present our work in using evolutionary algorithms to an automated antenna design system. Based on the primal individual structure of genetic programming (GP) is a tree form, a new data-structure computer program which can be represented as entire parameters of an antenna has been explored. The first experiment has been done successfully for automated design the antenna for 5G mobile device which is microstrip patch antenna (MPA) that operates at 3.5 GHz with 50-160 MHz of bandwidth. The innovative MPAs are obtained by this software. This work shows a great potential of the development of the intelligent computer program for automated synthesis antenna as well as conformal antenna. %K genetic algorithms, genetic programming %R 10.1109/ICEAA.2019.8879155 %U http://dx.doi.org/10.1109/ICEAA.2019.8879155 %P 1393-1397 %0 Book Section %T Using the Genetic Algorithm with a Variable Length Genome for Architectural %A Bachman, Brandon M. %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 bachman:2000:UGAVLGA %K genetic algorithms %P 33-39 %0 Journal Article %T Evolutionary computation: comments on the history and current state %A Back, Thomas %A Hammel, U. %A Schwefel, H.-P. %J IEEE Transactions on Evolutionary Computation %D 1997 %8 apr %V 1 %N 1 %@ 1089-778X %F back:1997:survey %X Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950’s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete %K genetic algorithms, genetic programming, EA, CS, evolutionstrategies, EP %9 journal article %U http://ls11-www.cs.uni-dortmund.de/people/schwefel/publications/BHS97.ps.gz %P 3-17 %0 Book Section %T Mutation operators %A Back, Thomas %A Fogel, David B. %A Whitley, Darrell %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 back:2000:EC1 %K genetic algorithms, genetic programming %U http://www.crcpress.com/product/isbn/9780750306645 %P 237-255 %0 Conference Proceedings %T Inverse Design of Cellular Automata by Genetic Algorithms: An Unconventional Programming Paradigm %A Baeck, Thomas %A Breukelaar, Ron %A Willmes, Lars %Y Banatre, Jean-Pierre %Y Fradet, Pascal %Y Giavitto, Jean-Louis %Y Michel, Olivier %S Unconventional Programming Paradigms: International Workshop UPP 2004 %S LNCS %D 2004 %8 sep 15 17 %V 3566 %I Springer %C Le Mont Saint Michel, France %G en %F Back:2004:UPP %O Revised Selected and Invited Papers, 2005 %X Evolving solutions rather than computing them certainly represents an unconventional programming approach. The general methodology of evolutionary computation has already been known in computer science since more than 40 years, but their use to program other algorithms is a more recent invention. In this paper, we outline the approach by giving an example where evolutionary algorithms serve to program cellular automata by designing rules for their iteration. Three different goals of the cellular automata designed by the evolutionary algorithm are outlined, and the evolutionary algorithm indeed discovers rules for the CA which solve these problems efficiently. %K genetic algorithms, genetic programming, CA %R 10.1007/11527800_13 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.535.7340 %U http://dx.doi.org/10.1007/11527800_13 %P 161-172 %0 Conference Proceedings %T Automatic Algorithm Configuration for Expensive Optimization Tasks %A Baeck, Thomas %Y Hu, Ting %Y Ofria, Charles %Y Trujillo, Leonardo %Y Winkler, Stephan %S Genetic Programming Theory and Practice XX %S Genetic and Evolutionary Computation %D 2023 %8 jun 1 3 %C Michigan State University, USA %F Baeck:2023:GPTP %O Keynote %K genetic algorithms, genetic programming %0 Conference Proceedings %T Learning with missing data using Genetic Programming %A Backer, Gerriet %S The 1st Online Workshop on Soft Computing (WSC1) %D 1996 %8 19–30 aug %I Nagoya University, Japan %F backer:1996:WSC %X Learning with imprecise or missing data has been a major challenge for machine learning. A new approach using Strongly Typed Genetic Programming is proposed here, which uses extra computations based on other input data to approximate the missing values. It eliminates the need for pre-processing and makes use of correlations between the input data. The decision process itself and the handling of unknown data can be extracted from the resulting program for an analysis afterwards. Comparing it to an alternative approach on a simple example shows the usefulness of this approach. %K genetic algorithms, genetic programming, Machine learning, Missing data, Strongly Typed Genetic Programming, STGP %U http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/files/backer.ps.gz %0 Conference Proceedings %T A Generative Representation for the Evolution of Jazz Solos %A Backman, Kjell %A Dahlstedt, Palle %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/BackmanD08 %X This paper describes a system developed to create computer based jazz improvisation solos. The generation of the improvisation material uses interactive evolution, based on a dual genetic representation: a basic melody line representation, with energy constraints (’rubber band’) and a hierarchic structure of operators that processes the various parts of this basic melody. To be able to listen to and evaluate the result in a fair way, the computer generated solos have been imported into a musical environment to form a complete jazz composition. The focus of this paper is on the data representations developed for this specific type of music. This is the first published part of an ongoing research project in generative jazz, based on probabilistic and evolutionary strategies. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-78761-7_40 %U http://dx.doi.org/10.1007/978-3-540-78761-7_40 %P 371-380 %0 Conference Proceedings %T Optimizing Convolutional Neural Networks for Embedded Systems by Means of Neuroevolution %A Badan, Filip %A Sekanina, Lukas %Y Martin-Vide, Carlos %Y Pond, Geoffrey %Y Vega-Rodriguez, Miguel A. %S International Conference on Theory and Practice of Natural Computing, TPNC 2019 %S LNCS %D 2019 %8 September 11 dec %V 11934 %I Springer %C Kingston, ON, Canada %F Badan:2019:TPNC %X Automated design methods for convolutional neural networks (CNNs) have recently been developed in order to increase the design productivity. We propose a neuroevolution method capable of evolving and optimizing CNNs with respect to the classification error and CNN complexity (expressed as the number of tunable CNN parameters), in which the inference phase can partly be executed using fixed point operations to further reduce power consumption. Experimental results are obtained with TinyDNN framework and presented using two common image classification benchmark problems: MNIST and CIFAR-10. %K genetic algorithms, genetic programming, Evolutionary Algorithm Convolutional neural network Neuroevolution Embedded Systems Energy Efficiency %R 10.1007/978-3-030-34500-6_7 %U http://dx.doi.org/10.1007/978-3-030-34500-6_7 %P 109-121 %0 Conference Proceedings %T A GP-based hyper-heuristic framework for evolving 3-SAT heuristics %A Bader-El-Den, Mohamed Bahy %A Poli, Riccardo %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 1277299 %X We present, GP-HH, a framework for evolving local search 3-SAT heuristics based on GP. Evolved heuristics are compared against well-known SAT solvers with very encouraging results. %K genetic algorithms, genetic programming: Poster, heuristics, hyper heuristic, SAT %R 10.1145/1276958.1277299 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1749.pdf %U http://dx.doi.org/10.1145/1276958.1277299 %P 1749-1749 %0 Conference Proceedings %T Generating SAT Local-Search Heuristics using a GP Hyper-Heuristic Framework %A Bader-El-Den, Mohamed %A Poli, Riccardo %Y Monmarché, Nicolas %Y Talbi, El-Ghazali %Y Collet, Pierre %Y Schoenauer, Marc %Y Lutton, Evelyne %S Evolution Artificielle, 8th International Conference %S Lecture Notes in Computer Science %D 2007 %8 29 31 oct %V 4926 %I Springer %C Tours, France %F bader-el-den07:_gener_sat_local_searc_heuris %X We present GP-HH, a framework for evolving local-search 3-SAT heuristics based on GP. The aim is to obtain disposable heuristics which are evolved and used for a specific subset of instances of a problem. We test the heuristics evolved by GP-HH against well-known local-search heuristics on a variety of benchmark SAT problems. Results are very encouraging. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-79305-2_4 %U https://pure.port.ac.uk/ws/portalfiles/portal/79937/AE2007.pdf %U http://dx.doi.org/10.1007/978-3-540-79305-2_4 %P 37-49 %0 Conference Proceedings %T Inc*: An Incremental Approach for Improving Local Search Heuristics %A Bader-El-Den, Mohamed Bahy %A Poli, Riccardo %Y van Hemert, Jano I. %Y Cotta, Carlos %S Proceedings of the 8th European Conference, Evolutionary Computation in Combinatorial Optimization, EvoCOP %S Lecture Notes in Computer Science %D 2008 %8 mar 26 28 %V 4972 %I Springer %C Naples, Italy %F Bader-El-Den:2008:evocop %K genetic algorithms, genetic programming %R 10.1007/978-3-540-78604-7_17 %U http://dx.doi.org/10.1007/978-3-540-78604-7_17 %P 194-205 %0 Conference Proceedings %T Analysis and Extension of the Inc* on the Satisfiability Testing Problem %A Bader-El-Den, Mohamed %A Poli, Riccardo %Y Wang, Jun %S 2008 IEEE World Congress on Computational Intelligence %D 2008 %8 January 6 jun %I IEEE %C Hong Kong %F Bader-El-Den:2008:WCCI %X Inc (star) is a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall performance of the heuristic. The general idea of the algorithm is the following. Rather than attempting to directly solve a difficult problem, the algorithm dynamically chooses a smaller instance of the problem, and then increases the size of the instance only after the previous simplified instances have been solved, until the full size of the problem is reached. Genetic programming is used to discover new strategies for Inc*. Preliminary experiments on the satisfiability problem (SAT) problem have shown that Inc* is a competitive approach. In this paper we enhance Inc* and we experimentally test it on larger set of benchmarks, including big instances of SAT. Furthermore, we provide an analysis of the algorithm’s behaviour. %K genetic algorithms, genetic programming, SAT %R 10.1109/CEC.2008.4631250 %U EC0725.pdf %U http://dx.doi.org/10.1109/CEC.2008.4631250 %P 3342-3349 %0 Book Section %T Evolving Effective Incremental Solvers for SAT with a Hyper-Heuristic Framework Based on Genetic Programming %A Bader-El-Den, Mohamed %A Poli, Riccardo %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 Bader-El-Den:2008:GPTP %X Hyper-heuristics could simply be defined as heuristics to choose other heuristics. In other words, they are methods for combining existing heuristics to generate new ones. we use a grammar-based genetic programming hyper-heuristic framework. The framework is used for evolving effective incremental solvers for SAT. The evolved heuristics perform very well against well-known local search heuristics on a variety of benchmark SAT problems. %K genetic algorithms, genetic programming, hyper-heuristic, HH, Inc, SAT, heuristics %R 10.1007/978-0-387-87623-8_11 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.206.3331.pdf %U http://dx.doi.org/10.1007/978-0-387-87623-8_11 %P 163-179 %0 Conference Proceedings %T Evolving Heuristics with Genetic Programming %A Bader-El-Den, Mohamed %A Poli, Riccardo %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 Bader-El-Den:2008:gecco %K genetic algorithms, genetic programming, heuristics, hyperheuristics, Inc*, SAT, Evolutionary combinatorial optimisation: Poster %R 10.1145/1389095.1389212 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p601.pdf %U http://dx.doi.org/10.1145/1389095.1389212 %P 601-602 %0 Conference Proceedings %T Grammar-Based Genetic Programming for Timetabling %A Bader El Den, Mohamed %A Poli, Riccardo %Y Tyrrell, Andy %S 2009 IEEE Congress on Evolutionary Computation %D 2009 %8 18 21 may %I IEEE Press %C Trondheim, Norway %F BaderElDen:2009:cec %X We present a grammar-based genetic programming framework for the solving the timetabling problem via the evolution of constructive heuristics. The grammar used for producing new generations is based on graph colouring heuristics that have previously proved to be effective in constructing timetables as well as different slot allocation heuristics. The framework is tested on a widely used benchmarks in the field of exam time-tabling and compared with highly-tuned state-of-the- art approaches. Results shows that the framework is very competitive with other constructive techniques. %K genetic algorithms, genetic programming, hyperheuristics %R 10.1109/CEC.2009.4983259 %U P677.pdf %U http://dx.doi.org/10.1109/CEC.2009.4983259 %P 2532-2539 %0 Thesis %T Investigation of the role of Genetic Programming in a Hyper-Heuristic Framework for Combinatorial Optimization Problems %A Bader El Den, Mohamed Bahr %D 2009 %C UK %C School of Computer Science and Electronic Engineering, University of Essex %F Bader-El-Den:thesis %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://ethos.bl.uk/OrderDetails.do?did=25&uin=uk.bl.ethos.510512 %0 Conference Proceedings %T Evolving Effective Bidding Functions for Auction based Resource Allocation Framework %A Bader-El-Den, Mohamed Bahy %A Fatima, Shaheen %Y Rosa, Agostinho %S International Conference on Evolutionary Computation (ICEC 2009) %D 2009 %8 May 7 oct %I INSTICC Press %C Madeira, Portugal %F conf/ijcci/Bader-El-DenF09 %X In this paper, we present an auction based resource allocation framework. This framework, called GPAuc, uses genetic programming for evolving bidding functions. We describe GPAuc in the context of the exam timetabling problem (ETTP). In the ETTP, there is a set of exams, which must be assigned to a predefined set of slots. Here, the exam time tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders’ optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam time-tabling. %K genetic algorithms, genetic programming %U https://www.researchgate.net/publication/221616501_Evolving_Effective_Bidding_Functions_for_Auction_based_Resource_Allocation_Framework %0 Journal Article %T Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework %A Bader-El-Den, Mohamed Bahy %A Poli, Riccardo %A Fatima, Shaheen %J Memetic Computing %D 2009 %V 1 %N 3 %F journals/memetic/Bader-El-DenPF09 %X This paper introduces a Grammar-based Genetic Programming Hyper-Heuristic framework (GPHH) for evolving constructive heuristics for timetabling. In this application GP is used as an online learning method which evolves heuristics while solving the problem. In other words, the system keeps on evolving heuristics for a problem instance until a good solution is found. The framework is tested on some of the most widely used benchmarks in the field of exam timetabling and compared with the best state-of-the-art approaches. Results show that the framework is very competitive with other constructive techniques, and did outperform other hyper-heuristic frameworks on many occasions. %K genetic algorithms, genetic programming, timetabling, Hyper-heuristics, Heuristics %9 journal article %R 10.1007/s12293-009-0022-y %U http://dx.doi.org/10.1007/s12293-009-0022-y %P 205-219 %0 Conference Proceedings %T Genetic Programming for Auction Based Scheduling %A Bader-El-Den, Mohamed %A Fatima, Shaheen %Y Esparcia-Alcazar, Anna Isabel %Y Ekart, Aniko %Y Silva, Sara %Y Dignum, Stephen %Y Uyar, A. Sima %S Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010 %S LNCS %D 2010 %8 July 9 apr %V 6021 %I Springer %C Istanbul %F Bader-El-Den:2010:EuroGP %X In this paper, we present a genetic programming (GP) framework for evolving agent’s binding function (GPAuc) in a resource allocation problem. The framework is tested on the exam timetabling problem (ETP). There is a set of exams, which have to be assigned to a predefined set of slots and rooms. Here, the exam time tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders’ optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam timetabling. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-12148-7_22 %U http://dx.doi.org/10.1007/978-3-642-12148-7_22 %P 256-267 %0 Conference Proceedings %T The roles of diversity preservation and mutation in preventing population collapse in multiobjective genetic programming %A Badran, Khaled M. S. %A Rockett, Peter I. %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 1277272 %X It has been observed previously that genetic programming populations can collapse to all single node trees when a parsimony measure (tree node count) is used in a multiobjective setting. We have investigated the circumstances under which this can occur for both the 6-parity boolean learning task and a range of benchmark machine learning problems. We conclude that mutation is an important and we believe a hitherto unrecognised factor in preventing population collapse in multiobjective genetic programming; without mutation we routinely observe population collapse. From systematic variation of the mutation operator, we conclude that a necessary condition to avoid collapse is that mutation produces, on average, an increase in tree sizes (bloating) at each generation which is then counterbalanced by the parsimony pressure applied during selection. Finally, we conclude that the use of a genotype diversity preserving mechanism is ineffective at preventing population collapse. %K genetic algorithms, genetic programming, bloat, diversity preservation, multiobjective optimisation, population collapse %R 10.1145/1276958.1277272 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1551.pdf %U http://dx.doi.org/10.1145/1276958.1277272 %P 1551-1558 %0 Conference Proceedings %T Integrating Categorical Variables with Multiobjective Genetic Programming for Classifier Construction %A Badran, Khaled M. S. %A Rockett, Peter %Y O’Neill, Michael %Y Vanneschi, Leonardo %Y Gustafson, Steven %Y Esparcia Alcazar, Anna Isabel %Y De Falco, Ivanoe %Y Della Cioppa, Antonio %Y Tarantino, Ernesto %S Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008 %S Lecture Notes in Computer Science %D 2008 %8 26 28 mar %V 4971 %I Springer %C Naples %F conf/eurogp/BadranR08 %K genetic algorithms, genetic programming %R 10.1007/978-3-540-78671-9_26 %U http://dx.doi.org/10.1007/978-3-540-78671-9_26 %P 301-311 %0 Journal Article %T The influence of mutation on population dynamics in multiobjective genetic programming %A Badran, Khaled %A Rockett, Peter I. %J Genetic Programming and Evolvable Machines %D 2010 %8 mar %V 11 %N 1 %@ 1389-2576 %F Badran:2009:GPEM %X Using multiobjective genetic programming with a complexity objective to overcome tree bloat is usually very successful but can sometimes lead to undesirable collapse of the population to all single-node trees. In this paper we report a detailed examination of why and when collapse occurs. We have used different types of crossover and mutation operators (depth-fair and sub-tree), different evolutionary approaches (generational and steady-state), and different datasets (6-parity Boolean and a range of benchmark machine learning problems) to strengthen our conclusion. We conclude that mutation has a vital role in preventing population collapse by counterbalancing parsimony pressure and preserving population diversity. Also, mutation controls the size of the generated individuals which tends to dominate the time needed for fitness evaluation and therefore the whole evolutionary process. Further, the average size of the individuals in a GP population depends on the evolutionary approach employed. We also demonstrate that mutation has a wider role than merely culling single-node individuals from the population; even within a diversity-preserving algorithm such as SPEA2 mutation has a role in preserving diversity. %K genetic algorithms, genetic programming, Multiobjective genetic programming, Population collapse, Mutation, Population dynamics, MOGP, bloat %9 journal article %R 10.1007/s10710-009-9084-3 %U http://dx.doi.org/10.1007/s10710-009-9084-3 %P 5-33 %0 Thesis %T Multi-objective genetic programming with an application to intrusion detection in computer networks %A Badran, Khaled %D 2009 %C UK %C University of Sheffield %F Badran:thesis %X The widespread connectivity of computers all over the world has encouraged intruders to threaten the security of computing systems by targeting the confidentiality and integrity of information, and the availability of systems. Traditional techniques such as user authentication, data encryption and firewalls have been implemented to defend computer security but still have problems and weak points. Therefore the development of intrusion detection systems (EDS) has aroused much research interest with the aim of preventing both internal and external attacks. In misuse-based, network-based IDS, huge history files of computer network usage are analysed hi order to extract useful information, and rules are extracted to judge future network usage as legal or illegal. This process is considered as data mining for intrusion detection in computer networks. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.505474 %0 Journal Article %T Multi-class pattern classification using single, multi-dimensional feature-space feature extraction evolved by multi-objective genetic programming and its application to network intrusion detection %A Badran, Khaled %A Rockett, Peter %J Genetic Programming and Evolvable Machines %D 2012 %8 mar %V 13 %N 1 %@ 1389-2576 %F Badran:2011:GPEM %O Special Section on Evolutionary Algorithms for Data Mining %X In this paper we investigate using multi-objective genetic programming to evolve a feature extraction stage for multiple-class classifiers. We find mappings which transform the input space into a new, multi-dimensional decision space to increase the discrimination between all classes; the number of dimensions of this decision space is optimised as part of the evolutionary process. A simple and fast multi-class classifier is then implemented in this multi-dimensional decision space. Mapping to a single decision space has significant computational advantages compared to k -class-to-2-class decompositions; a key design requirement in this work has been the ability to incorporate changing priors and/or costs associated with mislabelling without retraining. We have employed multi-objective optimization in a Pareto framework incorporating solution complexity as an independent objective to be minimised in addition to the main objective of the misclassification error. We thus give preference to simpler solutions which tend to generalise well on unseen data, in accordance with Occam’s Razor. We obtain classification results on a series of benchmark problems which are essentially identical to previous, more complex decomposition approaches. Our solutions are much simpler and computationally attractive as well as able to readily incorporate changing priors/costs. In addition, we have also applied our approach to the KDD-99 intrusion detection dataset and obtained results which are highly competitive with the KDD-99 Cup winner but with a significantly simpler classification framework. %K genetic algorithms, genetic programming, Multi-class pattern classification, Feature extraction, Feature selection, Multi-objective genetic programming %9 journal article %R 10.1007/s10710-011-9143-4 %U http://dx.doi.org/10.1007/s10710-011-9143-4 %P 33-63 %0 Journal Article %T Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection %A Badran, Khaled %A Rohim, Alaa %J International Journal of Computer Applications %D 2017 %8 jun %V 168 %N 1 %I Foundation of Computer Science (FCS), NY, USA %@ 0975-8887 %F Badran:2017:IJCA %X we compare the performance of three traditional robust classifiers (Neural Networks, Support Vector Machines, and Decision Trees) with and without using multi-objective genetic programming in the feature extraction phase. We argue that effective feature extraction can significantly enhance the performance of these classifiers. We have applied these three classifiers stand alone to real world five datasets from the UCI machine learning database and also to network intrusion KDD-99 cup dataset.Then,the experiments were repeated by adding the feature extraction phase.Theresults ofthetwo approachesare compared and conclude that the effective method is to evolve optimal feature extractors that transform input pattern space into a decision space in which the performance of traditional robust classifiers can be enhanced. %K genetic algorithms, genetic programming, Pattern Recognition, Classification, Network Intrusion, Feature Extraction, Neural Network, ANN, Support Vector Machines, SVM, Decision Trees %9 journal article %R 10.5120/ijca2017914276 %U https://www.ijcaonline.org/archives/volume168/number1/27841-2017914276 %U http://dx.doi.org/10.5120/ijca2017914276 %P 37-43 %0 Conference Proceedings %T Bimodal vowel recognition using fuzzy logic networks - naive approach %A Badura, Stefan %A Fratrik, Milan %A Skvarek, Ondrej %A Klimo, Martin %S ELEKTRO, 2014 %D 2014 %8 may %F Badura:2014:ELEKTRO %X We describe an audio visual speech recognition system (AVSR) based on fuzzy logic networks. Our system is able to recognise any time sequences and achieves positive results in the task of vowel recognition. Proposed design relies on new model and methods for training fuzzy logic circuits. We combine a simple combinatorial circuit, trained with genetic programming, with a fuzzy logic memory. Combinatorial structures are combined to a fuzzy logic network. This approach leads to design of a hierarchical, massive and layered structure for dynamic signal recognition. An AVSR system is effectively composed by fusion of such networks for audio and lip-reading parts. %K genetic algorithms, genetic programming, AVSR, bimodal, fusion, speech recognition, fuzzy logic %R 10.1109/ELEKTRO.2014.6847864 %U http://dx.doi.org/10.1109/ELEKTRO.2014.6847864 %P 22-25 %0 Journal Article %T Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling %A Bae, Hyeon %A Jeon, Tae-Ryong %A Kim, Sungshin %A Kim, Hyun-Soo %A Kim, DongSeop %A Han, Seung Soo %A May, Gary S. %J Soft Computing - A Fusion of Foundations, Methodologies and Applications %D 2010 %V 14 %N 2 %@ 1432-7643 %F journals/soco/BaeJKKKHM10 %X This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation. %K genetic algorithms, genetic programming, Neural network, Particle swarm optimization, Silicon solar cell fabrication %9 journal article %R 10.1007/s00500-009-0438-9 %U http://dx.doi.org/10.1007/s00500-009-0438-9 %P 161-169 %0 Conference Proceedings %T The Job Shop Problem Solved with Simple, Basic Evolutionary Search Elements %A Bael, Patrick Van %A Devogelaere, Dirk %A Rijckaert, 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 bael:1999:TJSPSSBESE %K genetic algorithms and classifier systems %P 665-669 %0 Conference Proceedings %T Open-Ended On-Board Evolutionary Robotics for Robot Swarms %A Baele, Guy %A Bredeche, Nicolas %A Haasdijk, Evert %A Maere, Steven %A Michiels, Nico %A Van de Peer, Yves %A Schwarzer, Christopher %A Thenius, Ronald %Y Tyrrell, Andy %S 2009 IEEE Congress on Evolutionary Computation %D 2009 %8 18 21 may %I IEEE Press %C Trondheim, Norway %F Baele:2009:cec %X The SYMBRION project stands at the crossroads of Artificial Life and Evolutionary Robotics: a swarm of real robots undergoes online evolution by exchanging information in a decentralized Evolutionary Robotics Scheme: the diffusion of each individual’s genotype depends both on its ability to survive in an unknown environment as well as its ability to maximize mating opportunities during its lifetime, which suggests an implicit fitness. This paper presents early research and prospective ideas in the context of large-scale swarm robotics projects, focusing on the open-ended evolutionary approach in the SYMBRION project. One key issue of this work is to perform on-board evolution in a spatially distributed population of robots. A real-world experiment is also described which yields important considerations regarding open-ended evolution with real autonomous robots. %K genetic algorithms, genetic programming %R 10.1109/CEC.2009.4983072 %U P485.pdf %U http://dx.doi.org/10.1109/CEC.2009.4983072 %P 1123-1130 %0 Conference Proceedings %T TensorGP - Genetic Programming Engine in TensorFlow %A Baeta, Francisco %A Correia, Joao %A Martins, Tiago %A Machado, Penousal %Y Castillo, Pedro %Y Jimenez-Laredo, Juanlu %S 24th International Conference, EvoApplications 2021 %S LNCS %D 2021 %8 July 9 apr %V 12694 %I Springer Verlag %C virtual event %F Baeta:2021:evoapplications %X we resort to the TensorFlow framework to investigate the benefits of applying data vectorisation and fitness caching methods to domain evaluation in Genetic Programming. For this purpose, an independent engine was developed, TensorGP, along with a testing suite to extract comparative timing results across different architectures and amongst both iterative and vectorized approaches. Our performance benchmarks demonstrate that by exploiting the TensorFlow eager execution model, performance gains of up to two orders of magnitude can be achieved on a parallel approach running on dedicated hardware when compared to a standard iterative approach. %K genetic algorithms, genetic programming, Parallelisation, Vectorisation, TensorFlow, GPU computing %R 10.1007/978-3-030-72699-7_48 %U http://dx.doi.org/10.1007/978-3-030-72699-7_48 %P 763-778 %0 Conference Proceedings %T Speed Benchmarking of Genetic Programming Frameworks %A Baeta, Francisco %A Correia, Joao %A Martins, Tiago %A Machado, Penousal %Y Chicano, Francisco %Y Tonda, Alberto %Y Krawiec, Krzysztof %Y Helbig, Marde %Y Cleghorn, Christopher W. %Y Wilson, Dennis G. %Y Yannakakis, Georgios %Y Paquete, Luis %Y Ochoa, Gabriela %Y Bacardit, Jaume %Y Gagne, Christian %Y Mostaghim, Sanaz %Y Jourdan, Laetitia %Y Schuetze, Oliver %Y Posik, Petr %Y Segura, Carlos %Y Tinos, Renato %Y Cotta, Carlos %Y Heywood, Malcolm %Y Zhang, Mengjie %Y Trujillo, Leonardo %Y Miikkulainen, Risto %Y Xue, Bing %Y Neumann, Aneta %Y Allmendinger, Richard %Y Ishikawa, Fuyuki %Y Medina-Bulo, Inmaculada %Y Neumann, Frank %Y Sutton, Andrew M. %S Proceedings of the 2021 Genetic and Evolutionary Computation Conference %S GECCO ’21 %D 2021 %8 jul 10 14 %I Association for Computing Machinery %C internet %F Baeta:2021:GECCO %X Genetic Programming (GP) is known to suffer from the burden of being computationally expensive by design. While, over the years, many techniques have been developed to mitigate this issue, data vectorisation, in particular, is arguably still the most attractive strategy due to the parallel nature of GP. In this work,we employ a series of benchmarks meant to compare both the performance and evolution capabilities of different vectorised and iterative implementation approaches across several existing frameworks. Namely, TensorGP, a novel open source engine written in Python, is shown to greatly benefit from the TensorFlow library to accelerate the domain evaluation phasein GP. The presented performance benchmarks demonstratethat the TensorGP engine manages to pull ahead, with relativespeedups above two orders of magnitude for problems with ahigher number of fitness cases. Additionally, as a consequenceof being able to compute larger domains, we argue that TensorGP performance gains aid the discovery of more accurate candidate solutions. %K genetic algorithms, genetic programming, Parallelisation, Vectorisation, TensorFlow, GPU Computing %R 10.1145/3449639.3459335 %U http://dx.doi.org/10.1145/3449639.3459335 %P 768-775 %0 Journal Article %T Exploring Genetic Programming in TensorFlow with TensorGP %A Baeta, Francisco %A Correia, Joao %A Martins, Tiago %A Machado, Penousal %J SN Computer Science %D 2022 %V 3 %N 2 %F baeta:2022:SN %K genetic algorithms, genetic programming, GPU %9 journal article %R 10.1007/s42979-021-01006-8 %U http://link.springer.com/article/10.1007/s42979-021-01006-8 %U http://dx.doi.org/10.1007/s42979-021-01006-8 %0 Conference Proceedings %T Exploring Evolutionary Generators within Generative Adversarial Networks %A Baeta, Francisco %A Correia, Joao %A Martins, Tiago %A Machado, Penousal %Y Mouret, Jean-Baptiste %Y Qin, Kai %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F baeta:2024:GECCOcomp %X Since their introduction, Generative Adversarial Networks (GANs) have represented the bulk of approaches used in image generation. Before GANs, such approaches used Machine Learning (ML) exclusively to tackle the training problems inherent to GANs. However, in recent years, evolutionary approaches have been making a comeback, not only across the field of ML but in generative modelling specifically. Successes in GPU-accelerated Genetic Programming (GP) led to the introduction of the TGPGAN framework, which used GP as a replacement for the deep convolutional network conventionally used as a GAN generator. In this paper, we delve further into the generative capabilities of evolutionary computation within adversarial models and extend the study performed in TGPGAN to analyse other evolutionary approaches. Similarly to TGPGAN, the presented approaches replace the generator component of a Deep Convolutional GAN (DCGAN): one with a line-drawing Genetic Algorithm (GA) and another with a Compositional Pattern Producing Network (CPPN). Our comparison of generative performance shows that the GA used manages to perform competitively with the original framework. More importantly, this work showcases the viability of other evolutionary approaches other than GP for the purpose of image generation. %K genetic algorithms, genetic programming, evolutionary computation, generative adversarial networks, TGPGAN, Evolutionary Machine Learning: Poster %R 10.1145/3638530.3654348 %U https://www.cisuc.uc.pt/download-file/20372/0maUqQ5FssF4ICu9v7wf %U http://dx.doi.org/10.1145/3638530.3654348 %P 251-254 %0 Conference Proceedings %T Learning Ranking Functions by Genetic Programming Revisited %A Baeza-Yates, Ricardo %A Cuzzocrea, Alfredo %A Crea, Domenico %A Lo Bianco, Giovanni %S Database and Expert Systems Applications %D 2018 %I Springer %F baeza-yates:2018:DESA %K genetic algorithms, genetic programming %R 10.1007/978-3-319-98812-2_34 %U http://link.springer.com/chapter/10.1007/978-3-319-98812-2_34 %U http://dx.doi.org/10.1007/978-3-319-98812-2_34 %0 Conference Proceedings %T An effective and efficient algorithm for ranking web documents via genetic programming %A Baeza-Yates, Ricardo %A Cuzzocrea, Alfredo %A Crea, Domenico %A Bianco, Giovanni Lo %Y Hung, Chih-Cheng %Y Papadopoulos, George A. %S Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019, Limassol, Cyprus, April 8-12, 2019 %D 2019 %I ACM %F DBLP:conf/sac/Baeza-YatesCCB19 %K genetic algorithms, genetic programming %R 10.1145/3297280.3297385 %U https://doi.org/10.1145/3297280.3297385 %U http://dx.doi.org/10.1145/3297280.3297385 %P 1065-1072 %0 Journal Article %T Multi-objective genetic programming strategies for topic-based search with a focus on diversity and global recall %A Baggio, Cecilia %A Lorenzetti, Carlos M. %A Cecchini, Rocio L. %A Maguitman, Ana Gabriela %J PeerJ Comput. Sci. %D 2023 %V 9 %F DBLP:journals/peerj-cs/BaggioLCM23 %K genetic algorithms, genetic programming %9 journal article %R 10.7717/PEERJ-CS.1710 %U https://doi.org/10.7717/peerj-cs.1710 %U http://dx.doi.org/10.7717/PEERJ-CS.1710 %P e1710 %0 Journal Article %T Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio %A Baghbani, Abolfazl %A Nguyen, Minh Duc %A Alnedawi, Ali %A Milne, Nick %A Baumgartl, Thomas %A Abuel-Naga, Hossam %J Applied Sciences %D 2023 %V 13 %N 8 %@ 2076-3417 %F baghbani:2023:AS %X Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R2 values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/app13084934 %U https://www.mdpi.com/2076-3417/13/8/4934 %U http://dx.doi.org/10.3390/app13084934 %P ArticleNo.4934 %0 Journal Article %T Predicting the Strength Performance of Hydrated-Lime Activated Rice Husk Ash-Treated Soil Using Two Grey-Box Machine Learning Models %A Baghbani, Abolfazl %A Soltani, Amin %A Kiany, Katayoon %A Daghistani, Firas %J Geotechnics %D 2023 %V 3 %N 3 %@ 2673-7094 %F baghbani:2023:Geotechnics %X Geotechnical engineering relies heavily on predicting soil strength to ensure safe and efficient construction projects. This paper presents a study on the accurate prediction of soil strength properties, focusing on hydrated-lime activated rice husk ash (HARHA) treated soil. To achieve precise predictions, the researchers employed two grey-box machine learning models–classification and regression trees (CART) and genetic programming (GP). These models introduce innovative equations and trees that readers can readily apply to new databases. The models were trained and tested using a comprehensive laboratory database consisting of seven input parameters and three output variables. The results indicate that both the proposed CART trees and GP equations exhibited excellent predictive capabilities across all three output variables–California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (Rvalue) (according to the in-situ cone penetrometer test). The GP proposed equations, in particular, demonstrated a superior performance in predicting the UCS and Rvalue parameters, while remaining comparable to CART in predicting the CBR. This research highlights the potential of integrating grey-box machine learning models with geotechnical engineering, providing valuable insights to enhance decision-making processes and safety measures in future infrastructural development projects. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/geotechnics3030048 %U https://www.mdpi.com/2673-7094/3/3/48 %U http://dx.doi.org/10.3390/geotechnics3030048 %P 894-920 %0 Journal Article %T Predicting the Compression Index of Clayey Soils Using a Hybrid Genetic Programming and XGBoost Model %A Baghbani, Abolfazl %A Kiany, Katayoon %A Abuel-Naga, Hossam %A Lu, Yi %J Applied Sciences %D 2025 %V 15 %N 4 %@ 2076-3417 %F baghbani:2025:AS %X The accurate prediction of the compression index (Cc) is crucial for understanding the settlement behaviour of clayey soils, which is a key factor in geotechnical design. Traditional empirical models, while widely used, often fail to generalise across diverse soil conditions due to their reliance on simplified assumptions and regional dependencies. This study proposed a novel hybrid method combining Genetic Programming (GP) and XGBoost methods. A large database (including 385 datasets) of geotechnical properties, including the liquid limit (LL), the plasticity index (PI), the initial void ratio (e0), and the water content (w), was used. The hybrid GP-XGBoost model achieved remarkable predictive performance, with an R2 of 0.966 and 0.927 and mean squared error (MSE) values of 0.001 and 0.001 for training and testing datasets, respectively. The mean absolute error (MAE) was also exceptionally low at 0.030 for training and 0.028 for testing datasets. Comparative analysis showed that the hybrid model outperformed the standalone GP (R2 = 0.934, MSE = 0.003) and XGBoost (R2 = 0.939, MSE = 0.002) models, as well as traditional empirical methods such as Terzaghi and Peck (R2 = 0.149, MSE = 0.090). Key findings highlighted that the initial void ratio and water content are the most influential predictors of Cc, with feature importance scores of 0.55 and 0.27, respectively. The novelty of the proposed method lies in its ability to combine the interpretability of GP with the computational efficiency of XGBoost and results in a robust and adaptable predictive tool. This hybrid approach has the potential to advance geotechnical engineering practices by providing accurate and interpretable models for diverse soil profiles and complex site conditions. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/app15041926 %U https://www.mdpi.com/2076-3417/15/4/1926 %U http://dx.doi.org/10.3390/app15041926 %P ArticleNo.1926 %0 Journal Article %T Artificial-Intelligence-Based Prediction of Crack and Shrinkage Intensity Factor in Clay Soils During Desiccation %A Baghbani, Abolfazl %A Choudhury, Tanveer %A Costa, Susanga %J Designs %D 2025 %V 9 %N 3 %@ 2411-9660 %F baghbani:2025:Designs %X Desiccation-induced cracking in clay soils significantly affects the structural performance and durability of geotechnical systems. This study presents a data-driven approach to predict the Crack and Shrinkage Intensity Factor (CSIF), a comprehensive index quantifying both crack formation and shrinkage behaviour in drying soils. A database of 100 controlled desiccation tests was developed using five clay mixtures with varying plasticity indices, which were subjected to a range of drying rates, soil thicknesses and initial conditions. Four predictive models–Multiple Linear Regression (MLR), Classification and Regression Random Forest (CRRF), Artificial Neural Network (ANN) and Genetic Programming (GP)–were evaluated. The ANN model using Bayesian Regularization demonstrated superior performance (R = 0.99, MAE = 5.44), followed by CRRF and symbolic GP equations. Sensitivity analysis identified drying rate and soil thickness as the most influential parameters, while initial moisture content and ambient conditions were found to be redundant when the drying rate was included. This study not only advances the predictive modelling of desiccation cracking but also introduces interpretable equations for practical engineering uses. The developed models offer valuable tools for crack risk assessment in clay liners, soil covers and expansive soil foundations. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/designs9030054 %U https://www.mdpi.com/2411-9660/9/3/54 %U http://dx.doi.org/10.3390/designs9030054 %P ArticleNo.54 %0 Journal Article %T The use of machine learning in boron-based geopolymers: Function approximation of compressive strength by ANN and GP %A Bagheri, Ali %A Nazari, Ali %A Sanjayan, Jay %J Measurement %D 2019 %V 141 %@ 0263-2241 %F BAGHERI:2019:Measurement %X This paper employs artificial intelligence methods in order to create a function for compressive strength of the boroaluminosilicate geopolymers based on mixture proportion variables. Boroaluminosilicate geopolymers (BASGs), a group of boron-based alkali-activated materials, not only minimise the carbon footprint in the construction industry but also decrease the consumption of energy and natural resources. Australian fly ash and iron making slag are activated in sodium and boron-based alkaline medium in order to produce the geopolymer binders. The current study employs artificial neural network in order to classify the collected data into train, test, and validation followed by genetic programming for developing a function to approximate the compressive strength of BASGs. The independent variables comprise the percentage of fly ash and slag as well as ratios of boron, silicon, and sodium ions in the alkaline solution. The performance of each method is assessed by the acquired regression and the error parameters. The obtained results show that the percent of silicon and boron ions, with positive direct correlation and the largest power in the function respectively, have the most significant effects on the compressive strength of BASG. The assessment factors, including R-squared 0.95 and root-mean-square error 0.07 in the testing data, indicate that the model explains all the variability of the response data around its mean. It implies a high level of accuracy and reliability for the model %K genetic algorithms, genetic programming, Boron-activated geopolymer, Artificial intelligence, Aluminosilicate, Machine learning, Energy and resources %9 journal article %R 10.1016/j.measurement.2019.03.001 %U http://www.sciencedirect.com/science/article/pii/S0263224119302106 %U http://dx.doi.org/10.1016/j.measurement.2019.03.001 %P 241-249 %0 Conference Proceedings %T Dejong Function Optimization by Means of a Parallel Approach to Fuzzified Genetic Algorithm %A Bagheri, Ebrahim %A Deldari, Hossein %Y Bellavista, Paolo %Y Chen, Chi-Ming %Y Corradi, Antonio %Y Daneshmand, Mahmoud %S Proceedings of the 11th IEEE Symposium on Computers and Communications (ISCC 2006) %D 2006 %8 26 29 jun %I IEEE Computer Society %C Cagliari, Sardinia, Italy %@ 0-7695-2588-1 %F DBLP:conf/iscc/BagheriD06 %X Genetic Algorithms are very powerful search methods that are used in different optimisation problems. Parallel versions of genetic algorithms are easily implemented and usually increase algorithm performance [4]. Fuzzy control as another optimisation solution along with genetic algorithms can significantly increase algorithm performance. Two variations for genetic algorithm and fuzzy system composition exist. In the first approach Genetic algorithms are used to optimise and model the structure of fuzzy systems through knowledge base or membership function design while the second approach exploits fuzzy to dynamically supervise genetic algorithm performance by speedily reaching an optimal solution. In this paper we propose a new method for fuzzy parallel genetic algorithms, in which a parallel client-server single population fuzzy genetic algorithm is configured to optimise the performance of the first three Dejong functions in order to reach a global solution in the least possible iterations. Simulations show much improvement in genetic algorithm performance evaluation. %K genetic algorithms %R 10.1109/ISCC.2006.57 %U http://dx.doi.org/10.1109/ISCC.2006.57 %P 675-680 %0 Journal Article %T Multi-expression programming based model for prediction of formation enthalpies of nitro-energetic materials %A Bagheri, Mehdi %A Gandomi, Amir Hossein %A Bagheri, Mehrdad %A Shahbaznezhad, Mohcen %J Expert Systems %D 2013 %8 feb %V 30 %N 1 %F journals/es/BagheriGBS13 %X There has been considerable interest in predicting the properties of nitro-energetic materials to improve their performance. Not to mention insightful physical knowledge, computational-aided molecular studies can expedite the synthesis of novel energetic materials through cost reduction labours and risky experimental tests. In this paper, quantitative structure-property relationship based on multi-expression programming employed to correlate the formation enthalpies of frequently used nitro-energetic materials with their molecular properties. The simple yet accurate obtained model is able to correlate the formation enthalpies of nitro-energetic materials to their molecular structure with the accuracy comparable to experimental precision. %K genetic algorithms, genetic programming, nitro-energetic materials, multi-expression programming, formation enthalpy, QSPR %9 journal article %R 10.1111/j.1468-0394.2012.00623.x %U http://dx.doi.org/10.1111/j.1468-0394.2012.00623.x %P 66-78 %0 Journal Article %T A simple modelling approach for prediction of standard state real gas entropy of pure materials %A Bagheri, M. %A Borhani, T. N. G. %A Gandomi, A. H. %A Manan, Z. A. %J SAR and QSAR in Environmental Research %D 2014 %V 25 %N 9 %F Bagheri:2015:SAR_QSAR_ER %X The performance of an energy conversion system depends on exergy analysis and entropy generation minimisation. A new simple four-parameter equation is presented in this paper to predict the standard state absolute entropy of real gases (SSTD). The model development and validation were accomplished using the Linear Genetic Programming (LGP) method and a comprehensive dataset of 1727 widely used materials. The proposed model was compared with the results obtained using a three-layer feed forward neural network model (FFNN model). The root-mean-square error (RMSE) and the coefficient of determination (r2) of all data obtained for the LGP model were 52.24 J/(mol K) and 0.885, respectively. Several statistical assessments were used to evaluate the predictive power of the model. In addition, this study provides an appropriate understanding of the most important molecular variables for exergy analysis. Compared with the LGP based model, the application of FFNN improved the r-squared to 0.914. The developed model is useful in the design of materials to achieve a desired entropy value. %K genetic algorithms, genetic programming, linear genetic programming (LGP), standard state absolute entropy of real gases (SSTD), feed forward neural network (FFNN), quantitative structure entropy relationship, exergy analysis %9 journal article %R 10.1080/1062936X.2014.942356 %U http://www.tandfonline.com/doi/abs/10.1080/1062936X.2014.942356 %U http://dx.doi.org/10.1080/1062936X.2014.942356 %P 695-710 %0 Journal Article %T Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review %A Bagheri, Majid %A Akbari, Ali %A Mirbagheri, Sayed Ahmad %J Process Safety and Environmental Protection %D 2019 %V 123 %@ 0957-5820 %F BAGHERI:2019:PSEP %X This paper critically reviews all artificial intelligence (AI) and machine learning (ML) techniques for the better control of membrane fouling in filtration processes, with the focus on water and wastewater treatment systems. Artificial neural networks (ANNs), fuzzy logic, genetic programming and model trees were found to be four successfully employed modeling techniques. The results show that well-known ANNs such as multilayer perceptron and radial basis function can predict membrane fouling with an R2 equal to 0.99 and an error approaching zero. Genetic algorithm (GA) and particle swarm optimization (PSO) are optimization methods successfully applied to optimize parameters related to membrane fouling. These optimization techniques indicated high capabilities in tuning various parameters such as transmembrane pressure, crossflow velocity, feed temperature, and feed pH. The results of this survey demonstrate that hybrid intelligent models using intelligent optimization methods such as GA and PSO for adjusting their weights and functions perform better than single models. Clustering analysis, image recognition, and feature selection are other employed intelligent techniques with positive role in the control of membrane fouling. The application of AI and ML techniques in an advanced control system can reduce the costs of treatment by monitoring of membrane fouling, and taking the best action when necessary %K genetic algorithms, genetic programming, Membrane bioreactors, Membrane fouling, Artificial intelligence, Machine learning, Control system %9 journal article %R 10.1016/j.psep.2019.01.013 %U http://www.sciencedirect.com/science/article/pii/S0957582018310863 %U http://dx.doi.org/10.1016/j.psep.2019.01.013 %P 229-252 %0 Conference Proceedings %T An Evolutionary Approach to Multiperiod Asset Allocation %A Baglioni, Stefania %A da Costa Pereira, Celia %A Sorbello, Dario %A Tettamanzi, Andrea G. B. %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 baglioni:2000:eampaa %X Portfolio construction can become a very complicated problem, as regulatory constraints, individual investor’s requirements, non-trivial indices of risk and subjective quality measures are taken into account, together with multiple investment horizons and cash-flow planning. This problem is approached using a tree of possible scenarios for the future, and an evolutionary algorithm is used to optimize an investment plan against the desired criteria and the possible scenarios. An application to a real defined benefit pension fund case is discussed. %K genetic algorithms, genetic programming: Poster %R 10.1007/978-3-540-46239-2_16 %U http://mago.crema.unimi.it/pub/BaglioniDaCostaPereiraSorbelloTettamanzi2000.ps %U http://dx.doi.org/10.1007/978-3-540-46239-2_16 %P 225-236 %0 Conference Proceedings %T Using an Adaptive Agent to Bid in a Simplified Model of the UK Market in Electricity %A Bagnall, A. J. %A Smith, G. D. %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 bagnall:1999:UAABSMUME %K genetic algorithms and classifier systems, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/Bagnall1999b.ps.gz %P 774 %0 Conference Proceedings %T On the Relevance of Using Gene Expression Programming in Destination-Based Traffic Engineering %A Bagula, Antoine B. %A Wang, Hong F. %S Computational Intelligence and Security %S Lecture Notes in Computer Science %D 2005 %V 3801 %F Bagula:2005:ciS %X This paper revisits the problem of Traffic Engineering (TE) to assess the relevance of using Gene Expression Programming (GEP) as a new fine-tuning algorithm in destination-based TE. We present a new TE scheme where link weights are computed using GEP and used as fine-tuning parameters in destination-based path selection. We apply the newly proposed TE scheme to compute the routing paths for the traffic offered to a 23- and 30-node test networks under different traffic conditions and differentiated services situations. We evaluate the performance achieved by the GEP algorithm compared to a memetic and the Open Shortest Path First (OSPF) algorithms in a simulated routing environment using the NS packet level simulator. Preliminary results reveal the relative efficiency of GEP compared to the memetic algorithm and OSPF routing. %K genetic algorithms, genetic programming, Gene Expression Programming %R 10.1007/11596448 %U http://dx.doi.org/10.1007/11596448 %P 224-229 %0 Conference Proceedings %T Traffic Engineering Next Generation IP Networks Using Gene Expression Programming %A Bagula, Antoine B. %S 10th IEEE/IFIP Network Operations and Management Symposium, NOMS 2006 %D 2006 %I IEEE %C Vancouver %F bagula_2006_NOMS %X This paper addresses the problem of Traffic Engineering (TE) to evaluate the performance of evolutionary algorithms when used as IP routing optimisers and assess the relevance of using ’Gene Expression Programming (GEP)’ as a new fine-tuning algorithm in destination- and flow-based TE. We consider a TE scheme where link weights are computed using GEP and used as either fine-tuning parameters in Open Shortest Path First (OSPF) routing or static routing cost in Constraint Based Rouiigg((CRR. Thh reeuutligg SPFa nd CBR algorithms are referred to as OSPFgepand CBRgep. The GEP algorithm is based on a hybrid optimisation model where local search complements the global search implemented by classical evolutionary algorithms to improve the genetic individuals fitness through hill-climbing. We apply the newly proposed TE scheme to compute the routing paths for the traffic offered to a 23-, 28- and 30-node test networks under different traffic conditions and differentiated services situations. We evaluate the performance achieved by the OSPFgep, CBRgepalgorithms and OSPFmal, a destination-based routing algorithm where OSPF path selection is driven by the link weights computed by a Memetic Algorithm (MA). We compare the performance achieved by the OSPFgepalgorithm to the performance of the OSPFmaand OSPF algorithms in a simulated routing environment using NS. We also compare the quality of the paths found by the CBRgepalgorithm to the quality of the paths computed by the Constraint Shortest Path First (CSPF) algorithm when routing bandwidth-guaranteed tunnels using connection-level simulation. %K genetic algorithms, genetic programming, Gene Expression Programming %R 10.1109/NOMS.2006.1687554 %U http://dx.doi.org/10.1109/NOMS.2006.1687554 %P 230-239 %0 Thesis %T Hybrid Routing in Next Generation IP Networks: QoS Routing Mechanisms and Network Control Strategies %A Bagula, Antoine B. %D 2006 %8 dec %C Stockholm, Sweden %C Royal Institute of Technology (KTH) %F urn_nbn_se_kth_diva-4213-2__fulltext %X Communication networks have evolved from circuit-switched and hop-by-hop routed systems into hybrid data/optical networks using the Internet as a common backbone carrying narrow- and broad-band traffic offered by a multitude of access networks. This data/optical backbone is built around a multi-technology/multi-protocol routing architecture which runs the IP protocols in a collapsed IP stack where ATM and SONET/SDH have been replaced by the suite of Generalised Multiprotocol Label Switching (GMPLS) protocols. A further evolution referred to as “IP over Photons” or “All IP - All Optical” is expected where “redundant intermediate layers” will be eliminated to run IP directly on top of optical cross-connects (OXCs) with the expectation of achieving savings on operation expenditures (OPEX) and capital expenditures (CAPEX). “IP over Photons” has been stalled by the immaturity in the control and data plane technologies leading to complex and time-consuming manual network planning and configurations which require a group of “layer experts” to operate and maintain a hybrid data/optical network. By making the status of each link and node of a data/optical network visible to a common control, GMPLS protocols have opened the way for automated operation and management allowing the different layers of an IP stack to be managed by a single network operator. GMPLS protocols provide the potential to make more efficient use of the IP backbone by having network management techniques such as Traffic Engineering (TE) and Network Engineering (NE), once the preserve of telecommunications, to be reinvented and deployed to effect different Quality of Service (QoS) requirements in the IP networks. NE moves bandwidth to where the traffic is offered to the network while TE moves traffic to where the bandwidth is available to achieve QoS agreements between the current and expected traffic and the available resources. However,several issues need to be resolved before TE and NE be effectively deployed in emerging and next generation IP networks. These include (1) the identification of QoS requirements of the different network layer interfaces of the emerging and next generation IP stack (2) the mapping of these QoS requirements into QoS routing mechanisms and network control strategies and (3) the deployment of these mechanisms and strategies within and beyond an Internet domain’s boundaries to maximise the engineering and economic efficiency. Building upon different frameworks and research fields, this thesis revisits the issue of Traffic and Network Engineering (TE and NE) to present and evaluate the performance of different QoS routing mechanisms and network control strategies when deployed at different network layer interfaces of a hybrid data/optical network where an IP over MPLS network is layered above an MP lambdaS/Fibre infrastructure. These include mechanisms and strategies to be deployed at the IP/MPLS, MPLS/MP LS and MP lambdaS/Fiber network layer interfaces. The main contributions of this thesis are threefold. First we propose and compare the performance of hybrid routing approaches to be deployed in IP/MPLS networks by combining connectionless routing mechanisms used by classical IGP protocols and the connection oriented routing approach borrowed from MPLS. Second, we present QoS routing mechanisms and network control strategies to be deployed at the MPLS/MP lambdaS network layer interface with a focus on contention-aware routing and inter-layer visibility to improve multi-layer optimality and resilience. Finally, we build upon fiber transmission characteristics to propose QoS routing mechanisms where the routing in the MPLS and MP lS layers is conducted by Photonic characteristics of the fiber such as the availability of the physical link and its failure risk group probability. %K genetic algorithms, genetic programming, Gene Expression Programming %9 Doctor of Technology %9 Ph.D. thesis %U http://kth.diva-portal.org/smash/record.jsf?pid=diva2:11272 %0 Journal Article %T Mixing gamma-Al2O3, silica-ZIF-8 and activated carbon nanoparticles in aqueous N-methyldiethanolamine+sulfolane as a nanofluid for application on CO2 absorption %A Bahadori, Mohammad Keshavarz %A Golhosseini, Reza %A Shokouhi, Mohammad %A Zoghi, Ali T. %J Journal of CO2 Utilization %D 2024 %V 79 %@ 2212-9820 %F BAHADORI:2024:jco2u %X Mixing of Al2O3, superactive carbon (SAC), and Si-ZIF-8 nanoparticles in alkanol amine solvents with aqueous and hybrid context were used to study the absorption of carbon dioxide. Hybrid-based nanofluids composed of aqueous N- methyldiethanolamine (MDEA) + Sulfolane (SFL) + small amount of nano particles including-gamma-Al2O3, Si-ZIF-8 and SAC in three concentrations levels, were prepared by weighting each components. The Zeta potential of suspensions was found thatgamma-Al2O3, Si-ZIF-8 and super activated carbon is stable in aqueous solvents. Absorption measurements were performed using the static method at a temperature of T = 313.15 K, gas partial pressure ranges up to about 1.2 MPa, nanoparticle concentrations of 0.02 - 0.50 by weight percent, and fixed concentration of MDEA (40 wtpercent) and SFL (30 wtpercent). It was found that in the hybrid context (i.e., MDEA 40 wtpercent + SFL 30 wtpercent), the addition of gamma-Al2O3 (0.1 wtpercent), Si-ZIF-8 (0.02 wtpercent) and SAC (0.1 wtpercent) has maximum effect on CO2 absorption. In aqueous context (i.e., MDEA 40 wtpercent), nanofluids used in the present study have no significant effect on CO2 capacity. The importance of the addition of SFL in nanofluids was discussed, and the experimental solubility data was modeled using Genetic Programming approach. Average absolute relative deviation (AARD), Coefficient of determination R2, as well as Root mean square error (RMSE) were 10.43percent & 8.94percent, 0.985 & 0.983, 0.051 & 0.054 for training and validating data, respectively. It is found that the determined parameters give satisfactory predictions in the modeling of the solubility %K genetic algorithms, genetic programming, Hybrid alkanolamine solution, Si-ZIF-8, gamma-AlO, Super activated carbon, Nanofluid %9 journal article %R 10.1016/j.jcou.2023.102650 %U https://www.sciencedirect.com/science/article/pii/S2212982023002615 %U http://dx.doi.org/10.1016/j.jcou.2023.102650 %P 102650 %0 Journal Article %T Measurements of density and viscosity of carbon dioxide-loaded and -unloaded nano-fluids: Experimental, genetic programming and physical interpretation approaches %A Bahadori, Mohammad Keshavarz %A Shokouhi, Mohammad %A Golhosseini, Reza %J Chemical Engineering Journal Advances %D 2024 %V 18 %@ 2666-8211 %F BAHADORI:2024:ceja %X In the present study, the density and viscosity of the CO2-loaded and -unloaded base solution and nano-fluid were experimentally measured and investigated from an intermolecular point of view. Nano-fluids are composed of nano-particles such as Al2O3 (0.1 wt.percent), Silica-2-methylimidazole, zinc salt (Si-ZIF-8) (0.02 wt.percent), and super activated carbon (SAC) (0.1 wt.percent) dispersed in aqueous and hybrid Methyl diethanolamine context (MDEA, 40 wt.percent) +Sulfolane (SFL), 30 wt.percent) +H2O) Experimental measurements were carried out at the low-temperature ranges 303.15-315.15 K, atmospheric pressure, and three different CO2 loadings. The results show that nanomaterials do not have a significant effect on the density and viscosity of the unloaded suspension; however, the density and viscosity of loaded suspensions and base solvent become more by increasing CO2 concentration. In the case of CO2-loaded fluids, the comparison of the results in the presence and absence of nanoparticles shows that the density of the solution is not much different in the two cases, but the viscosity of CO2-loaded in Si-ZIF-8, SAC, and gamma-Al2O3 base nano-fluids in comparison with base solvent shows an increase of 35 percent in high CO2 loading, 0.3 mol CO2 per mol MDEA. Density and viscosity experimental data were modeled using the Genetic Programming approach. The highest values of absolute average relative deviation (AARD) and root mean square error (RMSE) parameters obtained for modeling data are 3.04 and 0.317, respectively, and the lowest value of regression coefficient (R2) is 0.995, which indicates the appropriate fitting of the results %K genetic algorithms, genetic programming, Density, Viscosity, Si-zif-8, Super activated carbon, Nano-fluid %9 journal article %R 10.1016/j.ceja.2024.100600 %U https://www.sciencedirect.com/science/article/pii/S2666821124000188 %U http://dx.doi.org/10.1016/j.ceja.2024.100600 %P 100600 %0 Conference Proceedings %T Generating ternary stock trading signals using fuzzy genetic network programming %A Bahar, Hosein Hamisheh %A Zarandi, Mohammad Hossein Fazel %A Esfahanipour, Akbar %S 2016 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2016, El Paso, TX, USA, October 31 - November 4, 2016 %D 2016 %I IEEE %F DBLP:conf/nafips/BaharZE16 %K genetic algorithms, genetic programming %R 10.1109/NAFIPS.2016.7851630 %U https://doi.org/10.1109/NAFIPS.2016.7851630 %U http://dx.doi.org/10.1109/NAFIPS.2016.7851630 %P 1-6 %0 Journal Article %T On the Predictability of Risk Box Approach by Genetic Programming Method for Bankruptcy Prediction %A Bahiraie, Alireza %A Akma bt Ibrahim, Noor %A Azhar, A. K. M. %J American Journal of Applied Sciences %D 2009 %V 6 %N 9 %@ 1546-9239 %F Bahiraie:2009:AJAS %X \bf Problem statement: Theoretical based data representation is an important tool for model selection and interpretations in bankruptcy analysis since the numerical representation are much less transparent. Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-scalicity are solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. \bf Approach: This study presented new geometric technique for empirical analysis of bankruptcy risk using financial ratios. Within this framework, we proposed the use of a new ratio representation which named Risk Box measure (RB). We demonstrated the application of this geometric approach for variable representation, data visualization and financial ratios at different stages of corporate bankruptcy prediction models based on financial balance sheet ratios. These stages were the selection of variables (predictors), accuracy of each estimation model and the representation of each model for transformed and common ratios. \bf Results: We provided evidence of extent to which changes in values of this index were associated with changes in each axis values and how this may alter our economic interpretation of changes in the patterns and direction of risk components. Results of Genetic Programming (GP) models were compared as different classification models and results showed the classifiers outperform by modified ratios.\bf Conclusion/Recommendations: In this study, a new dimension to risk measurement and data representation with the advent of the Share Risk method (SR) was proposed. Genetic programming method is substantially superior to the traditional methods such as MDA or Logistic method. It was strongly suggested the use of SR methodology for ratio analysis, which provided a conceptual and complimentary methodological solution to many problems associated with the use of ratios. Respectively, GP will provide heuristic non linear regression as a tool in providing forecasting regression for studies associated with financial data. Genetic programming as one of the modern classification method out performs by the use of modified ratios. Our new method would be a general methodological guideline associated with financial data analysis. %K genetic algorithms, genetic programming, ratios analysis, risk box, bankruptcy prediction %9 journal article %U http://www.scipub.org/fulltext/ajas/ajas691748-1757.pdf %P 1748-1757 %0 Journal Article %T A novel approach for modeling and optimization of surfactant/polymer flooding based on Genetic Programming evolutionary algorithm %A Bahrami, Peyman %A Kazemi, Pezhman %A Mahdavi, Sedigheh %A Ghobadi, Hossein %J Fuel %D 2016 %V 179 %@ 0016-2361 %F Bahrami:2016:Fuel %X In this research, Genetic Programming (GP) as a novel method for modelling the Recovery Factor (RF) and the Net Present Value (NPV) in Surfactant-Polymer (SP) flooding is presented. The GP modelling, has the advantage that the created models did not require a fundamental description of the physical processes. The GP created mathematical functions for both outputs as a function of important parameters which involves in the SP flooding based on 202 different data. Moreover, 10-fold cross validation were employed to check the models overfitting. The Normalized Root Mean Squared Error (NRMSE) and the coefficient of determination (R2) of 4.83percent, 0.963 for the RF model, and 5.68percent, 0.946 for NPV model represented the accuracy of models. The importance and effect of variables on models were investigated, and simultaneous optimization was performed on both models to find the best results in terms of higher RF and NPV. The highest values of 55.03 and 7.3 Million US Dollars (MMUSD) for RF and NPV were achieved as a result of this optimization. %K genetic algorithms, genetic programming, RSM, Optimization, Polymer-surfactant flooding, 10-Fold cross validation %9 journal article %R 10.1016/j.fuel.2016.03.095 %U http://www.sciencedirect.com/science/article/pii/S0016236116301375 %U http://dx.doi.org/10.1016/j.fuel.2016.03.095 %P 289-298 %0 Journal Article %T Prediction of Maximum Pressure at the Roofs of Rectangular Water Tanks Subjected to Harmonic Base Excitation Using the Multi-Gene Genetic Programming Method %A Bahreini Toussi, Iman %A Mohammadian, Abdolmajid %A Kianoush, Reza %J Mathematical and Computational Applications %D 2021 %V 26 %N 1 %@ 2297-8747 %F Bahreini-Toussi:2021:MCA %X Liquid storage tanks subjected to base excitation can cause large impact forces on the tank roof, which can lead to structural damage as well as economic and environmental losses. The use of artificial intelligence in solving engineering problems is becoming popular in various research fields, and the Genetic Programming (GP) method is receiving more attention in recent years as a regression tool and also as an approach for finding empirical expressions between the data. In this study, an OpenFOAM numerical model that was validated by the authors in a previous study is used to simulate various tank sizes with different liquid heights. The tanks are excited in three different orientations with harmonic sinusoidal loadings. The excitation frequencies are chosen as equal to the tanks natural frequencies so that they would be subject to a resonance condition. The maximum pressure in each case is recorded and made dimensionless; then, using Multi-Gene Genetic Programming (MGGP) methods, a relationship between the dimensionless maximum pressure and dimensionless liquid height is acquired. Finally, some error measurements are calculated, and the sensitivity and uncertainty of the proposed equation are analysed. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/mca26010006 %U https://www.mdpi.com/2297-8747/26/1/6 %U http://dx.doi.org/10.3390/mca26010006 %0 Conference Proceedings %T Efficient evolutionary image processing using genetic programming: Reducing computation time for generating feature images of the Automatically Construction of Tree-Structural Image Transformation (ACTIT) %A Bai, Haiying %A Yata, Noriko %A Nagao, Tomoharu %S 10th International Conference on Intelligent Systems Design and Applications (ISDA 2010) %D 2010 %8 nov 29 dec 1 %F Bai:2010:ISDA %X Using well-established techniques of Genetic Programming (GP), we automatically optimise image feature filters over several inputs and within transformation images, improving the Automatic Construction of Tree-Structural Image Transformation (ACTIT) system. Our objective is to also produce optimal solutions in substantially less computation time than require for generating features of ACTIT. We improved the algorithm feature filters in the process through GP, which are expressed by trees in Automatic Construction of Tree-Structural Image Transformation, to reduce computation time. Through our experimentation, we show that our new approach is accurate and requires less computation time by maintaining the feature images in conjunction with the original images. %K genetic algorithms, genetic programming, ACTIT, automatically construction of tree-structural image transformation, evolutionary image processing, image feature filters, transformation images, image processing %R 10.1109/ISDA.2010.5687249 %U http://dx.doi.org/10.1109/ISDA.2010.5687249 %P 302-307 %0 Journal Article %T An oversampling method based on adaptive artificial immune network and SMOTE %A Bai, Lin %A Sun, Mengchen %A Jiang, Xianlin %A Liu, Jingxuan %A Liu, Jialu %A Pan, Xiaoying %J Genetic Programming and Evolvable Machines %D 2025 %V 26 %@ 1389-2576 %F Bai:2025:GPEM %O Online first %X ADAIN-SMOTE evolves a network structure capable of mapping the distribution of the original data, which is then used to augment the minority class %K genetic algorithms, genetic programming, AIS, Artificial immune network, Imbalanced data, Oversampling, Data distribution, AdaBoost, DT, SVM, XGBoost, Ablation %9 journal article %R 10.1007/s10710-025-09516-7 %U https://rdcu.be/eru8m %U http://dx.doi.org/10.1007/s10710-025-09516-7 %P Articleno:20 %0 Conference Proceedings %T Self-organizing primitives for automated shape composition %A Bai, Linge %A Eyiyurekli, Manolya %A Breen, David E. %S IEEE International Conference on Shape Modeling and Applications, SMI 2008 %D 2008 %8 jun %F Bai:2008:ieeeSMI %X Motivated by the ability of living cells to form into specific shapes and structures, we present a new approach to shape modeling based on self-organizing primitives whose behaviors are derived via genetic programming. The key concept of our approach is that local interactions between the primitives direct them to come together into a macroscopic shape. The interactions of the primitives, called morphogenic primitives (MP), are based on the chemotaxis-driven aggregation behaviors exhibited by actual living cells. Here, cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. MPs, though, do not attempt to completely mimic the behavior of real cells. The chemical fields are explicitly defined as mathematical functions and are not necessarily physically accurate. The explicit mathematical form of the chemical field functions are derived via genetic programming (GP), an evolutionary computing process that evolves a population of functions. A fitness measure, based on the shape that emerges from the chemical-field-driven aggregation, determines which functions will be passed along to later generations. This paper describes the cell interactions of MPs and the GP-based method used to define the chemical field functions needed to produce user- specified shapes from simple aggregating primitives. %K genetic algorithms, genetic programming, automated shape composition, cell behavior, chemical-field-driven aggregation, chemotaxis-driven aggregation behavior, cumulative chemical field, evolutionary computing process, fitness measure, macroscopic shape, mathematical function, morphogenic primitives, self-organizing primitive, shape formation, shape modeling, structure formation, computational geometry %R 10.1109/SMI.2008.4547962 %U http://dx.doi.org/10.1109/SMI.2008.4547962 %P 147-154 %0 Conference Proceedings %T Automated shape composition based on cell biology and distributed genetic programming %A Bai, Linge %A Eyiyurekli, Manolya %A Breen, David E. %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 Bai:2008:gecco %K genetic algorithms, genetic programming, chemotaxis, distributed genetic programming, morphogenesis, self-organisation, shape composition %R 10.1145/1389095.1389329 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1179.pdf %U http://dx.doi.org/10.1145/1389095.1389329 %P 1179-1186 %0 Conference Proceedings %T An Emergent System for Self-Aligning and Self-Organizing Shape Primitives %A Bai, Linge %A Eyiyurekli, Manolya %A Breen, David E. %S Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO ’08 %D 2008 %8 oct %F Bai:2008:SASO %X Motivated by the natural phenomenon of living cells self-organizing into specific shapes and structures, we present an emergent system that uses evolutionary computing methods for designing and simulating self-aligning and self-organizing shape primitives.Given the complexity of the emergent behavior, genetic programming is employed to control the evolution of our emergent system. The system has two levels of description. At the macroscopic level, a user-specified, pre-defined shape is given as input to the system. The system outputs local interaction rules that direct morphogenetic primitives (MP) to aggregate into the shape. At the microscopic level, MPs follow interaction rules based only on local interactions. All MPs are identical and do not know the final shape to be formed. The aggregate is then evaluated at the macroscopic level for its similarity to the user-defined shape. In this paper, we present (1) an emergent system that discovers local interaction rules that direct MPs to form user-defined shapes, (2) the simulation system that implements these rules and causes MPs to self-align and self-organize into a user-defined shape, and (3) the robustness and scalability qualities of the overall approach. %K genetic algorithms, genetic programming, direct morphogenetic primitives, emergent behavior, emergent system, evolutionary computing, living cells, local interaction rules, natural phenomenon, self-aligning shape primitives, self-organizing shape primitives, simulation system, user-defined shape, computational geometry %R 10.1109/SASO.2008.54 %U http://dx.doi.org/10.1109/SASO.2008.54 %P 445-454 %0 Book Section %T Chemotaxis-Inspired Cellular Primitives for Self-Organizing Shape Formation %A Bai, Linge %A Breen, David E. %E Doursat, Rene %E Sayama, Hiroki %E Michel, Olivier %B Morphogenetic Engineering %S Understanding Complex Systems %D 2012 %I Springer %G en %F Bai:2012:ME %X Motivated by the ability of living cells to form specific shapes and structures, we are investigating chemotaxis-inspired cellular primitives for self-organising shape formation. This chapter details our initial effort to create Morphogenetic Primitives (MPs), software agents that may be programmed to self-organise into user specified 2D shapes. The interactions of MPs are inspired by chemotaxis-driven aggregation behaviours exhibited by actual living cells. Cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. The artificial chemical fields of individual MPs are explicitly defined as mathematical functions. Genetic programming is used to discover the chemical field functions that produce an automated shape formation capability. We describe the cell-based behaviours of MPs and a distributed genetic programming method that discovers the chemical fields needed to produce macroscopic shapes from simple aggregating primitives. Several examples of aggregating MPs demonstrate that chemotaxis is an effective paradigm for spatial self-organization algorithms. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-33902-8_9 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.306.4523 %U http://dx.doi.org/10.1007/978-3-642-33902-8_9 %P 209-237 %0 Thesis %T Chemotaxis-based Spatial Self-Organization Algorithms %A Bai, Linge %D 2014 %8 aug %C Philadelphia, USA %C Department of Computer Science, Drexel University %F Bai_LingePhD %X Self-organization is a process that increases the order of a system as a result of local interactions among low-level, simple components, without the guidance of an outside source. Spatial self-organization is a process in which shapes and structures emerge at a global level from collective movements of low level shape primitives. Spatial self-organization is a stochastic process, and the outcome of the aggregation cannot necessarily be guaranteed. Despite the inherent ambiguity, self-organizing complex systems arise everywhere in nature. Motivated by the ability of living cells to form specific shapes and structures, we develop two self-organizing systems towards the ultimate goal of directing the spatial self-organizing process. We first develop a self-sorting system composed of a mixture of cells. The system consistently produces a sorted structure. We then extend the sorting system to a general shape formation system. To do so, we introduce morphogenetic primitives (MP), defined as software agents, which enable self-organizing shape formation of user-defined structures through a chemotaxis paradigm. One challenge that arises from the shape formation process is that the process may form two or more stable final configurations. In order to direct the self-organizing process, we find a way to characterize the macroscopic configuration of the MP swarm. We demonstrate that statistical moments of the primitives locations can successfully capture the macroscopic structure of the aggregated shape. We do so by predicting the final configurations produced by our spatial self-organization system at an early stage in the process using features based on the statistical moments. At the next stage, we focus on developing a technique to control the outcome of bifurcating aggregations. We identify thresholds of the moments and generate biased initial conditions whose statistical moments meet the thresholds. By starting simulations with biased, random initial configurations, we successfully control the aggregation for a number of swarms produced by the agent-based shape formation system. This thesis demonstrates that chemotaxis can be used as a paradigm to create an agent-based spatial self-organization system. Furthermore, statistical moments of the swarm can be used to robustly predict and control the outcomes of the aggregation process. %K genetic algorithms, genetic programming, Chemotaxis, Self-organizing systems %9 Ph.D. thesis %U https://www.cs.drexel.edu/~david/Abstracts/bai_phd-abs.html %0 Conference Proceedings %T Multi-Objective Genetic Programming for Imbalanced Classification with Adaptive Thresholds and a New Fitness Function %A Bai, Minghui %A Gao, Xiaoying %A Niu, Jiaxin %A Ma, Jianbin %Y Ekart, Aniko %Y Pillay, Nelishia %S Proceedings of the 2025 Genetic and Evolutionary Computation Conference %S GECCO ’25 %D 2025 %8 14 18 jul %I Association for Computing Machinery %C Malaga, Spain %F bai:2025:GECCO %X Genetic programming (GP) is widely used for classifier construction due to its flexible representation and feature construction characteristics. Traditional GP methods, however, often rely on a fixed threshold, typically 0, which fails to reflect the true distribution of the data in imbalanced datasets. To overcome this, we propose a multi-objective GP method that adaptively adjusts the threshold during evolution using Youden’s Index. This adaptive threshold adjustment allows the classifiers to better fit the data distribution. Additionally, we introduce a class separation metric, distt, aimed at enhancing the clarity of the classification boundaries and improving the generalization ability of the evolved classifiers. We use the multi-objective GP, along with the optimal threshold of each classifier, to jointly optimize the accuracy of the minority and majority classes, as well as the class separation metric distt, selecting the best classifier from the Pareto front for unseen data. Experiments on 7 imbalanced datasets demonstrate that our method outperforms single-objective GP with fixed thresholds and four GP-based algorithms, showcasing superior performance and improved classification clarity. Furthermore, our proposed clarity metric distt improves classification performance, ensuring better generalization and enhanced decision boundaries. %K genetic algorithms, genetic programming, multi-objective, threshold, classifier, imbalanced %R 10.1145/3712256.3726348 %U https://doi.org/10.1145/3712256.3726348 %U http://dx.doi.org/10.1145/3712256.3726348 %P 961-969 %0 Journal Article %T Towards trustworthy remaining useful life prediction through multi-source information fusion and a novel LSTM-DAU model %A Bai, Rui %A Noman, Khandaker %A Yang2, Yu %A Li, Yongbo %A Guo, Weiguo %J Reliability Engineering & System Safety %D 2024 %8 may %V 245 %@ 0951-8320 %F BAI:2024:ress %X Remaining useful life (RUL) prediction is a key part of the prognostic and health management of machines, which can effectively prevent catastrophic faults and decrease expensive unplanned maintenance. A good health indicator (HI) can ensure the accuracy and reliability of RUL prediction. However, most of the existing HI construction methods use only a single signal and rely heavily on prior knowledge, making it difficult to capture critical information about mechanical degradation, which in turn affects the performance of RUL prediction. To solve the above problems, a novel adaptive multi-source fusion method based on genetic programming is proposed for building a HI that can effectively reflect the health state of machines. Subsequently, a new LSTM model with a dual-attention mechanism is developed, which differentially handles the network input information and the recurrent information to improve the prediction performance and reduce the time complexity at the same time. Experimental validation is carried out on the real PRONOSTIA bearing dataset. The comparative results validate that the constructed fusion HI has better comprehensive performance than other fusion HIs, and the proposed prediction method is competitive with the current state-of-the-art methods %K genetic algorithms, genetic programming, Health index (HI), Trustworthy remaining useful life prediction, Multi-source fusion, LSTM, Dual attention unit %9 journal article %R 10.1016/j.ress.2024.110047 %U https://www.sciencedirect.com/science/article/pii/S0951832024001224 %U http://dx.doi.org/10.1016/j.ress.2024.110047 %P 110047 %0 Conference Proceedings %T Automatic generation of graph models for complex networks by genetic programming %A Bailey, Alexander %A Ventresca, Mario %A Ombuki-Berman, Beatrice %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 Bailey:2012:GECCO %X Complex networks have attracted a large amount of research attention, especially over the past decade, due to their prevalence and importance in our daily lives. Numerous human-designed models have been proposed that aim to capture and model different network structures, for the purpose of improving our understanding the real-life phenomena and its dynamics in different situations. Groundbreaking work in genetics, medicine, epidemiology, neuroscience, telecommunications, social science and drug discovery, to name some examples, have directly resulted. Because the graph models are human made (a very time consuming process) using a small subset of example graphs, they often exhibit inaccuracies when used to model similar structures. This paper represents the first exploration into the use of genetic programming for automating the discovery and algorithm design of graph models, representing a totally new approach with great interdisciplinary application potential. We present exciting initial results that show the potential of GP to replicate existing complex network algorithms. %K genetic algorithms, genetic programming %R 10.1145/2330163.2330263 %U http://cs.adelaide.edu.au/~brad/papers/alexanderThielPeacock.pdf %U http://dx.doi.org/10.1145/2330163.2330263 %P 711-718 %0 Conference Proceedings %T Automatic inference of hierarchical graph models using genetic programming with an application to cortical networks %A Bailey, Alexander %A Ombuki-Berman, Beatrice %A Ventresca, Mario %Y Blum, Christian %Y Alba, Enrique %Y Auger, Anne %Y Bacardit, Jaume %Y Bongard, Josh %Y Branke, Juergen %Y Bredeche, Nicolas %Y Brockhoff, Dimo %Y Chicano, Francisco %Y Dorin, Alan %Y Doursat, Rene %Y Ekart, Aniko %Y Friedrich, Tobias %Y Giacobini, Mario %Y Harman, Mark %Y Iba, Hitoshi %Y Igel, Christian %Y Jansen, Thomas %Y Kovacs, Tim %Y Kowaliw, Taras %Y Lopez-Ibanez, Manuel %Y Lozano, Jose A. %Y Luque, Gabriel %Y McCall, John %Y Moraglio, Alberto %Y Motsinger-Reif, Alison %Y Neumann, Frank %Y Ochoa, Gabriela %Y Olague, Gustavo %Y Ong, Yew-Soon %Y Palmer, Michael E. %Y Pappa, Gisele Lobo %Y Parsopoulos, Konstantinos E. %Y Schmickl, Thomas %Y Smith, Stephen L. %Y Solnon, Christine %Y Stuetzle, Thomas %Y Talbi, El-Ghazali %Y Tauritz, Daniel %Y Vanneschi, Leonardo %S GECCO ’13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference %D 2013 %8 June 10 jul %I ACM %C Amsterdam, The Netherlands %F Bailey:2013:GECCO %X The pathways that relay sensory information within the brain form a network of connections, the precise organisation of which is unknown. Communities of neurons can be discerned within this tangled structure, with inhomogeneously distributed connections existing between cortical areas. Classification and modelling of these networks has led to advancements in the identification of unhealthy or injured brains, however, the current models used are known to have major deficiencies. Specifically, the community structure of the cortex is not accounted for in existing algorithms, and it is unclear how to properly design a more representative graph model. It has recently been demonstrated that genetic programming may be useful for inferring accurate graph models, although no study to date has investigated the ability to replicate community structure. In this paper we propose the first GP system for the automatic inference of algorithms capable of generating, to a high accuracy, networks with community structure. We use a common cat cortex data set to highlight the efficacy of our approach. Our experiments clearly show that the inferred graph model generates a more representative network than those currently used in scientific literature. %K genetic algorithms, genetic programming %R 10.1145/2463372.2463498 %U http://dx.doi.org/10.1145/2463372.2463498 %P 893-900 %0 Journal Article %T Genetic Programming for the Automatic Inference of Graph Models for Complex Networks %A Bailey, Alexander %A Ventresca, Mario %A Ombuki-Berman, Beatrice %J IEEE Transactions on Evolutionary Computation %D 2014 %8 jun %V 18 %N 3 %@ 1089-778X %F Bailey:2014:ieeeTEC %X Complex networks are becoming an integral tool for our understanding of an enormous variety of natural and artificial systems. A number of human-designed network generation procedures have been proposed that reasonably model specific real-life phenomena in structure and dynamics. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. A graph model is an algorithm capable of constructing arbitrarily sized networks, whose end structure will exhibit certain statistical and structural properties. The process of deriving an accurate graph model is very time intensive and challenging and may only yield highly accurate models for very specific phenomena. An automated approach based on Genetic Programming was recently proposed by the authors. However, this initial system suffered from a number of drawbacks, including an under-emphasis on creating hub vertices, the requirement of user intervention to determine objective weights and the arbitrary approach to selecting the most representative model from a population of candidate models. In this paper we propose solutions to these problems and show experimentally that the new system represents a significant improvement and is very capable of reproducing existing common graph models from even a single small initial network. %K genetic algorithms, genetic programming, complex networks, Evolutionary Computation %9 journal article %R 10.1109/TEVC.2013.2281452 %U http://dx.doi.org/10.1109/TEVC.2013.2281452 %P 405-419 %0 Thesis %T Generation procedurale de niveaux de jeu alliant approche constructive et optimisation %A Bailly, Raphael %D 2022 %8 Dec %C France %C HESAM Universite %F bailly:tel-03971457 %X The research work in this thesis is positioned in the field of procedural content generation in video games. This study focuses more specifically on the questions related to the diversity and quality of the generated content. Indeed, in the video game industry, developers are constantly challenged to offer a diverse range of quality game environments to players. This perpetual quest led us to study numerous methods and algorithms, through a literature review of procedural content generation. Eventually, we addressed the following problem: How to achieve a generation method that offers a high degree of diversity of game experiences, while maintaining a certain structural quality in its results? Our study is centred in particular on the level design and the placement of objects in game levels. We also targeted a generation of non-linear open 3D levels for a first-person shooter, with a settlement infrastructure whose contents are positioned on a 2D grid. This thesis introduces a new method, named Genetic-WFC, with the aim of providing a diversity of game experiences with levels having a certain structural quality. It is a procedural generation pipeline that combines a Search-Based approach, consisting of a genetic algorithm and simulation-based evaluation, with a constructive method, the Wave Function Collapse, to generate levels targeting specific game experiences. The Wave Function Collapse, abbreviated WFC, is an algorithm for propagating local adjacency constraints. In our approach, it extracts these constraints from level examples, and allows us to perform genetic search on results that do not exhibit object placement errors. It serves, in fact, as a repair operator for the individuals in the population of the genetic algorithm. The driving of the WFC, by the search algorithm, is made possible by influencing the selection probability of its elements. We employ a level re-encoding solution that allows us to improve the optimisation process of our evolutionary algorithm. We also use a synthetic player to evaluate the game experience using three perception heuristics, namely, the novelty, safety and complexity, during a simulation of a walkthrough. Various experiments on our method have been conducted in order to establish its capabilities and performance. After looking at the computation time of the WFC for the generation of levels, a second experiment focused on comparing our approach to other similar methods, taking into account, in particular, the computation time and the score value of the results obtained. We also look at the visual differences between certain levels produced by these various methods. A last experiment is based on the evaluation of the diversity of game experiences that our procedural generation algorithm can provide. We conclude this thesis by mentioning several areas of improvement and further research, which can be pursued more thoroughly. For example, a user experience with fully playable settlements could be a next step in the study of our method. %K genetic algorithms, genetic programming, Genetic Algorithm, Wave Function Collapse, Diversity, Level Design, Video Games, Procedural Content Generation, Wave Function Collapse, Algorithme Genetique, Diversite, Level Design, Jeu Video, Generation Procedurale de Contenu, Ubisoft, FPS %9 Ph.D. thesis %U https://theses.hal.science/tel-03971457 %0 Conference Proceedings %T Evolving Algorithms for Constraint Satisfaction %A Bain, Stuart %A Thornton, John %A Sattar, Abdul %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 bain:2004:eafcs %X This paper proposes a framework for automatically evolving constraint satisfaction algorithms using genetic programming. The aim is to overcome the difficulties associated with matching algorithms to specific constraint satisfaction problems. A representation is introduced that is suitable for genetic programming and that can handle both complete and local search heuristics. In addition, the representation is shown to have considerably more flexibility than existing alternatives, being able to discover entirely new heuristics and to exploit synergies between heuristics. In a preliminary empirical study it is shown that the new framework is capable of evolving algorithms for solving the well-studied problem of boolean satisfiability testing. %K genetic algorithms, genetic programming, Combinatorial & numerical optimization %R 10.1109/CEC.2004.1330866 %U http://stuart.multics.org/publications/CEC2004.pdf %U http://dx.doi.org/10.1109/CEC.2004.1330866 %P 265-272 %0 Conference Proceedings %T Methods of Automatic Algorithm Generation %A Bain, Stuart %A Thornton, John %A Sattar, Abdul %Y Zhang, Chengqi %Y Guesgen, Hans W. %Y Yeap, Wai-Kiang %S 8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004 %S Lecture Notes in Computer Science %D 2004 %8 aug 9 13 %V 3157 %I Springer %C Auckland, New Zealand %F bain04methods %X Many methods have been proposed to automatically generate algorithms for solving constraint satisfaction problems. The aim of these methods has been to overcome the difficulties associated with matching algorithms to specific constraint satisfaction problems. This paper examines three methods of generating algorithms: a randomised search, a beam search and an evolutionary method. The evolutionary method is shown to have considerably more flexibility than existing alternatives, being able to discover entirely new heuristics and to exploit synergies between heuristics. %K genetic algorithms, genetic programming, CSP %R 10.1007/978-3-540-28633-2_17 %U http://www.ict.griffith.edu.au/~johnt/publications/PRICAI2004stuart.pdf %U http://dx.doi.org/10.1007/978-3-540-28633-2_17 %P 144-153 %0 Conference Proceedings %T Evolving variable-ordering heuristics for constrained optimisation %A Bain, Stuart %A Thornton, John %A Sattar, Abdul %Y van Beek, Peter %S Principles and Practice of Constraint Programming: CP’05 %S Lecture Notes in Computer Science %D 2005 %8 oct 1 5 %V 3709 %I Springer %C Sitges, Spain %F bain05evolving %X we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAX-SAT optimisation problems. Our approach offers two significant advantages over existing methods: it allows the evolution of more complex combinations of heuristics, and; it can identify fruitful synergies among heuristics. Using four different classes of MAX-SAT problems, we experimentally demonstrate that algorithms evolved with this method exhibit superior performance in comparison to general purpose methods. %K genetic algorithms, genetic programming, SAT, DIMACS %R 10.1007/11564751_54 %U http://www.ict.griffith.edu.au/~johnt/publications/CP2005stuart.pdf %U http://dx.doi.org/10.1007/11564751_54 %P 732-736 %0 Conference Proceedings %T A Comparison of Evolutionary Methods for the Discovery of Local Search Heuristics %A Bain, Stuart %A Thornton, John %A Sattar, Abdul %Y Zhang, Shichao %Y Jarvis, Ray %S Australian Conference on Artificial Intelligence: AI’05 %S Lecture Notes in Computer Science %D 2005 %8 dec 5 9 %V 3809 %I Springer %C Sydney %F bain05comparison %X Methods of adaptive constraint satisfaction have recently become of interest to overcome the limitations imposed on black-box search algorithms by the no free lunch theorems. Two methods that each use an evolutionary algorithm to adapt to particular classes of problem are the CLASS system of Fukunaga and the evolutionary constraint algorithm work of Bain et al. We directly compare these methods, demonstrating that although special purpose methods can learn excellent algorithms, on average standard evolutionary operators perform even better, and are less susceptible to the problems of bloat and redundancy. %K genetic algorithms, genetic programming %R 10.1007/11589990_142 %U http://www.ict.griffith.edu.au/~s661641/publications/AI2005stuart.pdf %U http://dx.doi.org/10.1007/11589990_142 %P 1068-1074 %0 Thesis %T Evolving Algorithms for Over-Constrained and Satisfaction Problems %A Bain, Stuart Iain %D 2006 %8 nov %C Brisbane, Queensland, Australia %C School of Information and Communication Technology, Griffith University %F Bain:thesis %X The notion that a universally effective problem solver may still exist, and is simply waiting to be found, is slowly being abandoned in the light of a growing body of work reporting on the narrow applicability of individual heuristics. As the formalism of the constraint satisfaction problem remains a popular choice for the representation of problems to be solved algorithmically, there exists an ongoing need for new algorithms to efficiently handle the disparate range of problems that have been posed in this representation. Given the costs associated with manually applying human algorithm development and problem solving expertise, methods that can automatically adapt to the particular features of a specific class of problem have begun to attract more attention. Whilst a number of authors have developed adaptive systems, the field, and particularly with respect to their application to constraint satisfaction problems, has seen only limited discussion as to what features are desirable for an adaptive constraint system. This may well have been a limiting factor with previous implementations, which have exhibited only subsets of the five features identified in this work as important to the utility of an adaptive constraint satisfaction system. Whether an adaptive system exhibits these features depends on both the chosen representation and the method of adaptation. In this thesis, a three-part representation for constraint algorithms is introduced, which defines an algorithm in terms of contention, preference and selection functions. An adaptive system based on genetic programming is presented that adapts constraint algorithms described using the mentioned three-part representation. This is believed to be the first use of standard genetic programming for learning constraint algorithms. Finally, to further demonstrate the efficacy of this adaptive system, its performance in learning specialised algorithms for hard, real-world problem instances is thoroughly evaluated. These instances include random as well as structured instances from known-hard benchmark distributions, industrial problems (specifically, SAT-translated planning and cryptographic problems) as well as over-constrained problem instances. The outcome of this evaluation is a set of new algorithms, valuable in their own right, specifically tailored to these problem classes. Partial results of this work have appeared in the following publications: [1] Stuart Bain, John Thornton, and Abdul Sattar (2004) Evolving algorithms for constraint satisfaction. In Proc. of the 2004 Congress on Evolutionary Computation, pages 265-272. [2] Stuart Bain, John Thornton, and Abdul Sattar (2004) Methods of automatic algorithm generation. In Proc. of the 9th Pacific Rim Conference on AI, pages 144-153. [3] Stuart Bain, John Thornton, and Abdul Sattar. (2005) A comparison of evolutionary methods for the discovery of local search heuristics. In Australian Conference on Artificial Intelligence: AI’05, pages 1068-1074. [4] Stuart Bain, John Thornton, and Abdul Sattar (2005) Evolving variable-ordering heuristics for constrained optimisation. In Principles and Practice of Constraint Programming: CP’05, pages 732-736. %K genetic algorithms, genetic programming %9 Ph.D. thesis %R 10.25904/1912/1794 %U http://stuart.freeshell.org/pubs/bain06evolving.pdf %U http://dx.doi.org/10.25904/1912/1794 %0 Journal Article %T Evolutionary computational methods to predict oral bioavailability QSPRs %A Bains, William %A Gilbert, Richard %A Sviridenko, Lilya %A Gascon, Jose-Miguel %A Scoffin, Robert %A Birchall, Kris %A Harvey, Inman %A Caldwell, John %J Current Opinion in Drug Discovery and Development %D 2002 %8 jan %V 5 %N 1 %@ 1367-6733 %F bains:2002:CODDD %X This review discusses evolutionary and adaptive methods for predicting oral bioavailability (OB) from chemical structure. Genetic Programming (GP), a specific form of evolutionary computing, is compared with some other advanced computational methods for OB prediction. The results show that classifying drugs into ’high’ and ’low’ OB classes on the basis of their structure alone is solvable, and initial models are already producing output that would be useful for pharmaceutical research. The results also suggest that quantitative prediction of OB will be tractable. Critical aspects of the solution will involve the use of techniques that can: (i) handle problems with a very large number of variables (high dimensionality); (ii) cope with ’noisy’ data; and (iii) implement binary choices to sub-classify molecules with behavior that are qualitatively different. Detailed quantitative predictions will emerge from more refined models that are hybrids derived from mechanistic models of the biology of oral absorption and the power of advanced computing techniques to predict the behavior of the components of those models in silico. %K genetic algorithms, genetic programming %9 journal article %P 44-51 %0 Journal Article %T HERG binding specificity and binding site structure: Evidence from a fragment-based evolutionary computing SAR study %A Bains, William %A Basman, Antranig %A White, Cat %J Progress in Biophysics and Molecular Biology %D 2004 %8 oct %V 86 %N 2 %F bains:2004:PBMB %X We describe the application of genetic programming, an evolutionary computing method, to predicting whether small molecules will block the HERG cardiac potassium channel. Models based on a molecular fragment-based descriptor set achieve an accuracy of 85-90% in predicting whether the IC50 of a ’blind’ set of compounds is <1 [mu]M. Analysis of the models provides a ’meta-SAR’, which predicts a pharmacophore of two hydrophobic features, one preferably aromatic and one preferably nitrogen-containing, with a protonatable nitrogen asymmetrically situated between them. Our experience of the approach suggests that it is robust, and requires limited scientist input to generate valuable predictive results and structural understanding of the target. %K genetic algorithms, genetic programming, HERG, IKr, QSAR, Molecular descriptors, Prediction %9 journal article %R 10.1016/j.pbiomolbio.2003.09.001 %U http://www.sciencedirect.com/science/article/B6TBN-4BS4DJM-1/2/2bd8833742e401378469ee988d571705 %U http://dx.doi.org/10.1016/j.pbiomolbio.2003.09.001 %P 205-233 %0 Journal Article %T Searching for IRES %A Baird, Stephen D. %A Turcotte, Marcel %A Korneluk, Robert G. %A Holcik, Martin %J RNA %D 2006 %8 oct %V 12 %N 10 %I RNA Society %F Baird:2006:RNA %X The cell has many ways to regulate the production of proteins. One mechanism is through the changes to the machinery of translation initiation. These alterations favor the translation of one subset of mRNAs over another. It was first shown that internal ribosome entry sites (IRESes) within viral RNA genomes allowed the production of viral proteins more efficiently than most of the host proteins. The RNA secondary structure of viral IRESes has sometimes been conserved between viral species even though the primary sequences differ. These structures are important for IRES function, but no similar structure conservation has yet to be shown in cellular IRES. With the advances in mathematical modeling and computational approaches to complex biological problems, is there a way to predict an IRES in a data set of unknown sequences? This review examines what is known about cellular IRES structures, as well as the data sets and tools available to examine this question. We find that the lengths, number of upstream AUGs, and %GC content of 5’-UTRs of the human transcriptome have a similar distribution to those of published IRES-containing UTRs. Although the UTRs containing IRESes are on the average longer, almost half of all 5’-UTRs are long enough to contain an IRES. Examination of the available RNA structure prediction software and RNA motif searching programs indicates that while these programs are useful tools to fine tune the empirically determined RNA secondary structure, the accuracy of de novo secondary structure prediction of large RNA molecules and subsequent identification of new IRES elements by computational approaches, is still not possible. %K genetic algorithms, genetic programming, IRES, RNA, secondary structure, prediction software %9 journal article %R 10.1261/rna.157806 %U http://dx.doi.org/10.1261/rna.157806 %P 1755-1785 %0 Conference Proceedings %T Function and terminal Set Selection for Evolving Goal Scoring Behaviour in Soccer Players %A Bajurnow, Andrei %A Ciesielski, Vic %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 Bajurnow:aspgp03 %K genetic algorithms, genetic programming %P 38-44 %0 Conference Proceedings %T Layered Learning for Evolving Goal Scoring Behavior in Soccer Players %A Bajurnow, Andrei %A Ciesielski, Vic %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 bajurnow:2004:llfegsbisp %X Layered learning allows decomposition of the stages of learning in a problem domain. We apply this technique to the evolution of goal scoring behavior in soccer players and show that layered learning is able to find solutions comparable to standard genetic programs more reliably. The solutions evolved with layers have a higher accuracy but do not make as many goal attempts. We compared three variations of layered learning and find that maintaining the population between layers as the encapsulated learnt layer is introduced to be the most computationally efficient. The quality of solutions found by layered learning did not exceed those of standard genetic programming in terms of goal scoring ability. %K genetic algorithms, genetic programming, Evolutionary intelligent agents, Evolutionary Computation and Games %R 10.1109/CEC.2004.1331118 %U http://goanna.cs.rmit.edu.au/~vc/papers/cec2004-bajurnow.pdf %U http://dx.doi.org/10.1109/CEC.2004.1331118 %P 1828-1835 %0 Conference Proceedings %T On the design of integrated HF radar systems for Homeland Security applications %A Baker, James %A Celik, Nuri %A Omaki, Nobutaka %A Kobashigawa, Jill %A Youn, Hyoung-Sun %A Iskander, Magdy F. %S 2010 IEEE International Conference on Wireless Information Technology and Systems (ICWITS) %D 2010 %8 28 oct sep 3 %F Baker:2010:ieeeICWITS %X In this paper, HCAC’s research and development efforts on the development of integrated and low cost HF radar for coastal surveillance and other Homeland Security applications are summarised. The proposed design incorporates electrically small antenna for rapid deployment, supports operation on floating platforms by using enhanced DSP algorithms to mitigate clutter, incorporates improved propagation modelling to more accurately select optimum frequency channels based on atmospheric conditionxs and overcome the errors due to terrain effects, uses Genetic Programming for automatic target recognition and classification, and provides for passive radar operation using existing broadcast transmitters to enable covert operation. %K genetic algorithms, genetic programming, DSP algorithm, broadcast transmitter, clutter, coastal surveillance, electrically small antenna, homeland security, integrated HF radar system, optimum frequency channel, propagation modeling, terrain effect, military radar, national security, radar antennas, radar clutter, signal processing %R 10.1109/ICWITS.2010.5611859 %U http://dx.doi.org/10.1109/ICWITS.2010.5611859 %0 Conference Proceedings %T Detecting Bacterial Vaginosis Using Machine Learning %A Baker, Yolanda S. %A Agrawal, Rajeev %A Foster, James A. %A Beck, Daniel %A Dozier, Gerry %S Proceedings of the 2014 ACM Southeast Regional Conference %D 2014 %8 mar 28 29 %I ACM %C Kennesaw, Georgia, USA %F Baker:2014:DBV:2638404.2638521 %X Bacterial Vaginosis (BV) is the most common of vaginal infections diagnosed among women during the years where they can bear children. Yet, there is very little insight as to how it occurs. There are a vast number of criteria that can be taken into consideration to determine the presence of BV. The purpose of this paper is two-fold; first to discover the most significant features necessary to diagnose the infection, second is to apply various classification algorithms on the selected features. It is observed that certain feature selection algorithms provide only a few features; however, the classification results are as good as using a large number of features. %K genetic algorithms, genetic programming %R 10.1145/2638404.2638521 %U http://dx.doi.org/10.1145/2638404.2638521 %P 46:1-46:4 %0 Conference Proceedings %T Applying machine learning techniques in detecting Bacterial Vaginosis %A Baker, Yolanda S. %A Agrawal, Rajeev %A Foster, James A. %A Beck, Daniel %A Dozier, Gerry %S 2014 International Conference on Machine Learning and Cybernetics %D 2014 %8 13 16 jul %V 1 %I IEEE %C Lanzhou, China %F Baker:2014:ICMLC %X There are several diseases which arise because of changes in the microbial communities in the body. Scientists continue to conduct research in a quest to find the catalysts that provoke these changes in the naturally occurring microbiota. Bacterial Vaginosis (BY) is a disease that fits the above criteria. BV afflicts approximately 29percent of women in child bearing age. Unfortunately, its causes are unknown. This paper seeks to uncover the most important features for diagnosis and in turn employ classification algorithms on those features. In order to fulfill our purpose, we conducted two experiments on the data. We isolated the clinical and medical features from the full set of raw data, we compared the accuracy, precision, recall and F-measure and time elapsed for each feature selection and classification grouping. We noticed that classification results were as good or better after performing feature selection although there was a wide range in the number of features produced from the feature selection process. After comparing the experiments, the algorithms performed best on the medical dataset. %K genetic algorithms, genetic programming, Bacterial Vaginosis, Machine learning, Feature selection, Classification %R 10.1109/ICMLC.2014.7009123 %U http://dx.doi.org/10.1109/ICMLC.2014.7009123 %P 241-246 %0 Thesis %T Feasibility Assessement and Optimal Scheduling of Water Supply Projects %A Bakkoury, Zohra %D 2002 %C School of Engineering and Computer Science, Exeter University %F bakkoury:thesis %K genetic algorithms %9 Ph.D. thesis %0 Journal Article %T To Accomplish Amelioration Of Classifier Using Gene-Mutation Tactics In Genetic Programming %A Bakshi, Ankit %A Pandit, Pallavi %A Easo, Santosh %J International Journal of Emerging Technology and Advanced Engineering %D 2012 %8 dec %V 2 %N 12 %@ 2250–2459 %G en %F Bakshi:2012:ijetae %X A phenomenon for designing classifier for three or more classes (Multiclass) problem using genetic programming (GP) is multiclass classifier. In this scenario we purported three methods named Double Tournament Method, Gene-Mutation Method and a Plain Crossover method. In Double Tournament Method, we pick out two idiosyncratic for the crossover operation on the basis of size and fitness. In Gene-Mutation tactic we are propagating two child from single parent and selecting one of them on the basis of fitness and also bring into play elitism on the child so that the mutation operation does not degrade the fitness of the distinct, whereas in Plain Crossover we select the two child for the succeeding generation on the basis of size, depth and fitness along with elitism on each step from the six child which is generated during crossover. To exhibit our approach we have designed a Multiclass Classifier using GP by taking some standard datasets. The results attained show that by applying Plain crossover together with Gene-Mutation refined the performance of the classifier. %K genetic algorithms, genetic programming, elitism, double tournament, plain crossover %9 journal article %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.3468 %P 319-322 %0 Conference Proceedings %T Non-photorealistic Rendering with Cartesian Genetic Programming Using Graphics Processing Units %A Bakurov, Illya %A Ross, Brian J. %Y Romero, Juan %Y Liapis, Antonios %Y Ekart, Aniko %S 7th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018 %S LNCS %D 2018 %8 April 6 apr %V 10783 %I Springer %C Parma, Italy %F Bakurov:2018:evoMusArt %X A non-photorealistic rendering system implemented with Cartesian genetic programming (CGP) is discussed. The system is based on Baniasadi’s NPR system using tree-based GP. The CGP implementation uses a more economical representation of rendering expressions compared to the tree-based system. The system borrows their many objective fitness evaluation scheme, which uses a model of aesthetics, colour testing, and image matching. GPU acceleration of the paint stroke application results in up to 6 times faster rendering times compared to CPU-based renderings. The convergence dynamics of CGP’s mu+lambda evolutionary strategy was more unstable than conventional GP runs with large populations. One possible reason may be the sensitivity of the smaller mu+lambda population to the many-objective ranking scheme, especially when objectives are in conflict with each other. This instability is arguably an advantage as an exploratory tool, especially when considering the subjectivity inherent in evolutionary art. %K genetic algorithms, genetic programming, Cartesian genetic programming, Evolutionary art, Non-photorealistic rendering, Graphics processing units, GPU %R 10.1007/978-3-319-77583-8_3 %U http://dx.doi.org/10.1007/978-3-319-77583-8_3 %P 34-49 %0 Conference Proceedings %T Supporting Medical Decisions for Treating Rare Diseases through Genetic Programming %A Bakurov, Illya %A Vanneschi, Leonardo %A Castelli, Mauro %A Freitas, Maria Joao %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 Bakurov:2019:evoapplications %X Casa dos Marcos is the largest specialized medical and residential center for rare diseases in the Iberian Peninsula. The large number of patients and the uniqueness of their diseases demand a considerable amount of diverse and highly personalized therapies, that are nowadays largely managed manually. This paper aims at catering for the emergent need of efficient and effective artificial intelligence systems for the support of the everyday activities of centres like Casa dos Marcos. We present six predictive data models developed with a genetic programming based system which, integrated into a web-application, enabled data-driven support for the therapists in Casa dos Marcos. The presented results clearly indicate the usefulness of the system in assisting complex therapeutic procedures for children suffering from rare diseases. %K genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Medical decisions, Rare diseases %R 10.1007/978-3-030-16692-2_13 %U http://hdl.handle.net/10362/91519 %U http://dx.doi.org/10.1007/978-3-030-16692-2_13 %P 187-203 %0 Conference Proceedings %T A Regression-like Classification System for Geometric Semantic Genetic Programming %A Bakurov, Illya %A Castelli, Mauro %A Fontanella, Francesco %A Vanneschi, Leonardo %Y Guervos, Juan Julian Merelo %Y Garibaldi, Jonathan M. %Y Linares-Barranco, Alejandro %Y Madani, Kurosh %Y Warwick, Kevin %S Proceedings of the 11th International Joint Conference on Computational Intelligence, IJCCI 2019, Vienna, Austria, September 17-19, 2019 %D 2019 %I ScitePress %F DBLP:conf/ijcci/BakurovCFV19 %K genetic algorithms, genetic programming %R 10.5220/0008052900400048 %U https://doi.org/10.5220/0008052900400048 %U http://dx.doi.org/10.5220/0008052900400048 %P 40-48 %0 Journal Article %T General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python %A Bakurov, Illya %A Buzzelli, Marco %A Castelli, Mauro %A Vanneschi, Leonardo %A Schettini, Raimondo %J Applied Sciences %D 2021 %8 January %V 11 %N 11 %@ 2076-3417 %F Bakurov:2021:AS %X Several interesting libraries for optimisation have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimisation and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction). %K genetic algorithms, genetic programming, optimization, evolutionary computation, swarm intelligence, local search, continuous optimisation, combinatorial optimization, inductive programming, supervised machine learning %9 journal article %R 10.3390/app11114774 %U https://www.mdpi.com/2076-3417/11/11/4774 %U http://dx.doi.org/10.3390/app11114774 %P Article-number4774 %0 Journal Article %T Genetic programming for stacked generalization %A Bakurov, Illya %A Castelli, Mauro %A Gau, Olivier %A Fontanella, Francesco %A Vanneschi, Leonardo %J Swarm and Evolutionary Computation %D 2021 %V 65 %@ 2210-6502 %F BAKUROV:2021:SEC %X In machine learning, ensemble techniques are widely used to improve the performance of both classification and regression systems. They combine the models generated by different learning algorithms, typically trained on different data subsets or with different parameters, to obtain more accurate models. Ensemble strategies range from simple voting rules to more complex and effective stacked approaches. They are based on adopting a meta-learner, i.e. a further learning algorithm, and are trained on the predictions provided by the single algorithms making up the ensemble. The paper aims at exploiting some of the most recent genetic programming advances in the context of stacked generalization. In particular, we investigate how the evolutionary demes despeciation initialization technique, ?-lexicase selection, geometric-semantic operators, and semantic stopping criterion, can be effectively used to improve GP-based systems’ performance for stacked generalization (a.k.a. stacking). The experiments, performed on a broad set of synthetic and real-world regression problems, confirm the effectiveness of the proposed approach %K genetic algorithms, genetic programming, Stacking, Ensemble Learning, Stacked Generalization %9 journal article %R 10.1016/j.swevo.2021.100913 %U https://www.sciencedirect.com/science/article/pii/S2210650221000742 %U http://dx.doi.org/10.1016/j.swevo.2021.100913 %P 100913 %0 Journal Article %T A novel binary classification approach based on geometric semantic genetic programming %A Bakurov, I. %A Castelli, M. %A Fontanella, F. %A Scotto di Freca, A. %A Vanneschi, L. %J Swarm and Evolutionary Computation %D 2022 %V 69 %@ 2210-6502 %F BAKUROV:2022:SEC %X Geometric semantic genetic programming (GSGP) is a recent variant of genetic programming. GSGP allows the landscape of any supervised regression problem to be transformed into a unimodal error surface, thus it has been applied only to this kind of problem. In a previous paper, we presented a novel variant of GSGP for binary classification problems that, taking inspiration from perceptron neural networks, uses a logistic-based activation function to constrain the output value of a GSGP tree in the interval [0,1]. This simple approach allowed us to use the standard RMSE function to evaluate the train classification error on binary classification problems and, consequently, to preserve the intrinsic properties of the geometric semantic operators. The results encouraged us to investigate this approach further. To this aim, in this paper, we present the results from 18 test problems, which we compared with those achieved by eleven well-known and widely classification schemes. We also studied how the parameter settings affect the classification performance and the use of the F-score function to deal with imbalanced data. The results confirmed the effectiveness of the proposed approach %K genetic algorithms, genetic programming, Binary classification, Geometric semantic genetic programming %9 journal article %R 10.1016/j.swevo.2021.101028 %U https://www.sciencedirect.com/science/article/pii/S2210650221001905 %U http://dx.doi.org/10.1016/j.swevo.2021.101028 %P 101028 %0 Conference Proceedings %T Genetic Programming for Structural Similarity Design at Multiple Spatial Scales %A Bakurov, Illya %A Buzzelli, Marco %A Castelli, Mauro %A Schettini, Raimondo %A Vanneschi, Leonardo %Y Rahat, Alma %Y Fieldsend, Jonathan %Y Wagner, Markus %Y Tari, Sara %Y Pillay, Nelishia %Y Moser, Irene %Y Aleti, Aldeida %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Hemberg, Erik %Y Cleghorn, Christopher %Y Sun, Chao-li %Y Yannakakis, Georgios %Y Bredeche, Nicolas %Y Ochoa, Gabriela %Y Derbel, Bilel %Y Pappa, Gisele L. %Y Risi, Sebastian %Y Jourdan, Laetitia %Y Sato, Hiroyuki %Y Posik, Petr %Y Shir, Ofer %Y Tinos, Renato %Y Woodward, John %Y Heywood, Malcolm %Y Wanner, Elizabeth %Y Trujillo, Leonardo %Y Jakobovic, Domagoj %Y Miikkulainen, Risto %Y Xue, Bing %Y Neumann, Aneta %Y Allmendinger, Richard %Y Medina-Bulo, Inmaculada %Y Bechikh, Slim %Y Sutton, Andrew M. %Y Oliveto, Pietro Simone %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F bakurov:2022:GECCO %X The growing production of digital content and its dissemination across the worldwide web require eficient and precise management. In this context, image quality assessment measures (IQAMs) play a pivotal role in guiding the development of numerous image processing systems for compression, enhancement, and restoration. The structural similarity index (SSIM) is one of the most common IQAMs for estimating the similarity between a pristine reference image and its corrupted variant. The multi-scale SSIM is one of its most popular variants that allows assessing image quality at multiple spatial scales. This paper proposes a two-stage genetic programming (GP) approach to evolve novel multi-scale IQAMs, that are simultaneously more effective and efficient. We use GP to perform feature selection in the first stage, while the second stage generates the final solutions. The experimental results show that the proposed approach outperforms the existing MS-SSIM. A comprehensive analysis of the feature selection indicates that, for extracting multi-scale similarities, spatially-varying convolutions are more effective than dilated convolutions. Moreover, we provide evidence that the IQAMs learned for one database can be successfully transferred to previously unseen databases. We conclude the paper by presenting a set of evolved multi-scale IQAMs and providing their interpretation. %K genetic algorithms, genetic programming, multi-scale context, image quality assessment, image processing, spatially-varying kernels, structural similarity, multi-scale structural similarity index, multi-scale processing, evolutionary computation, dilated convolutions %R 10.1145/3512290.3528783 %U http://dx.doi.org/10.1145/3512290.3528783 %P 911-919 %0 Thesis %T Soft computing for Ill Posed Problems in Computer Vision %A Bakurov, Illya Olegovich %D 2022 %8 19 sep %C Portugal %C NOVA Information Management School (NOVA IMS), NOVA University, Lisbon %G English %F Bakurov:thesis %X Soft computing (SC) includes computational techniques that are tolerant of approximations, missing information, and uncertainty, and aim at providing effective and efficient solutions to problems which may be unsolvable, or too time-consuming to solve, with exhaustive techniques. SC has found many applications in various domains of research and industry, including computer vision (CV). This dissertation focuses on tasks of full reference image quality assessment (FR-IQA) and fast scene understanding (FSU). The former consists of assessing images visual quality in regard to some pristine reference. The latter consists of classifying each pixel of a scene assuming a rapidly changing environment like, for instance, in a self-driving car. The current state-of-the-art (SOTA) in both FR-IQA and FSU rely upon convolutional neural networks (CNNs), which can be seen as a computational metaphor of the human visual cortex. Although CNNs achieved unprecedented results in many CV tasks, they also present several drawbacks: massive amounts of data and processing resources for training; the difficulty of outputs interpretation; reduced usability for compact battery-powered devices... This dissertation addresses FR-IQA and FSU using SC techniques other than CNNs. Initially, we created a flexible and efficient library to support our endeavors; it is publicly available and implements a wide range of metaheuristics to solve different problems. Then, we used swarm and evolutionary computation to optimize the parameters of several traditional FR-IQA measures (FR-IQAMs) that integrate the so called structural similarity paradigm; the novel parameters improve measures’ precision without affecting their complexity. Afterward, we applied genetic programming (GP) to automatically formulate novel FR-IQAMs that are simultaneously simple, accurate, and interpretable. Lastly, we used GP as a meta-model for stacking efficient CNNs for FSU; the approach allowed us to obtain simple and interpretable models that did not exceed processing preconditions for real-time applications while achieving high levels of precision. %K genetic algorithms, genetic programming, ANN, Evolutionary Computation, Swarm Intelligence, Ensemble Learning, Stacking, Computer Vision, Full Reference Image Quality Assessment, Semantic Segmentation %9 Ph.D. thesis %U http://hdl.handle.net/10362/144500 %0 Journal Article %T Full-Reference Image Quality Expression via Genetic Programming %A Bakurov, Illya %A Buzzelli, Marco %A Schettini, Raimondo %A Castelli, Mauro %A Vanneschi, Leonardo %J IEEE Trans. Image Process. %D 2023 %V 32 %F DBLP:journals/tip/BakurovBSCV23 %X Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TIP.2023.3244662 %U https://doi.org/10.1109/TIP.2023.3244662 %U http://dx.doi.org/10.1109/TIP.2023.3244662 %P 1458-1473 %0 Journal Article %T Semantic segmentation network stacking with genetic programming %A Bakurov, Illya %A Buzzelli, Marco %A Schettini, Raimondo %A Castelli, Mauro %A Vanneschi, Leonardo %J Genetic Programming and Evolvable Machines %D 2023 %8 dec %V 24 %N 2 %@ 1389-2576 %F Bakurov:2023:GPEM %O Special Issue on Highlights of Genetic Programming 2022 Events %K genetic algorithms, genetic programming, Stacking, Semantic segmentation, Ensemble learning, Deep learning, ANN %9 journal article %R 10.1007/s10710-023-09464-0 %U https://rdcu.be/drZeF %U http://dx.doi.org/10.1007/s10710-023-09464-0 %P Articlenumber:15 %0 Journal Article %T Geometric semantic genetic programming with normalized and standardized random programs %A Bakurov, Illya %A Munoz Contreras, Jose Manuel %A Castelli, Mauro %A Rodrigues, Nuno %A Silva, Sara %A Trujillo, Leonardo %A Vanneschi, Leonardo %J Genetic Programming and Evolvable Machines %D 2024 %V 25 %@ 1389-2576 %F Bakurov:2024:GPEM %O Online first %X Geometric semantic genetic programming (GSGP) represents one of the most promising developments in the area of evolutionary computation (EC) in the last decade. The results achieved by incorporating semantic awareness in the evolutionary process demonstrate the impact that geometric semantic operators have brought to the field of EC. An improvement to the geometric semantic mutation (GSM) operator is proposed, inspired by the results achieved by batch normalization in deep learning. While, in one of its most used versions, GSM relies on the use of the sigmoid function to constrain the semantics of two random programs responsible for perturbing the parent semantics, here a different approach is followed, which allows reducing the size of the resulting programs and overcoming the issues associated with the use of the sigmoid function, as commonly done in deep learning. The idea is to consider a single random program and use it to perturb the parent’s semantics only after standardization or normalization. The experimental results demonstrate the suitability of the proposed approach: despite its simplicity, the presented GSM variants outperform standard GSGP on the studied benchmarks, with a difference in terms of performance that is statistically significant. Furthermore, the individuals generated by the new GSM variants are easier to simplify, allowing us to create accurate but significantly smaller solutions. %K genetic algorithms, genetic programming, Geometric semantic mutation, Internal covariate shift, Sigmoid distribution bias, Model simplification %9 journal article %R 10.1007/s10710-024-09479-1 %U https://rdcu.be/dysci %U http://dx.doi.org/10.1007/s10710-024-09479-1 %P Articleno6 %0 Conference Proceedings %T Sharpness-Aware Minimization in Genetic Programming %A Bakurov, Illya %A Haut, Nathan %A Banzhaf, Wolfgang %Y Winkler, Stephan M. %Y Banzhaf, Wolfgang %Y Hu, Ting %Y Lalejini, Alexander %S Genetic Programming Theory and Practice XXI %S Genetic and Evolutionary Computation %D 2024 %8 jun 6 8 %I Springer %C University of Michigan, USA %F Bakurov:2024:GPTP %X Sharpness-Aware Minimization (SAM) was recently introduced as a regularization procedure for training deep neural networks. It simultaneously minimizes the fitness (or loss) function and the so-called fitness sharpness. The latter serves as a measure of the nonlinear behaviour of a solution and does so by finding solutions that lie in neighborhoods having uniformly similar loss values across all fitness cases. we adapt SAM for tree Genetic Programming (TGP) by exploring the semantic neighborhoods of solutions using two simple approaches. By capitalising upon perturbing input and output of program trees, sharpness can be estimated and used as a second optimization criterion during the evolution. To better understand the impact of this variant of SAM on TGP, we collect numerous indicators of the evolutionary process, including generalization ability, complexity, diversity, and a recently proposed genotype-phenotype mapping to study the amount of redundancy in trees. The experimental results demonstrate that using any of the two proposed SAM adaptations in TGP allows (i) a significant reduction of tree sizes in the population and (ii) a decrease in redundancy of the trees. When assessed on real-world benchmarks, the generalization ability of the elite solutions does not deteriorate. %K genetic algorithms, genetic programming, SAM %R 10.1007/978-981-96-0077-9_8 %U https://arxiv.org/abs/2405.10267 %U http://dx.doi.org/10.1007/978-981-96-0077-9_8 %P 151-175 %0 Conference Proceedings %T Analyzing Fitness Aggregation Strategies for Symbolic Regression Problem-Solving %A Bakurov, Illya %Y Banzhaf, Wolfgang %Y Burlacu, Bogdan %Y Kelly, Stephen %Y Lalejini, Alexander %Y Olivetti de Franca, Fabricio %S Genetic Programming Theory and Practice XXII %D 2025 %8 jun 5 7 %C Michigan State University, USA %F Bakurov:2025:GPTP %O lightning talk %K genetic algorithms, genetic programming %0 Conference Proceedings %T A comparison of tournament and lexicase selection paradigms in regression problems: error-based fitness versus correlation fitness %A Bakurov, Illya %A Murphy, Aidan %A Ofria, Charles %A Banzhaf, Wolfgang %Y Ekart, Aniko %Y Pillay, Nelishia %S Proceedings of the 2025 Genetic and Evolutionary Computation Conference %S GECCO ’25 %D 2025 %8 14 18 jul %I Association for Computing Machinery %C Malaga, Spain %F bakurov:2025:GECCO %X Lexicase parent selection considers training cases separately, postulating that aggregated fitness reduces the information about the behavior of individuals. Originally lexicase was proposed in the context of program synthesis, characterized by uncompromising problems that require qualitatively different actions for different inputs, but it has since been extended to regression problems. To facilitate valley-crossing a relaxation parameter epsilon was added broadening the pass condition at a given training case. Although epsilon-lexicase has demonstrated superior effectiveness, it was compared against selection methods that aggregated squared (or absolute) errors. Recent contributions, however, demonstrate that correlation fitness functions can lead to significant performance gains over the root mean square error (RMSE) in tournament-guided evolution for symbolic regression. Here we compare epsilon-lexicase (with and without down-sampling) against tournament selection using both error- and correlation-based fitness to guide Genetic Programming (GP). We also assess batch epsilon-lexicase selection as an intermediate condition. Finally, we explore different selection pressures to assess the exploration-exploitation trade-off. We analyze the experimental results using different metrics, including code redundancy, sharpness-awareness and selection impact. Our results demonstrate that tournament selection with correlation fitness function significantly outperforms epsilon-lexicase on regression problems and that its batch variant also benefits from correlation-based aggregation. %K genetic algorithms, genetic programming %R 10.1145/3712256.3726448 %U https://doi.org/10.1145/3712256.3726448 %U http://dx.doi.org/10.1145/3712256.3726448 %P 970-979 %0 Conference Proceedings %T On Sensor Evolution in Robotics %A Balakrishnan, Karthik %A Honavar, Vasant %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 balakrishnan:1996:ser %K Genetic Algorithms %U http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap75.pdf %P 455-460 %0 Conference Proceedings %T Spatial Learning for Robot Localization %A Balakrishnan, Karthik %A Honavar, Vasant %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 Balakrishnan:1997:slrl %K Artifical life and evolutionary robotics %P 389-397 %0 Journal Article %T Control System Synthesis by Means of Cartesian Genetic Programming %A Balandina, G. I. %J Procedia Computer Science %D 2017 %V 103 %@ 1877-0509 %F Balandina:2017:PCS %O XII International Symposium Intelligent Systems 2016, INTELS 2016, 5-7 October 2016, Moscow, Russia %X Cartesian Genetic Programming (CGP) is a type of Genetic Programming based on a program in a form of a directed graph. It also belongs to the methods of Symbolic Regression allowing to receive the optimal mathematical expression for a problem. Nowadays it becomes possible to use computers very effectively for symbolic regression calculations. CGP was developed by Julian Miller in 1999-2000. It represents a program for decoding a genotype (string of integers) into the phenotype (graph). The nodes of that graph contain references to functions from a function table, which could contain arithmetic, logical operations and/or user-defined functions. The inputs of those functions are connected to the node inputs, which itself could be connected to a node output or a graph input. As a result, it’s possible to construct several mathematical expressions for the outputs and calculate them for the given inputs. This CGP implementation use point mutation to form new mathematical expressions. Steady-state genetic algorithm is chosen as a search engine. Solution solving the control system synthesis problem is presented in a form of the Pareto set, which contains a set of satisfactory control functions. Nonlinear Duffing oscillator is taken as a dynamic object. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, Optimal control synthesis, nonlinear control systems %9 journal article %R 10.1016/j.procs.2017.01.051 %U http://www.sciencedirect.com/science/article/pii/S1877050917300522 %U http://dx.doi.org/10.1016/j.procs.2017.01.051 %P 176-182 %0 Journal Article %T Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming %A Balasubramaniam, P. %A Kumar, A. Vincent Antony %J Genetic Programming and Evolvable Machines %D 2009 %8 mar %V 10 %N 1 %@ 1389-2576 %F Balasubramaniam:2009:GPEM %X In this paper, we propose a novel approach to find the solution of the matrix Riccati differential equation (MRDE) for nonlinear singular systems using genetic programming (GP). The goal is to provide optimal control with reduced calculation effort by comparing the solutions of the MRDE obtained from the well known traditional Runge Kutta (RK) method to those obtained from the GP method. We show that the GP approach to the problem is qualitatively better in terms of accuracy. Numerical examples are provided to illustrate the proposed method. %K genetic algorithms, genetic programming, Matrix Riccati differential equation, Nonlinear, Optimal control, Runge Kutta method, Singular system %9 journal article %R 10.1007/s10710-008-9072-z %U http://dx.doi.org/10.1007/s10710-008-9072-z %P 71-89 %0 Conference Proceedings %T Hierarchical fuzzy system modeling by Genetic and Bacterial Programming approaches %A Balazs, Krisztian %A Botzheim, Janos %A Koczy, Laszlo T. %S IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Balazs:2010:ieee-fuzz %X In this paper a method is proposed for constructing hierarchical fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The resultant hierarchical rule base is the knowledge base, which is constructed by using structure constructing evolutionary techniques, namely, Genetic and Bacterial Programming Algorithms. Applying hierarchical fuzzy rule bases is a way of reducing the complexity of the knowledge base, whereas evolutionary methods ensure a relatively efficient learning process. This is the reason of the investigation of this combination. %K genetic algorithms, genetic programming %R 10.1109/FUZZY.2010.5584220 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/Balazs_2010_ieee-fuzz.pdf %U http://dx.doi.org/10.1109/FUZZY.2010.5584220 %P 1866-1871 %0 Conference Proceedings %T Hierarchical fuzzy system construction applying genetic and bacterial programming algorithms with expression tree building restrictions %A Balazs, Krisztian %A Botzheim, Janos %A Koczy, Laszlo T. %S World Automation Congress (WAC 2010) %D 2010 %8 19 23 sep %C Kobe, Japan %F Balazs:2010:WAC %X In this paper various restrictions are proposed in the construction of hierarchical fuzzy rule bases by using Genetic and Bacterial Programming algorithms in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The properties (learning speed, accuracy) of the established systems are observed based on simulation results and they are compared to each other. %K genetic algorithms, genetic programming, bacterial programming algorithm, black box system, genetic programming algorithm, hierarchical fuzzy rule system construction, input-output pairs, supervised machine learning problem, tree building restriction, fuzzy set theory, learning (artificial intelligence) %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/Balazs_2010_WAC.pdf %0 Conference Proceedings %T Hierarchical-interpolative fuzzy system construction by Genetic and Bacterial Programming Algorithms %A Balazs, Krisztian %A Koczy, Laszlo T. %S IEEE International Conference on Fuzzy Systems (FUZZ 2011) %D 2011 %8 27 30 jun %C Taipei, Taiwan %F Balazs:2011:ieeeFUZZ %X In this paper a method is proposed for constructing hierarchical-interpolative fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The resulting hierarchical rule base is the knowledge base, which is constructed by using evolutionary techniques, namely, Genetic and Bacterial Programming Algorithms. Applying hierarchical-interpolative fuzzy rule bases is an advanced way of reducing the complexity of the knowledge base, whereas evolutionary methods ensure a relatively efficient learning process. This is the reason of the investigation of this combination. %K genetic algorithms, genetic programming, bacterial programming, black box system, evolutionary technique, hierarchical-interpolative fuzzy rule bases construction, knowledge base, supervised machine learning problem, fuzzy logic, knowledge based systems, learning (artificial intelligence), mathematical programming %R 10.1109/FUZZY.2011.6007594 %U http://dx.doi.org/10.1109/FUZZY.2011.6007594 %P 2116-2122 %0 Conference Proceedings %T A genetic algorithm with dynamic population: Experimental results %A Balazs, Marton E. %A Richter, Daniel L. %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 balazs:1999:AE %K Genetic Algorithms %P 25-30 %0 Conference Proceedings %T Structural Topology Optimization of Braced Steel Frameworks Using Genetic Programming %A Baldock, Robert %A Shea, Kristina %Y Smith, Ian F. C. %S Intelligent Computing in Engineering and Architecture, 13th EG-ICE Workshop %S Lecture Notes in Computer Science %D 2006 %8 jun 25 30 %V 4200 %I Springer %C Ascona, Switzerland %@ 3-540-46246-5 %F conf/egice/BaldockS06 %O Revised Selected Papers %X This paper presents a genetic programming method for the topological optimisation of bracing systems for steel frameworks. The method aims to create novel, but practical, optimally-directed design solutions, the derivation of which can be readily understood. Designs are represented as trees with one-bay, one-story cellular bracing units, operated on by design modification functions. Genetic operators (reproduction, crossover, mutation) are applied to trees in the development of subsequent populations. The bracing design for a three-bay, 12-story steel framework provides a preliminary test problem, giving promising initial results that reduce the structural mass of the bracing in comparison to previous published benchmarks for a displacement constraint based on design codes. Further method development and investigations are discussed. %K genetic algorithms, genetic programming %R 10.1007/11888598 %U http://dx.doi.org/10.1007/11888598 %P 54-61 %0 Conference Proceedings %T Exploring the Application of Hybrid Evolutionary Computation Techniques to Physical Activity Recognition %A Baldominos, Alejandro %A del Barrio, Carmen %A Saez, Yago %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 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation %D 2016 %8 20 24 jul %I ACM %C Denver, Colorado, USA %F Baldominos:2016:GECCOcomp %X his paper focuses on the problem of physical activity recognition, i.e., the development of a system which is able to learn patterns from data in order to be able to detect which physical activity (e.g. running, walking, ascending stairs, etc.) a certain user is performing. While this field is broadly explored in the literature, there are few works that face the problem with evolutionary computation techniques. In this case, we propose a hybrid system which combines particle swarm optimization for clustering features and genetic programming combined with evolutionary strategies for evolving a population of classifiers, shaped in the form of decision trees. This system would run the segmentation, feature extraction and classification stages of the activity recognition chain. For this paper, we have used the PAMAP2 dataset with a basic preprocessing. This dataset is publicly available at UCI ML repository. Then, we have evaluated the proposed system using three different modes: a user-independent, a user-specific and a combined one. The results in terms of classification accuracy were poor for the first and the last mode, but it performed significantly well for the user-specific case. This paper aims to describe work in progress, to share early results an discuss them. There are many things that could be improved in this proposed system, but overall results were interesting especially because no manual data transformation took place. %K genetic algorithms, genetic programming %R 10.1145/2908961.2931732 %U http://dx.doi.org/10.1145/2908961.2931732 %P 1377-1384 %0 Journal Article %T Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments %A Baldominos, Alejandro %A Saez, Yago %A Isasi, Pedro %J Sensors %D 2018 %8 apr %V 18 %N 4 %@ 1424-8220 %F Baldominos:2018:Sensors %X Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. %K genetic algorithms, genetic programming, Grammatical Evolution, ANN %9 journal article %R 10.3390/s18041288 %U http://www.mdpi.com/1424-8220/18/4/1288 %U http://dx.doi.org/10.3390/s18041288 %0 Conference Proceedings %T System Identification of Fuzzy Cartesian Granules Feature Models Using Genetic Programming %A Baldwin, James F. %A Martin, Trevor P. %A Shanahan, James G. %Y Ralescu, Anca L. %Y Shanahan, James G. %S Fuzzy Logic in Artificial Intelligence, IJCAI’97 Workshop, Selected and Invited Papers %S Lecture Notes in Artificial Intelligence %D 1997 %8 aug 23 24 %V 1566 %I Springer %C Nagoya, Japan %@ 3-540-66374-6 %F Baldwin:1999:SIF %O Published 1999 %K genetic algorithms, genetic programming, artificial intelligence, fuzzy logic, IJCAI %U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-164-22-1637718-0,00.html %P 91-116 %0 Journal Article %T Controlling with words using automatically identified fuzzy Cartesian granule feature models %A Baldwin, James F. %A Martin, Trevor P. %A Shanahan, James G. %J International Journal of Approximate Reasoning %D 1999 %V 22 %N 1-2 %F Baldwin:1999:IJAR %X We present a new approach to representing and acquiring controllers based upon Cartesian granule features - multidimensional features formed over the cross product of words drawn from the linguistic partitions of the constituent input features - incorporated into additive models. Controllers expressed in terms of Cartesian granule features enable the paradigm ’controlling with words’ by translating process data into words that are subsequently used to interrogate a rule base, which ultimately results in a control action. The system identification of good, parsimonious additive Cartesian granule feature models is an exponential search problem. In this paper we present the G_DACG constructive induction algorithm as a means of automatically identifying additive Cartesian granule feature models from example data. G_DACG combines the powerful optimisation capabilities of genetic programming with a novel and cheap fitness function, which relies on the semantic separation of concepts expressed in terms of Cartesian granule fuzzy sets, in identifying these additive models. We illustrate the approach on a variety of problems including the modelling of a dynamical process and a chemical plant controller. %K genetic algorithms, genetic programming %9 journal article %U http://www.sciencedirect.com/science/article/B6V07-3XWJVTP-K/1/fca9fc7ee54707e1f2ed9847e29c1b7e %P 109-148 %0 Book %T Flexible Manufacturing Systems; Development - Structure - Operation - Handling - Tooling %A Balic, Joze %S Manufacturing technology %D 1999 %I DAAAM International %C Vienna %@ 3-901509-03-8 %F balic:book %K genetic algorithms, genetic programming %U http://www.amazon.com/Contribution-integrated-manufacturing-Publishing-Manufacturing/dp/3901509038/ref=sr_1_1?ie=UTF8&s=books&qid=1254069037&sr=1-1 %0 Generic %T Modeling Of Mechanical Parts Compositions Using Genetic Programming %A Balic, Joze %A Brezocnik, Miran %A Cus, Franci %D 2000 %G en %F oai:CiteSeerPSU:316448 %X The paper is a contribution to introducing biologically oriented ideas in conceiving a new and innovative idea in modern factories of the future. The intelligent system is treated as an autonomous organization structure efficiently adapting itself to the dynamic changes in the micro and macro environment. Simulation of self-organizing (genetic) composition of elementary (basic) mechanical parts into the product is presented as an example of the intelligent system. The genetic programming method is used. For conceiving the genetically based composition of parts, the parallels from the living systems are used. Composition takes place on the basis of the genetic content in the basic components and the influence of the environment. The genetically based composition takes place in a distributed way, non-deterministically, buttom-up, and in a self-organizing manner. The paper is also a contribution to the international research and development program Intelligent Manufacturing Systems which is one of the biggest projects ever introduced. %K genetic algorithms, genetic programming %U http://citeseer.ist.psu.edu/316448.html %0 Journal Article %T An on-line predictive system for steel wire straightening using genetic programming %A Balic, J. %A Nastran, M. %J Engineering Applications of Artificial Intelligence %D 2002 %8 dec %V 15 %N 6 %@ 0952-1976 %F Balic:2002:EAAI %X Dimensional stability of forming processes is becoming more and more important in the modern production world. Especially when mass production is concerned, the technological system has to be reliable and accurate. Growing market demands are forcing production engineers towards process optimisation in order to achieve high machinery efficiency and reduce the production costs. An important precondition for improving the process chain is the prediction of process behaviour in advance. The paper is presenting the use of genetic programming to predict the wire geometry after forming. The results can be used as the basis for later optimisation of forming processes. %K genetic algorithms, genetic programming, Production, Control, Prediction, Accuracy %9 journal article %R 10.1016/S0952-1976(03)00021-6 %U https://repozitorij.uni-lj.si/IzpisGradiva.php?id=43335&lang=slv %U http://dx.doi.org/10.1016/S0952-1976(03)00021-6 %P 559-565 %0 Journal Article %T Intelligent tool path generation for milling of free surfaces using neural networks %A Balic, Joze %A Korosec, Marjan %J International Journal of Machine Tools and Manufacture %D 2002 %V 42 %N 10 %@ 0890-6955 %F Balic20021171 %X The presented paper has an intention to show how with the help of Artificial Neural Network (ANN), the prediction of milling tool-path strategy could be made in order to establish which milling path strategy or their sequence will show the best results (will be the most appropriate) at free surface machining, according to set technological aim. In our case the best possible surface quality of machined surface was taken as the primary technological aim. Configuration of used Neural Network (NN) is presented, and the whole procedure is shown on an example of mould, for producing light switches. The verification of machined surface quality, according to average mean roughness, Ra, is also being done, and compared with the NN predicted results [COBISS.SI-ID 7318550] %K Neural network, CAD/CAM system, CAPP, ICAM, Milling strategy %9 journal article %R 10.1016/S0890-6955(02)00045-7 %U http://www.sciencedirect.com/science/article/B6V4B-45YG41B-6/2/09eff48a04f9b22be6b2ed2dd0e6d3b1 %U http://dx.doi.org/10.1016/S0890-6955(02)00045-7 %P 1171-1179 %0 Journal Article %T Intelligent Programming of CNC Turning Operations using Genetic Algorithm %A Balic, Joze %A Kovacic, Miha %A Vaupotic, Bostjan %J Journal of intelligent manufacturing %D 2006 %8 jun %V 17 %N 3 %@ 0956-5515 %F Balic:2006:JIM %X CAD/CAM systems are nowadays tightly connected to ensure that CAD data can be used for optimal tool path determination and generation of CNC programs for machine tools. The aim of our research is the design of a computer-aided, intelligent and genetic algorithm(GA) based programming system for CNC cutting tools selection, tool sequences planning and optimisation of cutting conditions. The first step is geometrical feature recognition and classification. On the basis of recognised features the module for GA-based determination of technological data determine cutting tools, cutting parameters (according to work piece material and cutting tool material) and detailed tool sequence planning. Material, which will be removed, is split into several cuts, each consisting of a number of basic tool movements. In the next step, GA operations such as reproduction, crossover and mutation are applied. The process of GA-based optimisation runs in cycles in which new generations of individuals are created with increased average fitness of a population. During the evaluation of calculated results (generated NC programmes) several rules and constraints like rapid and cutting tool movement, collision, clamping and minimum machining time, which represent the fitness function, were taken into account. A case study was made for the turning operation of a rotational part. The results show that the GA-based programming has a higher efficiency. The total machining time was reduced by 16percent. The demand for a high skilled worker on CAD/CAM systems and CNC machine tools was also reduced. %K genetic algorithms, genetic programming, CNC programming, GA, Intelligent CAM, Turning, Tool path generation %9 journal article %R 10.1007/s10845-005-0001-1 %U http://dx.doi.org/10.1007/s10845-005-0001-1 %P 331-340 %0 Journal Article %T Multicriterion Genetic Programming for Trajectory Planning of Underwater Vehicle %A Balicki, Jerzy %J IJCSNS International Journal of Computer Science and Network Security %D 2006 %8 dec %V 6 %N 12 %@ 1738-7906 %G en %F Balicki:2006:IJCSNS %X An autonomous underwater vehicle is supposed to find its trajectory, systematically. It can be obtained by using genetic programming for multi-criterion optimisation of the set of alternative paths. For assessment of an underwater vehicle trajectory, three crucial criteria can be used: a total length of a path, a smoothness of a trajectory, and a measure of safety. %K genetic algorithms, genetic programming, remote operating vehicle, multi-criterion optimisation %9 journal article %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.5889 %P 1-6 %0 Conference Proceedings %T Multi-Criterion Genetic Programming With Negative Selection for Finding Pareto Solutions %A Balicki, Jerzy Marian %Y Filipe, Joaquim %Y Shishkov, Boris %Y Helfert, Markus %S Proceedings of the Second International Conference on Software and Data Technologies, ICSOFT 2007 %D 2007 %8 22 25 jul %I INSTICC Press %C Barcelona, Spain %F conf/icsoft/Balicki07 %X Multi-criterion genetic programming (MGP) is a relatively new approach for a decision making aid and it can be applied to determine the Pareto solutions. This purpose can be obtained by formulation of a multi-criterion optimization problem that can be solved by genetic programming. An improved negative selection procedure to handle constraints in the MGP has been proposed. In the test instance, both a workload of a bottleneck computer and the cost of system are minimized; in contrast, a reliability of the distributed system is maximized. %K genetic algorithms, genetic programming %R 10.5220/0001336201200127 %U http://www.icsoft.org/Abstracts/2007/ICSOFT_2007_Abstracts.htm %U http://dx.doi.org/10.5220/0001336201200127 %P 120-127 %0 Conference Proceedings %T Genetic Programming with Negative Selection for Volunteer Computing System Optimization %A Balicki, Jerzy %A Korlub, Waldemar %A Krawczyk, Henryk %A Paluszak, Jacek %S The 6th International Conference on Human System Interaction (HSI 2013) %D 2013 %8 June 8 jun %F Balicki:2013:HSI %X Volunteer computing systems like BOINC or Comcute are strongly supported by a great number of volunteers who contribute resources of their computers via the Web. So, the high efficiency of such grid system is required, and that is why we have formulated a multi-criterion optimisation problem for a volunteer grid system design. In that dilemma, both the cost of the host system and workload of a bottleneck host are minimised. On the other hand, a reliability of this grid structure is maximised. Moreover, genetic programming has been applied to determine the Pareto solutions. Finally, a negative selection procedure to handle constraints has been discussed. %K genetic algorithms, genetic programming, volenteer grid systems, Internet %R 10.1109/HSI.2013.6577835 %U http://dx.doi.org/10.1109/HSI.2013.6577835 %P 271-278 %0 Book Section %T Genetic Programming for Interaction Efficient Supporting in Volunteer Computing Systems %A Balicki, Jerzy %A Korlub, Waldemar %A Krawczyk, Henryk %A Paluszak, Jacek %E Hippe, Zdzislaw S. %E Kulikowski, Juliusz L. %E Mroczek, Teresa %E Wtorek, Jerzy %B Issues and Challenges in Artificial Intelligence %S Studies in Computational Intelligence %D 2014 %V 559 %I Springer %F series/sci/BalickiKKP14 %X Volunteer computing systems provide a middleware for interaction between project owners and great number volunteers. In this chapter, a genetic programming paradigm has been proposed to a multi-objective scheduler design for efficient using some resources of volunteer computers via the web. In a studied problem, genetic scheduler can optimise both a workload of a bottleneck computer and cost of system. Genetic programming has been applied for finding the Pareto solutions by applying an immunological procedure. Finally, some numerical experiment outcomes have been discussed. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-06883-1_11 %U http://dx.doi.org/10.1007/978-3-319-06883-1_11 %P 129-139 %0 Conference Proceedings %T Big Data Paradigm Developed in Volunteer Grid System with Genetic Programming Scheduler %A Balicki, Jerzy %A Korlub, Waldemar %A Szymanski, Julian %A Zakidalski, Marcin %Y Rutkowski, Leszek %Y Korytkowski, Marcin %Y Scherer, Rafal %Y Tadeusiewicz, Ryszard %Y Zadeh, Lotfi A. %Y Zurada, Jacek M. %S Artificial Intelligence and Soft Computing - 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part I %S Lecture Notes in Computer Science %D 2014 %V 8467 %I Springer %F conf/icaisc/BalickiKSZ14 %K genetic algorithms, genetic programming %U http://dx.doi.org/10.1007/978-3-319-07173-2 %P 771-782 %0 Conference Proceedings %T Collective citizens’ behavior modelling with support of the Internet of Things and Big Data %A Balicki, Jerzy %A Beringer, Michal %A Korlub, Waldemar %A Przybylek, Piotr %A Tyszka, Maciej %A Zadroga, Marcin %S 8th International Conference on Human System Interaction (HSI) %D 2015 %8 jun %F Balicki:2015:HSI %X In this paper, collective human behaviours are modelled by a development of Big Data mining related to the Internet of Things. Some studies under MapReduce architectures have been carried out to improve an efficiency of Big Data mining. Intelligent agents in data mining have been analysed for smart city systems, as well as data mining has been described by genetic programming. Furthermore, artificial neural networks have been discussed in data mining as well as an analysis of Tweeter’s blogs for citizens has been proposed. Finally, some numerical experiments with fire spread around Tricity, Poland have been submitted. %K genetic algorithms, genetic programming %R 10.1109/HSI.2015.7170644 %U http://dx.doi.org/10.1109/HSI.2015.7170644 %P 61-67 %0 Journal Article %T Mobile link prediction: Automated creation and crowdsourced validation of knowledge graphs %A Ballandies, Mark C. %A Pournaras, Evangelos %J Microprocessors and Microsystems %D 2021 %V 87 %@ 0141-9331 %F BALLANDIES:2021:MM %X Building trustworthy knowledge graphs for cyber-physical social systems (CPSS) is a challenge. In particular, current approaches relying on human experts have limited scalability, while automated approaches are often not validated by users resulting in knowledge graphs of questionable quality. This paper introduces a novel pervasive knowledge graph builder for mobile devices that brings together automation, experts’ and crowdsourced citizens’ knowledge. The knowledge graph grows via automated link predictions using genetic programming that are validated by humans for improving transparency and calibrating accuracy. The knowledge graph builder is designed for pervasive devices such as smartphones and preserves privacy by localizing all computations. The accuracy, practicality, and usability of the knowledge graph builder is evaluated in a real-world social experiment that involves a smartphone implementation and a Smart City application scenario. The proposed methodology of knowledge graph building outperforms a baseline method in terms of accuracy while demonstrating its efficient calculations on smartphones and the feasibility of the pervasive human supervision process in terms of high interactions throughput. These findings promise new opportunities to crowdsource and operate pervasive reasoning systems for cyber-physical social systems in Smart Cities %K genetic algorithms, genetic programming, Knowledge graph, Ontology, Cyber-physical-social system, Link prediction, Crowdsourcing %9 journal article %R 10.1016/j.micpro.2021.104335 %U https://www.sciencedirect.com/science/article/pii/S0141933121004944 %U http://dx.doi.org/10.1016/j.micpro.2021.104335 %P 104335 %0 Journal Article %T Generation of algebraic data type values using evolutionary algorithms %A Ballesteros, Ignacio %A Benac-Earle, Clara %A Marino, Julio %A Fredlund, Lars-Ake %A Herranz, Angel %J Journal of Logical and Algebraic Methods in Programming %D 2025 %V 143 %@ 2352-2208 %F Ballesteros:2025:jlamp %X Automatic data generation is a key component of automated software testing. Random generation of test input data can uncover some bugs in software, but its effectiveness decreases when those inputs must satisfy complex properties in order to be meaningful. In this work, we study an evolutionary approach to generate values that can be encoded as algebraic data types plus additional properties. First, the approach is illustrated with the generation of sorted lists. Then, we generalise the technique to arbitrary algebraic data type definitions. Finally, we consider the problem of constrained data types where the data must satisfy some nontrivial property, using the well-known example of red-black trees for our experiments. This example will allow us to introduce the main principles of evolutionary algorithms and how these principles can be applied to obtain valid, nontrivial samples of a given data structure. Our experiments have revealed that this evolutionary approach is able to improve diversity, and increase the size of valid generated values with respect to simple random sampling techniques %K genetic algorithms, genetic programming, Evolutionary algorithms, Software testing, Property-based testing, Red-black tree %9 journal article %R 10.1016/j.jlamp.2024.101022 %U https://www.sciencedirect.com/science/article/pii/S2352220824000762 %U http://dx.doi.org/10.1016/j.jlamp.2024.101022 %P 101022 %0 Conference Proceedings %T An Annotated Dataset of Stack Overflow Post Edits %A Baltes, Sebastian %A Wagner, Markus %Y Alexander, Brad %Y Brownlee, Alexander (Sandy) %Y Haraldsson, Saemundur O. %Y Wagner, Markus %Y Woodward, John R. %S 9th edition of GI @ GECCO 2020 %D 2020 %8 jul 8 12 %I ACM %C Internet %F Baltes:2020:GI9 %X To improve software engineering, software repositories have been mined for code snippets and bug fixes. Typically, this mining takes place at the level of files or commits. To be able to dig deeper and to extract insights at a higher resolution, we hereby present an annotated dataset that contains over 7 million edits of code and text on Stack Overflow. Our preliminary study indicates that these edits might be a treasure trove for mining information about fine-grained patches, e.g., for the optimisation of non-functional properties. %K genetic algorithms, genetic programming, genetic improvement, SBSE, Software documentation, software evolution, patches, mining soft-ware repositories, stack overflow, SOTorrent %R 10.1145/3377929.3398108 %U https://dl.acm.org/doi/abs/10.1145/3377929.3398108 %U http://dx.doi.org/10.1145/3377929.3398108 %P 1923-1925 %0 Conference Proceedings %T All about the money: Cost modeling and optimization of cloud applications %A Baltes, Sebastian %S "12th International Workshop on Genetic Improvement %F Baltes:2023:GI %0 Journal Article %D 2023 %8 20 may %I IEEE %C Melbourne, Australia %F 2023"a %O Invited Keynote %X Cost is an essential non-functional property of cloud applications and is often a primary reason for companies to move to the cloud. One significant advantage of cloud platforms is the possibility to scale compute, storage, and networking resources up and down based on demand. However, as an application scales, so does the cost. Cost transparency of cloud applications is a common problem, and cloud providers have responded by providing means for detecting cost anomalies. However, detecting anomalies after billing is a workaround rather than a solution addressing the core problem. After introducing central cloud computing concepts and typical pricing approaches in the cloud, this talk outlines our vision of a vendor-agnostic cost model enabling reasoning about cost-optimal infrastructure and platform configurations based on expected workloads. The overall goal is to shift cost transparency left, i.e., to the developers and platform engineers who frequently provision cloud environments using web portals or Infrastructure-as-Code (IaC) files. The talk concludes by summarising the current trend towards Infrastructure-from-Code (IfC), where programming languages and cloud infrastructure descriptions converge into one paradigm, intending to automate infrastructure provisioning as much as possible. This area has huge potential for genetic improvement to optimize the IfC code and the provisioning mechanisms while balancing nonfunctional properties such as performance and cost. %K genetic algorithms, genetic programming, Genetic Improvement, cloud computing, IaC, IfC, Gartner, AWS, nonfunctional cost optimisation, software engineering, SBSE %9 journal article %R 10.1109/GI59320.2023.00008 %U http://gpbib.cs.ucl.ac.uk/gi2023/keynote_2023_gi.pdf %U http://dx.doi.org/10.1109/GI59320.2023.00008 %P x %0 Journal Article %T Towards Automated Artificial Evolution for Computer-generated Images %A Baluja, Shumeet %A Pomerleau, Dean %A Jochem, Todd %J Connection Science %D 1994 %V 6 %N 2 and 3 %F baluja:1994:taaecgi %X In 1991, Karl Sims presented work on artificial evolution in which he used genetic algorithms to evolve complex structures for use in computer generated images and animations. The evolution of the computer generated images progressed from simple, randomly generated shapes to interesting images which the users interactively created. The evolution advanced under the constant guidance and supervision of the user. This paper describes attempts to automate the process of image evolution through the use of artificial neural networks. The central objective of this study is to learn the user’s preferences, and to apply this knowledge to evolve aesthetically pleasing images which are similar to those evolved through interactive sessions with the user. This paper presents a detailed analysis of both the shortcomings and successes encountered in the use of five artificial neural network architectures. Further possibilities for improving the performance of a fully automated system are also discussed. %K genetic algorithms, genetic programming, artificial neural networks (ANN), simulated evolution, computer graphics %9 journal article %R 10.1080/09540099408915729 %U http://www.ri.cmu.edu/pub_files/pub3/baluja_shumeet_1994_1/baluja_shumeet_1994_1.pdf %U http://dx.doi.org/10.1080/09540099408915729 %P 325-354 %0 Journal Article %T A 15 Year Perspective on Automatic Programming %A Balzer, Robert %J IEEE Transactions on Software Engineering %D 1985 %8 nov %V SE-11 %N 11 %@ 0098-5589 %F Balzer:1985:ieeeTSE %X Automatic programming consists not only of an automatic compiler, but also some means of acquiring the high-level specification to be compiled, some means of determining that it is the intended specification, and some (interactive) means of translating this high-level specification into a lower-level one which can be automatically compiled. We have been working on this extended automatic programming problem for nearly 15 years, and this paper presents our perspective and approach to this problem and justifies it in terms of our successes and failures. Much of our recent work centers on an operational testbed incorporating usable aspects of this technology. This testbed is being used as a prototyping vehicle for our own research and will soon be released to the research community as a framework for development and evolution of Common Lisp systems. %K genetic algorithms, genetic programming, genetic improvement, Automatic programming, evolution, explanation, knowledge base, maintenance, prototyping, specification, transformation %9 journal article %R 10.1109/TSE.1985.231877 %U http://dx.doi.org/10.1109/TSE.1985.231877 %P 1257-1268 %0 Conference Proceedings %T Why Haven’t We Automated Programming %A Balzer, Robert %S Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research %D 2010 %I ACM %C Santa Fe, New Mexico, USA %F Balzer:2010:FoSER %K genetic improvement, automatic programming, interactive, refinement %R 10.1145/1882362.1882366 %U http://doi.acm.org/10.1145/1882362.1882366 %U http://dx.doi.org/10.1145/1882362.1882366 %P 13-16 %0 Journal Article %T Comparison between Genetic Programming and Dynamic Models for Compact Electrohydraulic Actuators %A Bamshad, Hamid %A Jang, Seongwon %A Jeong, Hyemi %A Lee, Jaesung %A Yang, Hyunseok %J Machines %D 2022 %V 10 %N 10 %@ 2075-1702 %F bamshad:2022:Machines %X A compact electrohydraulic actuator (C-EHA) is an innovative hydraulic system with a wide range of applications, particularly in automation, robotics, and aerospace. The actuator provides the benefits of hydraulics without the expense and space requirements of full-sized hydraulic systems and in a much cleaner manner. However, this actuator is associated with some disadvantages, such as a high level of nonlinearity, uncertainty, and a lack of studies. The development of a robust controller requires a thorough understanding of the system behaviour as well as an accurate dynamic model of the system; however, finding an accurate dynamic model of a system is not always straightforward, and it is considered a significant challenge for engineers, particularly for a C-EHA because the critical parameters inside cannot be accessed. Our research aims to evaluate and confirm the ability of genetic programming (GP) to model a nonlinear system for a C-EHA. In our paper, we present and develop a GP model for the C-EHA system. Furthermore, our study presents a dynamic model of the system for comparison with the GP model. As a result, by using this actuator in the 1-DOF arm system and conducting experiments, we confirmed that the GP model has a better performance with less positional error compared with the proposed dynamic model. The model can be used to conduct further studies, such as designing controllers or system simulations. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/machines10100961 %U https://www.mdpi.com/2075-1702/10/10/961 %U http://dx.doi.org/10.3390/machines10100961 %P ArticleNo.961 %0 Conference Proceedings %T A Dimensionally-Aware Genetic Programming Architecture for Automated Innovization %A Bandaru, Sunith %A Deb, Kalyanmoy %Y Purshouse, Robin C. %Y Fleming, Peter J. %Y Fonseca, Carlos M. %Y Greco, Salvatore %Y Shaw, Jane %S Proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013 %S Lecture Notes in Computer Science %D 2013 %8 mar 19 22 %V 7811 %I Springer %C Sheffield, UK %F conf/emo/BandaruD13 %X Automated innovization is an unsupervised machine learning technique for extracting useful design knowledge from Pareto-optimal solutions in the form of mathematical relationships of a certain structure. These relationships are known as design principles. Past studies have shown the applicability of automated innovization on a number of engineering design optimisation problems using a multiplicative form for the design principles. In this paper, we generalise the structure of the obtained principles using a tree-based genetic programming framework. While the underlying innovisation algorithm remains the same, evolving multiple trees, each representing a different design principle, is a challenging task. We also propose a method for introducing dimensionality information in the search process to produce design principles that are not just empirical in nature, but also meaningful to the user. The procedure is illustrated for three engineering design problems. %K genetic algorithms, genetic programming, dimensional awareness, automated innovization, multi-objective optimization, design principles, NSGA-II, Matlab, GA SmallGP %R 10.1007/978-3-642-37140-0_39 %U https://www.egr.msu.edu/~kdeb/papers/k2012015.pdf %U http://dx.doi.org/10.1007/978-3-642-37140-0_39 %P 513-527 %0 Thesis %T Automated Innovization: Knowledge discovery through multi-objective optimization %A Bandaru, Sunith %D 2013 %C India %C Indian Institute of Technology Kanpur %F Bandaru_thesis %X In recent years, there has been a growing interest in the field of post-optimality analysis. In a single objective scenario, this usually concerns optimality, sensitivity and robustness studies on the obtained solution. Multi-objective optimization on the other hand, poses an additional challenge in that there are a multitude of possible solutions (when the objectives are conflicting) which are all said to be Pareto-optimal. These solutions may collectively hold crucial design information. Properties that are common to all or most Pareto-optimal solutions can be considered as characteristic features that make good designs. Knowledge of such features, in addition to providing better insights into the problem at hand, enables the designer to hand craft solutions for other optimization tasks which are structurally similar to it; thus eliminating the need to actually optimize. Innovization is the process of extracting this information in the form of design principles, which are basically mathematical expressions relating various problem entities like variables, objectives and constraint functions. Manual innovization involves the visual identification of correlations between problem entities through two and three-dimensional plots. Thereafter, appropriate functions are used for regression and the design principles are obtained. Though the procedure has been applied to many engineering design problems, the human element involved in it limits its use in most practical applications. The present thesis firstly proposes automated innovization, an unsupervised machine learning technique that can identify correlations in any multi-dimensional space formed by variables, objectives, etc. specified by the user and subsequently performs a selective regression on the correlated part of the Pareto-optimal dataset to obtain a design principle. The correlations are automatically identified by a customized grid-based clustering algorithm and the design principle is evolved using a genetic algorithm. Next, the procedure is extended so that design principles hidden in all possible Euclidean spaces formed by the variables and objectives (and any other user-defined functions) can be obtained simultaneously, without any human interaction, in a single run of the algorithm. This is accomplished by introducing a niching strategy to evolve different species of design principles in the same population of a genetic algorithm. Automation in innovization is achieved at the cost of restricting the mathematical structure of the design principles to a certain form, the significance of which becomes clear by observing physical laws in nature. Later in this thesis, a tree-based genetic programming framework is integrated into automated innovization to obtain design principles of any generic mathematical structure. Dimensionality information is introduced in the search process to produce design principles that are meaningful to the designer. Next, the proposed automated innovization technique is used to obtain design principles for four real-world multi-objective design optimization problems from varied fields. They are: noise barrier design optimization, polymer extrusion process optimization, friction stir welding process optimization and MEMS (MicroElectroMechanical Systems) resonator design optimization. In each case the obtained design principles are presented to experts of the respective fields for interpretation to gain insights. Secondly, this thesis introduces two new innovization concepts, namely higher-level innovization and lower-level innovization. Multi-objective optimization problem formulations involve many settings that are not changed during the solution process. However, once the trade-off front is generated, the designer may wish to change them and rerun the optimization, thus obtaining more fronts. This happens in many real-world situations where the designer is initially unsure about problem elements such as constraints, variable bounds, parameters and even objective functions. Higher-level innovization answers questions like: Are the features of the original problem still valid for other generated fronts? If not, how do they change with the modified setting?. The name reflects the fact that higher-level design knowledge is gained in the process. Sometimes lower-level design knowledge may also be desired. Consider the situation when after obtaining a set of trade-off solutions for a multi-objective design problem, a posteriori decision-making approach is used to identify a region of preference on the trade-off front. Now the designer may be interested in knowing features that are common to solutions only in this partial set and are not seen in rest of the trade-off solutions, so that the designer is specifically aware of properties associated with the chosen solutions. In this thesis, the automated innovization technique is extended to perform both higher and lower-level innovization. Thirdly, this thesis studies the temporal evolution of design principles obtained using automated innovization during the course of optimization. Results on a few engineering design problems reveal that certain important design features start to evolve early on, whereas some detailed design features appear later during optimization. Interestingly, there exists a simile between evolution of design principles in engineering and human evolution. Such information about the hierarchy of key design principles should enable designers to have a deeper understanding of their problems. %K genetic algorithms, genetic programming, Innovization, MEMS %9 Ph.D. thesis %U https://drive.google.com/file/d/0B8WHZC_8VuhxZ3FWenBfa19MSDQ/view %0 Journal Article %T Generalized higher-level automated innovization with application to inventory management %A Bandaru, Sunith %A Aslam, Tehseen %A Ng, Amos H. C. %A Deb, Kalyanmoy %J European Journal of Operational Research %D 2015 %V 243 %N 2 %@ 0377-2217 %F Bandaru:2015:EJOR %X This paper generalises the automated innovization framework using genetic programming in the context of higher-level innovisation. Automated innovization is an unsupervised machine learning technique that can automatically extract significant mathematical relationships from Pareto-optimal solution sets. These resulting relationships describe the conditions for Pareto-optimality for the multi-objective problem under consideration and can be used by scientists and practitioners as thumb rules to understand the problem better and to innovate new problem solving techniques; hence the name innovization (innovation through optimisation). Higher-level innovization involves performing automated innovization on multiple Pareto-optimal solution sets obtained by varying one or more problem parameters. The automated innovization framework was recently updated using genetic programming. We extend this generalisation to perform higher-level automated innovization and demonstrate the methodology on a standard two-bar bi-objective truss design problem. The procedure is then applied to a classic case of inventory management with multi-objective optimisation performed at both system and process levels. The applicability of automated innovization to this area should motivate its use in other avenues of operational research. %K genetic algorithms, genetic programming, Automated innovization, Higher-level innovization, Inventory management, Knowledge discovery %9 journal article %R 10.1016/j.ejor.2014.11.015 %U http://www.sciencedirect.com/science/article/pii/S0377221714009199 %U http://dx.doi.org/10.1016/j.ejor.2014.11.015 %P 480-496 %0 Conference Proceedings %T Evolving Optimal Convolutional Neural Networks %A Banerjee, Subhashis %A Mitra, Sushmita %S 2020 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2020 %8 dec %F Banerjee:2020:SSCI %X Among the different Deep Learning (DL) models, the deep Convolutional Neural Networks (CNNs) have demonstrated impressive performance in a variety of image recognition or classification tasks. Although CNNs do not require feature engineering or manual extraction of features at the input level, yet designing a suitable CNN architecture necessitates considerable expert knowledge involving enormous amount of trial-and-error activities. In this paper we attempt to automatically design a competitive CNN architecture for a given problem while consuming reasonable machine resource(s) based on a modified version of Cartesian Genetic Programming (CGP). As CGP uses only the mutation operator to generate offsprings it typically evolves slowly. We develop a new algorithm which introduces crossover to the standard CGP to generate an optimal CNN architecture. The genotype encoding scheme is changed from integer to floating-point representation for this purpose. The function genes in the nodes of the CGP are chosen as the highly functional modules of CNN. Typically CNNs use convolution and pooling, followed by activation. Rather than using each of them separately as a function gene for a node, we combine them in a novel way to construct highly functional modules. Five types of functions, called ConvBlock, average pooling, max pooling, summation, and concatenation, were considered. We test our method on an image classification dataset CIFAR10, since it is being used as the benchmark for many similar problems. Experiments demonstrate that the proposed scheme converges fast and automatically finds the competitive CNN architecture as compared to state-of-the-art solutions which require thousands of generations or GPUs involving huge computational burden. %K genetic algorithms, genetic programming, cartesian genetic programming %R 10.1109/SSCI47803.2020.9308201 %U http://dx.doi.org/10.1109/SSCI47803.2020.9308201 %P 2677-2683 %0 Journal Article %T An Approach of Genetic Programming for Music Emotion Classification %A Bang, Sung-Woo %A Kim, Jaekwang %A Lee, Jee-Hyong %J International Journal of Control, Automation and Systems %D 2013 %8 dec %V 11 %N 6 %I Springer %@ 1598-6446 %G English %F Bang:2013:IJCAS %X In this paper, we suggest a new approach of genetic programming for music emotion classification. Our approach is based on Thayer’s arousal-valence plane which is one of representative human emotion models. Thayer’s plane which says human emotions is determined by the psychological arousal and valence. We map music pieces onto the arousal-valence plane, and classify the music emotion in that space. We extract 85 acoustic features from music signals, rank those by the information gain and choose the top k best features in the feature selection process. In order to map music pieces in the feature space onto the arousal-valence space, we apply genetic programming. The genetic programming is designed for finding an optimal formula which maps given music pieces to the arousal-valence space so that music emotions are effectively classified. k-NN and SVM methods which are widely used in classification are used for the classification of music emotions in the arousal-valence space. For verifying our method, we compare with other six existing methods on the same music data set. With this experiment, we confirm the proposed method is superior to others. %K genetic algorithms, genetic programming, Classification algorithm, emotion recognition, music information retrieval %9 journal article %R 10.1007/s12555-012-9407-7 %U http://dx.doi.org/10.1007/s12555-012-9407-7 %P 1290-1299 %0 Journal Article %T Computational Hybrids Towards Software Defect Predictions %A Banga, Manu %J International Journal of Scientific Engineering and Technology %D 2013 %V 2 %N 5 %@ 2277-1581 %G English %F Banga:2013:ijset %X In this paper, new computational intelligence sequential hybrid architectures involving Genetic Programming (GP) and Group Method of Data Handling (GMDH) viz. GPGMDH. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are also developed. We also performed GP based feature selection. The efficacy of Multiple Linear Regression (MLR), Polynomial Regression, Support Vector Regression (SVR), Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), Multilayer FeedForward Neural Network (MLFF), Radial Basis Function Neural Network (RBF), Counter Propagation Neural Network (CPNN), Dynamic Evolving Neuro–Fuzzy Inference System (DENFIS), TreeNet, Group Method of Data Handling and Genetic Programming is tested on the NASA dataset. Ten-fold cross validation and t-test are performed to see if the performances of the hybrids developed are statistically significant. %K genetic algorithms, genetic programming, MLR, SVR, CART, MARS, MPFF, RBF %9 journal article %U http://ijset.com/ijset/publication/v2s5/paper1.pdf %P 311-316 %0 Thesis %T Genetic Programming for Non-Photorealistic Rendering %A Baniasadi, Maryam %D 2013 %8 mar %C St. Catharines, Ontario, Canada L2S 3A1 %C Department of Computer Science, Brock University %F Brock_Baniasadi_Maryam_2013 %X This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities. %K genetic algorithms, genetic programming %9 Masters thesis %U http://hdl.handle.net/10464/4304 %0 Journal Article %T Exploring non-photorealistic rendering with genetic programming %A Baniasadi, Maryam %A Ross, Brian J. %J Genetic Programming and Evolvable Machines %D 2015 %8 jun %V 16 %N 2 %@ 1389-2576 %F Baniasadi:2015:GPEM %X The field of evolutionary art focuses on using artificial evolution as a means for generating and exploring artistic images and designs. Here, we use evolutionary computation to generate painterly styles of images. A source image is read into the system, and a genetic program is evolved that will re-render the image with non-photorealistic effects. A main contribution of this research is that the colour mixing expression is evolved, which permits a variety of interesting NPR effects to arise. The mixing expression evaluates mathematical properties of the dynamically changing canvas, which results in the evolution of adaptive NPR procedures. Automatic fitness evaluation includes Ralph’s aesthetic model, colour matching, and direct luminosity matching. A few simple techniques for economical brush stroke application on the canvas are supported, which produce different stylistic effects. Using our approach, a number of established, as well as innovative, non-photorealistic painting effects were produced. %K genetic algorithms, genetic programming, Evolutionary art %9 journal article %R 10.1007/s10710-014-9234-0 %U http://dx.doi.org/10.1007/s10710-014-9234-0 %P 211-239 %0 Conference Proceedings %T Forecasting US NASDAQ stock index values using hybrid forecasting systems %A Banik, Shipra %A Khan, A. F. M. Khodadad %S 2015 18th International Conference on Computer and Information Technology (ICCIT) %D 2015 %8 dec %F Banik:2015:ICCIT %X Capability to predict precise future stock values is the most important factor in financial market to make profit. Because of virtual trading, now a day this market has turn into one of the hot targets where any person can earn profit. Thus, predicting the correct future value of a stock has become an area of hot interest. This paper attempt to forecast NASDAQ stock index values using novel hybrid forecasting models based on widely used soft computing models and time series models. The daily historical US NASDAQ closing stock index for the periods of 08 February 1971 to 24 July 2015 is used and is applied our proposed hybrid forecasting models to see whether considered forecasting models can closely forecast daily NASDAQ stock index values. Mean absolute error and root mean square error between observed and predicted NASDAQ stock index are considered as evaluation criteria. The result is compared on the basis of selected individual forecasting time series model and individual soft computing forecasting models and the proposed hybrid forecasting models. Our experimental evidences show that the proposed hybrid back-propagation artificial neural network and genetic algorithm forecasting model has outperformed as compare to other considered forecasting models for forecasting daily US NASDAQ stock index. We trust that daily US NASDAQ stock index forecasts will be notice for a number of spectators who wish to construct strategies about this index. %K genetic algorithms, genetic programming %R 10.1109/ICCITechn.2015.7488083 %U http://dx.doi.org/10.1109/ICCITechn.2015.7488083 %P 282-287 %0 Journal Article %T Using evolvable genetic cellular automata to model breast cancer %A Bankhead III, Armand %A Heckendorn, Robert B. %J Genetic Programming and Evolvable Machines %D 2007 %8 dec %V 8 %N 4 %@ 1389-2576 %F Bankhead:2007:GPEM %O special issue on medical applications of Genetic and Evolutionary Computation %X Cancer is an evolutionary process. Mutated cells undergo selection for abnormal growth and survival creating a tumour. We model this process with cellular automata that use a simplified genetic regulatory network simulation to control cell behaviour and predict cancer etiology. Our genetic model gives us the ability to relate genetic mutation to cancerous outcomes. The simulation uses known histological morphology, cell types, and stochastic behavior to specifically model ductal carcinoma in situ (DCIS), a common form of non-invasive breast cancer. Using this model we examine the effects of hereditary predisposition on DCIS incidence and aggressiveness. Results show that we are able to reproduce in vivo pathological features to hereditary forms of breast cancer: earlier incidence and increased aggressiveness. We also show that a contributing factor to the different pathology of hereditary breast cancer results from the ability of progenitor cells to pass cancerous mutations on to offspring. %K genetic algorithms, Genetic cellular automata, DCIS, Progenitor hierarchy, Ductal simulation, Hereditary genetic predisposition, Hereditary breast cancer, CA %9 journal article %R 10.1007/s10710-007-9042-x %U http://dx.doi.org/10.1007/s10710-007-9042-x %P 381-393 %0 Conference Proceedings %T Parametric Regression Through Genetic Programming %A Banks, Edwin Roger %A Hayes, James %A Nunez, Edwin %Y Keijzer, Maarten %S Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference %D 2004 %8 26 jul %C Seattle, Washington, USA %F banks:2004:lbp %X Parametric regression in genetic programming can substantially speed up the search for solutions. Paradoxically, the same technique has difficulty finding a true optimum solution. The parametric formulation of a problem results in a fitness landscape that looks like an inverted brush with many bristles of almost equal length (individuals of high fitness), but with only one bristle that is very slightly longer than the rest, the optimum solution. As such it is easy to find very good, even outstanding solutions, but very difficult to locate the optimum solution. In this paper parametric regression is applied to a minimum-time-to-target problem. The solution is equivalent to the classical brachistochrone. Two formulations were tried: a parametric regression and the classical symbolic regression formulation. The parametric approach was superior without exception. We speculate the parametric approach is more generally applicable to other problems and suggest areas for more research. %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2004/LBP001.pdf %0 Conference Proceedings %T Parametric Regression Through Genetic Programming %A Banks, E. R. %A Hayes, J. C. %A Nunez, E. %Y Poli, R. %Y Cagnoni, S. %Y Keijzer, M. %Y Costa, E. %Y Pereira, F. %Y Raidl, G. %Y Upton, S. C. %Y Goldberg, D. %Y Lipson, H. %Y de Jong, E. %Y Koza, J. %Y Suzuki, H. %Y Sawai, H. %Y Parmee, I. %Y Pelikan, M. %Y Sastry, K. %Y Thierens, D. %Y Stolzmann, W. %Y Lanzi, P. L. %Y Wilson, S. W. %Y O’Neill, M. %Y Ryan, C. %Y Yu, T. %Y Miller, J. F. %Y Garibay, I. %Y Holifield, G. %Y Wu, A. S. %Y Riopka, T. %Y Meysenburg, M. M. %Y Wright, A. W. %Y Richter, N. %Y Moore, J. H. %Y Ritchie, M. D. %Y Davis, L. %Y Roy, R. %Y Jakiela, M. %S GECCO 2004 Workshop Proceedings %D 2004 %8 26 30 jun %C Seattle, Washington, USA %F banks:2004:msa:erban %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2004/WMSA003.pdf %0 Conference Proceedings %T Genetic Programming for Discrimination of Buried Unexploded Ordnance (UXO) %A Banks, Edwin Roger %A Nunez, Edwin %A Agarwal, Paul %A Owens, Claudette %A McBride, Marshall %A Liedel, Ron %Y Rothlauf, Franz %S Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO’2005) %D 2005 %8 25 29 jun %C Washington, D.C., USA %F Banks:gecco05lbp %X According to the Department of Defense, over 10 million acres of land in the US need to be cleared of buried unexploded ordnance (UXO). Worldwide, UXO injures thousands each year. Cleanup costs are prohibitively expensive due to the difficulties in discriminating buried UXO from other inert non-UXO objects. Government agencies are actively searching for improved sensor methodologies to detect and discriminate buried UXO from other objects. We describe the results of work performed on data gathered by the GeoPhex GEM-3 electromagnetic sensor during their attempts to discriminate buried UXO at the U.S. Army Jefferson Proving Ground (JPG). We used a variety of evolutionary computing (EC) approaches that included genetic programming, genetic algorithms, and decision-tree methods. All approaches were essentially formulated as regression problems whereby the EC algorithms used sensor data to evolve buried UXO discrimination chromosomes. Predictions were then compared with a ground-truth file and the number of false positives and negatives determined %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2005lbp/papers/66-banks.pdf %0 Conference Proceedings %T A Comparison of Evolutionary Computing Techniques Used to Model Bi-Directional Reflectance Distribution Functions %A Banks, Edwin Roger %A Nunez, Edwin %A Agarwal, Paul %A McBride, Marshall %A Liedel, Ronald %A Owens, Claudette %Y Grahl, Jörn %S Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO’2006) %D 2006 %8 August 12 jul %C Seattle, WA, USA %F Banks:gecco06lbp %X Bi-Directional Reflectance Distribution Functions are used in many fields including computer animation modeling, military defense (radar, lidar, etc.), and others. This paper explores a variety of approaches to modelling BRDFs using different evolutionary computing (EC) techniques. We concentrate on genetic programming (GP) and in hybrid GP approaches, obtaining very close correspondence between models and data. The problem of obtaining parameters that make particular BRDF models fit to laboratory-measured reflectance data is a classic symbolic regression problem. The goal of this approach is to discover the equations that model laboratory-measured data according to several criteria of fitness. These criteria involve closeness of fit, simplicity or complexity of the model (parsimony), form of the result, and speed of discovery. As expected, free form, unconstrained GP gave the best results in terms of minimising measurement errors. However, it also yielded the most complex model forms. Certain constrained approaches proved to be far superior in terms of speed of discovery. Furthermore, application of mild parsimony pressure resulted in not only simpler expressions, but also improved results by yielding small differences between the models and the corresponding laboratory measurements. %K genetic algorithms, genetic programming, evolutionary computation, hybrid genetic programming, symbolic regression, Bi-directional reflectance distribution function, BRDF, parsimony, Phong model %U http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/lbp128.pdf %0 Conference Proceedings %T A comparison of selection, recombination, and mutation parameter importance over a set of fifteen optimization tasks %A Banks, Edwin Roger %A Agarwal, Paul %A McBride, Marshall %A Owens, Claudette %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 Late-Breaking Papers %D 2009 %8 August 12 jul %I ACM %C Montreal %F DBLP:conf/gecco/BanksAMO09 %X How does one choose an initial set of parameters for an evolutionary computing algorithm? Clearly some choices are dictated by the problem itself, such as the encoding of a problem solution, or how much time is available for running the evolution. Others, however, are frequently found by trial-and-error. These may include population sizes, number of populations, type of selection, recombination and mutation rates, and a variety of other parameters. Sometimes these parameters are allowed to co-evolve along with the solutions rather than by trial-and-error. But in both cases, an initial setting is needed for each parameter. When there are hundreds of parameters to be adjusted, as in some evolutionary computation tools, one would like to just spend time adjusting those that are believed to be most important, or sensitive, and leave the rest to start with an initial default value. Thus the primary goal of this paper is to establish the relative importance of each parameter. Establishing general guidance to assist in the determination of these initial default values is another primary goal of this paper. We propose to develop this guidance by studying the solutions resulting from variations around the default starting parameters applied across fifteen different application types. %K genetic algorithms, genetic programming %R 10.1145/1570256.1570261 %U http://dx.doi.org/10.1145/1570256.1570261 %P 1971-1976 %0 Conference Proceedings %T Lessons learned in application of evolutionary computation to a set of optimization tasks %A Banks, Edwin Roger %A Agarwal, Paul %A McBride, Marshall %A Owens, Claudette %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 Late-Breaking Papers %D 2009 %8 August 12 jul %I ACM %C Montreal %F DBLP:conf/gecco/BanksAMO09a %X Many GECCO papers discuss lessons learned in a particular application, but few papers discuss lessons learned over an ensemble of problem areas. A scan of the tables of contents of the Proceedings from GECCO 2005 and 2006 showed no paper title stressing lessons learned although the term ’pitfall’ appeared occasionally in abstracts, typically applying to a particular practice. We present in this paper a set of broadly applicable ’lessons learned’ in the application of evolutionary computing (EC) techniques to a variety of problem areas and present advice related to encoding, running, monitoring, and managing an evolutionary computing task. %K genetic algorithms, genetic programming %R 10.1145/1570256.1570262 %U http://dx.doi.org/10.1145/1570256.1570262 %P 1977-1982 %0 Conference Proceedings %T Evolving Image Noise Filters through Genetic Programming %A Banks, Edwin Roger %A Agarwal, Paul %A McBride, Marshall %A Owens, Claudette %S DoD High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC), 2009 %D 2009 %8 15 18 jun %F Banks:2009:HPCMP-UGC %X A form of Evolutionary Computation (EC) called Genetic Programming (GP) was used to automatically discover sequences of image noise filters to remove two types of image noise and a type of communications noise associated with a remotely sensed imagery. Sensor noise was modelled by the addition of salt-and-pepper and grayscale noise to the image. Communication noise was modeled by inserting a series of blank pixels in selected image rows to replicate dropped pixel segments occurring during communication interruptions of sequential uncompressed image information. A known image was used for training the evolver. Heavy amounts of noise were added to the known image, and a filter was evolved. (The filtered image was compared to the original with the average image-to-image pixel error establishing the fitness function.). The evolved filter derived for the noisy image was then applied to never-before-seen imagery affected by similar noise conditions to judge the universal applicability of the evolved GP filter. Examples of all described images are included in the presentation. A variety of image filter primitives were used in this experiment. The evolved sequences of primitives were each then sequentially applied to produce the final filtered image. These filters were evolved over a typical run length of one week each on a small Linux cluster. Once evolved, the filters were then transported to a PC for application to the never-before-seen images, using an evolve-once, apply-many-times approach. The results of this image filtering experiment were quite dramatic. %K genetic algorithms, genetic programming, Linux cluster, communication interruptions, communications noise, evolutionary computation, grayscale noise, image filtering, image noise filters, remotely sensed imagery, salt-and-pepper noise, sensor noise, sequential uncompressed image information, Linux, filtering theory, image denoising, image resolution, image segmentation, image sequences %R 10.1109/HPCMP-UGC.2009.50 %U http://dx.doi.org/10.1109/HPCMP-UGC.2009.50 %P 307-312 %0 Conference Proceedings %T Toward a universal operator encoding for genetic programming %A Banks, Edwin Roger %A Agarwal, Paul %A McBride, Marshall %A Owens, Claudette %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 Late-Breaking Papers %D 2009 %8 August 12 jul %I ACM %C Montreal %F DBLP:conf/gecco/BanksAMO09b %X The 2002 CEC paper entitled ’Genetic Programming with Smooth Operators for Arithmetic Expressions: Diviplication and Subdition’ by Ursem and Krink \citeursem:2002:gpwsofaedas proposed to blend certain arithmetic operators by interpolation to smooth the transition from one operator to another in the fitness landscape. Inspired by their idea, herein it is shown how to generalise further by using combinations of more than two operators, requiring log(N) additional parameters for each N operators so combined. Comparative results are reported for the application of this methodology to a variety of optimisation tasks including symbolic regression, an aspherical lens system design, a UAV path optimization, and a remote sensor image noise filter design. %K genetic algorithms, genetic programming %R 10.1145/1570256.1570263 %U http://dx.doi.org/10.1145/1570256.1570263 %P 1983-1986 %0 Journal Article %T Automatic development of clinical prediction models with genetic programming: A case study in cardiovascular disease %A Bannister, C. A. %A Currie, C. J. %A Preece, A. %A Spasic, I. %J Value in Health %D 2014 %V 17 %N 3 %@ 1098-3015 %F Bannister:2014:VH %O ISPOR 19th Annual International Meeting Research Abstracts %X Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this study was to demonstrate the utility of genetic programming for the automatic development of clinical prediction models using cardiovascular disease as a case study. %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.jval.2014.03.1171 %U http://www.sciencedirect.com/science/article/pii/S1098301514012224 %U http://dx.doi.org/10.1016/j.jval.2014.03.1171 %P A200-A201 %0 Thesis %T Automated Development of Clinical Prediction Models Using Genetic Programming %A Bannister, Christian A. %D 2015 %8 sep %C UK %C School of Computer Science & Informatics, Cardiff University %F phd/ethos/Bannister15 %X Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Genetic programming is a general methodology, the specific implementation of which requires development of several different specific elements such as problem representation, fitness, selection and genetic variation. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this work was to develop a genetic programming approach for survival analysis and demonstrate its utility for the automatic development of clinical prediction models using cardiovascular disease as a case study. We developed a tree-based untyped steady-state genetic programming approach for censored longitudinal data, comparing its performance to the de facto statistical method (Cox regression) in the development of clinical prediction models for the prediction of future cardiovascular events in patients with symptomatic and asymptomatic cardiovascular disease, using large observational datasets. We also used genetic programming to examine the prognostic significance of different risk factors together with their non-linear combinations for the prognosis of health outcomes in cardiovascular disease. These experiments showed that Cox regression and the developed steady-state genetic programming approach produced similar results when evaluated in common validation datasets. Despite slight relative differences, both approaches demonstrated an acceptable level of discriminative and calibration at a range of times points. Whilst the application of genetic programming did not provide more accurate representations of factors that predict the risk of both symptomatic and asymptomatic cardiovascular disease when compared with existing methods, genetic programming did offer comparable performance. Despite generally comparable performance, albeit in slight favour of the Cox model, the predictors selected for representing their relationships with the outcome were quite different and, on average, the models developed using genetic programming used considerably fewer predictors. The results of the genetic programming confirm the prognostic significance of a small number of the most highly associated predictors in the Cox modelling; age, previous atherosclerosis, and albumin for secondary prevention; age, recorded diagnosis of other cardiovascular disease, and ethnicity for primary prevention in patients with type 2 diabetes. When considered as a whole, genetic programming did not produce better performing clinical prediction models, rather it used fewer predictors, most of which were the predictors that Cox regression estimated be most strongly associated with the outcome, whilst achieving comparable performance. This suggests that genetic programming may better represent the potentially non-linear relationship of (a smaller subset of) the strongest predictors. To our knowledge, this work is the first study to develop a genetic programming approach for censored longitudinal data and assess its value for clinical prediction in comparison with the well-known and widely applied Cox regression technique. Using empirical data this work has demonstrated that clinical prediction models developed by steady-state genetic programming have predictive ability comparable to those developed using Cox regression. The genetic programming models were more complex and thus more difficult to validate by domain experts, however these models were developed in an automated fashion, using fewer input variables, without the need for domain specific knowledge and expertise required to appropriately perform survival analysis. This work has demonstrated the strong potential of genetic programming as a methodology for automated development of clinical prediction models for diagnostic and prognostic purposes in the presence of censored data. This work compared untuned genetic programming models that were developed in an automated fashion with highly tuned Cox regression models that was developed in a very involved manner that required a certain amount of clinical and statistical expertise. Whilst the highly tuned Cox regression models performed slightly better in validation data, the performance of the automatically generated genetic programming models were generally comparable. The comparable performance demonstrates the utility of genetic programming for clinical prediction modelling and prognostic research, where the primary goal is accurate prediction. In aetiological research, where the primary goal is to examine the relative strength of association between risk factors and the outcome, then Cox regression and its variants remain as the de facto approach. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://orca.cf.ac.uk/90825/ %0 Report %T Genetic Programming for Pedestrians %A Banzhaf, Wolfgang %D 1993 %8 dec %N TR93-03 %I Mitsubishi Electric Research Laboratories, Inc. %C Cambridge, MA, USA %F banzhaf:mrl:tech %X We propose an extension to the Genetic Programming paradigm which allows users of traditional Genetic Algorithms to evolve computer programs. To this end, we have to introduce mechanisms like transscription, editing and repairing into Genetic Programming. We demonstrate the feasibility of the approach by using it to develop programs for the prediction of sequences of integer numbers. %K genetic algorithms, genetic programming %9 MERL Technical Report %U ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/pedes93.ps.gz %0 Conference Proceedings %T Genetic Programming for Pedestrians %A Banzhaf, Wolfgang %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 banzhaf:mrl %X We propose an extension to the Genetic Programming paradigm which allows users of traditional Genetic Algorithms to evolve computer programs. To this end, we have to introduce mechanisms like transcription, editing and repairing into Genetic Programming. We demonstrate the feasibility of the approach by using it to develop programs for the prediction of sequences of integer numbers. %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GenProg_forPed.ps.Z %P 628 %0 Conference Proceedings %T Genotype-Phenotype-Mapping and Neutral Variation – A case study in Genetic Programming %A Banzhaf, Wolfgang %Y Davidor, Yuval %Y Schwefel, Hans-Paul %Y Männer, Reinhard %S Parallel Problem Solving from Nature III %S LNCS %D 1994 %8 September 14 oct %V 866 %I Springer-Verlag %C Jerusalem %@ 3-540-58484-6 %F banzhaf:1994:ppsn3 %X We propose the application of a genotype-phenotype mapping to the solution of constrained optimization problems. The method consists of strictly separating the search space of genotypes from the solution space of phenotypes. A mapping from genotypes into phenotypes provides for the appropriate expression of information represented by the genotypes. The mapping is constructed as to guarantee feasibility of phenotypic solutions for the problem under study. This enforcing of constraints causes multiple genotypes to result in one and the same phenotype. Neutral variants are therefore frequent and play an important role in maintaining genetic diversity. As a specific example, we discuss Binary Genetic Programming (BGP), a variant of Genetic Programming that uses binary strings as genotypes and program trees as phenotypes. %K genetic algorithms, genetic programming %R 10.1007/3-540-58484-6_276 %U http://www.cs.mun.ca/~banzhaf/papers/ppsn94.pdf %U http://dx.doi.org/10.1007/3-540-58484-6_276 %P 322-332 %0 Conference Proceedings %T Generating Adaptive Behavior for a Real Robot using Function Regression within Genetic Programming %A Banzhaf, Wolfgang %A Nordin, Peter %A Olmer, Markus %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 banzhaf:1997:gabrrfr %X We discuss the generation of adaptive behaviour for an autonomous robot within the framework of a special kind of function regression used in compiling Genetic Programming (GP). The control strategy for the robot is derived, using an evolutionary algorithm, from a continuous improvement of machine language programs which are varied and selected against each other. We give an overview of our recent work on several fundamental behaviors like obstacle avoidance and object following adapted from programs that were originally random sequences of commands. It is argued that the method is generally applicable where there is a need for quick adaptation within real-time problem domains %K genetic algorithms, genetic programming, Khepera, Autonomous robots %U http://www.cs.mun.ca/~banzhaf/papers/robot_over.pdf %P 35-43 %0 Book Section %T Interactive Evolution %A Banzhaf, Wolfgang %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 Banzhaf:1997:HEC %X We present a different approach to directing the evolutionary process through interactive selection of solutions by the human user. First the general context of interactive evolution is set, then the standard interactive evolution algorithm is discussed together with more complicated variants. Finally, several application areas are discussed and uses for the new method are exemplified using design from the literature. %K genetic algorithms, genetic programming %R 10.1201/9781420050387.ptc %U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf %U http://dx.doi.org/10.1201/9781420050387.ptc %0 Book %T Genetic Programming – An Introduction; On the Automatic Evolution of Computer Programs and its Applications %A Banzhaf, Wolfgang %A Nordin, Peter %A Keller, Robert E. %A Francone, Frank D. %D 1998 %8 jan %I Morgan Kaufmann %C San Francisco, CA, USA %@ 1-55860-510-X %F banzhaf:1997:book %K genetic algorithms, genetic programming %U https://www.amazon.co.uk/Genetic-Programming-Introduction-Artificial-Intelligence/dp/155860510X %0 Conference Proceedings %T Genetic Programming %E Banzhaf, Wolfgang %E Poli, Riccardo %E Schoenauer, Marc %E Fogarty, Terence C. %S LNCS %D 1998 %8 14 15 apr %V 1391 %I Springer-Verlag %C Paris %@ 3-540-64360-5 %F banzhaf:1998:GP %K genetic algorithms, genetic programming %R 10.1007/BFb0055923 %U http://dx.doi.org/10.1007/BFb0055923 %0 Journal Article %T Les Robots inventeent la vie %J Le Monde %D 1998 %8 23 Avril %F lemonde:1998:23apr %O lemonde %K genetic algorithms, genetic programming %9 journal article %0 Conference Proceedings %T GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference %E Banzhaf, Wolfgang %E Daida, Jason %E Eiben, Agoston E. %E Garzon, Max H. %E Honavar, Vasant %E Jakiela, Mark %E Smith, Robert E. %D 1999 %8 13 17 jul %I Morgan Kaufmann %C Orlando, Florida, USA %@ 1-55860-611-4 %F banzhaf:1999:gecco99 %K genetic algorithms, genetic programming %U http://www.amazon.com/exec/obidos/ASIN/1558606114/qid%3D977054373/105-7666192-3217523 %0 Generic %T Artificial Intelligence: Genetic Programming %A Banzhaf, Wolfgang %D 2000 %8 jul 04 %G en %F oai:CiteSeerPSU:400591 %O Contract no: 20851A2/2/102 %X The term Genetic Programming describes a research area within the general field of Artificial Intelligence that deals with the evolution of computer code. This area is an out growth of two independent efforts in Artificial Intelligence, namely automatic programming and machine learning. Automatic programming is concerned with the induction of computer code which precisely fulfills certain functions, whereas machine learning studies improvement of computer programs through training and experience. %K genetic algorithms, genetic programming %U http://web.cs.mun.ca/~banzhaf/papers/ency.pdf %0 Report %T Hierarchical Genetic Programming Using Local Modules %A Banzhaf, Wolfgang %A Banscherus, Dirk %A Dittrich, Peter %D 1998 %N 50/98 %I University of Dortmund %C Dortmund, Germany %G en %F oai:CiteSeerPSU:324880 %X This paper presents detailed experimental results for a new modular approach to Genetic Programming, hierarchical GP (hGP) based on the introduction of local modules. A module in a hGP program is context-dependent and should not be expected to improve all programs of a population but rather a very specific subset providing the same context. This new modular approach allows for a natural hierarchy in that local modules themselves may define local sub-modules. %K genetic algorithms, genetic programming %U http://hdl.handle.net/2003/5365 %0 Journal Article %T Hierarchical Genetic Programming using Local Modules %A Banzhaf, Wolfgang %A Banscherus, Dirk %A Dittrich, Peter %J InterJournal Complex Systems %D 2000 %V 228 %F banzhaf:2000:IJ %X This paper presents a new modular approach to Genetic Programming, hierarchical GP (hGP) based on the introduction of local modules. A module in a hGP program is context-dependent and should not be expected to improve all programs of a population but rather a very specific subset providing the same context. This new modular approach allows for a natural recursiveness in that local modules themselves may define local sub-modules. %K genetic algorithms, genetic programming %9 journal article %U http://www.interjournal.org/manuscript_abstract.php?44691 %0 Journal Article %T Some considerations on the reason for bloat %A Banzhaf, W. %A Langdon, W. B. %J Genetic Programming and Evolvable Machines %D 2002 %8 mar %V 3 %N 1 %@ 1389-2576 %F banzhaf:2000:genpletter %X A representation-less model for genetic programming is presented. The model is intended to examine the mechanisms that lead to bloat in genetic programming (GP). We discuss two hypotheses (fitness causes bloat and neutral code is protective) and perform simulations to examine the predictions deduced from these hypotheses. Our observation is that predictions from both hypotheses are realized in the simulated model. %K genetic algorithms, genetic programming, linear genomes, effective fitness, neutral variations %9 journal article %R 10.1023/A:1014548204452 %U http://web.cs.mun.ca/~banzhaf/papers/genp_bloat.pdf %U http://dx.doi.org/10.1023/A:1014548204452 %P 81-91 %0 Journal Article %T Editorial Introduction %A Banzhaf, Wolfgang %J Genetic Programming and Evolvable Machines %D 2000 %8 apr %V 1 %N 1/2 %@ 1389-2576 %F banzhaf:2000:ei %9 journal article %R 10.1023/A:1010026829303 %U http://dx.doi.org/10.1023/A:1010026829303 %P 5-6 %0 Journal Article %T The artificial evolution of computer code %A Banzhaf, Wolfgang %J IEEE Intelligent Systems %D 2000 %8 may jun %V 15 %N 3 %@ 1094-7167 %F banzhaf:2000:IS %X Over the past decade, the artificial evolution of computer code has become a rapidly spreading technology with many ramifications. Originally conceived as a means to enforce computer intelligence, it has now spread to all areas of machine learning and is starting to conquer pattern-recognition applications such as data mining and the human-computer interface. %K genetic algorithms, genetic programming, machine code GP %9 journal article %R 10.1109/5254.846288 %U http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf %U http://dx.doi.org/10.1109/5254.846288 %P 74-76 %0 Journal Article %T Acknowledgement %A Banzhaf, W. %J Genetic Programming and Evolvable Machines %D 2000 %8 oct %V 1 %N 4 %@ 1389-2576 %F banzhaf:2000:ack %9 journal article %R 10.1023/A:1010022522223 %U http://dx.doi.org/10.1023/A:1010022522223 %P 307 %0 Book Section %T Artificial Intelligence: Genetic Programming %A Banzhaf, W. %E Smelser, Neil J. %E Baltes, Paul B. %B International Encyclopedia of the Social & Behavioral Sciences %D 2001 %I Pergamon %C Oxford %F Banzhaf2001789 %X Genetic Programming is a new method to generate computer programs. It was derived from the model of biological evolution. Programs are ’bred’ through continuous improvement of an initially random population of programs. Improvements are made possible by stochastic variation of programs and selection according to prespecified criteria for judging the quality of a solution. Programs of Genetic Programming systems evolve to solve predescribed automatic programming and machine learning problems. In this contribution the origins and the context of Genetic Programming are discussed. The primary mechanisms behind the working of the method are then outlined. Next is a review of the state-of-the-art of Genetic Programming, including the major achievements of the method in recent years. This leads to an overview of the application areas where GP is most frequently used to present. Among these areas is robotics and the control of behavior, both of real and virtual agents. The article will conclude with a section on methodological issues and future directions. The use of Genetic Programming for simulation in the social sciences is briefly sketched. %K genetic algorithms, genetic programming %R 10.1016/B0-08-043076-7/00557-X %U http://www.sciencedirect.com/science/article/B7MRM-4MT09VJ-403/2/fa4e06852750b95eb2734f9ca37ae6ad %U http://dx.doi.org/10.1016/B0-08-043076-7/00557-X %P 789-792 %0 Journal Article %T Editorial Introduction %A Banzhaf, W. %J Genetic Programming and Evolvable Machines %D 2001 %8 mar %V 2 %N 1 %@ 1389-2576 %F banzhaf:2001:intro %9 journal article %R 10.1023/A:1010076931170 %U http://dx.doi.org/10.1023/A:1010076931170 %P 5 %0 Journal Article %T Acknowledgement %A Banzhaf, W. %J Genetic Programming and Evolvable Machines %D 2001 %8 dec %V 2 %N 4 %@ 1389-2576 %F banzhaf:2001:ack %9 journal article %R 10.1023/A:1017497620393 %U http://dx.doi.org/10.1023/A:1017497620393 %P 315-315 %0 Journal Article %T Editorial Introduction %A Banzhaf, W. %J Genetic Programming and Evolvable Machines %D 2002 %8 mar %V 3 %N 1 %@ 1389-2576 %F banzhaf:2002:intro %9 journal article %R 10.1023/A:1017427619473 %U http://dx.doi.org/10.1023/A:1017427619473 %P 5-6 %0 Journal Article %T Acknowledgement %A Banzhaf, W. %J Genetic Programming and Evolvable Machines %D 2002 %8 dec %V 3 %N 4 %@ 1389-2576 %F banzhaf:2002:ack %9 journal article %R 10.1023/A:1020989508176 %U http://dx.doi.org/10.1023/A:1020989508176 %P 327 %0 Journal Article %T Editorial Introduction %A Banzhaf, Wolfgang %J Genetic Programming and Evolvable Machines %D 2003 %8 mar %V 4 %N 1 %@ 1389-2576 %F banzhaf:2003:intro %9 journal article %R 10.1023/A:1021808625350 %U http://dx.doi.org/10.1023/A:1021808625350 %P 5-6 %0 Book Section %T Artificial Regulatory Networks and Genetic Programming %A Banzhaf, Wolfgang %E Riolo, Rick L. %E Worzel, Bill %B Genetic Programming Theory and Practice %D 2003 %I Kluwer %@ 1-4020-7581-2 %F banzhaf:2003:GPTP %X An artificial regulatory network able to reproduce a number of phenomena found in natural genetic regulatory networks (such as heterochrony, evolution, stability and variety of network behaviour) is proposed. The connection to a new genetic representation for Genetic Programming is outlined. %K genetic algorithms, genetic programming, Regulatory Networks, Artificial Evolution, Evolutionary Algorithms, Development, Heterochrony %R 10.1007/978-1-4419-8983-3_4 %U http://www.cs.mun.ca/~banzhaf/papers/toy_world3.pdf %U http://dx.doi.org/10.1007/978-1-4419-8983-3_4 %P 43-61 %0 Book Section %T Genetic Programming and Its Application in Machining Technology %A Banzhaf, Wolfgang %A Brameier, Markus %A Stautner, Marc %A Weinert, Klaus %E Schwefel, Hans-Paul %E Wegener, Ingo %E Weinert, Klaus %B Advances in Computational Intelligence: Theory and Practice %S Natural Computing Series %D 2003 %I Springer %@ 3-540-43269-8 %F banzhaf:2003:ACI %X Genetic Programming (GP) denotes a variants of evolutionary algorithms that breeds solutions to problems in the form of computer programs. In recent years, GP has become increasingly important for real-world applications, including engineering tasks in particular. This contribution integrates both further development of the GP paradigm and its applications to challenging problems in machining technology. Different variants of program representations are investigated. %K genetic algorithms, genetic programming, Linear Genetic Programming %U http://www.cs.mun.ca/~banzhaf/papers/CI-book-chapter.pdf %P 194-241 %0 Journal Article %T Editorial Introduction %A Banzhaf, Wolfgang %J Genetic Programming and Evolvable Machines %D 2004 %8 mar %V 5 %N 1 %@ 1389-2576 %F banzhaf:2004:intro %9 journal article %R 10.1023/B:GENP.0000017050.75941.55 %U http://dx.doi.org/10.1023/B:GENP.0000017050.75941.55 %P 5 %0 Journal Article %T Artificial chemistries - Toward Constructive Dynamical Systems %A Banzhaf, Wolfgang %J Solid State Phenomena %D 2004 %8 apr %V 97/98 %@ 1662-9779 %F Banzhaf:2004:SSP %X we consider constructive dynamical systems, taking one particular Artificial Chemistry as an example. We argue that constructive dynamical systems are in fact widespread in combinatorial spaces of Artificial Chemistries. %K genetic algorithms, genetic programming, artificial chemistries, Self-Organization, Self-Assembly, Constructive Dynamical Systems %9 journal article %R 10.4028/www.scientific.net/SSP.97-98.43 %U http://dx.doi.org/10.4028/www.scientific.net/SSP.97-98.43 %P 43-50 %0 Journal Article %T Network motifs in natural and artificial transcriptional regulatory networks %A Banzhaf, Wolfgang %A Kuo, P. Dwight %J Journal of Biological Physics and Chemistry %D 2004 %8 jun %V 4 %N 2 %@ 1512-0856 %F Banzhaf:2004:JBPC %X We show that network motifs found in natural regulatory networks may also be found in an artificial regulatory network model created through a duplication/divergence process. It is shown that these network motifs exist more frequently in a genome created through the aforementioned process than in randomly generated genomes. These results are then compared with a network motif analysis of the gene expression networks of Escherichia coli and Saccharomyces cerevisiae. In addition, it is shown that certain individual network motifs may arise directly from the duplication/divergence mechanism. %K genetic algorithms, genetic programming %9 journal article %U https://www.cs.mun.ca/~banzhaf/papers/JBPC.pdf %P 85-92 %0 Journal Article %T Acknowledgement %A Banzhaf, W. %J Genetic Programming and Evolvable Machines %D 2004 %8 dec %V 5 %N 1 %@ 1389-2576 %F banzhaf:2004:ack %9 journal article %R 10.1023/B:GENP.0000017051.93386.43 %U http://dx.doi.org/10.1023/B:GENP.0000017051.93386.43 %P 7 %0 Journal Article %T Editorial Introduction %A Banzhaf, Wolfgang %A Foster, James %J Genetic Programming and Evolvable Machines %D 2004 %8 jun %V 5 %N 2 %@ 1389-2576 %F banzhaf:2004:biogec %K genetic algorithms, genetic programming, bioinformatics %9 journal article %R 10.1023/B:GENP.0000023710.47388.8b %U http://dx.doi.org/10.1023/B:GENP.0000023710.47388.8b %P 119-120 %0 Book Section %T Genetic Programming of an Algorithmic Chemistry %A Banzhaf, Wolfgang %A Lasarczyk, Christian W. G. %E O’Reilly, Una-May %E Yu, Tina %E Riolo, Rick L. %E Worzel, Bill %B Genetic Programming Theory and Practice II %D 2004 %8 13 15 may %I Springer %C Ann Arbor %@ 0-387-23253-2 %F banzhaf:2004:GPTP %X We introduce a new method of execution for GP-evolved programs consisting of register machine instructions. It is shown that this method can be considered as an artificial chemistry. It lends itself well to distributed and parallel computing schemes in which synchronisation and coordination are not an issue. %K genetic algorithms, genetic programming, artificial chemistry %R 10.1007/0-387-23254-0_11 %U http://www.cs.mun.ca/~banzhaf/papers/algochem.pdf %U http://dx.doi.org/10.1007/0-387-23254-0_11 %P 175-190 %0 Journal Article %T Network motifs in natural and artificial transcriptional regulatory networks %A Banzhaf, Wolfgang %A Kuo, P. Dwight %J Journal of Biological Physics and Chemistry %D 2004 %V 4 %N 2 %F banzhaf:2004:BPC %X We show that network motifs found in natural regulatory networks may also be found in an artificial regulatory network model created through a duplication / divergence process. It is shown that these network motifs exist more frequently in a genome created through the aforementioned process than in randomly generated genomes. These results are then compared with a network motif analysis of the gene expression networks of Escherichia Coli and Saccharomyces cerevisiae. In addition, it is shown that certain individual network motifs may arise directly from the duplication / divergence mechanism. %K artificial life %9 journal article %U http://www.cs.mun.ca/~kuo/Motifs_Numerical_journal.pdf %P 50-63 %0 Book Section %T The Challenge of Complexity %A Banzhaf, Wolfgang %A Miller, Julian %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 banzhaf:2004:cc %X the challenge provided by the problem of evolving large amounts of computer code via Genetic Programming. We argue that the problem is analogous to what Nature had to face when moving to multi-cellular life. We propose to look at developmental processes and there mechanisms to come up with solutions for this “challenge of complexity” in Genetic Programming %K genetic algorithms, genetic programming, Evolutionary Algorithm, Complexity, Scaling Problem, Development, Heterochrony %R 10.1007/1-4020-7782-3_11 %U http://www.cs.mun.ca/~banzhaf/papers/challenge_rev.pdf %U http://dx.doi.org/10.1007/1-4020-7782-3_11 %P 243-260 %0 Conference Proceedings %T Challenging the Program Counter %A Banzhaf, Wolfgang %Y Stepney, Susan %Y Emmott, Stephen %S The Grand Challenge in Non-Classical Computation: International Workshop %D 2005 %8 18 19 apr %C York, UK %F banzhaf:2005:cPC %K genetic algorithms, genetic programming, artificial chemistry %U http://www.cs.york.ac.uk/nature/workshop/papers/Banzhaf.pdf %0 Journal Article %T Editorial %A Banzhaf, Wolfgang %J Genetic Programming and Evolvable Machines %D 2005 %8 jun %V 5 %N 2 %@ 1389-2576 %F banzhaf:2005:intro %9 journal article %R 10.1007/s10710-005-6162-z %U http://dx.doi.org/10.1007/s10710-005-6162-z %P 135-136 %0 Journal Article %T Acknowledgement %A Banzhaf, W. %J Genetic Programming and Evolvable Machines %D 2005 %8 jun %V 5 %N 2 %@ 1389-2576 %F banzhaf:2005:ack %9 journal article %R 10.1007/s10710-005-6163-y %U http://dx.doi.org/10.1007/s10710-005-6163-y %P 137-138 %0 Book Section %T Evolution on Neutral Networks in Genetic Programming %A Banzhaf, Wolfgang %A Leier, Andre %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 banzhaf:2005:GPTP %X We examine the behaviour of an evolutionary search on neutral networks in a simple linear GP system of a Boolean function space problem. To this end we draw parallels between notions in RNA-folding problems and in Genetic Programming, observe parameters of neutral networks and discuss the population dynamics via the occupation probability of network nodes in runs on their way to the optimal solution. %K genetic algorithms, genetic programming, Neutrality, Linear GP, Networks, Population Dynamics %R 10.1007/0-387-28111-8_14 %U http://www.cs.mun.ca/~banzhaf/papers/GPTP3.pdf %U http://dx.doi.org/10.1007/0-387-28111-8_14 %P 207-221 %0 Journal Article %T Introduction %A Banzhaf, Wolfgang %J Genetic Programming and Evolvable Machines %D 2006 %8 mar %V 6 %N 1 %@ 1389-2576 %F banzhaf:2006:intro %9 journal article %R 10.1007/s10710-006-7015-0 %U http://dx.doi.org/10.1007/s10710-006-7015-0 %P 5-6 %0 Journal Article %T Acknowledgement %A Banzhaf, W. %J Genetic Programming and Evolvable Machines %D 2006 %8 mar %V 6 %N 1 %@ 1389-2576 %F banzhaf:2006:ack %9 journal article %R 10.1007/s10710-006-7016-z %U http://dx.doi.org/10.1007/s10710-006-7016-z %P 7 %0 Journal Article %T From Artificial Evolution to Computational Evolution: A Research Agenda %A Banzhaf, Wolfgang %A Beslon, Guillaume %A Christensen, Steffen %A Foster, James %A Kepes, Francois %A Lefort, Virginie %A Miller, Julian %A Radman, Miroslav %A Ramsden, Jeremy J. %J Nature Reviews Genetics %D 2006 %8 sep %V 7 %N 9 %@ 1471-0056 %F Banzhaf:2006:NRG %X Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimisation and design problems by building solutions that are ’more fit’ relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve previously unimaginable or intractable computational and biological problems. %K genetic algorithms, genetic programming %9 journal article %R 10.1038/nrg1921 %U http://dx.doi.org/10.1038/nrg1921 %P 729-735 %0 Journal Article %T Editorial introduction %A Banzhaf, Wolfgang %J Genetic Programming and Evolvable Machines %D 2007 %V 8 %N 1 %@ 1389-2576 %F banzhaf:2007:intro %X As we have moved into the corporate sphere of Springer there were a number of changes, some subtle, some not so subtle. One change that is somewhat behind the scenes and eludes the eye of a reader is how Springer uses its distribution channels to spread the journal. Genetic Programming and Evolvable Machines is now accessible in over 5000 libraries across the globe. I think that speaks to the ability of this publisher, and its will to get the word out about our community. In the absence of an officially calculated impact factor I have taken the initiative myself to address this issue. Using the citation base of Google Scholar, we have evaluated the impact of GPEM by looking at all the papers published since its inception in 2000, up to May 2006. It turns out that authors did very well if publishing in GPEM. Their GPEM papers regularly featured prominently among their papers in terms of citations. 50% of our authors can count their GPEM paper among the first, second or third most cited paper of theirs. While this is certainly only true for half of the authors, it is indeed an achievement. So if you publish in GPEM, be prepared that your work is read, and also cited. %9 journal article %R 10.1007/s10710-007-9022-1 %U http://dx.doi.org/10.1007/s10710-007-9022-1 %0 Journal Article %T Why Complex Systems Engineering needs Biological Development %A Banzhaf, Wolfgang %A Pillay, Nelishia %J Complexity %D 2007 %8 nov / dec %V 13 %N 2 %F Banzhaf:2007:Complexity %X Here we shall discuss the need of Complex Systems Engineering to adopt principles from natural development of complex biological organisms, besides principles of natural evolution, to accomplish the type of performance that biology achieves regularly. We shall situate Complex Systems Engineering and discuss an example of how it could be employed. %K genetic algorithms, genetic programming %9 journal article %R 10.1002/cplx.20199 %U https://onlinelibrary.wiley.com/doi/pdf/10.1002/cplx.20199.pdf %U http://dx.doi.org/10.1002/cplx.20199 %P 12-21 %0 Book Section %T Accelerating Genetic Programming through Graphics Processing Units %A Banzhaf, Wolfgang %A Harding, Simon %A Langdon, William B. %A Wilson, Garnett %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 Banzhaf:2008:GPTP %X Graphics Processing Units (GPUs) are in the process of becoming a major source of computational power for numerical applications. Originally designed for application of time-consuming graphics operations, GPUs are stream processors that implement the SIMD paradigm. The true degree of parallelism of GPUs is often hidden from the user, making programming even more flexible and convenient. In this chapter we survey Genetic Programming methods currently ported to GPUs. %K genetic algorithms, genetic programming, graphics processing units, parallel processing, GPU %R 10.1007/978-0-387-87623-8_15 %U http://dx.doi.org/10.1007/978-0-387-87623-8_15 %P 229-249 %0 Book Section %T Evolutionary Computation and Genetic Programming %A Banzhaf, Wolfgang %E Lakhtakia, Akhlesh %E Martin-Palma, Raul Jose %B Engineered Biomimicry %D 2013 %I Elsevier %C Boston %F Banzhaf:2013:EB %X This chapter focuses on evolutionary computation, in particular genetic programming, as examples of drawing inspiration from biological systems. We set the choice of evolution as a source for inspiration in context and discuss the history of evolutionary computation and its variants before looking more closely at genetic programming. After a discussion of methods and the state of the art, we review application areas of genetic programming and its strength in providing human-competitive solutions. %K genetic algorithms, genetic programming, Algorithms, Artificial intelligence, Automatic programming, Bioinspired computing, Breeding, Crossover, Differential evolution, Evolutionary computation, Evolutionary programming, Evolution strategies, Generation, Human-competitive, Machine learning, Mutation, Natural selection, Population, Reproduction, Search space %R 10.1016/B978-0-12-415995-2.00017-9 %U http://www.sciencedirect.com/science/article/pii/B9780124159952000179 %U http://dx.doi.org/10.1016/B978-0-12-415995-2.00017-9 %P 429-447 %0 Journal Article %T Genetic Programming and Emergence %A Banzhaf, Wolfgang %J Genetic Programming and Evolvable Machines %D 2014 %8 mar %V 15 %N 1 %@ 1389-2576 %F Banzhaf:2014:GPEM %X Emergence and its accompanying phenomena are a widespread process in nature. Despite its prominence, there is no agreement in the sciences about the concept and how to define or measure emergence. One of the most contentious issues discussed is that of top-down (or downward) causation as a defining characteristic of systems with emergence. In this contribution we shall argue that emergence happens in Genetic Programming, for all the world to see %K genetic algorithms, genetic programming, Emergence, Emergent phenomena, Top-down causation, Repetitive patterns, Modularity %9 journal article %R 10.1007/s10710-013-9196-7 %U http://dx.doi.org/10.1007/s10710-013-9196-7 %P 63-73 %0 Journal Article %T Response to comments on ”Genetic Programming and Emergence” %A Banzhaf, Wolfgang %J Genetic Programming and Evolvable Machines %D 2014 %8 mar %V 15 %N 1 %@ 1389-2576 %F Banzhaf_reply:2014:GPEM %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-013-9207-8 %U http://dx.doi.org/10.1007/s10710-013-9207-8 %P 103-108 %0 Journal Article %T Defining and Simulating Open-Ended Novelty: Requirements, Guidelines, and Challenges %A Banzhaf, Wolfgang %A Baumgaertner, Bert %A Beslon, Guillaume %A Doursat, Rene %A Foster, James %A McMullin, Barry %A de Melo, Vinicius Veloso %A Miconi, Thomas %A Spector, Lee %A Stepney, Susan %A White, Roger %J Theory in Biosciences %D 2016 %8 sep %V 135 %N 3 %F Banzhaf:2016:TBS %O Special Issue in Commemoration of Olaf Breidbach - Part I %X The open-endedness of a system is often defined as a continual production of novelty. Here we pin down this concept more fully by defining several types of novelty that a system may exhibit, classified as variation, innovation, and emergence. We then provide a meta-model for including levels of structure in a system’s model. From there, we define an architecture suitable for building simulations of open-ended novelty-generating systems and discuss how previously proposed systems fit into this framework. We discuss the design principles applicable to those systems and close with some challenges for the community. %K genetic algorithms, genetic programming, open-ended evolution, Modeling and simulation, Open-ended evolution, Novelty, Innovation, Major transitions, Emergence %9 journal article %R 10.1007/s12064-016-0229-7 %U http://dx.doi.org/10.1007/s12064-016-0229-7 %P 131-161 %0 Conference Proceedings %T Genetic Programming Theory and Practice XV %E Banzhaf, Wolfgang %E Olson, Randal S. %E Tozier, William %E Riolo, Rick %S Genetic and Evolutionary Computation %D 2017 %8 19 21 may %I Springer %C Ann Arbor, USA %F Banzhaf:2017:GPTP %X Provides papers describing cutting-edge work on the theory and applications of genetic programming (GP) Offers large-scale, real-world applications (big data) of GP to a variety of problem domains, including commercial and scientific applications as well as bioinformatics problems Explores controlled semantics, lexicase and other selection methods, crossover techniques, diversity analysis and understanding of convergence tendencies These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: exploiting subprograms in genetic programming, schema frequencies in GP, Accessible AI, GP for Big Data, lexicase selection, symbolic regression techniques, co-evolution of GP and LCS, and applying ecological principles to GP. It also covers several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-90512-9 %U http://www.springer.com/gb/book/9783319905112 %U http://dx.doi.org/10.1007/978-3-319-90512-9 %0 Book Section %T Some Remarks on Code Evolution with Genetic Programming %A Banzhaf, Wolfgang %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 Banzhaf:2017:miller %X In this chapter we take a fresh look at the current status of evolving computer code using Genetic Programming methods. The emphasis is not so much on what has been achieved in detail in the past few years, but on the general research direction of code evolution and its ramifications for GP. We begin with a quick glance at the area of Search-based Software Engineering (SBSE), discuss the history of GP as applied to code evolution, consider various application scenarios, and speculate on techniques that might lead to a scaling-up of present-day approaches. %K genetic algorithms, genetic programming, genetic improvement, SBSE, machine learning, ILP %R 10.1007/978-3-319-67997-6_6 %U http://dx.doi.org/10.1007/978-3-319-67997-6_6 %P 145-156 %0 Conference Proceedings %T Genetic Programming Theory and Practice XVI %E Banzhaf, Wolfgang %E Spector, Lee %E Sheneman, Leigh %D 2018 %8 17 20 may %I Springer %C Ann Arbor, USA %F Banzhaf:2018:GPTP %X Contents: \citedolson:2018:GPTP \citehintze:2018:GPTP \citekelly:2018:GPTP \citekorns:2018:GPTP \citekronberger:2018:GPTP \citelalejini:2018:GPTP \citemetevier:2018:GPTP \citemiller:2018:GPTP \citeoneill:2018:GPTP \citetrujillo:2018:GPTP \citeyang:2018:GPTP %K genetic algorithms, genetic programming %R 10.1007/978-3-030-04735-1 %U http://dx.doi.org/10.1007/978-3-030-04735-1 %0 Conference Proceedings %T Genetic Programming Theory and Practice XVII %E Banzhaf, Wolfgang %E Goodman, Erik %E Sheneman, Leigh %E Trujillo, Leonardo %E Worzel, Bill %D 2019 %8 16 19 may %I Springer %C East Lansing, MI, USA %F Banzhaf:2019:GPTP %K genetic algorithms, genetic programming %R 10.1007/978-3-030-39958-0 %U https://link.springer.com/book/10.1007/978-3-030-39958-0 %U http://dx.doi.org/10.1007/978-3-030-39958-0 %0 Conference Proceedings %T Genetic Programming Theory and Practice XVIII %E Banzhaf, Wolfgang %E Trujillo, Leonardo %E Winkler, Stephan %E Worzel, Bill %S Genetic and Evolutionary Computation %D 2021 %8 19 21 may %I Springer %C East Lansing, USA %F Banzhaf:2021:GPTP %X Note GPTP 2020 not held due to corvid pandemic Contents \citeBayer:2021:GPTP, \citeDolson:2021:GPTP, \citeFleck:2021:GPTP, \citeFonseca:2021:GPTP, \citeGuadalupe-Hernandez:2021:GPTP, \citeKorns:2021:GPTP, \citeKotanchek:2021:GPTP, \citeLangdon:2021:GPTP, \citeMiller:2021:GPTP, \citeSaini:2021:GPTP, \citeSloss:2021:GPTP, Index A Action program, 2 multi-action program, 6 Activity dependence, 166 Ascension, 203 Automated program repair, 46 B Bees algorithm, 117 Benchmarking, 8, 84 C Cache, 52, 161 Cambrian explosion, 199 Causality, 71 Classification, 166 Co-evolution, 202 Competition, 89, 114, 205 Context-free grammar, 48 Convergence phenotypic, 150, 199 Crossover asymmetry of GP subtree crossover, 151 fatherless crossover, 158 unbiased subtree crossover, 150 D Data balancing, 133, 141 Deep learning, 2, 109, 165 with genetic programming, 109 Diagnostics exploration diagnostics, 104 selection scheme diagnostics, 104 Discriminant analysis, 113 Diversity, 2, 53, 63, 88, 139, 199 phenotypic, 64 phenotypic diversity, 89 phylogenetic, 64 phylogenetic diversity, 84 E Eco-EA, 66 Efficiency, 84, 114, 129, 155, 203 Ensembles, 133, 138, 139 Exploration diagnostic, 67 Exponential growth, 206 F Feedback loop, 71 Fitness predicting evaluation time of, 160 Fitness sharing, 66 G General artificial intelligence, 165 Genetic learning, 182 Genetic programming BalancedGP, 133, 137 grammar-based vectorial GP, 22 networked runs genetic programming, 109 OrdinalGP, 134, 137 PushGP, 52, 102, 190 template-constrained genetic program- ming, 45, 109 vectorial GP, 22 Grammar-guided, 22 Graph, 28, 111, 183 Growing neural networks, 111, 168 H High performance, 143, 195 Homeostatis, 172 Horizontal gene transfer, 203 I Inefficient threads avoiding, 143 causes, 143 measurement, 143 prediction, 143 Information loss, 33, 40 Inplace crossover, 143 shuffle, 143 speedup, 143 Intellectual property, 202 L Lexicase selection, 65, 66, 83, 191 cohort lexicase selection, 83 down-sampled lexicase selection, 83 epsilon lexicase selection, 83 novelty lexicase selection, 83 Linear genetic programming, 3, 7, 18, 69, 184 Liquid types, 50, 51 M Memory bandwidth, 143 Memory use minimising, 143 Metrics, 70 Mitochondria, 203 Modular, 167, 194 Modularity, 2, 7, 69, 181, 194 Moore Law, 197, 206 N Novelty, 90, 199 P Panmictic, 146, 150 Parent selection, 65, 83, 191 Pareto tournament, 131 Partially observable, 1 Population diversity, 2, 66, 97, 199 Population initialization, 2, 5, 12, 55, 90, 174 Predicting success based on diversity, 63 Program dendrite program, 168 evolving modular program, 182 neuron program, 176 program graph, 2, 183 program representation, 46 program synthesis, 47, 52, 84 program synthesis benchmark suite, 190 programming languages, 48 Program synthesis, 47, 84, 190 program synthesis benchmark suite, 190 R Rampant mutation, 2 Reinforcement learning, 2, 17, 176 Resilience, 203, 207 S Selection offspring selection, 34 selection pressure, 34, 157, 161 Semantic constraints, 48 Semiconductor industry, 197 SMT solvers, 48, 58 Social evolution, 203, 205 Strongly-typed, 25 Sustainability, 202 Symbolic regression, 24, 30, 87, 88, 115, 116 T Tags, 183 Tangled program graphs, 2, 183 Team, 3, 183 Tournament selection, 65, 85, 90, 91, 103, 130, 143, 145, 150, 161 Tree-based GP, 26, 28 Tree depth, 57 Type-aware, 50 %K genetic algorithms, genetic programming %R 10.1007/978-981-16-8113-4 %U https://link.springer.com/book/9789811681127 %U http://dx.doi.org/10.1007/978-981-16-8113-4 %0 Conference Proceedings %T Genetic Programming Theory and Practice XIX %E Trujillo, Leonardo %E Winkler, Stephan M. %E Silva, Sara %E Banzhaf, Wolfgang %S Genetic and Evolutionary Computation %D 2022 %8 February 4 jun %I Springer %C East Lansing, USA %F Banzhaf:2022:GPTP %X published after the workshop in 2023. http://gptp-workshop.com/contributions.html Contents \citeAffenzeller:2022:GPTP \citeBanzhaf:2022:GPTP.2 \citegold:2022:GPTP \citeHu:2022:GPTP \citeKotanchek:2022:GPTP \citeLaCava:2022:GPTP x, \citeMachado:2022:GPTP \citeMoore:2022:GPTP x, \citeOlague:2022:GPTP \citeO’Reilly:2022:GPTP x, \citeSpector:2022:GPTP x, \citeUrbanowicz:2022:GPTP \citeWright:2022:GPTP April 2023 added: \citeStepney:2022:GPTP \citeWorzel:2022:GPTP %K genetic algorithms, genetic programming %R 10.1007/978-981-19-8460-0 %U https://link.springer.com/book/9789811984594 %U http://dx.doi.org/10.1007/978-981-19-8460-0 %0 Conference Proceedings %T Correlation versus RMSE Loss Functions in Symbolic Regression Tasks %A Haut, Nathan %A Banzhaf, Wolfgang %A Punch, Bill %Y Trujillo, Leonardo %Y Winkler, Stephan M. %Y Silva, Sara %Y Banzhaf, Wolfgang %S Genetic Programming Theory and Practice XIX %S Genetic and Evolutionary Computation %D 2022 %8 jun 2 4 %I Springer %C Ann Arbor, USA %F Banzhaf:2022:GPTP.2 %X The use of correlation as a fitness function is explored in symbolic regression tasks and its performance is compared against a more typical RMSE fitness function. Using correlation with an alignment step to conclude the evolution led to significant performance gains over RMSE as a fitness function. Employing correlation as a fitness function led to solutions being found in fewer generations compared to RMSE. We also found that fewer data points were needed in a training set to discover correct equations. The Feynman Symbolic Regression Benchmark as well as several other old and recent GP benchmark problems were used to evaluate performance. %K genetic algorithms, genetic programming %R 10.1007/978-981-19-8460-0_2 %U http://dx.doi.org/10.1007/978-981-19-8460-0_2 %P 31-55 %0 Conference Proceedings %T How the Combinatorics of Neutral Spaces Leads Genetic Programming to Discover Simple Solutions %A Banzhaf, Wolfgang %A Hu, Ting %A Ochoa, Gabriela %Y Winkler, Stephan %Y Trujillo, Leonardo %Y Ofria, Charles %Y Hu, Ting %S Genetic Programming Theory and Practice XX %S Genetic and Evolutionary Computation %D 2023 %8 jun 1 3 %I Springer %C Michigan State University, USA %F Banzhaf:2023:GPTP %X For various evolutionary systems it was found that the abundance of phenotypes in a search space, defined as the size of their respective neutral networks, is key to understanding the trajectory an evolutionary process takes from an initial to a target solution. we use a Linear Genetic Programming system to demonstrate that the abundance of phenotypes is determined by the combinatorics offered in its neutral components. This translates into the size of the neutral space available to a phenotype and also can explain the beautiful and rather curious observation that the abundance of phenotypes is dependent on their complexity in a negative exponential fashion. %K genetic algorithms, genetic programming %R 10.1007/978-981-99-8413-8_4 %U http://dx.doi.org/10.1007/978-981-99-8413-8_4 %P 65-86 %0 Generic %T On The Nature Of The Phenotype In Tree Genetic Programming %A Banzhaf, Wolfgang %A Bakurov, Illya %D 2024 %I arXiv %F DBLP:journals/corr/abs-2402-08011 %K genetic algorithms, genetic programming %R 10.48550/ARXIV.2402.08011 %U https://doi.org/10.48550/arXiv.2402.08011 %U http://dx.doi.org/10.48550/ARXIV.2402.08011 %0 Journal Article %T “The physics of evolution” by Michael W. Roth, CRC press, 2023 %A Banzhaf, Wolfgang %J Genetic Programming and Evolvable Machines %D 2024 %8 dec %V 25 %N 2 %@ 1389-2576 %F Banzhaf:2024:GPEM %O Book review %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-024-09489-z %U https://rdcu.be/dK8KO %U http://dx.doi.org/10.1007/s10710-024-09489-z %P Articleno16 %0 Conference Proceedings %T On the Nature of the Phenotype in Tree Genetic Programming %A Banzhaf, Wolfgang %A Bakurov, Illya %Y Hu, Ting %Y Ekart, Aniko %Y Handl, Julia %Y Li, Xiaodong %Y Wagner, Markus %Y Garza-Fabre, Mario %Y Smith-Miles, Kate %Y Allmendinger, Richard %Y Bi, Ying %Y Dick, Grant %Y Gandomi, Amir H. %Y Martins, Marcella Scoczynski Ribeiro %Y Assimi, Hirad %Y Veerapen, Nadarajen %Y Sun, Yuan %Y Munyoz, Mario Andres %Y Kheiri, Ahmed %Y Su, Nguyen %Y Thiruvady, Dhananjay %Y Song, Andy %Y Neumann, Frank %Y Silva, Carla %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F banzhaf:2024:GECCO %X In this contribution, we discuss the basic concepts of genotypes and phenotypes in tree-based GP (TGP), and then analyze their behavior using five real-world datasets. We show that TGP exhibits the same behavior that we can observe in other GP representations: At the genotypic level trees show frequently unchecked growth with seemingly ineffective code, but on the phenotypic level, much smaller trees can be observed. To generate phenotypes, we provide a unique technique for removing semantically ineffective code from GP trees. The approach extracts considerably simpler phenotypes while not being limited to local operations in the genotype. We generalize this transformation based on a problem-independent parameter that enables a further simplification of the exact phenotype by coarse-graining to produce approximate phenotypes. The concept of these phenotypes (exact and approximate) allows us to clarify what evolved solutions truly predict, making GP models considered at the phenotypic level much better interpretable. %K genetic algorithms, genetic programming, genotype-phenotype map, simplication, neutrality, explainability, symbolic regression %R 10.1145/3638529.3654129 %U http://dx.doi.org/10.1145/3638529.3654129 %P 868-877 %0 Conference Proceedings %T Linear Genetic Programming %A Banzhaf, Wolfgang %A Hu, Ting %Y Zhang, Mengjie %Y Hart, Emma %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F banzhaf:2024:GECCOcomp %O Tutorial %K genetic algorithms, genetic programming %R 10.1145/3638530.3648422 %U http://dx.doi.org/10.1145/3638530.3648422 %P 759-771 %0 Conference Proceedings %T Linear Genetic Programming %A Banzhaf, Wolfgang %A Hu, Ting %Y Zhang, Mengjie %Y Hart, Emma %S Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion %S GECCO ’25 Companion %D 2025 %8 14 18 jul %I Association for Computing Machinery %C Malaga, Spain %F banzhaf:2025:GECCOcomp %O Tutorial %K genetic algorithms, genetic programming %R 10.1145/3712255.3716528 %U https://doi.org/10.1145/3712255.3716528 %U http://dx.doi.org/10.1145/3712255.3716528 %P 1749-1766 %0 Conference Proceedings %T An Improved Genetic Programming Based Factor Construction for Stock Price Prediction %A Bao, Hailu %A Zhang, Changsheng %A Zhang, Chen %A Zhang, Bin %Y Zhang, Songmao %Y Zhang, Yonggang %S Third International Conference on Artificial Intelligence Logic and Applications, AILA 2023 %S CCIS %D 2023 %8 aug 5 6 %V 1917 %I Springer %C Changchun, China %F bao:2023:AILA %X In the process of stock price forecasting, there are the following problems: how to find the more effective factors for stock price forecasting, and how to calculate the weight of the constructed stock correlation factor sets. To solve the above problems, this paper proposes a method of factor construction in the field of stock price prediction based on genetic programming. The method can automatically construct the factor by reading the original data set of the stock, and calculate the weight of each factor. In addition, this paper also proposes a new crossover operator, which can dynamically adjust the selection of crossover nodes by using the information in the execution process of genetic programming algorithm, so as to improve the quality of the constructed factor set. A lot of experiments have been carried out with this method. The results show that the factors constructed by this method can improve the accuracy of the stock price prediction algorithm in most cases. %K genetic algorithms, genetic programming %R 10.1007/978-981-99-7869-4_18 %U http://link.springer.com/chapter/10.1007/978-981-99-7869-4_18 %U http://dx.doi.org/10.1007/978-981-99-7869-4_18 %P 227-240 %0 Conference Proceedings %T A Review on Cutting-Edge Techniques in Evolutionary Algorithms %A Bao, Yun %A Zhao, Erbo %A Gan, Xiaocong %A Luo, Dan %A Han, Zhangang %S Fifth International Conference on Natural Computation, 2009. ICNC ’09 %D 2009 %8 aug %V 5 %F Bao:2009:ICNC %X There are vast amount researches in Evolutionary Algorithms (EA). We need an overview of the current state of EA research every few years. This paper reviews some of the interesting researches at the current state in both theory and application of EA. Works in EA performance improvements are introduced in the sense of balancing between convergence speed and diversity in the population. The combination of EA with other methods is highlighted as a prospective area that may give fertility results. Some smart applications are reviewed in this paper, for example, application in nuclear power plant. The authors point out some research highlights and drawbacks in the conclusion. Future research suggestions are also given. %K genetic algorithms, genetic programming, EA performance improvements, convergence speed, cutting-edge techniques, evolutionary algorithms, nuclear power plant, evolutionary computation %R 10.1109/ICNC.2009.459 %U http://dx.doi.org/10.1109/ICNC.2009.459 %P 347-351 %0 Thesis %T Modelling floodplain biogeomorphology %A Baptist, Martin Josephus %D 2005 %8 18 apr %C Holland %C Technische Universiteit Delft %F ceg_baptist_20050418 %X There is an increasing awareness that rivers need more room in order to safeguard flood safety under climate change conditions. Contemporary river management is creating room in the floodplains and allowing, within certain bounds, natural processes of sedimentation and erosion. One of the aims is to restore dynamic conditions, so as to get a sustainable and more diverse river ecosystem that can cope with floods. This new approach requires understanding of the interaction between the biotic and abiotic components of river systems. More specifically, it requires a better understanding of the interaction between flora and fauna and geomorphological factors. This is the object of investigation of the interdiscipline of biogeomorphology. Modelling biogeomorphological processes in river floodplains is the topic of this thesis. To reduce flood risks in the Netherlands, measures to increase the flood conveyance capacity of the Rhine River will be implemented. However, it is expected that floodplain sedimentation and softwood forest development in rehabilitated floodplains will gradually reduce the conveyance capacity and the biodiversity. Moreover, in regulated rivers, such as the Rhine River, erosion and sedimentation processes caused by channel migration, which periodically interrupt vegetation succession, cannot be allowed. Therefore, a floodplain management strategy was proposed that would meet both flood protection and nature rehabilitation objectives. This strategy, ’Cyclic Floodplain Rejuvenation (CFR)’, aims at mimicking the effects of channel migration by removal of softwood forests, by lowering floodplains or by (re)constructing secondary channels. In chapter 2, the effects of CFR measures on reducing flood levels and enhancing biodiversity along the Waal River were assessed. A one-dimensional hydraulic modelling system, SOBEK, was applied together with rule-based models for floodplain vegetation succession and floodplain sedimentation. The model simulations demonstrated that the flood management strategy of Cyclic Floodplain Rejuvenation is able to sustain safe flood levels in the Waal River. Rejuvenation is then needed every 25 to 35 years on average, each time in an area of about 15percent of the total floodplain area. The rejuvenation strategy led to a diverse floodplain vegetation distribution that largely complies to the historical reference for the Waal River. Cyclic Floodplain Rejuvenation may be the appropriate answer to find symbiosis between flood protection and nature rehabilitation in highly regulated rivers. ... %K genetic algorithms, genetic programming, water, flow, biogeomorphology, flood management, hydraulic modelling, nature management, hydraulic roughness, river morphology, bed shear stress, floodplain vegetation %9 Ph.D. thesis %U https://repository.tudelft.nl/islandora/object/uuid%3Ab2739720-e2f6-40e2-b55f-1560f434cbee %0 Journal Article %T On inducing equations for vegetation resistance %A Baptist, M. J. %A Babovic, Vladan %A Rodriguez Uthurburu, J. %A Keijzer, M. %A Uittenbogaard, R. E. %A Mynett, A. %A Verwey, A. %J Journal of Hydraulic Research %D 2007 %V 45 %N 4 %@ 0022-1686 %F Baptist:2007:JHR %X The paper describes the process of induction of equations for the description of vegetation-induced roughness from several angles. Firstly, it describes two approaches for obtaining theoretically well-founded analytical expressions for vegetation resistance. The first of the two is based on simplified assumptions for the vertical flow profile through and over vegetation, whereas the second is based on an analytical solution to the momentum balance for flow through and over vegetation. In addition to analytical expressions the paper also outlines a numerical 1-DV k-e turbulence model which includes several important features related to the influence plants exhibit on the flow. Last but not least, the paper presents a novel way of applying genetic programming to the results of the 1-DV model, in order to obtain an expression for roughness based on synthetic data. The resulting expressions are evaluated and compared with an independent data set of flume experiments %K genetic algorithms, genetic programming, vegetation, roughness, resistance, knowledge discovery %9 journal article %R 10.1080/00221686.2007.9521778 %U https://research.tudelft.nl/en/publications/on-inducing-equations-for-vegetation-resistance/ %U http://dx.doi.org/10.1080/00221686.2007.9521778 %P 435-450 %0 Journal Article %T Parasitic Computing %A Barabasi, Albert-Laszlo %A Freeh, Vincent W. %A Jeong, Hawoong %A Brockman, Jay B. %J Nature %D 2001 %8 30 aug %V 412 %F BarabasiEtAl01 %X Reliable communication on the Internet is guaranteed by a standard set of protocols, used by all computers. Here we show that these protocols can be exploited to compute with the communication infrastructure, transforming the Internet into a distributed computer in which servers unwittingly perform computation on behalf of a remote node. In this model, which we call ’parasitic computing’, one machine forces target computers to solve a piece of a complex computational problem merely by engaging them in standard communication. Consequently, the target computers are unaware that they have performed computation for the benefit of a commanding node. As experimental evidence of the principle of parasitic computing, we harness the power of several web servers across the globe, which unknown to them work together to solve an NP complete problem %K 16-SAT %9 journal article %R 10.1038/35091039 %U http://www.nd.edu/~alb/Publication06/082%20Parasitic%20computing/Parasitic%20computing.pdf %U http://dx.doi.org/10.1038/35091039 %P 894-897 %0 Conference Proceedings %T Assembling Strategies in Extrinsic Evolvable Hardware with Bidirectional Incremental Evolution %A Baradavka, Igor %A Kalganova, Tatiana %Y Ryan, Conor %Y Soule, Terence %Y Keijzer, Maarten %Y Tsang, Edward %Y Poli, Riccardo %Y Costa, Ernesto %S Genetic Programming, Proceedings of EuroGP’2003 %S LNCS %D 2003 %8 14 16 apr %V 2610 %I Springer-Verlag %C Essex %@ 3-540-00971-X %F baradavka03 %X Bidirectional incremental evolution (BIE) has been proposed as a technique to overcome the “stalling” effect in evolvable hardware applications. However preliminary results show perceptible dependence of performance of BIE and quality of evaluated circuit on assembling strategy applied during reverse stage of incremental evolution. The purpose of this paper is to develop assembling strategy that will assist BIE to produce relatively optimal solution with minimal computational effort (e.g. the minimal number of generations). %K genetic algorithms, genetic programming, evolvable hardware: Poster %R 10.1007/3-540-36599-0_25 %U http://dx.doi.org/10.1007/3-540-36599-0_25 %P 276-285 %0 Journal Article %T Automatic Interpretation of Affective Facial Expressions in the Context of Interpersonal Interaction %A Barakova, Emilia I. %A Gorbunov, Roman %A Rauterberg, Matthias %J IEEE Transactions on Human-Machine Systems %D 2015 %8 aug %V 45 %N 4 %@ 2168-2291 %F Barakova:2015:ieeeMMS %X This paper proposes a method for interpretation of the emotions detected in facial expressions in the context of the events that cause them. The method was developed to analyse the video recordings of facial expressions depicted during a collaborative game played as a part of the Mars-500 experiment. In this experiment, six astronauts were isolated for 520 days in a space station to simulate a flight to Mars. Seven time-dependent components of facial expressions were extracted from the video recordings of the experiment. To interpret these dynamic components, we proposed a mathematical model of emotional events. Genetic programming was used to find the locations, types, and intensities of the emotional events as well as the way the recorded facial expressions represented reactions to them. By classification of different statistical properties of the data, we found that there are significant relations between the facial expressions of different crew members and a memory effect between the collective emotional states of the crew members. The model of emotional events was validated on previously unseen video recordings of the astronauts. We demonstrated that both genetic search and optimisation of the parameters improve the accuracy of the proposed model. This method is a step toward automating the analysis of affective expressions in terms of the cognitive appraisal theory of emotion, which relies on the dependence of the expressed emotion on the causing event. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/THMS.2015.2419259 %U http://dx.doi.org/10.1109/THMS.2015.2419259 %P 409-418 %0 Journal Article %T Impedance spectroscopy of Gd-doped ceria analyzed by genetic programming (ISGP) method %A Baral, Ashok Kumar %A Tsur, Yoed %J Solid State Ionics %D 2017 %V 304 %@ 0167-2738 %F Baral:2017:SSI %X This work presents the distribution function of relaxation time (DFRT) analysis of Gd doped ceria (GDC) and cobalt co-doped GDC prepared by precipitation method. Ionic transport properties and grain-boundary phenomena are discussed thoroughly based on the DFRT. The impedance results, especially the bulk and grain-boundary conductivities ( sigma b and sigma gb) and activation energies (Eb and Egb) obtained from the ISGP, are compared with the values obtained from the Equivalent Circuit Model. Grain boundary space charge (SC) effects discussed so far in the literature, generally do not consider the defect interaction between the oxygen vacancies and acceptor dopants in ceria and other oxide ion conductors. However, ISGP study clearly evidence the co-existence of SC effect and defect association in grain boundary regions, and both contribute to the grain boundary resistance (Rgb) at lower temperatures. The effect of sintering aid (Co) on the grain boundary activity is discussed considering both phenomena. Lower sintering temperature of the samples results in a relatively smaller grain boundary potential (Phi(0)) i.e., 0.15, 0.17 and 0.19 V at 300 degreeC in 0, 1 and 3 molpercent Co co-doped GDC, respectively. %K genetic algorithms, genetic programming, Impedance spectroscopy, DFRT, Grain boundary properties, Doped ceria %9 journal article %R 10.1016/j.ssi.2017.04.003 %U http://www.sciencedirect.com/science/article/pii/S0167273816309419 %U http://dx.doi.org/10.1016/j.ssi.2017.04.003 %P 145-149 %0 Journal Article %T 316L(N) Creep Modeling with Phenomenological Approach and Artificial Intelligence Based Methods %A Baraldi, Daniele %A Holmstrom, Stefan %A Nilsson, Karl-Fredrik %A Bruchhausen, Matthias %A Simonovski, Igor %J Metals %D 2021 %V 11 %N 5 %@ 2075-4701 %F baraldi:2021:Metals %X A model that describes creep behaviour is essential in the design or life assessment of components and systems that operate at high temperatures. Using the RCC-MRx data and the LCSP (logistic creep strain prediction) model, processed design data were generated over the whole creep regime of 316L(N) steel–i.e., primary, secondary, and tertiary creep. The processed design data were used to develop three models with different approaches for the creep rate: a phenomenological approach; an artificial neural network; and an artificial intelligence method based on symbolic regression and genetic programming. It was shown that all three models are capable of describing the true creep rate as a function of true creep strain and true stress over a wide range of engineering stresses and temperatures without the need of additional micro-structural information. Furthermore, the results of finite element simulations reproduce the trends of experimental data from the literature. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/met11050698 %U https://www.mdpi.com/2075-4701/11/5/698 %U http://dx.doi.org/10.3390/met11050698 %0 Conference Proceedings %T Micro Genetic Algorithms in Finding the Optimal Frequency for Stabilizing Atoms by High-intensity Laser Fields %A Barash, Danny %A Orel, Ann %A Vemuri, V. Rao %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 barash:1998:mGAofsalf %X The goal of this paper is to explore the power of genetic algorithms, in particular the so called micro genetic algorithms, to solve a challenging problem in experimental physics. The problem is to find an optimum frequency to stabilise atoms by high-intensity laser fields. The standard approach to search for optimal laser parameters has been by trial and error. This is the first known application of a genetic algorithm technique to model atomic stabilisation. The micro genetic algorithm worked exceptionally well for this problem as a way to automate the search in a time efficient manner. A parallel platform is used to perform the genetic search efficiently. Locating the best frequency to achieve a suppression of ionization, which is predicted to occur at high intensities, can help design a laboratory experiment and tune to that frequency in order to identify a stabilization effect. The micro genetic algorithm did successfully identify this optimum frequency. It is indeed possible to ex... %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.1378 %P 7-13 %0 Conference Proceedings %T Automatic Design of Vision-Based Obstacle Avoidance Controllers Using Genetic Programming %A Barate, Renaud %A Manzanera, Antoine %Y Monmarché, Nicolas %Y Talbi, El-Ghazali %Y Collet, Pierre %Y Schoenauer, Marc %Y Lutton, Evelyne %S Artificial Evolution %S Lecture Notes in Computer Science %D 2007 %8 oct 29 31 %V 4926 %I Springer %C Tours, France %F DBLP:conf/ae/BarateM07 %X The work presented in this paper is part of the development of a robotic system able to learn context dependent visual clues to navigate in its environment. We focus on the obstacle avoidance problem as it is a necessary function for a mobile robot. As a first step, we use an off-line procedure to automatically design algorithms adapted to the visual context. This procedure is based on genetic programming and the candidate algorithms are evaluated in a simulation environment. The evolutionary process selects meaningful visual primitives in the given context and an adapted strategy to use them. The results show the emergence of several different behaviors outperforming hand-designed controllers. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-79305-2_3 %U http://dx.doi.org/10.1007/978-3-540-79305-2_3 %P 25-36 %0 Conference Proceedings %T Generalization performance of vision based controllers for mobile robots evolved with genetic programming %A Barate, Renaud %A Manzanera, Antoine %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 Barate:2008:gecco %K genetic algorithms, genetic programming, generalisation, Obstacle avoidance, robotic simulation, vision, Poster %R 10.1145/1389095.1389349 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1331.pdf %U http://dx.doi.org/10.1145/1389095.1389349 %P 1331-1332 %0 Conference Proceedings %T Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation %A Barate, Renaud %A Manzanera, Antoine %Y Asada, Minoru %Y Hallam, John C. T. %Y Meyer, Jean-Arcady %Y Tani, Jun %S From Animals to Animats 10, Proceedings of the 10th International Conference on Simulation of Adaptive Behavior, SAB 2008 %S Lecture Notes in Computer Science %D 2008 %8 jul 7 12 %V 5040 %I Springer %C Osaka, Japan %F DBLP:conf/sab/BarateM08 %X We present a system that automatically selects and parameterises a vision based obstacle avoidance method adapted to a given visual context. This system uses genetic programming and a robotic simulation to evaluate the candidate algorithms. As the number of evaluations is restricted, we introduce a novel method using imitation to guide the evolution toward promising solutions. We show that for this problem, our two-phase evolution process performs better than other techniques. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-69134-1_8 %U http://dx.doi.org/10.1007/978-3-540-69134-1_8 %P 73-82 %0 Conference Proceedings %T Learning Vision Algorithms for Real Mobile Robots with Genetic Programming %A Barate, Renaud %A Manzanera, Antoine %S ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS ’08 %D 2008 %8 aug %F Barate:2008:ECSIS-LAB-RS %X We present a genetic programming system to evolve vision based obstacle avoidance algorithms. In order to develop autonomous behavior in a mobile robot, our purpose is to design automatically an obstacle avoidance controller adapted to the current context. We first record short sequences where we manually guide the robot to move away from the walls. This set of recorded video images and commands is our learning base. Genetic programming is used as a supervised learning system to generate algorithms that exhibit this corridor centering behavior. We show that the generated algorithms are efficient in the corridor that was used to build the learning base, and that they generalize to some extent when the robot is placed in a visually different corridor. More, the evolution process has produced algorithms that go past a limitation of our system, that is the lack of adequate edge extraction primitives. This is a good indication of the ability of this method to find efficient solutions for different kinds of environments. %K genetic algorithms, genetic programming, learning vision algorithms, mobile robots, obstacle avoidance algorithms, supervised learning system, control engineering computing, learning (artificial intelligence), mobile robots, robot vision %R 10.1109/LAB-RS.2008.20 %U http://dx.doi.org/10.1109/LAB-RS.2008.20 %P 47-52 %0 Thesis %T Learning Visual Functions for a Mobile Robot with Genetic Programming %A Barate, Renaud %D 2008 %8 nov %C 32 Bd Victor, Paris 75015, France %C ENSTA %F Barate:thesis %O In French %X Existing techniques used to learn artificial vision for mobile robots generally represent an image with a set of visual features that are computed with a hard-coded method. This impairs the system’s adaptability to a changing visual environment. We propose a method to describe and learn vision algorithms globally, from the perceived image to the final decision. The target application is the obstacle avoidance function, which is necessary for any mobile robot. We formally describe the structure of vision-based obstacle avoidance algorithms with a grammar. Our system uses this grammar and genetic programming techniques to learn controllers adapted to a given visual context automatically. We use a simulation environment to test this approach and evaluate the performance of the evolved algorithms. We propose several techniques to speed up the evolution and improve the performance and generalization abilities of evolved controllers. In particular, we compare several methods that can be used to guide the evolution and we introduce a new one based on the imitation of a recorded behavior. Next we validate these methods on a mobile robot moving in an indoor environment. Finally, we indicate how this system can be adapted for other vision based applications and we give some hints for the online adaptation of the robot’s behavior. %K genetic algorithms, genetic programming, Vision, mobile robotics, obstacle avoidance %9 Ph.D. thesis %U http://www.ensta.fr/~manzaner/Publis/these-barate.pdf %0 Journal Article %T Development of empirical models with high accuracy for estimation of drag coefficient of flow around a smooth sphere: An evolutionary approach %A Barati, Reza %A Salehi Neyshabouri, Seyed Ali Akbar %A Ahmadi, Goodarz %J Powder Technology %D 2014 %V 257 %@ 0032-5910 %F Barati:2014:PT %X An accurate correlation for the smooth sphere drag coefficient with wide range of applicability is a useful tool in the field of particle technology. The present study focuses on the development of high accurate drag coefficient correlations from low to very high Reynolds numbers (up to 1000000) using a multi-gene Genetic Programming (GP) procedure. A clear superiority of GP over other methods is that GP is able to determine the structure and parameters of the model, simultaneously, while the structure of the model is imposed by the user in traditional regression analysis, and only the parameters of the model are assigned. In other words, in addition to the parameters of the model, the structure of it can be optimised using GP approach. Among two new and high accurate models of the present study, one of them is acceptable for the region before drag dip, and the other is applicable for the whole range of Reynolds numbers up to 1 million including the transient region from laminar to turbulent. The performances of the developed models are examined and compared with other reported models. The results indicate that these models respectively give 16.2percent and 69.4percent better results than the best existing correlations in terms of the sum of squared of logarithmic deviations (SSLD). On the other hand, the proposed models are validated with experimental data. The validation results show that all of the estimated drag coefficients are within the bounds of 7percent of experimental values. %K genetic algorithms, genetic programming, Particle motion, Sphere drag, Reynolds number %9 journal article %R 10.1016/j.powtec.2014.02.045 %U http://www.sciencedirect.com/science/article/pii/S003259101400182X %U http://dx.doi.org/10.1016/j.powtec.2014.02.045 %P 11-19 %0 Journal Article %T Genetic Programming in Civil, Structural and Environmental Engineering %A Barbosa, Helio J. C. %A Bernardino, Heder S. %J Computational Technology Reviews %D 2011 %V 4 %I Civil-Comp %@ 2044-8430 %F Barbosa:2011:CTR %X Soft computing techniques have been receiving considerable attention in recent years due to their wide applicability and low ratio of implementation effort to succeed in producing good results. In civil and environmental engineering one is often faced with the problem of inferring a mathematical model from a set of observed data. Also, in structural engineering, nature-inspired techniques, especially evolutionary algorithms, have been extensively applied, mainly to parametric design optimization problems. This paper provides an overview of the applications of one of the most versatile soft computing tools available - genetic programming - to relevant design, optimization, and identification of problems arising in civil, structural, and environmental engineering. Genetic programming (GP) is a domain-independent sub-area of the evolutionary computation field. The candidate solutions are referred to as programs, a high-level structure able to represent a large class of computational artefacts. A program can be a standard computer program, a numerical function or a classifier in symbolic form, a candidate design (such as the structure of a building), among many other possibilities. In the following sections tree-based, linear, and graph-based GPs are discussed. Moreover, grammatical evolution (GE) is presented in some detail, a relatively recent GP technique in which candidate solution’s genotypes are binary encoded and space transformations create the programs employing a user-defined grammar. The most common classes of problems in civil, structural, and environmental engineering in which GP has been applied are loosely grouped here into two large classes, namely model inference and design. Both types of problems correspond to activities traditionally assigned only to humans, as they require intelligence and creativity not (yet) available elsewhere. Some representative papers from the literature were reviewed and are summarized in nine tables. The tables indicate the reference number, the GP technique adopted, the class of problem considered, a short description of the application, and the main results and conclusions of the paper. Our survey indicated a much larger number of papers dealing with model inference than with design applications in the civil, structural, and environmental engineering literature. Also, as expected, the standard tree-based genetic programming (TGP) is by far the most often adopted technique. Contrary to our expectations, gene expression programming (GEP) seems to be more popular than GE, which is probably due to the fact that GE, although more elegant and flexible, requires the specification of a problem dependent grammar by the user. Genetic programming has been proving its versatility in many different fields. Due to its great expressiveness, GP is able to evolve complex artifacts, either when inducing understandable and communicable models or generating novel designs. %K genetic algorithms, genetic programming %9 journal article %R 10.4203/ctr.4.5 %U http://www.ctresources.info/ctr/paper.html?id=32 %U http://dx.doi.org/10.4203/ctr.4.5 %P 115-145 %0 Conference Proceedings %T Semantically Rich Local Dataset Generation for Explainable AI in Genomics %A Barbosa, Pedro %A Savisaar, Rosina %A Fonseca, Alcides %Y Mouret, Jean-Baptiste %Y Qin, Kai %Y Handl, Julia %Y Li, Xiaodong %Y Wagner, Markus %Y Garza-Fabre, Mario %Y Smith-Miles, Kate %Y Allmendinger, Richard %Y Bi, Ying %Y Dick, Grant %Y Gandomi, Amir H. %Y Martins, Marcella Scoczynski Ribeiro %Y Assimi, Hirad %Y Veerapen, Nadarajen %Y Sun, Yuan %Y Munyoz, Mario Andres %Y Kheiri, Ahmed %Y Su, Nguyen %Y Thiruvady, Dhananjay %Y Song, Andy %Y Neumann, Frank %Y Silva, Carla %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F barbosa:2024:GECCO %X Black box deep learning models trained on genomic sequences excel at predicting the outcomes of different gene regulatory mechanisms. Therefore, interpreting these models may provide novel insights into the underlying biology, supporting downstream biomedical applications. Due to their complexity, interpretable surrogate models can only be built for local explanations (e.g., a single instance). However, accomplishing this requires generating a dataset in the neighborhood of the input, which must maintain syntactic similarity to the original data while introducing semantic variability in the model’s predictions. This task is challenging due to the complex sequence-to-function relationship of DNA.We propose using Genetic Programming to generate datasets by evolving perturbations in sequences that contribute to their semantic diversity. Our custom, domain-guided individual representation effectively constrains syntactic similarity, and we provide two alternative fitness functions that promote diversity with no computational effort. Applied to the RNA splicing domain, our approach quickly achieves good diversity and significantly outperforms a random baseline in exploring the search space, as shown by our proof-of-concept, short RNA sequence. Furthermore, we assess its generalizability and demonstrate scalability to larger sequences, resulting in a approx30% improvement over the baseline. %K genetic algorithms, genetic programming, XAI, evolutionary computation, instance generation, combinatorial optimization, local explainability, RNA splicing, Evolutionary Machine Learning %R 10.1145/3638529.3653990 %U http://dx.doi.org/10.1145/3638529.3653990 %P 267-276 %0 Conference Proceedings %T A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures %A Barbosa Diniz, Jessica %A Cordeiro, Filipe R. %A Miranda, Pericles B. C. %A Tomaz da Silva, Laura A. %Y Maua, Denis D. %Y Naldi, Murilo %S Anais do XV Encontro Nacional de Inteligencia Artificial e Computacional %D 2018 %8 22 25 oct %I Sociedade Brasileira de Computacao %C Sao Paulo, Brazil %F Barbosa-Diniz:2018:eniac %X Deep Learning is a research area under the spotlight in recent years due to its successful application to many domains, such as computer vision and image recognition. The most prominent technique derived from Deep Learning is Convolutional Neural Network, which allows the network to automatically learn representations needed for detection or classification tasks. However, Convolutional Neural Networks have some limitations, as designing these networks are not easy to master and require expertise and insight. In this work, we present the use of Genetic Algorithm associated to Grammar-based Genetic Programming to optimize Convolution Neural Network architectures. To evaluate our proposed approach, we adopted CIFAR-10 dataset to validate the evolution of the generated architectures, using the metric of accuracy to evaluate its classification performance in the test dataset. The results demonstrate that our method using Grammar-based Genetic Programming can easily produce optimized CNN architectures that are competitive and achieve high accuracy results. %K genetic algorithms, genetic programming, Grammatical Evolution, PonyGE2, ANN, CNN, Keras, image processing %R 10.5753/eniac.2018.4406 %U https://sol.sbc.org.br/index.php/eniac/article/view/4406 %U http://dx.doi.org/10.5753/eniac.2018.4406 %P 82-93 %0 Conference Proceedings %T Grammar-Based Evolutionary Approach for Automatic Workflow Composition with Open Preprocessing Sequence %A Barbudo, Rafael %A Ventura, Sebastian %A Romero, Jose Raul %Y Abraham, Ajith %Y Engelbrecht, Andries %Y Scotti, Fabio %Y Gandhi, Niketa %Y Mishra, Pooja Manghirmalani %Y Fortino, Giancarlo %Y Sakalauskas, Virgilijus %Y Pllana, Sabri %S Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) %S LNNS %D 2021 %V 417 %I Springer %F Barbudo:2021:SoCPaR %X Knowledge discovery is a complex process involving several phases. Some of them are repetitive and time-consuming, so they are susceptible of being automated. As an example, the large number of machine learning algorithms, together with their hyper-parameters, constitutes a vast search space to explore. In this vein, the term AutoML was coined to encompass those approaches automating such phases. The automatic workflow composition is an AutoML task that involves both the selection and the hyper-parameter optimisation of the algorithms addressing different phases, thus giving a more comprehensive assistance during the knowledge discovery process. Unlike other proposals that predetermine the structure of the preprocessing sequence, and in some cases the size of the workflow, our proposal generates workflows made up of an arbitrary number of preprocessing algorithms of any type and a classifier. This allows returning more accurate results since its avoids the oversimplification of the solution space. The optimisation is conducted by a grammar-guided genetic programming algorithm. The proposal has been validated and compared against TPOT and RECIPE generating workflows with greater predictive performance. %K genetic algorithms, genetic programming, Evolutionary Algorithms, Grammar-Based Genetic Programming, TPOT %R 10.1007/978-3-030-96302-6_61 %U http://dx.doi.org/10.1007/978-3-030-96302-6_61 %P 647-656 %0 Journal Article %T GEML: A grammar-based evolutionary machine learning approach for design-pattern detection %A Barbudo, Rafael %A Ramirez, Aurora %A Servant, Francisco %A Romero, Jose Raul %J Journal of Systems and Software %D 2021 %8 may %V 175 %@ 0164-1212 %F BARBUDO:2021:JSS %X Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis of either software metrics or specific properties of the source code. We propose GEML, a novel detection approach based on evolutionary machine learning using software properties of diverse nature. Firstly, GEML makes use of an evolutionary algorithm to extract those characteristics that better describe the DP, formulated in terms of human-readable rules, whose syntax is conformant with a context-free grammar. Secondly, a rule-based classifier is built to predict whether new code contains a hidden DP implementation. GEML has been validated over five DPs taken from a public repository recurrently adopted by machine learning studies. Then, we increase this number up to 15 diverse DPs, showing its effectiveness and robustness in terms of detection capability. An initial parameter study served to tune a parameter setup whose performance guarantees the general applicability of this approach without the need to adjust complex parameters to a specific pattern. Finally, a demonstration tool is also provided %K genetic algorithms, genetic programming, Design pattern detection, Reverse engineering, Machine learning, Associative classification, Grammar-guided genetic programming %9 journal article %R 10.1016/j.jss.2021.110919 %U https://www.sciencedirect.com/science/article/pii/S0164121221000169 %U http://dx.doi.org/10.1016/j.jss.2021.110919 %P 110919 %0 Journal Article %T Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity %A Barbudo, Rafael %A Ramirez, Aurora %A Romero, Jose Raul %J Applied Soft Computing %D 2024 %V 153 %@ 1568-4946 %F BARBUDO:2024:asoc %X The process of extracting valuable and novel insights from raw data involves a series of complex steps. In the realm of Automated Machine Learning (AutoML), a significant research focus is on automating aspects of this process, specifically tasks like selecting algorithms and optimising their hyper-parameters. A particularly challenging task in AutoML is automatic workflow composition (AWC). AWC aims to identify the most effective sequence of data preprocessing and machine learning algorithms, coupled with their best hyper-parameters, for a specific dataset. However, existing AWC methods are limited in how many and in what ways they can combine algorithms within a workflow. Addressing this gap, this paper introduces EvoFlow, a grammar-based evolutionary approach for AWC. EvoFlow enhances the flexibility in designing workflow structures, empowering practitioners to select algorithms that best fit their specific requirements. EvoFlow stands out by integrating two innovative features. First, it employs a suite of genetic operators, designed specifically for AWC, to optimise both the structure of workflows and their hyper-parameters. Second, it implements a novel updating mechanism that enriches the variety of predictions made by different workflows. Promoting this diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow builds an ensemble whose workflows differ in their misclassified instances. To evaluate EvoFlow’s effectiveness, we carried out empirical validation using a set of classification benchmarks. We begin with an ablation study to demonstrate the enhanced performance attributable to EvoFlow’s unique components. Then, we compare EvoFlow with other AWC approaches, encompassing both evolutionary and non-evolutionary techniques. Our findings show that EvoFlow’s specialised genetic operators and updating mechanism substantially outperform current leading methods in predictive performance. Additionally, EvoFlow is capable of discovering workflow structures that other approaches in the literature have not considered %K genetic algorithms, genetic programming, AutoML, Automated workflow composition, Algorithm selection, Hyper-parameter optimisation, Grammar-guided genetic programming, Ensemble learning, Classification %9 journal article %R 10.1016/j.asoc.2024.111292 %U https://www.sciencedirect.com/science/article/pii/S1568494624000668 %U http://dx.doi.org/10.1016/j.asoc.2024.111292 %P 111292 %0 Conference Proceedings %T Meteorological time series modeling using an adaptive gene expression programming %A Barbulescu, Alina %A Bautu, Elena %Y Mastorakis, Nikos E. %Y Croitoru, Anca %Y Balas, Valentina Emilia %Y Son, Eduard %Y Mladenov, Valeri %S Proceedings of the 10th WSEAS International Conference on Evolutionary Computation %D 2009 %8 23 25 mar %I World Scientific and Engineering Academy and Society (WSEAS) %C Prague, Czech %F Barbulescu:2009:WSEAS %X The precipitations are characterised by important spatial and temporal variation. Model determination for such series is of high importance for hydrological purposes (e.g. weather forecasting, agriculture, flood areas, administrative planning), even if discovering patterns in such series is a very difficult problem. The objective of the current study is to describe the use of an adaptive evolutionary technique that give promising results for the development of non-linear time series models. %K genetic algorithms, genetic programming, Gene Expression Programming %U http://www.wseas.us/e-library/conferences/2009/prague/EVOLUTIONARY/EC02.pdf %P 17-22 %0 Conference Proceedings %T ARIMA Models versus Gene Expression Programming in Precipitation Modeling %A Barbulescu, Alina %A Bautu, Elena %Y Mastorakis, Nikos E. %Y Croitoru, Anca %Y Balas, Valentina Emilia %Y Son, Eduard %Y Mladenov, Valeri %S Proceedings of the 10th WSEAS International Conference on Evolutionary Computation %D 2009 %8 23 25 mar %I World Scientific and Engineering Academy and Society (WSEAS) %C Prague, Czech %F Barbulescu:2009:WSEASb %X In this paper we present a case study: the application of some conceptually different approaches to the problem of identifying a model for a hydrological time series. The problem is particularly challenging, due to the size of the time series and more importantly, to the many complex phenomena that influence such time series and that reflect in the characteristics of the data. We use well established statistical methods to detect change points in the time series, and we model the subseries obtained by ARIMA, GEP and the adaptive variant and a combination of the two. The models obtained state the efficiency of combining pure statistical tests and methods with heuristic approaches. %K genetic algorithms, genetic programming, Gene Expression Programming, rain, time series modelling, statistical analysis %U http://www.wseas.us/e-library/conferences/2009/prague/EVOLUTIONARY/EC16.pdf %P 112-117 %0 Journal Article %T Alternative Models in Precipitation Analysis %A Barbulescu, Alina %A Bautu, Elena %J Analele Stiintifice ale Universitatii Ovidius Constanta, Seria Matematica %D 2009 %V XVII %N 3 %@ 1844-0835 %F Barbulescu20091 %X Precipitation time series intrinsically contain important information concerning climate variability and change. Well-fit models of such time series can shed light upon past weather related phenomena and can help to explain future events. The objective of this study is to investigate the application of some conceptually different methods to construct models for large hydrological time series. We perform a thorough statistical analysis of the time series, which covers the identification of the change points in the time series. Then, the subseries delimited by the change points are modelled with classical Box-Jenkins methods to construct ARIMA models and with a computational intelligence technique, gene expression programming, which produces non-linear symbolic models of the series. The combination of statistical techniques with computational intelligence methods, such as gene expression programming, for modelling time series, offers increased accuracy of the models obtained. This affirmation is illustrated with examples. %K genetic algorithms, genetic programming, Gene Expression Programming %9 journal article %U http://www.anstuocmath.ro/mathematics/pdf19/Barbulescu_Bautu.pdf %P 45-68 %0 Journal Article %T Time Series Modeling Using an Adaptive Gene Expression Programming Algorithm %A Barbulescu, Alina %A Bautu, Elena %J International Journal of Mathematical Models and Methods in Applied Sciences %D 2009 %V 3 %N 2 %@ 1998-0140 %F Barbulescu20092 %X Meteorological time series are characterised by important spatial and temporal variation. Model determination and the prediction of evolution of such series is of high importance for different practical purposes, even if discovering evolution patterns in such series is a very difficult problem. In this article we describe an adaptive evolutionary technique and we apply it for modelling the precipitation and temperatures collected in a region of Romania. The results are promising for the analysis of such time series. %K genetic algorithms, genetic programming, Gene Expression Programming %9 journal article %U http://www.naun.org/journals/m3as/mmmas-134.pdf %P 85-93 %0 Journal Article %T Mathematical models of climate evolution in Dobrudja %A Barbulescu, Alina %A Bautu, Elena %J Theoretical and Applied Climatology %D 2010 %8 mar %V 100 %N 1 %I Springer Wien %@ 0177-798X %F Barbulescu201003 %X The understanding of processes that occur in climate change evolution and their spatial and temporal variations are of major importance in environmental sciences. Modelling these processes is the first step in the prediction of weather change. In this context, this paper presents the results of statistical investigations of monthly and annual meteorological data collected between 1961 and 2007 in Dobrudja (South-East of Romania between the Black Sea and the lower Danube River) and the models obtained using time series analysis and gene expression programming. Using two fundamentally different approaches, we provide a comprehensive analysis of temperature variability in Dobrudja, which may be significant in understanding the processes that govern climate changes in the region. %K genetic algorithms, genetic programming, gene expression programming, ARIMA, Earth and Environmental Science %9 journal article %R 10.1007/s00704-009-0160-7 %U http://dx.doi.org/10.1007/s00704-009-0160-7 %P 29-44 %0 Journal Article %T Generating Milling Tool Paths for Prismatic Parts Using Genetic Programming %A Barclay, Jack %A Dhokia, Vimal %A Nassehi, Aydin %J Procedia CIRP %D 2015 %V 33 %@ 2212-8271 %F Barclay:2015:Procedia %O 9th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME 14 %X The automatic generation of milling tool paths traditionally relies on applying complex tool path generation algorithms to a geometric model of the desired part. For parts with unusual geometries or intricate intersections between sculpted surfaces, manual intervention is often required when normal tool path generation methods fail to produce efficient tool paths. In this paper, a simplified model of the machining process is used to create a domain-specific language that enables tool paths to be generated and optimised through an evolutionary process - formulated, in this case, as a genetic programming system. The driving force behind the optimisation is a fitness function that promotes tool paths whose result matches the desired part geometry and favours those that reach their goal in fewer steps. Consequently, the system is not reliant on tool path generation algorithms, but instead requires a description of the desired characteristics of a good solution, which can then be used to measure and evaluate the relative performance of the candidate solutions that are generated. The performance of the system is less sensitive to different geometries of the desired part and doesn’t require any additional rules to deal with changes to the initial stock (e.g. when rest roughing). The method is initially demonstrated on a number of simple test components and the genetic programming process is shown to positively influence the outcome. Further tests and extensions to the work are presented. %K genetic algorithms, genetic programming, Computer numerical control (CNC), Milling %9 journal article %R 10.1016/j.procir.2015.06.060 %U http://www.sciencedirect.com/science/article/pii/S2212827115007039 %U http://dx.doi.org/10.1016/j.procir.2015.06.060 %P 490-495 %0 Journal Article %T Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions %A Bardhan, Abidhan %A Gokceoglu, Candan %A Burman, Avijit %A Samui, Pijush %A Asteris, Panagiotis G. %J Engineering Geology %D 2021 %V 291 %@ 0013-7952 %F BARDHAN:2021:EG %X California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked conditions, which is an exhaustive and time-consuming process. Therefore, to sidestep the operation of conducting actual laboratory tests, this study presents the development of four efficient soft computing techniques, namely multivariate adaptive regression splines with piecewise linear models (MARS-L), multivariate adaptive regression splines with piecewise cubic models (MARS-C), Gaussian process regression, and genetic programming. For this purpose, a wide range of experimental results of soaked CBR was collected from an ongoing railway project of Indian Railways. Three explicit expressions are proposed to estimate the CBR of soils in soaked conditions. Separate laboratory experiments were performed to evaluate the generalization capabilities of the developed models. Furthermore, simulated datasets were used to validate the feasibility of the best-performing model. Experimental results reveal that the proposed MARS-L model attained the most accurate prediction (R2 = 0.9686 and RMSE = 0.0359 against separate laboratory experiments) in predicting the soaked CBR at all stages. Based on the accuracies attained, the proposed MARS-L model is very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects %K genetic algorithms, genetic programming, Soaked CBR, Machine learning, MARS, GPR, Sub-grade design %9 journal article %R 10.1016/j.enggeo.2021.106239 %U https://www.sciencedirect.com/science/article/pii/S0013795221002507 %U http://dx.doi.org/10.1016/j.enggeo.2021.106239 %P 106239 %0 Journal Article %T ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions %A Bardhan, Abidhan %A Samui, Pijush %A Ghosh, Kuntal %A Gandomi, Amir H. %A Bhattacharyya, Siddhartha %J Applied Soft Computing %D 2021 %V 110 %@ 1568-4946 %F BARDHAN:2021:ASC %X This study proposes novel integration of extreme learning machine (ELM) and adaptive neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio (CBR) of soils for the subgrade layers of railway tracks, a critical real-time problem of geotechnical engineering. Particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients (TAC) was employed to optimize the learning parameters of ELM. Three novel ELM-based ANSI models, namely ELM coupled-modified PSO (ELM-MPSO), ELM coupled-TAC PSO (ELM-TPSO), and ELM coupled-improved PSO (ELM-IPSO) were developed for predicting the CBR of soils in soaked conditions. Compared to standard PSO (SPSO), the modified and improved version of PSO are capable of converging to a high-quality solution at early iterations. A detailed comparison was made between the proposed models and other conventional soft computing techniques, such as conventional ELM, artificial neural network, genetic programming, support vector machine, group method of data handling, and three ELM-based swarm intelligence optimized models (ELM-based grey wolf optimization, ELM-based slime mould algorithm, and ELM-based Harris hawks optimization). Experimental results reveal that the proposed ELM-based ANSI models can attain the most accurate prediction and confirm the dominance of MPSO over SPSO. Considering the consequences and robustness of the proposed models, it can be concluded that the newly constructed ELM-based ANSI models, especially ELM-MPSO, can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering %K genetic algorithms, genetic programming, Swarm intelligence, Soft computing, CBR, DFC, Indian Railways, Particle swarm optimization %9 journal article %R 10.1016/j.asoc.2021.107595 %U https://www.sciencedirect.com/science/article/pii/S1568494621005160 %U http://dx.doi.org/10.1016/j.asoc.2021.107595 %P 107595 %0 Journal Article %T Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer %A Bardhan, Abidhan %A Manna, Priyadip %A Kumar, Vinay %A Burman, Avijit %A Zlender, Bojan %A Samui, Pijush %J CMES - Computer Modeling in Engineering and Sciences %D 2021 %V 128 %N 3 %@ 1526-1492 %F Bardhan:2021:CMES %X In many civil engineering projects, Piled Raft Foundations (PRFs) are usually preferred where the incoming load from the superstructures is very high. In geotechnical engineering practice, the settlement of soil layers is a critical issue for the serviceability of the structures. Thus, assessment of risk associated with the structures corresponding to the maximum allowable settlement of soils needs to be carried out in the design phase. In this study, reliability analysis of PRF based on settlement criteria is performed using a high-performance hybrid soft computing model. The new approach is an integration of the artificial neural network (ANN) and a recently developed meta-heuristic algorithm called equilibrium optimiser (EO). The concept of reliability index was used to explore the feasibility of a newly constructed hybrid model of ANN and EO (i.e., ANN-EO) against the conventional approach of calculating the probability of failure of PRF. Experimental results show that the proposed ANN-EO attained the most accurate prediction with R2 = 0.9914 and RMSE = 0.0518 in the testing phase, which are significantly better than those obtained from conventional ANN, multivariate adaptive regression splines, and genetic programming, including the ANN optimised with particle swarm optimisation developed in this study. Based on the experimental results of different settlement values, the newly constructed ANN-EO is very potential to analyse the risk associated with civil engineering structures. Also, the present study would significantly contribute to the knowledge pool of reliability studies related to piled raft systems because the works of literature on reliability analysis of piled raft systems are relatively scarce %K genetic algorithms, genetic programming, Risk analysis, soil, meta-heuristic optimization, particle swarm optimization, PSO, ANN %9 journal article %R 10.32604/cmes.2021.015885 %U https://www.sciencedirect.com/science/article/pii/S1526149221001545 %U http://dx.doi.org/10.32604/cmes.2021.015885 %P 1033-1067 %0 Journal Article %T A novel integrated approach of ELM and modified equilibrium optimizer for predicting soil compression index of subgrade layer of Dedicated Freight Corridor %A Bardhan, Abidhan %A GuhaRay, Anasua %A Gupta, Shubham %A Pradhan, Biswajeet %A Gokceoglu, Candan %J Transportation Geotechnics %D 2022 %8 jan %V 32 %@ 2214-3912 %F BARDHAN:2022:TG %X This study proposes a high-performance machine learning model to sidestep the time of conducting actual laboratory tests of soil compression index (Cc), one of the important criteria for determining the settlement of subgrade layers of roadways, railways, and airport runways. The suggested method combines the modified equilibrium optimizer (MEO) and the extreme learning machine (ELM) in a novel way. In this study, Gaussian mutation with an exploratory search mechanism was incorporated to construct the MEO and used to enhance the performance of conventional ELM by optimizing its learning parameters. PCA (Principal component analysis)-based results exhibit that the developed ELM-MEO attained the most precise prediction with R2 = 0.9746, MAE = 0.0184, and RMSE = 0.0284 in training, and R2 = 0.9599, MAE = 0.0232, and RMSE = 0.0357 in the testing phase. The results showed that the proposed ELM-MEO model outperformed the other developed models, confirming the ELM-MEO model’s superiority over the other models, such as random forest, gradient boosting machine, genetic programming, including the ELM and artificial neural network (ANN)-based models optimized with equilibrium optimizer, particle swarm optimization, Harris hawks optimization, slime mould algorithm, and marine predators algorithm. Based on the experimental results, the proposed ELM-MEO can be used as a promising alternative to predict soil Cc in civil engineering projects, including rail and road projects %K genetic algorithms, genetic programming, Subgrade layer design, Railway embankment, Soft computing, Consolidation parameter, Meta-heuristic optimization, Indian Railways, Swarm intelligence %9 journal article %R 10.1016/j.trgeo.2021.100678 %U https://www.sciencedirect.com/science/article/pii/S2214391221001689 %U http://dx.doi.org/10.1016/j.trgeo.2021.100678 %P 100678 %0 Journal Article %T Probabilistic assessment of heavy-haul railway track using multi-gene genetic programming %A Bardhan, Abidhan %J Applied Mathematical Modelling %D 2024 %V 125 %@ 0307-904X %F BARDHAN:2024:apm %X This study presented a probabilistic assessment of heavy-haul railway track using a high-performance computational model called multi-gene genetic programming (MGGP). A reliability analysis (RA) method based on MGGP and the first-order second-moment method (FOSM) has been proposed in this study. First, GP was used to map the implicit performance functions; therefore, arriving at GP-based explicit performance functions. Subsequently, the developed GP model was used to perform RA of a soil slope of heavy-haul railway track under both seismic and non-seismic conditions. Using the FOSM, soil uncertainties were mapped based on the concepts of probability theory and statistics, and a ready-made expression was developed. Simulated results demonstrate that the GP-based FOSM approach can predict the probability of failure (POF) of slope with rational accuracy. The probabilistic analysis against bearing capacity failure was also investigated in this study to ensure serviceability of the soil slope. Based on the outcomes, it can be deduced that the coefficient of variation of soil properties affects the POF of slope significantly. With the aid of the developed expression, the POF of the soil slope of heavy-haul railway track can be assessed rationally and efficiently %K genetic algorithms, genetic programming, Railway embankment, Reliability analysis, Bearing capacity, Heavy-haul freight corridor, Slope/W modelling, Artificial intelligence %9 journal article %R 10.1016/j.apm.2023.08.009 %U https://www.sciencedirect.com/science/article/pii/S0307904X2300358X %U http://dx.doi.org/10.1016/j.apm.2023.08.009 %P 687-720 %0 Journal Article %T Phase stability conditions of clathrate hydrates for methane + aqueous solution of water soluble organic promoter system: Modeling using a thermodynamic framework %A Bardool, Roghayeh %A Javanmardi, Jafar %A Roosta, Aliakbar %A Mohammadi, Amir H. %J Journal of Molecular Liquids %D 2016 %V 224, Part B %@ 0167-7322 %F Bardool:2016:JML %X A thermodynamic model is presented for predicting the phase stability conditions of clathrate hydrates for methane + water-soluble organic promoter aqueous solution. A new equation is then proposed to estimate the enthalpy of hydrate dissociation for methane + aqueous solution of water-soluble organic promoter using Genetic Programming (GP) and Teaching-Learning-Based Optimization (TLBO) evolutionary algorithm. The model reliably predicts the hydrate dissociation conditions for methane + aqueous solutions of tetrahydrofuran, 1,3-dioxolane, 1,4-dioxane and acetone. The van Laar model is used to calculate the activity coefficient of water in aqueous solution of water-soluble organic promoter. About 30percent of the reported experimental data were used for finding the empirical relationships to estimate the enthalpy of hydrate dissociation and the remaining 70percent was used to test the accuracy and the predictive capability of the correlation. The average absolute error for methane hydrate dissociation temperatures was found to be 0.33 K, which indicates the accuracy of the model. %K genetic algorithms, genetic programming, Gas hydrate, Clathrate hydrate, Methane, Water-soluble organic promoter, Thermodynamic model, Correlation %9 journal article %R 10.1016/j.molliq.2016.09.084 %U http://www.sciencedirect.com/science/article/pii/S016773221630335X %U http://dx.doi.org/10.1016/j.molliq.2016.09.084 %P 1117-1123 %0 Journal Article %T Combining classifiers generated by multi-gene genetic programming for protein fold recognition using genetic algorithm %A Bardsiri, Mahshid Khatibi %A Eftekhari, Mahdi %A Mousavi, Reza %J Int. J. of Bioinformatics Research and Applications %D 2015 %8 mar 17 %V 11 %N 2 %I Inderscience Publishers %@ 1744-5493 %F Bardsiri:2015:IJBRA %X In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy. %K genetic algorithms, genetic programming, multi-gene genetic programming, protein fold recognition, bioinformatics, weighted voting, classifiers, classification accuracy %9 journal article %R 10.1504/IJBRA.2015.068092 %U http://www.inderscience.com/link.php?id=68092 %U http://dx.doi.org/10.1504/IJBRA.2015.068092 %P 171-186 %0 Journal Article %T Determining normalized friction torque of an industrial robotic manipulator using the symbolic regression method %A Sandi, Segota Baressi %A Vedran, Mrzljak %A Jasna, Prpic-Orsic %A Car, Zlatan %J Industry 4.0 %D 2023 %V 8 %N 1 %@ 2534-8582 %F Segota-Sandi:2023:Industry4.0 %O Dominant technologies in %X The goal of the paper is estimating the normalized friction torque of a joint in an industrial robotic manipulator. For this purpose a source data, given as a figure, is digitized using a tool WebPlotDigitizer in order to obtain numeric data. The numeric data is the used within the machine learning algorithm genetic programming (GP), which performs the symbolic regression in order to obtain the equation that regresses the dataset in question. The obtained model shows a coefficient of determination equal to 0.87, which indicates that the model in question may be used for the wide approximation of the normalized friction torque using the torque load, operating temperature and joint velocity as inputs. %K genetic algorithms, genetic programming, friction torque prediction, industrial robotic manipulator, friction, machine learning, symbolic regression %9 journal article %U https://stumejournals.com/journals/i4/2023/1/21.full.pdf %P 21-24 %0 Journal Article %T An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow %A Barge, Jonathan T. %A Sharif, Hatim O. %J Water %D 2016 %V 8 %N 6 %@ 2073-4441 %F barge:2016:Water %X This study focused on employing Linear Genetic Programming (LGP), Ensemble Empirical Mode Decomposition (EEMD), and the Self-Organising Map (SOM) in modelling the rainfall-runoff relationship in a mid-size catchment. Models were assessed with regard to their ability to capture daily discharge at Lock and Dam 10 along the Kentucky River as well as the hybrid design of EEM-SOM-LGP to make predictions multiple time-steps ahead. Different model designs were implemented to demonstrate the improvements of hybrid designs compared to LGP as a standalone application. Additionally, LGP was used to gain a better understanding of the catchment in question and to assess its ability to capture different aspects of the flow hydrograph. As a standalone application, LGP was able to outperform published Artificial Neural Network (ANN) results over the same dataset, posting an average absolute relative error (AARE) of 17.118 and Nash-Sutcliff (E) of 0.937. Using EEMD derived IMF runoff subcomponents for forecasting daily discharge resulted in an AARE of 14.232 and E of 0.981. Clustering the EEMD-derived input space through an SOM before LGP application returned the strongest results, posting an AARE of 10.122 and E of 0.987. Applying LGP to the distinctive low and high flow seasons demonstrated a loss in correlation for the low flow season with an under-predictive nature signified by a normalised mean biased error (NMBE) of -2.353. Separating the rising and falling trends of the hydrograph showed that the falling trends were more easily captured with an AARE of 8.511 and E of 0.968 compared to the rising trends AARE of 38.744 and E of 0.948. Using the EEMD-SOM-LGP design to make predictions multiple-time-steps ahead resulted in a AARE of 43.365 and E of 0.902 for predicting streamflow three days ahead. The results demonstrate the effectiveness of using EEMD and an SOM in conjunction with LGP for streamflow forecasting. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/w8060247 %U https://www.mdpi.com/2073-4441/8/6/247 %U http://dx.doi.org/10.3390/w8060247 %0 Conference Proceedings %T Non-photorealistic Rendering Using Genetic Programming %A Barile, Perry %A Ciesielski, Victor %A Trist, Karen %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 DBLP:conf/seal/BarileCT08 %X We take a novel approach to Non-Photorealistic Rendering by adapting genetic programming in combination with computer graphics drawing techniques. As a GP tree is evaluated, upon encountering certain nodes referred to as Draw nodes, information contained within such nodes are sent to one of three virtual canvasses and a mark is deposited on the canvas. For two of the canvasses the user is able to define custom brushes to be applied to the canvas. Drawing functions are supplied with little localised information regarding the target image. Based on this local data, the drawing functions are enabled to apply contextualized information to the canvas. The obtained results include a Shroud of Turin effect, a Decal effect and a Starburst effect. %K genetic algorithms, genetic programming, non-photorealistic rendering, evolutionary computation %R 10.1007/978-3-540-89694-4_31 %U http://dx.doi.org/10.1007/978-3-540-89694-4_31 %P 299-308 %0 Conference Proceedings %T Animated drawings rendered by genetic programming %A Barile, Perry %A Ciesielski, Victor %A Berry, Marsha %A Trist, Karen %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/BarileCBT09 %X We describe an approach to generating animations of drawings that start as a random collection of strokes and gradually resolve into a recognizable subject. The strokes are represented as tree based genetic programs. An animation is generated by rendering the best individual in a generation as a frame of a movie. The resulting animations have an engaging characteristic in which the target slowly emerges from a random set of strokes. We have generated two qualitatively different kinds of animations, ones that use grey level straight line strokes and ones that use binary Bezier curve stokes. Around 100,000 generations are needed to generate engaging animations. Population sizes of 2 and 4 give the best convergence behaviour. Convergence can be accelerated by using information from the target in drawing a stroke. Our approach provides a large range of creative opportunities for artists. Artists have control over choice of target and the various stroke parameters. %K genetic algorithms, genetic programming %R 10.1145/1569901.1570030 %U http://dx.doi.org/10.1145/1569901.1570030 %P 939-946 %0 Conference Proceedings %T Towards Scene Text Recognition with Genetic Programming %A Barlow, Brendan %A Song, Andy %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 Barlow:2013:CEC %K genetic algorithms, genetic programming %R 10.1109/CEC.2013.6557716 %U http://dx.doi.org/10.1109/CEC.2013.6557716 %P 1310-1317 %0 Thesis %T Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming %A Barlow, Gregory J. %D 2004 %8 mar %C Raleigh, NC, USA %C North Carolina State University %F barlow2004-thesis %X Unmanned aerial vehicles (UAVs) have become increasingly popular for many applications, including search and rescue, surveillance, and electronic warfare, but almost all UAVs are controlled remotely by humans. Methods of control must be developed before UAVs can become truly autonomous. While the field of evolutionary robotics (ER) has made strides in using evolutionary computation (EC) to develop controllers for wheeled mobile robots, little attention has been paid to applying EC to UAV control. EC is an attractive method for developing UAV controllers because it allows the human designer to specify the set of high level goals that are to be solved by artificial evolution. In this research, autonomous navigation controllers were developed using multi-objective genetic programming (GP) for fixed wing UAV applications. Four behavioral fitness functions were derived from flight simulations. Multi-objective GP used these fitness functions to evolve controllers that were able to locate an electromagnetic energy source, to navigate the UAV to that source efficiently using on-board sensor measurements, and to circle around the emitter. Controllers were evolved in simulation. To narrow the gap between simulated and real controllers, the simulation environment employed noisy radar signals and a sensor model with realistic inaccuracies. All computations were performed on a 92-processor Beowulf cluster parallel computer. To gauge the success of evolution, baseline fitness values for a successful controller were established by selecting values for a minimally successful controller. Two sets of experiments were performed, the first evolving controllers directly from random initial populations, the second using incremental evolution. In each set of experiments, autonomous navigation controllers were evolved for a variety of radar types. Both the direct evolution and incremental evolution experiments were able to evolve controllers that performed acceptably. However, incremental evolution vastly increased the success rate of incremental evolution over direct evolution. The final incremental evolution experiment on the most complex radar investigated in this research evolved controllers that were able to handle all of the radar types. Evolved UAV controllers were successfully transferred to a wheeled mobile robot. An acoustic array on-board the mobile robot replaced the radar sensor, and a speaker emitting a tone was used as the target. Using the evolved navigation controllers, the mobile robot moved to the speaker and circled around it. Future research will include testing the best evolved controllers by using them to fly real UAVs. %K genetic algorithms, genetic programming, mobile robotics, evolutionary robotics, multi-objective optimization, incremental evolution, unmanned aerial vehicles %9 Masters thesis %U http://www.andrew.cmu.edu/user/gjb/includes/publications/thesis/barlow2004-thesis/barlow2004-thesis.pdf %0 Conference Proceedings %T Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming %A Barlow, Gregory J. %A Oh, Choong K. %A Grant, Edward %Y Keijzer, Maarten %S Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference %D 2004 %8 26 jul %C Seattle, Washington, USA %F barlow:2004:lbp %X Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using incremental evolution with multi-objective genetic programming (GP). We designed four fitness functions derived from flight simulations and used multi-objective GP to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle closely around the emitter. We selected realistic flight parameters and sensor inputs to aid in the transference of evolved controllers to physical UAVs. We used both direct and environmental incremental evolution to evolve controllers for four types of radars: 1) continuously emitting, stationary radars, 2) continuously emitting, mobile radars, 3) intermittently emitting, stationary radars, and 4) intermittently emitting, mobile radars. The use of incremental evolution drastically increased evolution’s chances of evolving a successful controller compared to direct evolution. This technique can also be used to develop a single controller capable of handling all four radar types. In the next stage of research, the best evolved controllers will be tested by using them to fly real UAVs. %K genetic algorithms, genetic programming %U http://www.andrew.cmu.edu/user/gjb/includes/publications/other/barlow2004-geccolbp/barlow2004-geccolbp.pdf %0 Conference Proceedings %T Autonomous Controller Design for Unmanned Aerial Vehicles using Multi-objective Genetic Programming %A Barlow, Gregory J. %Y Poli, R. %Y Cagnoni, S. %Y Keijzer, M. %Y Costa, E. %Y Pereira, F. %Y Raidl, G. %Y Upton, S. C. %Y Goldberg, D. %Y Lipson, H. %Y de Jong, E. %Y Koza, J. %Y Suzuki, H. %Y Sawai, H. %Y Parmee, I. %Y Pelikan, M. %Y Sastry, K. %Y Thierens, D. %Y Stolzmann, W. %Y Lanzi, P. L. %Y Wilson, S. W. %Y O’Neill, M. %Y Ryan, C. %Y Yu, T. %Y Miller, J. F. %Y Garibay, I. %Y Holifield, G. %Y Wu, A. S. %Y Riopka, T. %Y Meysenburg, M. M. %Y Wright, A. W. %Y Richter, N. %Y Moore, J. H. %Y Ritchie, M. D. %Y Davis, L. %Y Roy, R. %Y Jakiela, M. %S Proceedings of the Graduate Student Workshop at the 2004 Genetic and Evolutionary Computation Conference (GECCO-2004) %D 2004 %8 24 26 jun %C Seattle, Washington, USA %F barlow:2004:geccogsw %X Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using multi-objective genetic programming (GP). Four fitness functions derived from flight simulations were designed and multi-objective GP was used to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. Controllers were evolved for three different kinds of radars: stationary, continuously emitting radars, stationary, intermittently emitting radars, and mobile, continuously emitting radars. In this study, realistic flight parameters and sensor inputs were selected to aid in the transference of evolved controllers to physical UAVs. %K genetic algorithms, genetic programming, evolutionary robotics, multi-objective optimisation, unmanned aerial vehicles %U http://gpbib.cs.ucl.ac.uk/gecco2004/WGSW001.pdf %0 Conference Proceedings %T Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming %A Barlow, Gregory J. %A Oh, Choong K. %A Grant, Edward %S Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems (CIS) %D 2004 %8 January 3 dec %I IEEE %C Singapore %F barlow2004-cis %X Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using incremental evolution with multi-objective genetic programming (GP). We designed four fitness functions derived from flight simulations and used multi-objective GP to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle closely around the emitter. We selected realistic flight parameters and sensor inputs to aid in the transference of evolved controllers to physical UAVs. We used both direct and environmental incremental evolution to evolve controllers for four types of radars: 1) continuously emitting, stationary radars, 2) continuously emitting, mobile radars, 3) intermittently emitting, stationary radars, and 4) intermittently emitting, mobile radars. The use of incremental evolution drastically increased evolution’s chances of evolving a successful controller compared to direct evolution. This technique can also be used to develop a single controller capable of handling all four radar types. In the next stage of research, the best evolved controllers will be tested by using them to fly real UAVs. %K genetic algorithms, genetic programming, incremental evolution, multi-objective optimisation %U http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2004-cis/barlow2004-cis.pdf %P 688-693 %0 Conference Proceedings %T Transference of Evolved Unmanned Aerial Vehicle Controllers to a Wheeled Mobile Robot %A Barlow, Gregory J. %A Mattos, Leonardo S. %A Grant, Edward %A Oh, Choong K. %Y Dillmann, Ruediger %S Proceedings of the IEEE International Conference on Robotics and Automation %D 2005 %8 18 22 apr %I IEEE %C Barcelona, Spain %F barlow2005-icra %X Transference of controllers evolved in simulation to real vehicles is an important issue in evolutionary robotics (ER). We have previously evolved autonomous navigation controllers for fixed wing UAV applications using multi-objective genetic programming (GP). Controllers were evolved to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. We successfully tested an evolved UAV controller on a wheeled mobile robot. A passive sonar system on the robot was used in place of the radar sensor, and a speaker emitting a tone was used as the target in place of a radar. Using the evolved navigation controller, the mobile robot moved to the speaker and circled around it. The results from this experiment demonstrate that our evolved controllers are capable of transference to real vehicles. Future research will include testing the best evolved controllers by using them to fly real UAVs. %K genetic algorithms, genetic programming %U http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2005-icra/barlow2005-icra.pdf %0 Conference Proceedings %T Robustness analysis of genetic programming controllers for unmanned aerial vehicles %A Barlow, Gregory J. %A Oh, Choong K. %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 1144023 %K genetic algorithms, genetic programming, Artificial Life Evolutionary Robotics, Adaptive Behavior, autonomous vehicles, program synthesis, reliability, robustness, synthesis, transference, unmanned aerial vehicles %R 10.1145/1143997.1144023 %U http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p135.pdf %U http://dx.doi.org/10.1145/1143997.1144023 %P 135-142 %0 Conference Proceedings %T Evolving cooperative control on sparsely distributed tasks for UAV teams without global communication %A Barlow, Gregory J. %A Oh, Choong K. %A Smith, Stephen F. %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 Barlow:2008:gecco %K genetic algorithms, genetic programming, evolutionary robotics, multiagent systems, multiobjective optimisation, unmanned aerial vehicles, Artificial life, adaptive behaviour, evolvable hardware %R 10.1145/1389095.1389125 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p177.pdf %U http://dx.doi.org/10.1145/1389095.1389125 %P 177-184 %0 Journal Article %T Solid dispersions in the development of a nimodipine floating tablet formulation and optimization by artificial neural networks and genetic programming %A Barmpalexis, Panagiotis %A Kachrimanis, Kyriakos %A Georgarakis, Emanouil %J European Journal of Pharmaceutics and Biopharmaceutics %D 2011 %V 77 %N 1 %@ 0939-6411 %F Barmpalexis2011122 %X The present study investigates the use of nimodipine-polyethylene glycol solid dispersions for the development of effervescent controlled release floating tablet formulations. The physical state of the dispersed nimodipine in the polymer matrix was characterised by differential scanning calorimetry, powder X-ray diffraction, FT-IR spectroscopy and polarised light microscopy, and the mixture proportions of polyethylene glycol (PEG), polyvinyl-pyrrolidone (PVP), hydroxypropylmethylcellulose (HPMC), effervescent agents (EFF) and nimodipine were optimised in relation to drug release (percent release at 60 min, and time at which the 90percent of the drug was dissolved) and floating properties (tablet’s floating strength and duration), employing a 25-run D-optimal mixture design combined with artificial neural networks (ANNs) and genetic programming (GP). It was found that nimodipine exists as mod I microcrystals in the solid dispersions and is stable for at least a three-month period. The tablets showed good floating properties and controlled release profiles, with drug release proceeding via the concomitant operation of swelling and erosion of the polymer matrix. ANNs and GP both proved to be efficient tools in the optimization of the tablet formulation, and the global optimum formulation suggested by the GP equations consisted of PEG = 9percent, PVP = 30percent, HPMC = 36percent, EFF = 11percent, nimodipine = 14percent. %K genetic algorithms, genetic programming, Solid dispersions, Nimodipine, Controlled release, Effervescent floating tablets, Artificial neural networks %9 journal article %R 10.1016/j.ejpb.2010.09.017 %U http://www.sciencedirect.com/science/article/B6T6C-51696TP-1/2/61fc7d46e9a66d451646234b5e96dedb %U http://dx.doi.org/10.1016/j.ejpb.2010.09.017 %P 122-131 %0 Journal Article %T Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation %A Barmpalexis, P. %A Kachrimanis, K. %A Tsakonas, A. %A Georgarakis, E. %J Chemometrics and Intelligent Laboratory Systems %D 2011 %8 may %V 107 %N 1 %@ 0169-7439 %F Barmpalexis201175 %X Symbolic regression via genetic programming (GP) was used in the optimisation of a pharmaceutical zero-order release matrix tablet, and its predictive performance was compared to that of artificial neural network (ANN) models. Two types of GP algorithms were employed: 1) standard GP, where a single population is used with a restricted or an extended function set, and 2) multi-population (island model) GP, where a finite number of populations is adopted. The amounts of four polymers, namely PEG4000, PVP K30, HPMC K100 and HPMC E50LV were selected as independent variables, while the percentage of nimodipine released in 2 and 8 h (Y2h, and Y8h), respectively, and the time at which 90% of the drug was dissolved (t90%), were selected as responses. Optimal models were selected by minimisation of the Euclidian distance between predicted and optimum release parameters. It was found that the prediction ability of GP on an external validation set was higher compared to that of the ANNs, with the multi population and standard GP combined with an extended function set, showing slightly better predictive performance. Similarity factor (f2) values confirmed GP’s increased prediction performance for multi-population GP (f2 = 85.52) and standard GP using an extended function set (f2 = 84.47). %K genetic algorithms, genetic programming, Artificial neural networks, Controlled release, Experimental design, Optimisation %9 journal article %R 10.1016/j.chemolab.2011.01.012 %U http://ikee.lib.auth.gr/record/226012 %U http://dx.doi.org/10.1016/j.chemolab.2011.01.012 %P 75-82 %0 Journal Article %T Comparison of multi-linear regression, particle swarm optimization artificial neural networks and genetic programming in the development of mini-tablets %A Barmpalexis, Panagiotis %A Karagianni, Anna %A Karasavvaides, Grigorios %A Kachrimanis, Kyriakos %J International Journal of Pharmaceutics %D 2018 %V 551 %N 1 %@ 0378-5173 %F BARMPALEXIS:2018:IJP %X In the present study, the preparation of pharmaceutical mini-tablets was attempted in the framework of Quality by Design (QbD) context, by comparing traditionally used multi-linear regression (MLR), with artificially-intelligence based regression techniques (such as standard artificial neural networks (ANNs), particle swarm optimization (PSO) ANNs and genetic programming (GP)) during Design of Experiment (DoE) implementation. Specifically, the effect of diluent type and particle size fraction for three commonly used direct compression diluents (lactose, pregelatinized starch and dibasic calcium phosphate dihydrate, DCPD) blended with either hydrophilic or hydrophobic flowing aids was evaluated in terms of: a) powder blend properties (such as bulk (Y1) and tapped (Y2) density, Carr’s compressibility index (Y3, CCI), Kawakita’s compaction fitting parameters a (Y4) and 1/b (Y5)), and b) mini-tablet’s properties (such as relative density (Y6), average weight (Y7) and weight variation (Y8)). Results showed better flowing properties for pregelatinized starch and improved packing properties for lactose and DPCD. MLR analysis showed high goodness of fit for the Y1, Y2, Y4, Y6 and Y8 with RMSE values of Y1a =a 0.028, Y2a =a 0.032, Y4a =a 0.019, Y6a =a 0.015 and Y8a =a 0.130; while for rest responses, high correlation was observed from both standard ANNs and GP. PSO-ANNs fitting was the only regression technique that was able to adequately fit all responses simultaneously (RMSE values of Y1a =a 0.026, Y2a =a 0.022, Y3a =a 0.025, Y4a =a 0.010, Y5a =a 0.063, Y6a =a 0.013, Y7a =a 0.064 and Y8a =a 0.104) %K genetic algorithms, genetic programming, Mini-tablets, Quality by design (QbD), Particle swarm optimization ANNs, Flow properties, DoE optimization %9 journal article %R 10.1016/j.ijpharm.2018.09.026 %U http://www.sciencedirect.com/science/article/pii/S037851731830677X %U http://dx.doi.org/10.1016/j.ijpharm.2018.09.026 %P 166-176 %0 Journal Article %T Development of a New Aprepitant Liquisolid Formulation with the Aid of Artificial Neural Networks and Genetic Programming %A Barmpalexis, Panagiotis %A Grypioti, Agni %A Eleftheriadis, Georgios K. %A Fatouros, Dimitris G. %J AAPS PharmSciTech %D 2018 %V 19 %N 2 %F barmpalexis:2018:AAPSPST %K genetic algorithms, genetic programming %9 journal article %R 10.1208/s12249-017-0893-z %U http://link.springer.com/article/10.1208/s12249-017-0893-z %U http://dx.doi.org/10.1208/s12249-017-0893-z %0 Generic %T Viewing Anthropogenic Change Through an AI Lens %A Barnes, Elizabeth %E Banzhaf, Wolfgang %E Trujillo, Leonardo %E Winkler, Stephan %E Worzel, Bill %D 2021 %8 19 21 may %C East Lansing, USA %F Barnes:2021:GPTP %O keynote %K genetic algorithms, genetic programming %0 Conference Proceedings %T Meta-genetic programming for static quantum circuits %A Barnes, Kenton M. %A Gale, Michael B. %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 Barnes:2019:GECCOcomp %X Quantum programs are difficult for humans to develop due to their complex semantics that are rooted in quantum physics. It is there-fore preferable to write specifications and then use techniques such as genetic programming (GP) to generate quantum programs in-stead. We present a new genetic programming system for quantumcircuits which can evolve solutions to the full-adder and quantumFourier transform problems in fewer generations than previouswork, despite using a general set of gates. This means that it is nolonger required to have any previous knowledge of the solutionand choose a specialised gate set based on it. %K genetic algorithms, genetic programming, quantum computing %R 10.1145/3319619.3326907 %U http://wrap.warwick.ac.uk/119812/1/WRAP-meta-genetic-programming-static-quantum-circuits-Gale-2019.pdf %U http://dx.doi.org/10.1145/3319619.3326907 %P 2016-2019 %0 Generic %T A quantum circuit for OR %A Barnum, Howard %A Bernstein, Herbert J. %A Spector, Lee %D 1999 %8 oct 08 %I arXiv.or %F oai:arXiv.org:quant-ph/9907056 %X We give the first quantum circuit for computing $f(0)$ OR $f(1)$ more reliably than is classically possible with a single evaluation of the function. OR therefore joins XOR (i.e. parity, $f(0) øplus f(1)$) to give the full set of logical connectives (up to relabeling of inputs and outputs) for which there is quantum speedup. The XOR algorithm is of fundamental importance in quantum computation; our OR algorithm (found with the aid of genetic programming), may represent a new quantum computational effect, also useful as a “subroutine”. %K genetic algorithms, genetic programming %U http://arxiv.org/abs/quant-ph/9907056 %0 Report %T Quantum circuits for OR and AND of OR’s %A Barnum, Howard %A Bernstein, Herbert J. %A Spector, Lee %D 2000 %8 aug %I University of Bristol %C UK %F 2000-barnum-2 %X We give the first quantum circuit, derived with the aid of genetic programming, for computing $f(0)$ OR $f(1)$ more reliably than is classically possible with a single evaluation of the function. OR therefore joins XOR (i.e. parity, $f(0) øplus f(1)$) to give the full set of logical connectives (up to relabeling of inputs and outputs) for which there is quantum speedup. %K genetic algorithms, genetic programming %U http://www.cs.bris.ac.uk/Publications/Papers/1000497.pdf %0 Journal Article %T Quantum circuits for OR and AND of ORs %A Barnum, Howard %A Bernstein, Herbert J. %A Spector, Lee %J Journal of Physics A: Mathematical and General %D 2000 %8 17 nov %V 33 %N 45 %F barnum:2000:qc %X We give the first quantum circuit for computing f(0) or f(1) more reliably than is classically possible with a single evaluation function. Or therefor joins XOR (ie parity) to give the full set of logical connectives (up to relabelling of inputs and outputs) for which there is a quantum speedup %K genetic algorithms, genetic programming %9 journal article %U http://hampshire.edu/lspector/pubs/jpa.pdf %P 8047-8057 %0 Conference Proceedings %T Systemions to model alternative issues in problem solving %A Baron, Christophe %A Gouarderes, Guy %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 baron:1999:S %P 31-37 %0 Journal Article %T A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals %A Barone, Francesco Pio %A Dell’Aquila, Daniele %A Russo, Marco %J Machine Learning: Science and Technology %D 2023 %8 dec %V 4 %N 4 %I IOP Publishing %@ 2632-2153 %F Barone:2023:MLST %X Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave (GW) signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extremely complex by the incredibly small amplitude of the target signals. In this scenario, the development of effective GW detection algorithms is crucial. We propose a novel layered framework for real-time detection of GWs inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if, in the present implementation, the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. Compared to more complex approaches, such as convolutional neural networks, which comprise a parameter set of several tens of MB and were tested exclusively for fixed length data samples, our framework has lower accuracy (e.g. it identifies of low signal-to-noise-ration GW signals, against of the state-of-the-art, at a false alarm probability of 10−2), but has a much lower computational complexity (it exploits only 4 numerical features in the present implementation) and a higher degree of modularity. Furthermore, the exploitation of short-term features makes the results of the new framework virtually independent against time-position of GW signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with new generation interferometers. %K genetic algorithms, genetic programming, BP, gravitational-wave science, analysis of noisy timeseries, fuzzy-classification of signals, speech-processing, artificial neural networks, ANN %9 journal article %R 10.1088/2632-2153/ad1200 %U https://iopscience.iop.org/article/10.1088/2632-2153/ad1200 %U http://dx.doi.org/10.1088/2632-2153/ad1200 %P 045054 %0 Conference Proceedings %T Enhancing Tournament Selection to Prevent Code Bloat in Genetic Programming %A Baronti, Flavio %A Starita, Antonina %Y Cantú-Paz, Erick %S Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002) %D 2002 %8 jul %I AAAI %C New York, NY %F baronti:2002:gecco:lbp %K genetic algorithms, genetic programming %P 17-22 %0 Conference Proceedings %T The Plastic Surgery Hypothesis %A Barr, Earl T. %A Brun, Yuriy %A Devanbu, Premkumar %A Harman, Mark %A Sarro, Federica %Y Orso, Alessandro %Y Storey, Margaret-Anne %Y Cheung, Shing-Chi %S 22nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2014) %D 2014 %8 16 12 nov %I ACM %C Hong Kong %F Barr:2014:FSE %X Recent work on genetic-programming-based approaches to automatic program patching have relied on the insight that the content of new code can often be assembled out of fragments of code that already exist in the code base. This insight has been dubbed the plastic surgery hypothesis; successful, well-known automatic repair tools such as GenProg rest on this hypothesis, but it has never been validated. We formalise and validate the plastic surgery hypothesis and empirically measure the extent to which raw material for changes actually already exists in projects. In this paper, we mount a large-scale study of several large Java projects, and examine a history of 15,723 commits to determine the extent to which these commits are graftable, i.e., can be reconstituted from existing code, and find an encouraging degree of graftability, surprisingly independent of commit size and type of commit. For example, we find that changes are 43percent graftable from the exact version of the software being changed. With a view to investigating the difficulty of finding these grafts, we study the abundance of such grafts in three possible sources: the immediately previous version, prior history, and other projects. We also examine the contiguity or chunking of these grafts, and the degree to which grafts can be found in the same file. Our results are quite promising and suggest an optimistic future for automatic program patching methods that search for raw material in already extant code in the project being patched. %K genetic improvement, SBSE, APR, Distribution, Maintenance, and Enhancement, Reusable Software, Experimentation, Languages, Measurement, Software graftability, code reuse, empirical software engineering, mining software repositories, automated program repair %U http://earlbarr.com/publications/psh.pdf %0 Conference Proceedings %T Automated Software Transplantation %A Barr, Earl T. %A Harman, Mark %A Jia, Yue %A Marginean, Alexandru %A Petke, Justyna %Y Xie, Tao %Y Young, Michal %S International Symposium on Software Testing and Analysis, ISSTA 2015 %D 2015 %8 14 17 jul %I ACM %C Baltimore, Maryland, USA %F Barr:2015:ISSTA %O ACM SIGSOFT Distinguished Paper Award %X Automated transplantation would open many exciting avenues for software development: suppose we could autotransplant code from one system into another, entirely unrelated, system. This paper introduces a theory, an algorithm, and a tool that achieve this. Leveraging lightweight annotation, program analysis identifies an organ (interesting behaviour to transplant); testing validates that the organ exhibits the desired behavior during its extraction and after its implantation into a host. While we do not claim automated transplantation is now a solved problem, our results are encouraging: we report that in 12 of 15 experiments, involving 5 donors and 3 hosts (all popular real-world systems), we successfully autotransplanted new functionality and passed all regression tests. Autotransplantation is also already useful: in 26 hours computation time we successfully autotransplanted the H.264 video encoding functionality from the x264 system to the VLC media player; compare this to upgrading x264 within VLC, a task that we estimate, from VLC’s version history, took human programmers an average of 20 days of elapsed, as opposed to dedicated, time %K genetic algorithms, genetic programming, genetic improvement, Automated software transplantation, autotransplantation, TXL %R 10.1145/2771783.2771796 %U http://crest.cs.ucl.ac.uk/autotransplantation/ %U http://dx.doi.org/10.1145/2771783.2771796 %P 257-269 %0 Conference Proceedings %T Confidence intervals of success rates in evolutionary computation %A Barrero, David F. %A Camacho, David %A R-Moreno, Maria D. %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 Barrero:2010:gecco %X Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examined in detail. We address some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical distribution with a large corpus of tools available that can be used in the context of EC research.One of those tools, confidence intervals (CIs), is studied. %K genetic algorithms, genetic programming: Poster %R 10.1145/1830483.1830657 %U http://dx.doi.org/10.1145/1830483.1830657 %P 975-976 %0 Conference Proceedings %T Statistical Distribution of Generation-to-Success in GP: Application to Model Accumulated Success Probability %A Barrero, David F. %A Castaño, Bonifacio %A R-Moreno, Maria D. %A Camacho, David %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 barrero:2011:EuroGP %X Many different metrics have been defined in Genetic Programming. Depending on the experiment requirements and objectives, a collection of measures are selected in order to achieve an understanding of the algorithm behaviour. One of the most common metrics is the accumulated success probability, which evaluates the probability of an algorithm to achieve a solution in a certain generation. We propose a model of accumulated success probability composed by two parts, a binomial distribution that models the total number of success, and a lognormal approximation to the generation-to-success, that models the variation of the success probability with the generation. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-20407-4_14 %U http://dx.doi.org/10.1007/978-3-642-20407-4_14 %P 154-165 %0 Conference Proceedings %T An Empirical Study on the Accuracy of Computational Effort in Genetic Programming %A Barrero, David F. %A R-Moreno, Maria %A Castano, Bonifacio %A Camacho, David %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 Barrero:2011:AESotAoCEiGP %X Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza’s performance measures have been widely used in the literature, their behaviour is not well known. In this paper we try to study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error. %K genetic algorithms, genetic programming, computational effort, estimation, variability sources, computational complexity, estimation theory %R 10.1109/CEC.2011.5949748 %U http://dx.doi.org/10.1109/CEC.2011.5949748 %P 1169-1176 %0 Conference Proceedings %T Effects of the Lack of Selective Pressure on the Expected Run-Time Distribution in Genetic Programming %A Barrero, David F. %A R-Moreno, Maria D. %A Castano, Bonifacio %A Camacho, David %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 Barrero:2013:CEC %K genetic algorithms, genetic programming %R 10.1109/CEC.2013.6557772 %U http://dx.doi.org/10.1109/CEC.2013.6557772 %P 1748-1755 %0 Journal Article %T A study on Koza’s performance measures %A Barrero, David F. %A Castano, Bonifacio %A R-Moreno, Maria D. %A Camacho, David %J Genetic Programming and Evolvable Machines %D 2015 %8 sep %V 16 %N 3 %@ 1389-2576 %F Barrero:2015:GPEM %X John R. Koza defined several metrics to measure the performance of an Evolutionary Algorithm that have been widely used by the Genetic Programming community. Despite the importance of these metrics, and the doubts that they have generated in many authors, their reliability has attracted little research attention, and is still not well understood. The lack of knowledge about these metrics has likely contributed to the decline in their usage in the last years. This paper is an attempt to increase the knowledge about these measures, exploring in which circumstances they are more reliable, providing some clues to improve how they are used, and eventually making their use more justifiable. Specifically, we investigate the amount of uncertainty associated with the measures, taking an analytical and empirical approach and reaching theoretical boundaries to the error. Additionally, a new method to calculate Koza’s performance measures is presented. It is shown that these metrics, under common experimental configurations, have an unacceptable error, which can be arbitrary large in certain conditions. %K genetic algorithms, genetic programming, Computational effort, Performance measures, Experimental methods, Measurement error %9 journal article %R 10.1007/s10710-014-9238-9 %U http://dx.doi.org/10.1007/s10710-014-9238-9 %P 327-349 %0 Conference Proceedings %T Evolved nonlinear predictor functions for lossless image compression %A Barresi, Kevin M. %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 Barresi:2014:GECCOcomp %X Due to the increased quantity of digital data, especially in the form of digital images, the need for effective image compression techniques is greater than ever. The JPEG lossless mode relies on predictive coding, in which accurate predictive models are critical. This study presents an efficient method of generating predictor models for input images via genetic programming. It is shown to always produce error images with entropy equal to or lower than those produced by the JPEG lossless mode. This method is demonstrated to have practical use as a real-time asymmetric image compression algorithm due to its ability to quickly and reliably derive prediction models. %K genetic algorithms, genetic programming: Poster %R 10.1145/2598394.2598503 %U http://doi.acm.org/10.1145/2598394.2598503 %U http://dx.doi.org/10.1145/2598394.2598503 %P 129-130 %0 Journal Article %T Mining parasite data using genetic programming %A Barrett, John %A Kostadinova, Aneta %A Raga, Juan Antonio %J Trends in Parasitology %D 2005 %8 may %V 21 %N 5 %F Barrett:2005:TP %X Genetic programming is a technique that can be used to tackle the hugely demanding data-processing problems encountered in the natural sciences. Application of genetic programming to a problem using parasites as biological tags demonstrates its potential for developing explanatory models using data that are both complex and noisy. %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.pt.2005.03.007 %U http://dx.doi.org/10.1016/j.pt.2005.03.007 %P 207-209 %0 Conference Proceedings %T Recurring Analytical Problems within Drug Discovery and Development %A Barrett, S. J. %Y Scheffer, Tobias %Y Leser, Ulf %S Data Mining and Text Mining for Bioinformatics: Proceedings of the European Workshop %D 2003 %8 22 sep %C Dubrovnik, Croatia %F barrett:2003:dmtmb %O Invited talk %X The overall processes driving pharmaceuticals discovery and development research involve many disparate kinds of problems and problem-solving at multiple levels of generality and specificity. The discovery/pre-clinical processes are also highly technology-driven and specific aspects may be more dynamic over time compared to developmental research which is conducted in a more conservatively controlled manner, conducive to regulatory requirements. %K genetic algorithms, genetic programming, SVM, SNP %U http://www2.informatik.hu-berlin.de/~scheffer/publications/ProceedingsWS2003.pdf %P 6-7 %0 Conference Proceedings %T Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development %A Barrett, S. J. %A Langdon, W. B. %Y Tiwari, Ashutosh %Y Knowles, Joshua %Y Avineri, Erel %Y Dahal, Keshav %Y Roy, Rajkumar %S Applications of Soft Computing: Recent Trends %S Advances in Soft Computing %D 2006 %8 19 sep 7 oct 2005 %V 36 %I Springer %C On the World Wide Web %@ 3-540-29123-7 %F barrett:2005:WSC %X Machine learning tools, in particular support vector machines (SVM), Particle Swarm Optimisation (PSO) and Genetic Programming (GP), are increasingly used in pharmaceuticals research and development. They are inherently suitable for use with noisy, high dimensional (many variables) data, as is commonly used in cheminformatic (i.e. In silico screening), bioinformatic (i.e. bio-marker studies, using DNA chip data) and other types of drug research studies. These aspects are demonstrated via review of their current usage and future prospects in context with drug discovery activities. %K genetic algorithms, genetic programming, Pharmaceutical applications, Drug design, Particle swarm optimisation, Support vector machines %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/barrett_2005_WSC.pdf %P 99-110 %0 Journal Article %T Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems John Wiley & Sons Ltd., Chichester, UK, Keedwell, Edward and Narayanan, Ajit, 2005, 280 p., Hardcover, ISBN 0-470-02175-6 %A Barrett, Steven J. %J Genetic Programming and Evolvable Machines %D 2006 %8 oct %V 7 %N 3 %@ 1389-2576 %F Barrett:2006:GPEM %O Book review %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-006-7003-4 %U https://rdcu.be/dR8e5 %U http://dx.doi.org/10.1007/s10710-006-7003-4 %P 283-284 %0 Conference Proceedings %T Modeling human expertise on a cheese ripening industrial process using GP %A Barriere, Olivier %A Lutton, Evelyne %A Baudrit, Cedric %A Sicard, Mariette %A Pinaud, Bruno %A Perrot, Nathalie %Y Rudolph, Gunter %Y Jansen, Thomas %Y Lucas, Simon %Y Poloni, Carlo %Y Beume, Nicola %S Parallel Problem Solving from Nature - PPSN X %S LNCS %D 2008 %8 13 17 sep %V 5199 %I Springer %C Dortmund %@ 3-540-87699-5 %F Barriere:2008:PPSN %X Industrial agrifood processes often strongly rely on human expertise, expressed as know-how and control procedures based on subjective measurements (colour, smell, texture), which are very difficult to capture and model. We deal in this paper with a cheese ripening process (of French Camembert), for which experimental data have been collected within a cheese ripening laboratory chain. A global and a monopopulation cooperative/coevolutive GP scheme (Parisian approach) have been developed in order to simulate phase prediction (i.e. a subjective estimation of human experts) from microbial proportions and Ph measurements. These two GP approaches are compared to Bayesian network modelling and simple multilinear learning algorithms. Preliminary results show the effectiveness and robustness of the Parisian GP approach. %K genetic algorithms, genetic programming, pH acidity %R 10.1007/978-3-540-87700-4_85 %U http://dx.doi.org/10.1007/978-3-540-87700-4_85 %P 859-868 %0 Report %T Modeling an agrifood industrial process using cooperative coevolution Algorithms %A Barriere, Olivier %A Lutton, Evelyne %A Wuillemin, Pierre-Henri %A Baudrit, Cedric %A Sicard, Mariette %A Pinaud, Bruno %A Perrot, Nathalie %D 2009 %8 June %N inria-00381681, version 1 %I INRIA %C Parc Orsay, France %G EN %F inria-00381681 %X This report presents two experiments related to the modeling of an industrial agrifood process using evolutionary techniques. Experiments have been focused on a specific problem which is the modeling of a Camembert-cheese ripening process. Two elated complex optimisation problems have been considered: – a deterministic modeling problem, the phase prediction problem, for which a search for a closed form tree expression has been performed using genetic programming (GP), – a Bayesian network structure estimation problem, considered as a two-stage problem, i.e. searching first for an approximation of an independence model using EA, and then deducing, via a deterministic algorithm, a Bayesian network which represents the equivalence class of the independence model found at the first stage. In both of these problems, cooperative-coevolution techniques (also called “Parisian” approaches) have been proved successful. These approaches actually allow to represent the searched solution as an aggregation of several individuals (or even as a whole population), as each individual only bears a part of the searched solution. This scheme allows to use the artificial Darwinism principles in a more economic way, and the gain in terms of robustness and efficiency is important. %K genetic algorithms, genetic programming, Parisian, Computer Science, Artificial Intelligence, Life Sciences/Food and Nutrition, Agrifood, Cheese ripening, Cooperative coevolution, Parisian approach, Bayesian Network %U http://hal.inria.fr/inria-00381681/en/ %0 Conference Proceedings %T Pricing Rainfall Derivatives by Genetic Programming: A Case Study %A Barro, Diana %A Parpinel, Francesca %A Pizzi, Claudio %Y Corazza, Marco %Y Perna, Cira %Y Pizzi, Claudio %Y Sibillo, Marilena %S Mathematical and Statistical Methods for Actuarial Sciences and Finance %D 2022 %I Springer %F barro:2022:MSMASF %X we consider a genetic programming approach to price rainfall derivatives and we test it on a case study based on data collected from a meteorological station in a city in the northeast region of Friuli Venezia Giulia (Italy), characterized by a fairly abundant rainfall. %K genetic algorithms, genetic programming %R 10.1007/978-3-030-99638-3_11 %U http://link.springer.com/chapter/10.1007/978-3-030-99638-3_11 %U http://dx.doi.org/10.1007/978-3-030-99638-3_11 %P 64-69 %0 Conference Proceedings %T Towards the automatic design of decision tree induction algorithms %A Barros, Rodrigo C. %A Basgalupp, Marcio P. %A de Carvalho, Andre C. P. L. F. %A Freitas, Alex A. %Y Pappa, Gisele L. %Y Freitas, Alex A. %Y Swan, Jerry %Y Woodward, John %S GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms %D 2011 %8 December 16 jul %I ACM %C Dublin, Ireland %F Barros:2011:GECCOcomp %X Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes two different approaches for automatically generating generic decision tree induction algorithms. Both approaches are based on the evolutionary algorithms paradigm, which improves solutions based on metaphors of biological processes. We also propose guidelines to design interesting fitness functions for these evolutionary algorithms, which take into account the requirements and needs of the end-user. %K genetic algorithms, genetic programming %R 10.1145/2001858.2002050 %U http://dx.doi.org/10.1145/2001858.2002050 %P 567-574 %0 Conference Proceedings %T A grammatical evolution approach for software effort estimation %A Barros, Rodrigo C. %A Basgalupp, Marcio P. %A Cerri, Ricardo %A da Silva, Tiago S. %A de Carvalho, Andre C. P. L. F. %Y Blum, Christian %Y Alba, Enrique %Y Auger, Anne %Y Bacardit, Jaume %Y Bongard, Josh %Y Branke, Juergen %Y Bredeche, Nicolas %Y Brockhoff, Dimo %Y Chicano, Francisco %Y Dorin, Alan %Y Doursat, Rene %Y Ekart, Aniko %Y Friedrich, Tobias %Y Giacobini, Mario %Y Harman, Mark %Y Iba, Hitoshi %Y Igel, Christian %Y Jansen, Thomas %Y Kovacs, Tim %Y Kowaliw, Taras %Y Lopez-Ibanez, Manuel %Y Lozano, Jose A. %Y Luque, Gabriel %Y McCall, John %Y Moraglio, Alberto %Y Motsinger-Reif, Alison %Y Neumann, Frank %Y Ochoa, Gabriela %Y Olague, Gustavo %Y Ong, Yew-Soon %Y Palmer, Michael E. %Y Pappa, Gisele Lobo %Y Parsopoulos, Konstantinos E. %Y Schmickl, Thomas %Y Smith, Stephen L. %Y Solnon, Christine %Y Stuetzle, Thomas %Y Talbi, El-Ghazali %Y Tauritz, Daniel %Y Vanneschi, Leonardo %S GECCO ’13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference %D 2013 %8 June 10 jul %I ACM %C Amsterdam, The Netherlands %F Barros:2013:GECCO %X Software effort estimation is an important task within software engineering. It is widely used for planning and monitoring software project development as a means to deliver the product on time and within budget. Several approaches for generating predictive models from collected metrics have been proposed throughout the years. Machine learning algorithms, in particular, have been widely-employed to this task, bearing in mind their capability of providing accurate predictive models for the analysis of project stakeholders. In this paper, we propose a grammatical evolution approach for software metrics estimation. Our novel algorithm, namely SEEGE, is empirically evaluated on public project data sets, and we compare its performance with state-of-the-art machine learning algorithms such as support vector machines for regression and artificial neural networks, and also to popular linear regression. Results show that SEEGE outperforms the other algorithms considering three different evaluation measures, clearly indicating its effectiveness for the effort estimation task. %K genetic algorithms, genetic programming %R 10.1145/2463372.2463546 %U http://dx.doi.org/10.1145/2463372.2463546 %P 1413-1420 %0 Journal Article %T Evolutionary model trees for handling continuous classes in machine learning %A Barros, Rodrigo C. %A Ruiz, Duncan D. %A Basgalupp, Marcio P. %J Information Sciences %D 2011 %V 181 %N 5 %@ 0020-0255 %F Barros2011954 %X Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. %K genetic algorithms, genetic programming, Evolutionary algorithms, Model trees, Continuous classes, Machine learning %9 journal article %R 10.1016/j.ins.2010.11.010 %U http://www.sciencedirect.com/science/article/B6V0C-51GHWYC-1/2/2ba74d92cb03abc637a4c377b47a4dbe %U http://dx.doi.org/10.1016/j.ins.2010.11.010 %P 954-971 %0 Journal Article %T Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets %A Barros, Rodrigo C. %A Basgalupp, Marcio P. %A Freitas, Alex A. %A de Carvalho, Andre C. P. L. F. %J IEEE Transactions on Evolutionary Computation %D 2014 %8 dec %V 18 %N 6 %@ 1089-778X %F Barros:2014:ieeeTEC %X Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TEVC.2013.2291813 %U http://dx.doi.org/10.1109/TEVC.2013.2291813 %P 873-892 %0 Journal Article %T Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification %A Barros, Rodrigo C. %A Basgalupp, Marcio P. %A de Carvalho, Andre C. P. L. F. %J Genetic Programming and Evolvable Machines %D 2015 %8 sep %V 16 %N 3 %@ 1389-2576 %F Barros:2015:GPEM %X In this paper, we analyse in detail the impact of different strategies to be used as fitness function during the evolutionary cycle of a hyper-heuristic evolutionary algorithm that automatically designs decision-tree induction algorithms (HEAD-DT). We divide the experimental scheme into two distinct scenarios: (1) evolving a decision-tree induction algorithm from multiple balanced data sets; and (2) evolving a decision-tree induction algorithm from multiple imbalanced data sets. In each of these scenarios, we analyse the difference in performance of well-known classification performance measures such as accuracy, F-Measure, AUC, recall, and also a lesser-known criterion, namely the relative accuracy improvement. In addition, we analyse different schemes of aggregation, such as simple average, median, and harmonic mean. Finally, we verify whether the best-performing fitness functions are capable of providing HEAD-DT with algorithms more effective than traditional decision-tree induction algorithms like C4.5, CART, and REPTree. Experimental results indicate that HEAD-DT is a good option for generating algorithms tailored to (im)balanced data, since it outperforms state-of-the-art decision-tree induction algorithms with statistical significance. %K genetic algorithms, genetic programming, Hyper-heuristics, Decision trees, Fitness function, Imbalanced data %9 journal article %R 10.1007/s10710-014-9235-z %U http://dx.doi.org/10.1007/s10710-014-9235-z %P 241-281 %0 Conference Proceedings %T Aliasing in XCS and the Consecutive State Problem: 1 - Effects %A Barry, Alwyn %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 barry:1999:AXCSPE %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-317.pdf %P 19-26 %0 Conference Proceedings %T Aliasing in XCS and the Consecutive State Problem: 2 - Solutions %A Barry, Alwyn %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 barry:1999:AXCSPS %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-336.pdf %P 27-34 %0 Conference Proceedings %T GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference %E Barry, Alwyn M. %D 2002 %8 August %I AAAI %C New York %F barry:2002:gecco:workshop %K genetic algorithms, genetic programming, optimization, fuzzy model, design optimization, case-based reasoning, evolutionary algorithm, evolution strategies, simulated annealing, agents, evolutionary computation, co-evolution, parallel implementation, learning classifier system, time series prediction, grammatical evolution, multi-objective optimization, planning, scheduling, industrial applications, machine learning, niching, linkage learning %0 Conference Proceedings %T GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference %E Barry, Alwyn M. %D 2003 %8 November %I AAAI %C Chigaco %F barry:2003:gecco:workshop %K genetic algorithms, genetic programming, optimization, fuzzy model, design optimization, case-based reasoning, evolutionary algorithm, evolution strategies, simulated annealing, agents, evolutionary computation, co-evolution, parallel implementation, learning classifier system, time series prediction, grammatical evolution, multi-objective optimization, planning, scheduling, machine learning, representations %U http://gpbib.cs.ucl.ac.uk/gecco2003wks.bib %0 Conference Proceedings %T Evolving PSO algorithm design in vector fields using geometric semantic GP %A Bartashevich, Palina %A Bakurov, Illya %A Mostaghim, Sanaz %A Vanneschi, Leonardo %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 Bartashevich:2018:GECCOcomp %X This paper investigates the possibility of evolving new particle swarm equations representing a collective search mechanism, acting in environments with unknown external dynamics, using Geometric Semantic Genetic Programming (GSGP). The proposed method uses a novel initialization technique - the Evolutionary Demes Despeciation Algorithm (EDDA)- which allows to generate solutions of smaller size than using the traditional ramped half-and-half algorithm. We show that EDDA, using a mixture of both GP and GSGP mutation operators, allows us to evolve new search mechanisms with good generalization ability. %K genetic algorithms, genetic programming %R 10.1145/3205651.3205760 %U http://dx.doi.org/10.1145/3205651.3205760 %P 262-263 %0 Conference Proceedings %T PSO-based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming %A Bartashevich, Palina %A Bakurov, Illya %A Mostaghim, Sanaz %A Vanneschi, Leonardo %Y Auger, Anne %Y Fonseca, Carlos M. %Y Lourenco, Nuno %Y Machado, Penousal %Y Paquete, Luis %Y Whitley, Darrell %S 15th International Conference on Parallel Problem Solving from Nature %S LNCS %D 2018 %8 August 12 sep %V 11101 %I Springer %C Coimbra, Portugal %F Bartashevich:2018:PPSN %X In this paper, we study Particle Swarm Optimization (PSO) as a collective search mechanism for individuals (such as aerial micro-robots) which are supposed to search in environments with unknown external dynamics. In order to deal with the unknown disturbance, we present new PSO equations which are evolved using Genetic Programming (GP) with a semantically diverse starting population, seeded by the Evolutionary Demes Despeciation Algorithm (EDDA), that generalizes better than standard GP in the presence of unknown dynamics. The analysis of the evolved equations shows that with only small modifications in the velocity equation, PSO can achieve collective search behaviour while being unaware of the dynamic external environment, mimicking the zigzag upwind flights of birds towards the food source. %K genetic algorithms, genetic programming, Particle swarm optimization, Vector fields, Semantics, EDDA %R 10.1007/978-3-319-99253-2_4 %U https://www.springer.com/gp/book/9783319992587 %U http://dx.doi.org/10.1007/978-3-319-99253-2_4 %P 41-53 %0 Conference Proceedings %T Gene Expression Programming in Correction Modelling of Nonlinear Dynamic Objects %A Bartczuk, Lukasz %Y Borzemski, Leszek %Y Grzech, Adam %Y Swiatek, Jerzy %Y Wilimowska, Zofia %S Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology - ISAT 2015 - Part I %S Advances in Intelligent Systems and Computing %D 2015 %V 429 %I Springer %F conf/isat/Bartczuk15 %X In this paper we shown the applying of gene expression programming algorithm to correction modelling of non-linear dynamic objects. The correction modelling is the non-linear modelling method based on equivalent linearisation technique that allows to incorporate in modelling process the known linear model of the same or similar object or phenomenon. The usefulness of the proposed method will be shown on a practical example of the continuous stirred tank reactor modelling. %K genetic algorithms, genetic programming, gene expression programming, nonlinear modelling, dynamic objects %R 10.1007/978-3-319-28555-9_11 %U http://dx.doi.org/10.1007/978-3-319-28555-9_11 %P 125-134 %0 Conference Proceedings %T New Method for Non-linear Correction Modelling of Dynamic Objects with Genetic Programming %A Bartczuk, Lukasz %A Przybyl, Andrzej %A Koprinkova-Hristova, Petia D. %Y Rutkowski, Leszek %Y Korytkowski, Marcin %Y Scherer, Rafal %Y Tadeusiewicz, Ryszard %Y Zadeh, Lotfi A. %Y Zurada, Jacek M. %S Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Zakopane, Poland, June 14-28, 2015, Proceedings, Part II %S Lecture Notes in Computer Science %D 2015 %V 9120 %I Springer %F conf/icaisc/BartczukPK15 %K genetic algorithms, genetic programming %R 10.1007/978-3-319-19369-4_29 %U http://dx.doi.org/10.1007/978-3-319-19369-4 %U http://dx.doi.org/10.1007/978-3-319-19369-4_29 %P 318-329 %0 Conference Proceedings %T A New Method for Generating Nonlinear Correction Models of Dynamic Objects Based on Semantic Genetic Programming %A Bartczuk, Lukasz %A Galushkin, Alexander I. %Y Rutkowski, Leszek %Y Korytkowski, Marcin %Y Scherer, Rafal %Y Tadeusiewicz, Ryszard %Y Zadeh, Lotfi A. %Y Zurada, Jacek M. %S Artificial Intelligence and Soft Computing - 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part II %S Lecture Notes in Computer Science %D 2016 %V 9693 %I Springer %F conf/icaisc/BartczukG16 %K genetic algorithms, genetic programming %U http://dx.doi.org/10.1007/978-3-319-39384-1 %P 249-261 %0 Conference Proceedings %T A New Method for Generating of Fuzzy Rules for the Nonlinear Modelling Based on Semantic Genetic Programming %A Bartczuk, Lukasz %A Lapa, Krystian %A Koprinkova-Hristova, Petia D. %Y Rutkowski, Leszek %Y Korytkowski, Marcin %Y Scherer, Rafal %Y Tadeusiewicz, Ryszard %Y Zadeh, Lotfi A. %Y Zurada, Jacek M. %S Artificial Intelligence and Soft Computing - 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part II %S Lecture Notes in Computer Science %D 2016 %V 9693 %I Springer %F conf/icaisc/BartczukLK16 %K genetic algorithms, genetic programming %U http://dx.doi.org/10.1007/978-3-319-39384-1 %P 262-278 %0 Conference Proceedings %T The Concept on Nonlinear Modelling of Dynamic Objects Based on State Transition Algorithm and Genetic Programming %A Bartczuk, Lukasz %A Dziwinski, Piotr %A Redko, Vladimir G. %Y Rutkowski, Leszek %Y Korytkowski, Marcin %Y Scherer, Rafal %Y Tadeusiewicz, Ryszard %Y Zadeh, Lotfi A. %Y Zurada, Jacek M. %S Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Zakopane, Poland, June 11-15, 2017, Proceedings, Part II %S Lecture Notes in Computer Science %D 2017 %V 10246 %I Springer %F conf/icaisc/BartczukDR17 %K genetic algorithms, genetic programming %R 10.1007/978-3-319-59060-8_20 %U http://dx.doi.org/10.1007/978-3-319-59060-8_20 %P 209-220 %0 Journal Article %T Exhaustive Symbolic Regression %A Bartlett, Deaglan J. %A Desmond, Harry %A Ferreira, Pedro G. %J IEEE Transactions on Evolutionary Computation %D 2024 %8 aug %V 28 %N 4 %@ 1941-0026 %F Bartlett:TEVC %X Symbolic Regression (SR) algorithms attempt to learn analytic expressions which fit data accurately and in a highly interpretable manner. Conventional SR suffers from two fundamental issues which we address here. First, these methods search the space stochastically (typically using genetic programming) and hence do not necessarily find the best function. Second, the criteria used to select the equation optimally balancing accuracy with simplicity have been variable and subjective. To address these issues we introduce Exhaustive Symbolic Regression (ESR), which systematically and efficiently considers all possible equations-made with a given basis set of operators and up to a specified maximum complexity- and is therefore guaranteed to find the true optimum (if parameters are perfectly optimised) and a complete function ranking subject to these constraints. We implement the minimum description length principle as a rigorous method for combining these preferences into a single objective. To illustrate the power of ESR we apply it to a catalogue of cosmic chronometers and the Pantheon+ sample of supernovae to learn the Hubble rate as a function of redshift, finding 40 functions (out of 5.2 million trial functions) that fit the data more economically than the Friedmann equation. These low-redshift data therefore do not uniquely prefer the expansion history of the standard model of cosmology. We make our code and full equation sets publicly available. %K genetic algorithms, genetic programming, Mathematical models, Complexity theory, Optimisation, Numerical models, Biological system modelling, Standards, Search problems, Symbolic regression, data analysis, minimum description length, MDL, model selection, cosmology %9 journal article %R 10.1109/TEVC.2023.3280250 %U http://dx.doi.org/10.1109/TEVC.2023.3280250 %P 950-964 %0 Conference Proceedings %T Genetic programming for developing simple cognitive models %A Bartlett, Laura K. %A Pirrone, Angelo %A Javed, Noman %A Lane, Peter C. R. %A Gobet, Fernand %Y Goldwater, M. %Y Anggoro, F. K. %Y Hayes, B. K. %Y Ong, D. C. %S Proceedings of the 45th Annual Meeting of the Cognitive Science Society %D 2023 %8 jul 26 29 %C Sydney, Australia %F bartlett:2023:cogsci %X Frequently in psychology, simple tasks that are designed to tap a particular feature of cognition are used without considering the other mechanisms that might be at play. For example, the delayed-match-to-sample (DMTS) task is often used to examine short-term memory; however, a number of cognitive mechanisms interact to produce the observed behaviour, such as decision-making and attention processes. As these simple tasks form the basis of more complex psychological experiments and theories, it is critical to understand what strategies might be producing the recorded behaviour. The current paper uses the GEMS methodology, a system that generates models of cognition using genetic programming, and applies it to differing DMTS experimental conditions. We investigate the strategies that participants might be using, while looking at similarities and differences in strategy depending on task variations; in this case, changes to the interval between study and recall affected the strategies used by the generated models. %K genetic algorithms, genetic programming, delayed-match-to-sample,memory, psychology %U http://hdl.handle.net/2299/27181 %P 2833-2839 %0 Conference Proceedings %T GP-based Electricity Price Forecasting %A Bartoli, Alberto %A Davanzo, Giorgio %A De Lorenzo, Andrea %A Medvet, Eric %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 Bartoli:2011:EuroGP %X The electric power market is increasingly relying on competitive mechanisms taking the form of day-ahead auctions, in which buyers and sellers submit their bids in terms of prices and quantities for each hour of the next day. Methods for electricity price forecasting suitable for these contexts are crucial to the success of any bidding strategy. Such methods have thus become very important in practice, due to the economic relevance of electric power auctions. In this work we propose a novel forecasting method based on Genetic Programming. Key feature of our proposal is the handling of outliers, i.e., regions of the input space rarely seen during the learning. Since a predictor generated with Genetic Programming can hardly provide acceptable performance in these regions, we use a classifier that attempts to determine whether the system is shifting toward a difficult-to-learn region. In those cases, we replace the prediction made by Genetic Programming by a constant value determined during learning and tailored to the specific subregion expected. We evaluate the performance of our proposal against a challenging baseline representative of the state-of-the-art. The baseline analyses a real-world dataset by means of a number of different methods, each calibrated separately for each hour of the day and recalibrated every day on a progressively growing learning set. Our proposal exhibits smaller prediction error, even though we construct one single model, valid for each hour of the day and used unmodified across the entire testing set. We believe that our results are highly promising and may open a broad range of novel solutions. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-20407-4_4 %U http://dx.doi.org/10.1007/978-3-642-20407-4_4 %P 37-48 %0 Conference Proceedings %T Automatic generation of regular expressions from examples with genetic programming %A Bartoli, Alberto %A Davanzo, Giorgio %A De Lorenzo, Andrea %A Mauri, Marco %A Medvet, Eric %A Sorio, Enrico %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 Companion ’12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion %D 2012 %8 July 11 jul %I ACM %C Philadelphia, Pennsylvania, USA %F Bartoli:2012:GECCOcomp %X We explore the practical feasibility of a system based on genetic programming (GP) for the automatic generation of regular expressions. The user describes the desired task by providing a set of labeled examples, in the form of text lines. The system uses these examples for driving the evolutionary search towards a regular expression suitable for the specified task. Usage of the system should require neither familiarity with GP nor with regular expressions syntax. In our GP implementation each individual represents a syntactically correct regular expression. We performed an experimental evaluation on two different extraction tasks applied to real-world datasets and obtained promising results in terms of precision and recall, even in comparison to an earlier state-of-the-art proposal. %K genetic algorithms, Genetic programming: Poster %R 10.1145/2330784.2331000 %U http://dx.doi.org/10.1145/2330784.2331000 %P 1477-1478 %0 Conference Proceedings %T Playing Regex Golf with Genetic Programming %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %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: Proceeding of the sixteenth annual conference on genetic and evolutionary computation conference %D 2014 %8 December 16 jul %I ACM %C Vancouver, BC, Canada %F Bartoli:2014:GECCO %X Regex golf has recently emerged as a specific kind of code golf, i.e., unstructured and informal programming competitions aimed at writing the shortest code solving a particular problem. A problem in regex golf consists in writing the shortest regular expression which matches all the strings in a given list and does not match any of the strings in another given list. The regular expression is expected to follow the syntax of a specified programming language, e.g., Javascript or PHP. In this paper, we propose a regex golf player internally based on Genetic Programming. We generate a population of candidate regular expressions represented as trees and evolve such population based on a multi-objective fitness which minimises the errors and the length of the regular expression. We assess experimentally our player on a popular regex golf challenge consisting of 16 problems and compare our results against those of a recently proposed algorithm—the only one we are aware of. Our player obtains scores which improve over the baseline and are highly competitive also with respect to human players. The time for generating a solution is usually in the order of tens minutes, which is arguably comparable to the time required by human players. %K genetic algorithms, genetic programming %R 10.1145/2576768.2598333 %U http://machinelearning.inginf.units.it/publications/international-conference-publications/playingregexgolfwithgeneticprogramming %U http://dx.doi.org/10.1145/2576768.2598333 %P 1063-1070 %0 Conference Proceedings %T Compressing Regular Expression Sets for Deep Packet Inspection %A Bartoli, Alberto %A Cumar, Simone %A De Lorenzo, Andrea %A Medvet, Eric %Y Bartz-Beielstein, Thomas %Y Branke, Juergen %Y Filipic, Bogdan %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 Bartoli:2014:PPSN %X The ability to generate security-related alerts while analysing network traffic in real time has become a key mechanism in many networking devices. This functionality relies on the application of large sets of regular expressions describing attack signatures to each individual packet. Implementing an engine of this form capable of operating at line speed is considerably difficult and the corresponding performance problems have been attacked from several points of view. In this work we propose a novel approach complementing earlier proposals: we suggest transforming the starting set of regular expressions to another set of expressions which is much smaller yet classifies network traffic in the same categories as the starting set. Key component of the transformation is an evolutionary search based on Genetic Programming: a large population of expressions represented as abstract syntax trees evolves by means of mutation and crossover, evolution being driven by fitness indexes tailored to the desired classification needs and which minimise the length of each expression. We assessed our proposals on real datasets composed of up to 474 expressions and the outcome has been very good, resulting in compressions in the order of 74percent. Our results are highly encouraging and demonstrate the power of evolutionary techniques in an important application domain. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-10762-2_39 %U http://machinelearning.inginf.units.it/publications/international-conference-publications/compressingregularexpressionsetsfordeeppacketinspection %U http://dx.doi.org/10.1007/978-3-319-10762-2_39 %P 394-403 %0 Journal Article %T Automatic Synthesis of Regular Expressions from Examples %A Bartoli, Alberto %A Davanzo, Giorgio %A De Lorenzo, Andrea %A Medvet, Eric %A Sorio, Enrico %J IEEE Computer %D 2014 %8 dec %V 47 %N 12 %@ 0018-9162 %F Bartoli:2014:Computer %X We propose a system for the automatic generation of regular expressions for text-extraction tasks. The user describes the desired task only by means of a set of labelled examples. The generated regexes may be used with common engines such as those that are part of Java, PHP, Perl and so on. Usage of the system does not require any familiarity with regular expressions syntax. We performed an extensive experimental evaluation on 12 different extraction tasks applied to real-world datasets. We obtained very good results in terms of precision and recall, even in comparison to earlier state-of-the-art proposals. Our results are highly promising toward the achievement of a practical surrogate for the specific skills required for generating regular expressions, and significant as a demonstration of what can be achieved with GP-based approaches on modern IT technology. %K genetic algorithms, genetic programming, text extraction, NLP %9 journal article %R 10.1109/MC.2014.344 %U http://dx.doi.org/10.1109/MC.2014.344 %P 72-80 %0 Conference Proceedings %T Learning Text Patterns using Separate-and-Conquer Genetic Programming %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %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 Bartoli:2015:EuroGP %X The problem of extracting knowledge from large volumes of unstructured textual information has become increasingly important. We consider the problem of extracting text slices that adhere to a syntactic pattern and propose an approach capable of generating the desired pattern automatically, from a few annotated examples. Our approach is based on Genetic Programming and generates extraction patterns in the form of regular expressions that may be input to existing engines without any post-processing. Key feature of our proposal is its ability of discovering automatically whether the extraction task may be solved by a single pattern, or rather a set of multiple patterns is required. We obtain this property by means of a separate-and-conquer strategy: once a candidate pattern provides adequate performance on a subset of the examples, the pattern is inserted into the set of final solutions and the evolutionary search continues on a smaller set of examples including only those not yet solved adequately. Our proposal outperforms an earlier state-of-the-art approach on three challenging datasets %K genetic algorithms, genetic programming, Regular expressions, Multiple pattern, Programming by example, Text extraction %R 10.1007/978-3-319-16501-1_2 %U http://dx.doi.org/10.1007/978-3-319-16501-1_2 %P 16-27 %0 Conference Proceedings %T Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %A Virgolin, Marco %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 Bartoli:2015:GECCO %X There is an increasing interest in the development of techniques for automatic relation extraction from unstructured text. The biomedical domain, in particular, is a sector that may greatly benefit from those techniques due to the huge and ever increasing amount of scientific publications describing observed phenomena of potential clinical interest. In this paper, we consider the problem of automatically identifying sentences that contain interactions between genes and proteins, based solely on a dictionary of genes and proteins and a small set of sample sentences in natural language. We propose an evolutionary technique for learning a classifier that is capable of detecting the desired sentences within scientific publications with high accuracy. The key feature of our proposal, that is internally based on Genetic Programming, is the construction of a model of the relevant syntax patterns in terms of standard part-of-speech annotations. The model consists of a set of regular expressions that are learned automatically despite the large alphabet size involved. We assess our approach on two realistic datasets and obtain 74percent accuracy, a value sufficiently high to be of practical interest and that is in line with significant baseline methods. %K genetic algorithms, genetic programming, Real World Applications %R 10.1145/2739480.2754706 %U http://doi.acm.org/10.1145/2739480.2754706 %U http://dx.doi.org/10.1145/2739480.2754706 %P 1183-1190 %0 Journal Article %T Predicting the effectiveness of pattern-based entity extractor inference %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %J Applied Soft Computing %D 2016 %V 46 %@ 1568-4946 %F Bartoli:2016:ASC %X An essential component of any workflow leveraging digital data consists in the identification and extraction of relevant patterns from a data stream. We consider a scenario in which an extraction inference engine generates an entity extractor automatically from examples of the desired behaviour, which take the form of user-provided annotations of the entities to be extracted from a dataset. We propose a methodology for predicting the accuracy of the extractor that may be inferred from the available examples. We propose several prediction techniques and analyse experimentally our proposals in great depth, with reference to extractors consisting of regular expressions. The results suggest that reliable predictions for tasks of practical complexity may indeed be obtained quickly and without actually generating the entity extractor. %K genetic algorithms, genetic programming, String similarity metrics, Information extraction, Hardness estimation %9 journal article %R 10.1016/j.asoc.2016.05.023 %U http://www.sciencedirect.com/science/article/pii/S1568494616302241 %U http://dx.doi.org/10.1016/j.asoc.2016.05.023 %P 398-406 %0 Journal Article %T Inference of Regular Expressions for Text Extraction from Examples %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %J IEEE Transactions on Knowledge and Data Engineering %D 2016 %8 may %V 28 %N 5 %@ 1041-4347 %F Bartoli:2016:ieeeTKDE %X A large class of entity extraction tasks from text that is either semistructured or fully unstructured may be addressed by regular expressions, because in many practical cases the relevant entities follow an underlying syntactical pattern and this pattern may be described by a regular expression. In this work, we consider the long-standing problem of synthesizing such expressions automatically, based solely on examples of the desired behaviour. We present the design and implementation of a system capable of addressing extraction tasks of realistic complexity. Our system is based on an evolutionary procedure carefully tailored to the specific needs of regular expression generation by examples. The procedure executes a search driven by a multiobjective optimization strategy aimed at simultaneously improving multiple performance indexes of candidate solutions while at the same time ensuring an adequate exploration of the huge solution space. We assess our proposal experimentally in great depth, on a number of challenging datasets. The accuracy of the obtained solutions seems to be adequate for practical usage and improves over earlier proposals significantly. Most importantly, our results are highly competitive even with respect to human operators. A prototype is available as a web application at regex.inginf.units.it %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TKDE.2016.2515587 %U http://www.human-competitive.org/sites/default/files/bartoli-delorenzo-medvet-tarlao-text.txt %U http://dx.doi.org/10.1109/TKDE.2016.2515587 %P 1217-1230 %0 Journal Article %T Can A Machine Replace Humans In Building Regular Expressions? A Case Study %A Bartoli, Alberto %A Medvet, Eric %A De Lorenzo, Andrea %A Tarlao, Fabiano %J IEEE Intelligent Systems %D 2016 %8 nov %V 31 %N 6 %@ 1541-1672 %F Bartoli:2016:ieeeIS %X Regular expressions are routinely used in a variety of different application domains. Building a regular expression involves a considerable amount of skill, expertise and creativity. In this work we investigate whether a machine may surrogate these qualities and construct automatically regular expressions for tasks of realistic complexity. We discuss a large scale experiment involving more than 1700 users on 10 challenging tasks. We compared the solutions constructed by these users to those constructed by a tool based on Genetic Programming that we have recently developed and made publicly available. The quality of automatically-constructed solutions turned out to be similar to the quality of those constructed by the most skilled user group; and, the time for automatic construction was similar to the time required by human users. %K genetic algorithms, genetic programming, Buildings, Creativity, Intelligent systems, Object recognition, Pattern matching, Web pages %9 journal article %R 10.1109/MIS.2016.46 %U http://www.human-competitive.org/sites/default/files/bartoli-delorenzo-medvet-tarlao-text.txt %U http://dx.doi.org/10.1109/MIS.2016.46 %P 15-21 %0 Conference Proceedings %T Active learning approaches for learning regular expressions with genetic programming %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %Y Ossowski, Sascha %S Proceedings of the 31st Annual ACM Symposium on Applied Computing, Pisa, Italy, April 4-8, 2016 %D 2016 %I ACM %F conf/sac/BartoliLMT16 %X We consider the long-standing problem of the automatic generation of regular expressions for text extraction, based solely on examples of the desired behaviour. We investigate several active learning approaches in which the user annotates only one desired extraction and then merely answers extraction queries generated by the system. The resulting framework is attractive because it is the system, not the user, which digs out the data in search of the samples most suitable to the specific learning task. We tailor our proposals to a state-of-the-art learner based on Genetic Programming and we assess them experimentally on a number of challenging tasks of realistic complexity. The results indicate that active learning is indeed a viable framework in this application domain and may thus significantly decrease the amount of costly annotation effort required. %K genetic algorithms, genetic programming, entity extraction, information extraction, machine learning, programming by examples %R 10.1145/2851613.2851668 %U http://dx.doi.org/10.1145/2851613.2851668 %P 97-102 %0 Journal Article %T Regex-based Entity Extraction with Active Learning and Genetic Programming %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %J ACM SIGAPP Applied Computing Review %D 2016 %8 jun %V 16 %N 2 %I ACM %C New York, NY, USA %@ 1559-6915 %F Bartoli:2016:acmACR %X We consider the long-standing problem of the automatic generation of regular expressions for text extraction, based solely on examples of the desired behaviour. We investigate several active learning approaches in which the user annotates only one desired extraction and then merely answers extraction queries generated by the system. The resulting framework is attractive because it is the system, not the user, which digs out the data in search of the samples most suitable to the specific learning task. We tailor our proposals to a state-of-the-art learner based on Genetic Programming and we assess them experimentally on a number of challenging tasks of realistic complexity. The results indicate that active learning is indeed a viable framework in this application domain and may thus significantly decrease the amount of costly annotation effort required. %K genetic algorithms, genetic programming, entity extraction, information extraction, machine learning, programming by examples %9 journal article %R 10.1145/2993231.2993232 %U https://sites.google.com/site/machinelearningts/publications/international-journal-publications/regex-basedentityextractionwithactivelearningandgeneticprogramming/2016-ACR-RegexEntityExtractionActiveLearningGP.pdf %U http://dx.doi.org/10.1145/2993231.2993232 %P 7-15 %0 Conference Proceedings %T On the Automatic Construction of Regular Expressions from Examples (GP vs. Humans 1-0) %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %Y Doerr, Benjamin %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 2016 Hot of the Press %D 2016 %8 20 24 jul %I ACM %C Denver, USA %F Bartoli:2016:GECCOcomp %X Regular expressions are systematically used in a number of different application domains. Writing a regular expression for solving a specific task is usually quite difficult, requiring significant technical skills and creativity. We have developed a tool based on Genetic Programming capable of constructing regular expressions for text extraction automatically, based on examples of the text to be extracted. We have recently demonstrated that our tool is human-competitive in terms of both accuracy of the regular expressions and time required for their construction. We base this claim on a large-scale experiment involving more than 1700 users on 10 text extraction tasks of realistic complexity. The F-measure of the expressions constructed by our tool was almost always higher than the average F-measure of the expressions constructed by each of the three categories of users involved in our experiment (Novice, Intermediate, Experienced). The time required by our tool was almost always smaller than the average time required by each of the three categories of users. The experiment is described in full detail in Can a machine replace humans? A case study. IEEE Intelligent Systems, 2016 \citeBartoli:2016:ieeeIS %K genetic algorithms, genetic programming %R 10.1145/2908961.2930946 %U http://dx.doi.org/10.1145/2908961.2930946 %P 155-156 %0 Conference Proceedings %T Syntactical Similarity Learning by means of Grammatical Evolution %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %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 Bartoli:2016:PPSN %X Several research efforts have shown that a similarity function synthesized from examples may capture an application-specific similarity criterion in a way that fits the application needs more effectively than a generic distance definition. In this work, we propose a similarity learning algorithm tailored to problems of syntax-based entity extraction from unstructured text streams. The algorithm takes in input pairs of strings along with an indication of whether they adhere or not adhere to the same syntactic pattern. Our approach is based on Grammatical Evolution and explores systematically a similarity definition space including all functions that may be expressed with a specialized, simple language that we have defined for this purpose. We assessed our proposal on patterns representative of practical applications. The results suggest that the proposed approach is indeed feasible and that the learned similarity function is more effective than the Levenshtein distance and the Jaccard similarity index. %K genetic algorithms, genetic programming, Grammatical Evolution %R 10.1007/978-3-319-45823-6_24 %U http://dx.doi.org/10.1007/978-3-319-45823-6_24 %P 260-269 %0 Journal Article %T Active Learning of Regular Expressions for Entity Extraction %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %J IEEE Transactions on Cybernetics %D 2018 %8 mar %V 48 %N 3 %@ 2168-2267 %F Bartoli:2017:ieeeTC %X We consider the automatic synthesis of an entity extractor, in the form of a regular expression, from examples of the desired extractions in an unstructured text stream. This is a long-standing problem for which many different approaches have been proposed, which all require the preliminary construction of a large dataset fully annotated by the user. we propose an active learning approach aimed at minimizing the user annotation effort: the user annotates only one desired extraction and then merely answers extraction queries generated by the system. During the learning process, the system digs into the input text for selecting the most appropriate extraction query to be submitted to the user in order to improve the current extractor. We construct candidate solutions with genetic programming (GP) and select queries with a form of querying-by-committee, i.e., based on a measure of disagreement within the best candidate solutions. All the components of our system are carefully tailored to the peculiarities of active learning with GP and of entity extraction from unstructured text. We evaluate our proposal in depth, on a number of challenging datasets and based on a realistic estimate of the user effort involved in answering each single query. The results demonstrate high accuracy with significant savings in terms of computational effort, annotated characters, and execution time over a state-of-the-art baseline. %K genetic algorithms, genetic programming, automatic programming, evolutionary computation, inference mechanisms, man machine systems, semisupervised learning, text processing %9 journal article %R 10.1109/TCYB.2017.2680466 %U http://dx.doi.org/10.1109/TCYB.2017.2680466 %P 1067-1080 %0 Journal Article %T Multi-level diversity promotion strategies for Grammar-guided Genetic Programming %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Squillero, Giovanni %J Applied Soft Computing %D 2019 %V 83 %@ 1568-4946 %F BARTOLI:2019:ASC %X Grammar-guided Genetic Programming (G3P) is a family of Evolutionary Algorithms that can evolve programs in any language described by a context-free grammar. The most widespread members of this family are based on an indirect representation: a sequence of bits or integers (the genotype) is transformed into a string of the language (the phenotype) by means of a mapping function, and eventually into a fitness value. Unfortunately, the flexibility brought by this mapping is also likely to introduce non-locality phenomena, reduce diversity, and hamper the effectiveness of the algorithm. In this paper, we experimentally characterize how population diversity, measured at different levels, varies for four popular G3P approaches. We then propose two strategies for promoting diversity which are general, independent both from the specific problem being tackled and from the other components of the Evolutionary Algorithm, such as genotype-phenotype mapping, selection criteria, and genetic operators. We experimentally demonstrate their efficacy in a wide range of conditions and from different points of view. The results also confirm the preponderant importance of the phenotype-level analyzes in diversity promotion %K genetic algorithms, genetic programming, Representation, Grammatical evolution, CFGGP, SGE, WHGE %9 journal article %R 10.1016/j.asoc.2019.105599 %U http://www.sciencedirect.com/science/article/pii/S1568494619303795 %U http://dx.doi.org/10.1016/j.asoc.2019.105599 %P 105599 %0 Journal Article %T Weighted Hierarchical Grammatical Evolution %A Bartoli, Alberto %A Castelli, Mauro %A Medvet, Eric %J IEEE Transactions on Cybernetics %D 2020 %8 feb %V 50 %N 2 %@ 2168-2267 %F Bartoli:2019:ieeeTCyber %X Grammatical Evolution (GE) is one of the most widespread techniques in evolutionary computation. Genotypes in GE are bit strings while phenotypes are strings of a language defined by a user-provided context-free grammar (CFG). In this work, we propose a novel procedure for mapping genotypes to phenotypes that we call Weighted Hierarchical GE (WHGE). WHGE imposes a form of hierarchy on the genotype and encodes grammar symbols with a varying number of bits based on the relative expressive power of those symbols. WHGE does not impose any constraint on the overall GE framework, in particular, WHGE may handle recursive grammars, uses the classical genetic operators, and does not need to define any bound in advance on the size of phenotypes. We assessed experimentally our proposal in depth on a set of challenging and carefully selected benchmarks, comparing the results to the standard GE framework as well as to two of the most significant enhancements proposed in the literature: Position-independent GE and Structured GE. Our results show that WHGE delivers very good results in terms of fitness as well as in terms of the properties of the genotype-phenotype mapping procedure. %K genetic algorithms, genetic programming, Grammatical Evolution, genotype-phenotype, mapping, representation %9 journal article %R 10.1109/TCYB.2018.2876563 %U https://sites.google.com/site/machinelearningts/publications/international-journal-publications/weightedhierarchicalgrammaticalevolution/2018-TCyb-WHGE.pdf %U http://dx.doi.org/10.1109/TCYB.2018.2876563 %P 476-488 %0 Journal Article %T Automatic Search-and-Replace From Examples With Coevolutionary Genetic Programming %A Bartoli, Alberto %A De Lorenzo, Andrea %A Medvet, Eric %A Tarlao, Fabiano %J IEEE Transactions on Cybernetics %D 2021 %8 may %V 51 %N 5 %@ 2168-2267 %F Bartoli:ieeeTcybernetics %X We describe the design and implementation of a system for executing search-and-replace text processing tasks automatically, based only on examples of the desired behaviour. The examples consist of pairs describing the original string and the desired modified string. Their construction, thus, does not require any specific technical skill. The system constructs a solution to the specified task that can be used unchanged on popular existing software for text processing. The solution consists of a search pattern coupled with a replacement expression: the former is a regular expression which describes both the strings to be replaced and their portions to be reused in the latter, which describes how to build the modified strings. Our proposed system is internally based on Genetic Programming and implements a form of cooperative coevolution in which two separate populations are evolved independently, one for search patterns and the other for replacement expressions. We assess our proposal on six tasks of realistic complexity obtaining very good results, both in terms of absolute quality of the solutions and with respect to the challenging baselines considered. %K genetic algorithms, genetic programming, diversity promotion, find-and-replace, programming by examples, regular expressions %9 journal article %R 10.1109/TCYB.2019.2918337 %U http://dx.doi.org/10.1109/TCYB.2019.2918337 %P 2612-2624 %0 Journal Article %T Commentary on “Jaws 30”, by W. B. Langdon %A Bartoli, Alberto %A Manzoni, Luca %A Medvet, Eric %J Genetic Programming and Evolvable Machines %D 2023 %8 dec %V 24 %N 2 %@ 1389-2576 %F bartoli:2023:GPEM %O Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection %X While genetic programming has had a huge impact on the research community, it is fair to say that its impact on industry and practitioners has been much smaller. In this commentary we elaborate on this claim and suggest some broad research goals aimed at greatly increasing such impact. %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-023-09471-1 %U https://rdcu.be/drZe8 %U http://dx.doi.org/10.1007/s10710-023-09471-1 %P Articlenumber:23 %0 Conference Proceedings %T Computational Intelligence Techniques Applied to Magnetic Resonance Spectroscopy Data of Human Brain Cancers %A Barton, Alan J. %A Valdes, Julio J. %Y Chan, Chien-Chung %Y Grzymala-Busse, Jerzy W. %Y Ziarko, Wojciech %S Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2008 %S Lecture Notes in Computer Science %D 2008 %8 oct 23 25 %V 5306 %I Springer %C Akron, OH, USA %F DBLP:conf/rsctc/BartonV08 %X Computational intelligence techniques were applied to human brain cancer magnetic resonance spectral data. In particular, two approaches, Rough Sets and a Genetic Programming-based Neural Network were investigated and then confirmed via a systematic Individual Dichotomization algorithm. Good preliminary results were obtained with 100percent training and 100percent testing accuracy that differentiate normal versus malignant samples. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-88425-5_50 %U http://dx.doi.org/10.1007/978-3-540-88425-5_50 %P 485-494 %0 Conference Proceedings %T Learning the neuron functions within a neural network via Genetic Programming: Applications to geophysics and hydrogeology %A Barton, Alan J. %A Valdes, Julio J. %A Orchard, Robert %S International Joint Conference on Neural Networks, IJCNN 2009 %D 2009 %8 jun 14 19 %C Atlanta, Georgia, USA %F Barton:2009:IJCNN %X A neural network classifier is sought. Classical neural network neurons are aggregations of a weight multiplied by an input value and then controlled via an activation function. This paper learns everything within the neuron using a variant of genetic programming called gene expression programming. That is, this paper does not explicitly use weights or activation functions within a neuron, nor bias nodes within a layer. Promising preliminary results are reported for a study of the detection of underground caves (a 1 class problem) and for a study of the interaction of water and minerals near a glacier in the Arctic (a 5 class problem). %K genetic algorithms, genetic programming, gene expression programming, geophysics, geophysics computing, hydrology, neural nets, geophysics, hydrogeology, neural network classifier, neural network neurons, neuron functions %R 10.1109/IJCNN.2009.5178731 %U http://dx.doi.org/10.1109/IJCNN.2009.5178731 %P 264-271 %0 Journal Article %T Neural networks with multiple general neuron models: A hybrid computational intelligence approach using Genetic Programming %A Barton, Alan J. %A Valdes, Julio J. %A Orchard, Robert %J Neural Networks %D 2009 %V 22 %N 5-6 %@ 0893-6080 %F Barton2009614 %O Advances in Neural Networks Research: IJCNN2009, 2009 International Joint Conference on Neural Networks %X Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions. %K genetic algorithms, genetic programming, General neuron model, Evolutionary Computation, Hybrid algorithm, Machine learning, Parameter space, Visualization %9 journal article %R 10.1016/j.neunet.2009.06.043 %U http://www.sciencedirect.com/science/article/B6T08-4WNRK15-3/2/d8803b07859caa7efcd99475af7005ae %U http://dx.doi.org/10.1016/j.neunet.2009.06.043 %P 614-622 %0 Conference Proceedings %T Searching for a single mathematical function to address the nonlinear retention time shifts problem in nanoLC-MS data: A fuzzy-evolutionary computational proteomics approach %A Barton, Alan J. %S 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) %D 2010 %8 may %F Barton:2010:ieeeCIBCB %X Proteomics involves collecting and analysing information about proteins within one or more complex samples in order to address a biological problem. One methodology is the use of high performance liquid chromatography coupled mass spectrometry (nanoLC-MS). In such a case, the accurate determination of non-linear peptide retention times between runs is expected to increase the number of identified peptides and hence, proteins. There are many approaches when using a computer for such a problem; including very interactive to completely non-interactive algorithms for finding global and local functions that may be either explicit or implicit. This paper extends previous work and explores finding an explicit global function for which two stages are involved: i) computation of a set of candidate functions (results) by the algorithm, and ii) searching within the set for patterns of interest. For the first stage, three classes of approximating global functions are considered: Class 1 functions that have a completely unknown structure, Class 2 functions that have a tiny amount of domain knowledge incorporated, and Class 3 functions that have a small amount of domain knowledge incorporated. For the second stage, some issues with current similarity measures for mathematical expressions are discussed and a new measure is proposed. Preliminary experimental results with an Evolutionary Computation algorithm called Gene Expression Programming (a variant of Genetic Programming) when used with a fuzzy membership within the fitness function are reported. %K genetic algorithms, genetic programming, gene expression programming, fuzzy-evolutionary computational proteomics approach, liquid chromatography coupled mass spectrometry, mathematical function, nanoLC-MS, nanoLC-MS data, nonlinear retention time shifts problem, biocomputing, evolutionary computation, fuzzy set theory, proteins, proteomics %R 10.1109/CIBCB.2010.5510688 %U http://dx.doi.org/10.1109/CIBCB.2010.5510688 %0 Journal Article %T Towards Efficient Indexing of Arbitrary Similarity %A Bartos, Tomas %A Skopal, Tomas %A Mosko, Juraj %J SIGMOD Record %D 2013 %8 jul %V 42 %N 2 %I ACM %C New York, NY, USA %@ 0163-5808 %F Bartos:2013:SIGMOD %O Vision Paper. ACM Special Interest Group on Management of Data %X The popularity of similarity search expanded with the increased interest in multimedia databases, bioinformatics, or social networks, and with the growing number of users trying to find information in huge collections of unstructured data. During the exploration, the users handle database objects in different ways based on the used similarity models, ranging from simple to complex models. Efficient indexing techniques for similarity search are required especially for growing databases. In this paper, we study implementation possibilities of the recently announced theoretical framework SIMDEX, the task of which is to algorithmically explore a given similarity space and find possibilities for efficient indexing. Instead of a fixed set of indexing properties, such as metric space axioms, SIMDEX aims to seek for alternative properties that are valid in a particular similarity model (database) and, at the same time, provide efficient indexing. In particular, we propose to implement the fundamental parts of SIMDEX by means of the genetic programming (GP) which we expect will provide high-quality resulting set of expressions (axioms) useful for indexing. %K genetic algorithms, genetic programming %9 journal article %R 10.1145/2503792.2503794 %U http://doi.acm.org/10.1145/2503792.2503794 %U http://dx.doi.org/10.1145/2503792.2503794 %P 5-10 %0 Conference Proceedings %T Efficient indexing of similarity models with inequality symbolic regression %A Bartos, Tomas %A Skopal, Tomas %A Mosko, Juraj %Y Blum, Christian %Y Alba, Enrique %Y Auger, Anne %Y Bacardit, Jaume %Y Bongard, Josh %Y Branke, Juergen %Y Bredeche, Nicolas %Y Brockhoff, Dimo %Y Chicano, Francisco %Y Dorin, Alan %Y Doursat, Rene %Y Ekart, Aniko %Y Friedrich, Tobias %Y Giacobini, Mario %Y Harman, Mark %Y Iba, Hitoshi %Y Igel, Christian %Y Jansen, Thomas %Y Kovacs, Tim %Y Kowaliw, Taras %Y Lopez-Ibanez, Manuel %Y Lozano, Jose A. %Y Luque, Gabriel %Y McCall, John %Y Moraglio, Alberto %Y Motsinger-Reif, Alison %Y Neumann, Frank %Y Ochoa, Gabriela %Y Olague, Gustavo %Y Ong, Yew-Soon %Y Palmer, Michael E. %Y Pappa, Gisele Lobo %Y Parsopoulos, Konstantinos E. %Y Schmickl, Thomas %Y Smith, Stephen L. %Y Solnon, Christine %Y Stuetzle, Thomas %Y Talbi, El-Ghazali %Y Tauritz, Daniel %Y Vanneschi, Leonardo %S GECCO ’13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference %D 2013 %8 June 10 jul %I ACM %C Amsterdam, The Netherlands %F Bartovs:2013:GECCO %X The increasing amount of available unstructured content introduced a new concept of searching for information, the content-based retrieval. The principle behind is that the objects are compared based on their content which is far more complex than simple text or metadata based searching. Many indexing techniques arose to provide an efficient and effective similarity searching. However, these methods are restricted to a specific domain such as the metric space model. If this prerequisite is not fulfilled, indexing cannot be used, while each similarity search query degrades to sequential scanning which is unacceptable for large datasets. Inspired by previous successful results, we decided to apply the principles of genetic programming to the area of database indexing. We developed the GP-SIMDEX which is a universal framework that is capable of finding precise and efficient indexing methods for similarity searching for any given similarity data. For this purpose, we introduce the inequality symbolic regression principle and show how it helps the GP-SIMDEX Framework to find appropriate results that in most cases outperform the best-known indexing methods. %K genetic algorithms, genetic programming %R 10.1145/2463372.2463487 %U http://dx.doi.org/10.1145/2463372.2463487 %P 901-908 %0 Conference Proceedings %T Designing Similarity Indexes with Parallel Genetic Programming %A Bartos, Tomas %A Skopal, Tomas %Y Brisaboa, Nieves R. %Y Pedreira, Oscar %Y Zezula, Pavel %S Proceedings of the 6th International Conference on Similarity Search and Applications (SISAP 2013) %S Lecture Notes in Computer Science %D 2013 %8 oct 2 4 %V 8199 %I Springer %C A Coruna, Spain %F conf/sisap/BartosS13 %X The increasing diversity of unstructured databases leads to the development of advanced indexing techniques as the metric indexing model does not fit to the general similarity models. Once the most critical postulate, namely the triangle inequality, does not hold, the metric model produces notable errors during the query evaluation. To overcome this situation and to obtain more qualitative results, we want to discover better indexing models for databases using arbitrary similarity measures. However, each database is unique in a specific way, so we outline the automatic way of exploring the best indexing method. We introduce the exploration approach using parallel genetic programming principles in a multi-threaded environment built upon recently introduced SIMDEX Framework. Furthermore, we introduce smart pivot table which is an intelligent indexing method capable of incorporating obtained results. We supplement the theoretical background with experiments showing the achieved improvements in comparison to the single-threaded evaluations. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-41062-8_29 %U http://dx.doi.org/10.1007/978-3-642-41062-8 %U http://dx.doi.org/10.1007/978-3-642-41062-8_29 %P 294-299 %0 Book Section %T A Computational Intelligence Approach to Railway Track Intervention Planning %A Bartram, Derek %A Burrow, Michael %A Yao, Xin %E Yu, Tina %E Davis, David %E Baydar, Cem %E Roy, Rajkumar %B Evolutionary Computation in Practice %S Studies in Computational Intelligence %D 2008 %V 88 %I Springer %F Bartram:2008:ECP %X Railway track intervention planning is the process of specifying the location and time of required maintenance and renewal activities. To facilitate the process, decision support tools have been developed and typically use an expert system built with rules specified by track maintenance engineers. However, due to the complex interrelated nature of component deterioration, it is problematic for an engineer to consider all combinations of possible deterioration mechanisms using a rule based approach. To address this issue, this chapter describes an approach to the intervention planning using a variety of computational intelligence techniques. The proposed system learns rules for maintenance planning from historical data and incorporates future data to update the rules as they become available thus the performance of the system improves over time. To determine the failure type, historical deterioration patterns of sections of track are first analysed. A Rival Penalised Competitive Learning algorithm is then used to determine possible failure types. We have devised a generalised two stage evolutionary algorithm to produce curve functions for this purpose. The approach is illustrated using an example with real data which demonstrates that the proposed methodology is suitable and effective for the task in hand. %K genetic algorithms, genetic programming, k-means, RPCL, learning %R 10.1007/978-3-540-75771-9_8 %U http://dx.doi.org/10.1007/978-3-540-75771-9_8 %P 163-198 %0 Journal Article %T Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder %A Bartsch Jr., Georg %A Mitra, Anirban P. %A Mitra, Sheetal A. %A Almal, Arpit A. %A Steven, Kenneth E. %A Skinner, Donald G. %A Fry, David W. %A Lenehan, Peter F. %A Worzel, William P. %A Cote, Richard J. %J The Journal of Urology %D 2016 %V 195 %N 2 %@ 0022-5347 %F BartschJr:2016:TJU %X Purpose Due to the high recurrence risk of non-muscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with non muscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumour. Materials and Methods Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. Results The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77percent sensitivity and 85percent specificity to predict recurrence in the training set, and 69percent and 62percent, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80percent sensitivity and 90percent specificity in the training set, and 71percent and 67percent, respectively, in the test set. Conclusions Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management. %K genetic algorithms, genetic programming, urinary bladder neoplasms, neoplasm recurrence, local, genome, algorithms, software %9 journal article %R 10.1016/j.juro.2015.09.090 %U http://www.sciencedirect.com/science/article/pii/S0022534715049629 %U http://dx.doi.org/10.1016/j.juro.2015.09.090 %P 493-498 %0 Conference Proceedings %T Evolving Cellular Automata to Grow Microstructures %A Basanta, David %A Miodownik, Mark A. %A Holm, Elizabeth A. %Y Ryan, Conor %Y Soule, Terence %Y Keijzer, Maarten %Y Tsang, Edward %Y Poli, Riccardo %Y Costa, Ernesto %S Genetic Programming, Proceedings of EuroGP’2003 %S LNCS %D 2003 %8 14 16 apr %V 2610 %I Springer-Verlag %C Essex %@ 3-540-00971-X %F basanta03 %X The properties of engineering structures such as cars, cell phones or bridges rely on materials and on the properties of these materials. The study of these properties, which are determined by the internal architecture of the material or microstructure, has significant importance for material scientists. One of the things needed for this study is a tool that can create microstructural patterns. In this paper we explore the use of a genetic algorithm to evolve the rules of an effector automata to recreate these microstructural patterns. %K genetic algorithms, genetic programming %R 10.1007/3-540-36599-0_1 %U http://rcswww.urz.tu-dresden.de/~basanta/eurogp03.pdf %U http://dx.doi.org/10.1007/3-540-36599-0_1 %P 1-10 %0 Conference Proceedings %T Evolving and Growing Microstructures of Materials using Biologically Inspired CA %A Basanta, David %A Miodownik, Mark A. %A Bentley, Peter J. %A Holm, Elizabeth A. %Y Zebulum, R. S. %S 2004 NASA/DoD Conference on Evolvable Hardware %D 2004 %8 jun 24 26 %I IEEE Computer Society %C Seattle, Washington, USA %@ 0-7695-2145-2 %F Basanta:2004:EH %X The properties of engineering structures, such as robotic arms, aircrafts or bridges, rely on the properties of the materials used to build them. The internal architecture of the material or microstructure determines its properties and therefore, its study is of great interest for engineers and material scientists. Although there are tools that can provide 2D microstructural information, tools that can be used to obtain 3D characterisations of microstructures for routine analysis are not yet available to material scientists. In this paper we will describe Microconstructor. Microconstructor comprises a genetic algorithm that evolves populations of Cellular Automata inspired by developmental biology that self organise into 3D patterns that can be used for microstructural analysis. %K genetic algorithms, genetic programming %R 10.1109/EH.2004.1310841 %U http://dx.doi.org/10.1109/EH.2004.1310841 %P 275-275 %0 Journal Article %T A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation %A Baser, Furkan %A Demirhan, Haydar %J Energy %D 2017 %V 123 %@ 0360-5442 %F Baser:2017:Energy %X Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that uses fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics. %K genetic algorithms, genetic programming, Artificial intelligence, Fuzzy regression, METEONORM, Solar radiation model, Support vector machines %9 journal article %R 10.1016/j.energy.2017.02.008 %U http://www.sciencedirect.com/science/article/pii/S0360544217301822 %U http://dx.doi.org/10.1016/j.energy.2017.02.008 %P 229-240 %0 Conference Proceedings %T A grammatical evolution based hyper-heuristic for the automatic design of split criteria %A Basgalupp, Marcio Porto %A Barros, Rodrigo Coelho %A Barabasz, Tiago %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 Basgalupp:2014:GECCO %X Top-down induction of decision trees (TDIDT) is a powerful method for data classification. A major issue in TDIDT is the decision on which attribute should be selected for dividing the nodes in subsets, creating the tree. For performing such a task, decision trees make use of a split criterion, which is usually an information-theory based measure. Apparently, there is no free-lunch regarding decision-tree split criteria, as is the case of most things in machine learning. Each application may benefit from a distinct split criterion, and the problem we pose here is how to identify the suitable split criterion for each possible application that may emerge. We propose in this paper a grammatical evolution algorithm for automatically generating split criteria through a context-free grammar. We name our new approach ESC-GE (Evolutionary Split Criteria with Grammatical Evolution). It is empirically evaluated on public gene expression datasets, and we compare its performance with state-of-the-art split criteria, namely the information gain and gain ratio. Results show that ESC-GE outperforms the baseline criteria in the domain of gene expression data, indicating its effectiveness for automatically designing tailor-made split criteria. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1145/2576768.2598327 %U http://doi.acm.org/10.1145/2576768.2598327 %U http://dx.doi.org/10.1145/2576768.2598327 %P 1311-1318 %0 Conference Proceedings %T Grammar-based Evolutionary Approaches for Software Effort Estimation %A Basgalupp, Marcio P. %A Barros, Rodrigo C. %A Cerri, Ricardo %A Neri, Ferrante %A Miranda, Pericles B. C. %A Ludermir, Teresa %Y Jin, Yaochu %Y Baeck, Thomas %S 2025 IEEE Congress on Evolutionary Computation (CEC) %D 2025 %8 August 12 jun %I IEEE %C Hangzhou, China %F basgalupp:2025:CEC %X Software effort estimation predicts resources needed for a project, including person-hours and costs, and is vital for effective planning and budgeting. This paper compares two grammar-based evolutionary algorithms: grammar-based genetic programming (GGP) and grammatical evolution (GE). Both algorithms are tested on public project datasets and compared with machine learning models such as support vector machines, artificial neural networks, and least-squares linear regression. Results demonstrate that GGP and GE outperform alternative methods across two evaluation metrics, highlighting their effectiveness in estimating software effort. %K genetic algorithms, genetic programming, Support vector machines, SVM, Costs, Computational modeling, Linear regression, Estimation, Evolutionary computation, Germanium, Software, Standards, software effort estimation, grammar-based genetic programming, grammatical evolution %R 10.1109/CEC65147.2025.11042977 %U http://dx.doi.org/10.1109/CEC65147.2025.11042977 %0 Conference Proceedings %T Managing Diversity and Many Objectives in Evolutionary Design %A Basher, Sheikh Faishal %A Ross, Brian J. %Y Coello, Carlos A. Coello %Y Mostaghim, Sanaz %S 2022 IEEE Congress on Evolutionary Computation (CEC) %D 2022 %8 18 23 jul %C Padua, Italy %F Basher:2022:CEC %X A new approach to evolving a diversity of high-quality solutions for problems having many objectives is presented. Mouret and Clunes MAP-Elites algorithm has been proposed as a way to evolve an assortment of diverse solutions to a problem. We extend MAP-Elites in a number of ways. We introduce into MAP-elites the many objective strategy called sum-of-ranks, which enables problems with many objectives (4 and more) to be considered in the MAP. We also enhance MAP-Elites by extending it with multiple solutions per cell (the original MAP-Elites saves only a single solution per cell). Different ways of selecting cell members for reproduction are also considered. We test the new MAP-Elites strategies on the evolutionary art application of image generation. Using procedural textures, genetic programming is used with upwards of 15 light weight image features to guide fitness. The goal is to evolve images that share image features with a given target image. Our experiments show that the new MAP-Elites algorithms produce a large number of diverse solutions, which can be competitive in quality to those from standard GP runs. %K genetic algorithms, genetic programming, Visualization, Image synthesis, Image color analysis, Sociology, Search problems, Entropy, Performance analysis, Diversity, Many-objective Optimization, Evolutionary Art, Procedural Texture %R 10.1109/CEC55065.2022.9870353 %U http://dx.doi.org/10.1109/CEC55065.2022.9870353 %0 Journal Article %T Combining pre-retrieval query quality predictors using genetic programming %A Bashir, Shariq %J Appl. Intell %D 2014 %V 40 %N 3 %F journals/apin/Bashir14 %K genetic algorithms, genetic programming %9 journal article %U http://dx.doi.org/10.1007/s10489-013-0475-z %P 525-535 %0 Journal Article %T Opinion-Based Entity Ranking using learning to rank %A Bashir, Shariq %A Afzal, Wasif %A Baig, Abdul Rauf %J Applied Soft Computing %D 2016 %V 38 %@ 1568-4946 %F Bashir:2016:ASC %X As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time. The users have to read a large number of opinions of different entities before making any decision. Recently a new retrieval task in information retrieval known as Opinion-Based Entity Ranking (OpER) has emerged. OpER directly ranks relevant entities based on how well opinions on them are matched with a user’s preferences that are given in the form of queries. With such a capability, users do not need to read a large number of opinions available for the entities. Previous research on OpER does not take into account the importance and subjectivity of query keywords in individual opinions of an entity. Entity relevance scores are computed primarily on the basis of occurrences of query keywords match, by assuming all opinions of an entity as a single field of text. Intuitively, entities that have positive judgements and strong relevance with query keywords should be ranked higher than those entities that have poor relevance and negative judgments. This paper outlines several ranking features and develops an intuitive framework for OpER in which entities are ranked according to how well individual opinions of entities are matched with the user’s query keywords. As a useful ranking model may be constructed from many ranking features, we apply learning to rank approach based on genetic programming (GP) to combine features in order to develop an effective retrieval model for OpER task. The proposed approach is evaluated on two collections and is found to be significantly more effective than the standard OpER approach. %K genetic algorithms, genetic programming, Entity Ranking, Opinion analysis, Learning to rank %9 journal article %R 10.1016/j.asoc.2015.10.001 %U http://www.sciencedirect.com/science/article/pii/S156849461500616X %U http://dx.doi.org/10.1016/j.asoc.2015.10.001 %P 151-163 %0 Generic %T Darwinian Data Structure Selection %A Basios, Michail %A Li, Lingbo %A Wu, Fan %A Kanthan, Leslie %A Lawrence, Donald %A Barr, Earl T. %D 2017 %8 October %I arXiv %F DBLP:journals/corr/BasiosLWKLB17 %X Data structure selection and tuning is laborious but can vastly improve application performance and memory footprint. We introduce ARTEMIS a multiobjective, cloud-based optimisation framework that automatically finds optimal, tuned data structures and rewrites applications to use them. ARTEMIS achieves substantial performance improvements for every project in a set of 29 Java programs uniformly sampled from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution for each project that improves all measures. The median improvement across all these best solutions is 8.38percent for execution time, 24.27percent for memory consumption and 11.61percent for CPU usage. In detail, ARTEMIS improved the memory consumption of JUnit4, a ubiquitous Java testing framework, by 45.42percent memory, while also improving its execution time 2.29percent at the cost a 1.25percent increase in CPU usage. LinkedIn relies on the Cleo project as their autocompletion engine for search. ARTEMIS improves its execution time by 12.17percent, its CPU usage by 4.32percent and its memory consumption by 23.91percent. %K genetic algorithms, genetic programming, genetic improvement, Search-based software engineering, SBSE, Software analysis and optimisation, Multi-objective optimisation, SBSE, Software Engineering %U http://arxiv.org/abs/1706.03232 %0 Conference Proceedings %T Optimising Darwinian Data Structures on Google Guava %A Basios, Michail %A Li, Lingbo %A Wu, Fan %A Kanthan, Leslie %A Barr, Earl T. %Y Menzies, Tim %Y Petke, Justyna %S Proceedings of the 9th International Symposium on Search Based Software Engineering, SSBSE 2017 %S LNCS %D 2017 %8 sep 9 11 %V 10452 %I Springer %C Paderborn, Germany %F Basios:2017:SSBSE %O Best Challenge Paper Award %X Data structure selection and tuning is laborious but can vastly improve application performance and memory footprint. In this paper, we demonstrate how artemis, a multiobjective, cloud-based optimisation framework can automatically find optimal, tuned data structures and how it is used for optimising the Guava library. From the proposed solutions that artemis found, 27percent of them improve all measures (execution time, CPU usage, and memory consumption). More specifically, artemis managed to improve the memory consumption of Guava by up 13percent, execution time by up to 9percent, and 4percent CPU usage. %K genetic algorithms, genetic improvement, SBSE, Software analysis and optimisation, Multi-objective optimisation %R 10.1007/978-3-319-66299-2_14 %U http://dx.doi.org/10.1007/978-3-319-66299-2_14 %P 161-167 %0 Conference Proceedings %T Darwinian Data Structure Selection %A Basios, Michail %A Li, Lingbo %A Wu, Fan %A Kanthan, Leslie %A Barr, Earl T. %Y Leavens, Gary T. %Y Garcia, Alessandro %Y Pasareanu, Corina S. %S Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2018 %D 2018 %8 April 9 nov %I ACM %C Lake Buena Vista, FL, USA %F Basios:2018:FSE %X Data structure selection and tuning is laborious but can vastly improve an applications performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for every project in 5 Java projects from DaCapo benchmark, 8 popular projects and 30 uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves all measures for 86percent (37/43) of the projects. The median improvement across the best solutions is 4.8percent, 10.1percent, 5.1percent for runtime, memory and CPU usage. These aggregate results understate ARTEMIS potential impact. Some of the benchmarks it improves are libraries or utility functions. Two examples are gson, a ubiquitous Java serialization framework, and xalan, Apache XML transformation tool. ARTEMIS improves gson by 16.5percent, 1percent and 2.2percent for memory, runtime, and CPU; ARTEMIS improves xalan’s memory consumption by 23.5percent. Every client of these projects will benefit from these performance improvements. %K genetic algorithms, genetic programming, Genetic Improvement, Search-based Software Engineering, SBSE, Software Analysis and Optimisation %R 10.1145/3236024.3236043 %U http://human-competitive.org/sites/default/files/artemis.pdf %U http://dx.doi.org/10.1145/3236024.3236043 %P 118-128 %0 Thesis %T Darwinian Code Optimisation %A Basios, Michail %D 2019 %8 18 jan %C UK %C Department of Computer Science, University College London %F Basios_10070648_thesis %X Programming is laborious. A long-standing goal is to reduce this cost through automation. Genetic Improvement (GI) is a new direction for achieving this goal. It applies search to the task of program improvement. The research conducted in this thesis applies GI to program optimisation and to enable program optimisation. In particular, it focuses on automatic code optimisation for complex managed runtimes,such as Java and Ethereum Virtual Machines. We introduce the term Darwinian Data Structures (DDS) for the data structures of a program that share a common interface and enjoy multiple implementations. We call them Darwinian since we can subject their implementations to the survival of the fittest. We introduce ARTEMIS, a novel cloud-based multi-objective multi-language optimisation framework that automatically finds optimal, tuned data structures and rewrites the source code of applications accordingly to use them. ARTEMIS achieves substantial performance improvements for 44 diverse programs. ARTEMIS achieves 4.8percent, 10.1percent, 5.1percent median improvement for runtime, memory and CPU usage. Even though GI has been applied successfully to improve properties of programs running in different runtimes, GI has not been applied in Blockchains, such as Ethereum. The code immutability of programs running on top of Ethereum limits the application of GI. The first step of applying GI in Ethereum is to overcome the code immutability limitations. Thus, to enable optimisation, we present PROTEUS, a state of the art framework that automatically extends the functionality of smart contracts written in Solidity and makes them upgradeable. Thus, allowing developers to introduce alternative optimised versions of code (e.g., code that consumes less gas), found by GI, in newer versions. %K genetic algorithms, genetic programming, genetic improvement, SBSE, APR, Blockchain, Java, JVM, Immortal bugs, Proteus grammar, solidify, ETH, MultiSig, ICO, trampoline %9 Ph.D. thesis %U https://discovery.ucl.ac.uk/id/eprint/10070648 %0 Conference Proceedings %T Learning Task-specific Activation Functions using Genetic Programming %A Basirat, Mina %A Roth, Peter M. %Y Tremeau, Alain %Y Farinella, Giovanni Maria %Y Braz, Jose %S Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019, Volume 5: VISAPP, Prague, Czech Republic, February 25-27, 2019 %D 2019 %I SciTePress %F DBLP:conf/visapp/BasiratR19 %K genetic algorithms, genetic programming %R 10.5220/0007408205330540 %U https://doi.org/10.5220/0007408205330540 %U http://dx.doi.org/10.5220/0007408205330540 %P 533-540 %0 Conference Proceedings %T A new methodology for the GP theory toolbox %A Bassett, Jeffrey %A Kamath, Uday %A De Jong, Kenneth %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 Bassett:2012:GECCO %X Recently Quantitative Genetics has been successfully employed to understand and improve operators in some Evolutionary Algorithms (EAs) implementations. This theory offers a phenotypic view of an algorithm’s behavior at a population level, and suggests new ways of quantifying and measuring concepts such as exploration and exploitation. In this paper, we extend the quantitative genetics approach for use with Genetic Programming (GP), adding it to the set of GP analysis techniques. We use it in combination with some existing diversity and bloat measurement tools to measure, analyze and predict the evolutionary behavior of several GP algorithms. GP specific benchmark problems, such as ant trail and symbolic regression, are used to provide new insight into how various evolutionary forces work in combination to affect the search process. Finally, using the tools, a multivariate phenotypic crossover operator is designed to both improve performance and control bloat on the difficult ant trail problem. %K genetic algorithms, genetic programming %R 10.1145/2330163.2330264 %U http://dx.doi.org/10.1145/2330163.2330264 %P 719-726 %0 Thesis %T Methods for Improving the Design and Performance of Evolutionary Algorithms %A Bassett, Jeffrey Kermes %D 2012 %8 Fall %C USA %C The Volgenau School of Engineering, George Mason University %F Bassett:thesis %X Evolutionary Algorithms (EAs) can be applied to almost any optimization or learning problem by making some simple customizations to the underlying representation and/or reproductive operators. This makes them an appealing choice when facing a new or unusual problem. Unfortunately, while making these changes is often easy, getting a customized EA to operate effectively (i.e. find a good solution quickly) can be much more difficult. Ideally one would hope that theory would provide some guidance here, but in these cases, evolutionary computation (EC) theories are not easily applied. They either require customization themselves, or they require information about the problem that essentially includes the solution. Consequently most practitioners rely on an ad-hoc approach, incrementally modifying and testing various customizations until they find something that works reasonably well. The drawback that most EC theories face is that they are closely associated with the underlying representation of an individual (i.e. the genetic code). There has been some success at addressing this limitation by applying a biology theory called quantitative genetics to EAs. This approach allows one to monitor the behaviour of an EA by observing distributions of an outwardly observable phenotypic trait (usually fitness), and thus avoid modelling the algorithm’s internal details. Unfortunately, observing a single trait does not provide enough information to diagnose most problems within an EA. It is my hypothesis that using multiple traits will allow one to observe how the population is traversing the search space, thus making more detailed diagnosis possible. In this work, I adapt a newer multivariate form of quantitative genetics theory for use with evolutionary algorithms and derive a general equation of population variance dynamics. This provides a foundation for building a set of tools that can measure and visualize important characteristics of an algorithm, such as exploration, exploitation, and heritability, throughout an EA run. Finally I demonstrate that the tools can actually be used to identify and fix problems in two well known EA variants: Pittsburgh approach rule systems and genetic programming trees. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://hdl.handle.net/1920/8122 %0 Journal Article %T Identifying fuzzy models utilizing genetic programming %A Bastian, Andreas %J Fuzzy Sets and Systems %D 2000 %8 January %V 113 %N 3 %F Bastian:2000:FSS %X Fuzzy models offer a convenient way to describe complex nonlinear systems. Moreover, they permit the user to deal with uncertainty and vagueness. Due to these advantages fuzzy models are employed in various fields of applications, e.g. control, forecasting, and pattern recognition. Nevertheless, it has to be emphasised that the identification of a fuzzy model is a complex optimisation task with many local minima. Genetic programming provides a way to solve such complex optimization problems. In this work, the use of genetic programming to identify the input variables, the rule base and the involved membership functions of a fuzzy model is proposed. For this purpose, several new reproduction operators are introduced. %K genetic algorithms, genetic programming, System identification, Fuzzy modeling %9 journal article %U http://www.sciencedirect.com/science/article/B6V05-4234BFC-1/1/261a04fa056f3f0dfe0fb79a773a971a %P 333-350 %0 Journal Article %T An Automatic Generation of Textual Pattern Rules for Digital Content Filters Proposal, Using Grammatical Evolution Genetic Programming %A Basto-Fernandes, Vitor %A Yevseyeva, Iryna %A Frantz, Rafael Z. %A Grilo, Carlos %A Perez Diaz, Noemi %A Emmerich, Michael %J Procedia Technology %D 2014 %V 16 %@ 2212-0173 %F BastoFernandes:2014:PT %O CENTERIS 2014 - Conference on ENTERprise Information Systems / ProjMAN 2014 - International Conference on Project MANagement / HCIST 2014 - International Conference on Health and Social Care Information Systems and Technologies %X This work presents a conceptual proposal to address the problem of intensive human specialised resources that are nowadays required for the maintenance and optimised operation of digital contents filtering in general and anti-spam filtering in particular. The huge amount of spam, malware, virus, and other illegitimate digital contents distributed through network services, represents a considerable waste of physical and technical resources, experts and end users time, in continuous maintenance of anti-spam filters and deletion of spam messages, respectively. The problem of cumbersome and continuous maintenance required to keep anti-spam filtering systems updated and running in an efficient way, is addressed in this work by the means of genetic programming grammatical evolution techniques, for automatic rules generation, having SpamAssassin anti-spam system and SpamAssassin public corpus as the references for the automatic filtering customisation. %K genetic algorithms, genetic programming, Grammatical Evolution, Spam filtering, Digital Content Filters, Classification %9 journal article %R 10.1016/j.protcy.2014.10.030 %U http://www.sciencedirect.com/science/article/pii/S2212017314002576 %U http://dx.doi.org/10.1016/j.protcy.2014.10.030 %P 806-812 %0 Journal Article %T Hands-on introduction to genetic programming %A Batenkov, Dmitry %J XRDS Crossroads %D 2010 %8 sep 2010 %V 17 %N 1 %I ACM %@ 1528-4972 %F Batenkov:2010:HIG:1836543.1836558 %O The ACM Magazine for Students %X The idea to mimic the principles of Darwinian evolution in computing has been around at least since the 1950s, so long, in fact, that it has grown into the field called evolutionary computing (EC). In this tutorial, we’ll learn the basic principles of EC and its offspring, genetic programming (GP), on a ’toy problem’ of symbolic regression. We’ll also learn how to use OpenBeagle, a generic C++ object-oriented EC framework. %K genetic algorithms, genetic programming, Coding Tools and Techniques, Expressions and Their Representation, Object-oriented Programming, Problem Solving, Control Methods, Search %9 journal article %R 10.1145/1836543.1836558 %U http://xrds.acm.org/article.cfm?aid=1836558 %U http://dx.doi.org/10.1145/1836543.1836558 %P 46-51 %0 Journal Article %T Open BEAGLE: a generic framework for evolutionary computations %A Batenkov, Dmitry %J Genetic Programming and Evolvable Machines %D 2011 %8 sep %V 12 %N 3 %I Springer %@ 1389-2576 %F Batenkov:2011:GPEM %O Software review %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-011-9135-4 %U https://rdcu.be/dR8fs %U http://dx.doi.org/10.1007/s10710-011-9135-4 %P 329-331 %0 Conference Proceedings %T Evolutionary reinforcement learning in FX order book and order flow analysis %A Bates, R. G. %A Dempster, M. A. H. %A Romahi, Y. S. %S IEEE International Conference on Computational Intelligence for Financial Engineering %D 2003 %8 20 23 mar %C Hong Kong %F Bates:2003:ICCIFE %X As macroeconomic fundamentals based modelling of FX time series have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone. %K genetic algorithms, genetic programming %R 10.1109/CIFER.2003.1196282 %U http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2003/WP06.pdf %U http://dx.doi.org/10.1109/CIFER.2003.1196282 %P 355-362 %0 Conference Proceedings %T Genetic Programming on Program Traces as an Inference Engine for Probabilistic Languages %A Batishcheva, Vita %A Potapov, Alexey %Y Bieger, Jordi %Y Goertzel, Ben %Y Potapov, Alexey %S Proceedings of the 8th International Conference Artificial General Intelligence, AGI 2015 %S Lecture Notes in Computer Science %D 2015 %8 jul 22 25 %V 9205 %I Springer %C Berlin, Germany %F conf/agi/BatishchevaP15 %X Methods of simulated annealing and genetic programming over probabilistic program traces are developed firstly. These methods combine expressiveness of Turing-complete probabilistic languages, in which arbitrary generative models can be defined, and search effectiveness of meta-heuristic methods. To use these methods, one should only specify a generative model of objects of interest and a fitness function over them without necessity to implement domain-specific genetic operators or mappings from objects to and from bit strings. On the other hand, implemented methods showed better quality than the traditional mh-query on several optimization tasks. Thus, these results can contribute to both fields of genetic programming and probabilistic programming. %K genetic algorithms, genetic programming, Probabilistic programming, Program traces %R 10.1007/978-3-319-21365-1_2 %U http://dx.doi.org/10.1007/978-3-319-21365-1 %U http://dx.doi.org/10.1007/978-3-319-21365-1_2 %P 14-24 %0 Thesis %T Studying elements of genetic programming for multiclass classification %A Silva Pombinho Batista, Joao Eduardo %D 2018 %C Lisbon, Portugal %C Department of Informatics, Faculdade de Ciencias, Universidade de Lisboa %F Batista:mastersthesis %X Although Genetic Programming (GP) has been very successful in both symbolic regression and binary classification by solving many difficult problems from various domains, it requires improvements in multiclass classification, which due to the high complexity of this kind of problems, requires specialised classifiers. In this project, we explored a multiclass classification GP-based algorithm, the M3GP [4]. The individuals in standard GP only have one node at their root. This means that their output space is in R. Unlike standard GP, M3GP allows each individual to have n nodes at its root. This variation changes the output space to Rn, allowing them to construct clusters of samples and use a cluster-based classification. Although M3GP is capable of creating interpretable models while having competitive results with state-of-the-art classifiers, such as Random Forests and Neural Networks, it has downsides. The focus of this project is to improve the algorithm by exploring two components, the fitness function, and the genetic operators’ selection method. The original fitness function was accuracy-based. Since using this kind of functions does not allow a smooth evolution of the output space, we tried to improve the algorithm by exploring two distance-based fitness functions as an attempt to separate the clusters while bringing the samples closer to their respective centroids. Until now, the genetic operators in M3GP were selected with a fixed probability. Since some operators have a better effect on the fitness at different stages of the evolution, the fixed probabilities allow operators to be selected at the wrong stages of the evolution, slowing down the learning process. In this project, we try to evolve the probability the genetic operators have of being chosen over the generations. On a later stage, we proposed a new crossover genetic operator that uses three individuals for the M3GP algorithm. The results obtained show significantly better results in the training set in half the datasets, while improving the test accuracy in two datasets. %K genetic algorithms, genetic programming, M3GP, Java, Machine Learning, Classification, Multiclass, Multidimensional clustering, Programacao genetica, Aprendizagem automatica, Classificacao, Multi-classe, Aglomeracao multi-dimensional %9 Masters thesis %U http://hdl.handle.net/10451/35287 %0 Conference Proceedings %T To adapt or not to adapt, or the beauty of random settings %A Batista, Joao E. %A Rodrigues, Nuno M. %A Silva, Sara %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 Batista:2019:GECCOcomp %K genetic algorithms, genetic programming %R 10.1145/3319619.3321994 %U http://dx.doi.org/10.1145/3319619.3321994 %P 336-337 %0 Conference Proceedings %T Improving the Detection of Burnt Areas in Remote Sensing using Hyper-features Evolved by M3GP %A Batista, Joao E. %A Silva, Sara %Y Jin, Yaochu %S 2020 IEEE Congress on Evolutionary Computation, CEC 2020 %D 2020 %8 19 24 jul %I IEEE Press %C internet %F Batista:2020:CEC %X One problem found when working with satellite images is the radiometric variations across the image and different images. Intending to improve remote sensing models for the classification of burnt areas, we set two objectives. The first is to understand the relationship between feature spaces and the predictive ability of the models, allowing us to explain the differences between learning and generalization when training and testing in different datasets. We find that training on datasets built from more than one image provides models that generalize better. These results are explained by visualizing the dispersion of values on the feature space. The second objective is to evolve hyper-features that improve the performance of different classifiers on a variety of test sets. We find the hyper-features to be beneficial, and obtain the best models with XGBoost, even if the hyper-features are optimized for a different method %K genetic algorithms, genetic programming, Classification, RemoteSensing, Feature Spaces, Hyper-features, Transfer Learning %R 10.1109/CEC48606.2020.9185630 %U https://arxiv.org/abs/2002.00053 %U http://dx.doi.org/10.1109/CEC48606.2020.9185630 %P paperid24404 %0 Journal Article %T Improving Land Cover Classification Using Genetic Programming for Feature Construction %A Batista, Joao E. %A Cabral, Ana I. R. %A Vasconcelos, Maria J. P. %A Vanneschi, Leonardo %A Silva, Sara %J Remote Sensing %D 2021 %V 13 %N 9 %@ 2072-4292 %F batista:2021:remotesensing %X Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS. %K genetic algorithms, genetic programming, evolutionary computation, machine learning, classification, multi-class classification, feature construction, hyperfeatures, spectral indices %9 journal article %R 10.3390/rs13091623 %U https://www.mdpi.com/2072-4292/13/9/1623 %U http://dx.doi.org/10.3390/rs13091623 %0 Conference Proceedings %T Evolving a Cloud-Robust Water Index with Genetic Programming %A Batista, Joao %A Silva, Sara %Y Ong, Yew-Soon %Y Gupta, Abhishek %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F batista:2022:GECCOlba %X Over the years, remote sensing (RS) experts created many indices to help them study satellite imagery by highlighting characteristics like vegetation, water, or burnt areas, among others. In this work, we study water indices. Although there is a large number of water indices that work perfectly in unclouded imagery, clouds and shadows cast by clouds are often mistaken for water. This work is focused on the automatic feature construction using genetic programming (GP), in an attempt to make features that are more robust to these issues. To do this, we use a dataset containing pixels from areas where we could find these issues to evolve models that learn how to classify those pixels correctly. The results indicate improvements when comparing evolved features with indices, but further improvements are required to tackle other issues found. %K genetic algorithms, genetic programming, feature construction, water indices, remote sensing, satellite imagery %R 10.1145/3520304.3533946 %U http://dx.doi.org/10.1145/3520304.3533946 %P 55-56 %0 Conference Proceedings %T Comparative study of classifier performance using automatic feature construction by M3GP %A Batista, Joao E. %A Silva, Sara %Y Coello, Carlos A. Coello %Y Mostaghim, Sanaz %S 2022 IEEE Congress on Evolutionary Computation (CEC) %D 2022 %8 18 23 jul %C Padua, Italy %F Batista:2022:CEC %X The M3GP algorithm, originally designed to perform multiclass classification with genetic programming, is also a powerful feature construction method. Here we explore its ability to evolve hyper-features that are tailored not only to the problem to be solved, but also to the learning algorithm that is used to solve it. We pair M3GP with six different machine learning algorithms and study its performance in eight classification problems from different scientific domains, with substantial variety in the number of classes, features and samples. The results show that automatic feature construction with M3GP, when compared to using the standalone classifiers without feature construction, achieves statistically significant improvements in the majority of the test cases, sometimes by a very large margin, while degrading the weighted f-measure in only one out of 48 cases. We observe the differences in the number and size of the hyper-features evolved for each case, hypothesising that the simpler the classifier, the larger the amount of problem complexity is being captured in the hyperfeatures. Our results also reveal that the M3GP algorithm can be improved, both in execution time and in model quality, by replacing its default classifier with support vector machines or random forest classifiers. %K genetic algorithms, genetic programming, M3GP, Python, naive Bayes, decision trees, Support vector machines, random forests, xgboost, Machine learning algorithms, Computational modeling, Evolutionary computation, Classification algorithms, Complexity theory, Feature Construction, Multiclass Classification %R 10.1109/CEC55065.2022.9870343 %U http://dx.doi.org/10.1109/CEC55065.2022.9870343 %0 Journal Article %T Optical time series for the separation of land cover types with similar spectral signatures: cocoa agroforest and forest %A Batista, Joao E. %A Rodrigues, Nuno M. %A Cabral, Ana I. R. %A Vasconcelos, Maria J. P. %A Venturieri, Adriano %A Silva, Luiz G. T. %A Silva, Sara %J International Journal of Remote Sensing %D 2022 %V 43 %N 9 %I Taylor & Francis %F Batista:2022:IJRS %X One of the main applications of machine learning (ML) in remote sensing (RS) is the pixel-level classification of satellite images into land cover types. Although classes with different spectral signatures can be easily separated, e.g. aquatic and terrestrial land cover types, others have similar spectral signatures and are hard to separate using only the information within a single pixel. This work focused on the separation of two cover types with similar spectral signatures, cocoa agroforest and forest, over an area in Para, Brazil. For this, we study the training and application of several ML algorithms on datasets obtained from a single composite image, a time-series (TS) composite obtained from the same location and by preprocessing the TS composite using simple TS preprocessing techniques. As expected, when ML algorithms are applied to a dataset obtained from a composite image, the median producer accuracy (PA) and user accuracy (UA) in those two classes are significantly lower than the median overall accuracy (OA) for all classes. The second dataset allows the ML models to learn the evolution of the spectral signatures over 5 months. Compared to the first dataset, the results indicate that ML models generalise better using TS data, even if the series are short and without any preprocessing. This generalization is further improved in the last dataset. The ML models are subsequently applied to an area with different geographical bounds. These last results indicate that, out of seven classifiers, the popular random forest (RF) algorithm ranked fourth, while XGBoost (xGB) obtained the best results. The best OA, as well as the best PA/UA balance, were obtained by performing feature construction using the M3GP algorithm and then applying XGB to the new extended dataset. %K genetic algorithms, genetic programming, Cocoa agroforest classification, land cover mapping, machine learning, time series, tropical areas %9 journal article %R 10.1080/01431161.2022.2089540 %U http://dx.doi.org/10.1080/01431161.2022.2089540 %P 3298-3319 %0 Conference Proceedings %T M6GP: Multiobjective Feature Engineering %A Batista, Joao Eduardo %A Rodrigues, Nuno Miguel %A Vanneschi, Leonardo %A Silva, Sara %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F batista:2024:CEC %X The current trend in machine learning is to use powerful algorithms to induce complex predictive models that often fall under the category of black-box models. Thanks to this, there is also a growing interest in studying model explainability and interpretability so that human experts can understand, validate, and correct those models. With the objective of promoting the creation of inherently interpretable models, we present M6GP. This wrapper-based multi-objective automatic feature engineering algorithm combines key components of the M3GP and NSGA-II algorithms. Wrapping M6GP around another machine learning algorithm evolves a set of features optimised for this algorithm while potentially increasing its robustness. We compare our results with M3GP and M4GP, two ancestors from the same algorithm family, and verify that, by using a multi-objective approach, M6GP obtains equal or better results. In addition, by using complexity metrics on the list of objectives, the M6GP models come down to one-fifth of the size of the M3GP models, making them easier to read by comparison. %K genetic algorithms, genetic programming, Measurement, Training, Machine learning algorithms, Power demand, Machine learning, Predictive models, Market research, Multiobjective Optimization, Feature Engineering, Explainable AI, Interpretability %R 10.1109/CEC60901.2024.10612107 %U http://hdl.handle.net/10362/172920 %U http://dx.doi.org/10.1109/CEC60901.2024.10612107 %0 Conference Proceedings %T Measuring Structural Complexity of GP Models for Feature Engineering over the Generations %A Batista, Joao Eduardo %A Pindur, Adam Kotaro %A Iba, Hitoshi %A Silva, Sara %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F batista:2024:CEC2 %X Feature engineering is a necessary step in the machine learning pipeline. Together with other preprocessing methods, it allows the conversion of raw data into a dataset containing only the necessary features to solve the task at hand, reducing the computational complexity of inducing models and creating models that are potentially simpler, more robust, and more interpretable. We use M3GP, a wrapper-based feature engineering algorithm, to induce a set of features that are adapted in number and in shape to several classifiers with different levels of predictive power, from decision trees with depth 3 to random forests with 100 estimators and no depth limit. Intuition tells us that classifiers that are restricted in the number of features should compensate for this restriction by using features with a high degree of correlation with the target objective. By opposition, the principle behind the boosting algorithm tells us that we can create a strong classifier using a large set of weak features. This indicates that classifiers with no restrictions should prefer many but weaker features. Our results confirm this hypothesis while also revealing that M3GP induces unnecessarily complex features. We measure complexity using several structural complexity metrics found in the literature and show that, although our pipeline consistently obtains good results, the structural complexity of the induced models varies drastically across runs. Additionally, while the test performance peaks in the early stages of the evolution, the complexity of the feature engineering models continues to grow, with little to no return in test performance. This work promotes using several complexity metrics to measure model interpretability and identifies issues related to model complexity in M3GP, proposing solutions to improve the computational cost of inducing models and the complexity of the final models. %K genetic algorithms, genetic programming, Measurement, Analytical models, Computational modeling, Pipelines, Predictive models, Prediction algorithms, Complexity theory, Model Complexity, Feature Engineering, Model Interpretability, Classification %R 10.1109/CEC60901.2024.10611989 %U http://dx.doi.org/10.1109/CEC60901.2024.10611989 %0 Journal Article %T Complexity, interpretability and robustness of GP-based feature engineering in remote sensing %A Batista, Joao E. %A Pindur, Adam K. %A Cabral, Ana I. R. %A Iba, Hitoshi %A Silva, Sara %J Swarm and Evolutionary Computation %D 2025 %V 92 %@ 2210-6502 %F Batista:2025:swevo %X Feature engineering is a crucial step in machine learning that provides better data for the learning algorithms to induce robust models, and this effort should be adapted to the capabilities of each algorithm. For example, classifiers that do not perform data transformations (e.g., cluster-based) perform better when the different classes are separated, typically requiring preprocessed data. Other models (e.g., decision trees) can perform several splits in the feature space, easily obtaining perfect results in training data, but have a higher risk of overfitting with unprocessed data. We use the rbd-GP and M3GP genetic programming algorithms to induce new features based on the original features, to be used by shallow and deep decision tree and random forest models. M3GP is wrapped around a learning algorithm, using its performance as fitness. This way, the induced features are adapted to the classifier, allowing us to compare the complexity of the features induced for the different classifiers. We measure the complexity of the induced features using several structural and functional complexity metrics found in the literature, also proposing a new metric that measures the separability of classes in the feature space. Like other authors, we use complexity as an interpretability metric, selecting three models to discuss and validate based on their performance and size. We apply these methods to remote sensing classification problems and solve two tasks that are hard due to the high similarity between the land cover classes: detecting cocoa agroforest and forecasting forest degradation up to one year in the future %K genetic algorithms, genetic programming, Remote sensing, Model complexity, Model interpretability, Feature construction, Forest degradation, Classification, Time series %9 journal article %R 10.1016/j.swevo.2024.101761 %U https://www.sciencedirect.com/science/article/pii/S2210650224002992 %U http://dx.doi.org/10.1016/j.swevo.2024.101761 %P 101761 %0 Conference Proceedings %T Solving the Unknown Complexity Formula Problem with Genetic Programming %A Batista, Rayco %A Segredo, Eduardo %A Segura, Carlos %A Leon, Coromoto %A Rodriguez, Casiano %Y Rojas, Ignacio %Y Caparrós, Gonzalo Joya %Y Cabestany, Joan %S Advances in Computational Intelligence - 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part I %S Lecture Notes in Computer Science %D 2013 %V 7902 %I Springer %F conf/iwann/BatistaSSLR13 %X The Unknown Complexity Formula Problem (ucfp) is a particular case of the symbolic regression problem in which an analytical complexity formula that fits with data obtained by multiple executions of certain algorithm must be given. In this work, a set of modifications has been added to the standard Genetic Programming (GP) algorithm to deal with the ucfp. This algorithm has been applied to a set of well-known benchmark functions of the symbolic regression problem. Moreover, a real case of the ucfp has been tackled. Experimental evaluation has demonstrated the good behaviour of the proposed approach in obtaining high quality solutions, even for a real instance of the ucfp. Finally, it is worth pointing out that the best published results for the majority of benchmark functions have been improved. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-38679-4_22 %U http://dx.doi.org/10.1007/978-3-642-38679-4 %U http://dx.doi.org/10.1007/978-3-642-38679-4_22 %P 232-240 %0 Conference Proceedings %T Injecting Social Diversity in Multi-Objective Genetic Programming: the Case of Model Well-formedness Rule Learning %A Batot, Edouard %A Sahraoui, Houari %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 2018_Batot_Sahraoui_SSBSE18 %X Software modelling activities typically involve a tedious and time-consuming effort by specially trained personnel. This lack of automation hampers the adoption of the Model Driven Engineering (MDE) paradigm. Nevertheless, in the recent years, much research work has been dedicated to learn MDE artefacts instead of writing them manually. In this context, mono- and multi-objective Genetic Programming (GP) has proven being an efficient and reliable method to derive automation knowledge by using, as training data, a set of input/out examples representing the expected behaviour of an artefact. Generally, the conformance to the training example set is the main objective to lead the search for a solution. Yet, single fitness peak, or local optima deadlock, one of the major drawbacks of GP, happens when adapted to MDE and hinder the results of the learning. We aim at showing in this paper that an improvement in population social diversity carried out during the evolutionary %K genetic algorithms, genetic programming, SBSE, Model Driven Engineering (MDE), Metamodel, Modelling Space, WFR, OCL, NSGA-II, SSDM, TF-IDF %R 10.1007/978-3-319-99241-9_8 %U http://www-ens.iro.umontreal.ca/~batotedo/papers/2018_Batot_Sahraoui_SSBSE18.pdf %U http://dx.doi.org/10.1007/978-3-319-99241-9_8 %P 166-181 %0 Thesis %T From examples to knowledge in model-driven engineering : a holistic and pragmatic approach %A Batot, Edouard %D 2018 %8 nov %C Canada %C Departement d’informatique et de recherche operationnelle, Universite de Montreal %F Batot_Edouard_2018_These %X Model-Driven Engineering (MDE) is a software development approach that proposes to raise the level of abstraction of languages in order to shift the design and understanding effort from a programmer point of view to the one of decision makers. However, the manipulation of these abstract representations, or models, has become so complex that traditional techniques are not enough to automate its inherent tasks. For its part, the Search-Based Software Engineering (SBSE) proposes to reformulate the automation of MDE tasks as optimization problems. Once reformulated, the problem will be solved by metaheuristic algorithms. With a plethora of studies on the subject, the power of automation of SBSE has been well established. Based on this observation, the Example-Based MDE community (EB-MDE) started using application examples to feed the reformulation into SBSE of the MDE task learning problem. In this context, the concordance of the output of the solutions with the examples becomes an effective barometer for evaluating the ability of a solution to solve a task. This measure has proved to be a semantic goal of choice to guide the metaheuristic search for solutions. However, while it is commonly accepted that the representativeness of the examples has an impact on the generalizability of the solutions, the study of this impact suffers from a flagrant lack of consideration. In this thesis, we propose a thorough formulation of the learning process in an MDE context including a complete methodology to characterize and evaluate the relation that exists between two important properties of the examples, their size and coverage, and the generalizability of the solutions. We perform an empirical analysis, and propose a detailed plan for further investigation of the concept of representativeness, or of other representativities. %K genetic algorithms, genetic programming, SBSE, Software Engineering, Model-Driven Engineering, Search Based Software Engineering, Learning from examples, Generalization and Representativeness, Machine Learning, Aritficial Intelligence, Genie logiciel, Apprentissage machine, Intelligence artificielle, Genie logiciel experimental, Apprentissage a partir d’exemples %9 Ph.D. thesis %U https://papyrus.bib.umontreal.ca/xmlui/bitstream/1866/21737/2/Batot_Edouard_2018_These.pdf %0 Journal Article %T Promoting social diversity for the automated learning of complex MDE artifacts %A Batot, Edouard R. %A Sahraoui, Houari %J Software and Systems Modeling %D 2022 %8 jun %V 21 %@ 1619-1366 %F Batot:2022:SSM %X Software modelling activities typically involve a tedious and time-consuming effort by specially trained personnel. This lack of automation hampers the adoption of model-driven engineering (MDE). Nevertheless, in the recent years, much research work has been dedicated to learn executable MDE artifacts instead of writing them manually. In this context, mono- and multi-objective genetic programming (GP) has proven being an efficient and reliable method to derive automation knowledge by using, as training data, a set of examples representing the expected behaviour of an artifact. Generally, conformance to the training example set is the main objective to lead the learning process. Yet, single fitness peak, or local optima deadlock, a common challenge in GP, hinders the application of GP to MDE. we propose a strategy to promote populations social diversity during the GP learning process. We evaluate our approach with an empirical study featuring the case of learning well-formedness rules in MDE with a multi-objective genetic programming algorithm. Our evaluation shows that integration of social diversity leads to more efficient search, faster convergence, and more generalizable results. Moreover, when the social diversity is used as crowding distance, this convergence is uniform through a hundred of runs despite the probabilistic nature of GP. It also shows that genotypic diversity strategies cannot achieve comparable results %K genetic algorithms, genetic programming, Model-driven engineering, Social diversity, MOGP, NSGA-II, fitness sharing, ROUGE %9 journal article %R 10.1007/s10270-021-00969-9 %U https://rdcu.be/c69Rx %U http://dx.doi.org/10.1007/s10270-021-00969-9 %P 1159-1178 %0 Conference Proceedings %T Genetic Programming of Full Knowledge Bases for Fuzzy Logic Controllers %A Battle, Daryl %A Homaifar, Abdollah %A Tunstel, Edward %A Dozier, Gerry %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 battle:1999:GPFKBFLC %X Genetic programming (GP) is applied to automatic discovery of full knowledge bases for use in fuzzy logic control applications. An extension to a rule learning GP system is presented that achieves this objective. In addition, GP is employed to handle selection of fuzzy set intersection operators (t-norms). The new GP system is applied to design a mobile robot path tracking controller and performance is shown to be comparable to that of a manually designed controller. %K genetic algorithms, genetic programming, real world applications %U http://gpbib.cs.ucl.ac.uk/gecco1999/RW-730.pdf %P 1463-1468 %0 Conference Proceedings %T Tailored Source Code Transformations to Synthesize Computationally Diverse Program Variants %A Baudry, Benoit %A Allier, Simon %A Monperrus, Martin %S Proceedings of the 2014 International Symposium on Software Testing and Analysis, ISSTA 2014 %D 2014 %8 jul 21 25 %I ACM %C San Jose, CA, USA %F Baudry:2014:ISSTA %X The predictability of program execution provides attackers a rich source of knowledge who can exploit it to spy or remotely control the program. Moving target defence addresses this issue by constantly switching between many diverse variants of a program, which reduces the certainty that an attacker can have about the program execution. The effectiveness of this approach relies on the availability of a large number of software variants that exhibit different executions. However, current approaches rely on the natural diversity provided by of-the-shelf components, which is very limited. In this paper, we explore the automatic synthesis of large sets of program variants, called sosies. Sosies provide the same expected functionality as the original program, while exhibiting different executions. They are said to be computationally diverse. This work addresses two objectives: comparing different transformations for increasing the likelihood of sosie synthesis (densifying the search space for sosies); demonstrating computation diversity in synthesized sosies. We synthesized 30 184 sosies in total, for 9 large, real-world, open source applications. For all these programs we identified one type of program analysis that systematically increases the density of sosies; we measured computation diversity for sosies of 3 programs and found diversity in method calls or data in more than 40percent of sosies. This is a step towards controlled massive unpredictability of software. %K genetic algorithms, genetic programming, Genetic Improvement, SBSE, Program Transformation, Software Diversity %R 10.1145/2610384.2610415 %U https://hal.archives-ouvertes.fr/hal-00938855/document %U http://dx.doi.org/10.1145/2610384.2610415 %P 149-159 %0 Journal Article %T The Multiple Facets of Software Diversity: Recent Developments in Year 2000 and Beyond %A Baudry, Benoit %A Monperrus, Martin %J ACM Computer Surveys %D 2015 %8 sep %V 48 %N 1 %I ACM %C New York, NY, USA %@ 0360-0300 %F baudry:2015:acmcs %X Early experiments with software diversity in the mid 1970s investigated N-version programming and recovery blocks to increase the reliability of embedded systems. Four decades later, the literature about software diversity has expanded in multiple directions: goals (fault tolerance, security, software engineering), means (managed or automated diversity), and analytical studies (quantification of diversity and its impact). Our article contributes to the field of software diversity as the first work that adopts an inclusive vision of the area, with an emphasis on the most recent advances in the field. This survey includes classical work about design and data diversity for fault tolerance, as well as the cybersecurity literature that investigates randomization at different system levels. It broadens this standard scope of diversity to include the study and exploitation of natural diversity and the management of diverse software products. Our survey includes the most recent works, with an emphasis from 2000 to the present. The targeted audience is researchers and practitioners in one of the surveyed fields who miss the big picture of software diversity. Assembling the multiple facets of this fascinating topic sheds a new light on the field. %K genetic algorithms, genetic programming, Software Engineering, Software diversity, design principles, program transformation %9 journal article %R 10.1145/2807593 %U http://dx.doi.org/10.1145/2807593 %U http://arxiv.org/abs/1409.7324 %P 16:1-16:26 %0 Conference Proceedings %T A spoonful of DevOps helps the GI Go Down %A Baudry, Benoit %A Harrand, Nicolas %A Schulte, Eric %A Timperley, Christopher %A Tan, Shin Hwei %A Selakovic, Marija %A Ugherughe, Emamurho %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 Baudry:2018:GI %X DevOps emphasizes a high degree of automation at all phases of the software development lifecyle. Meanwhile, Genetic Improvement (GI) focuses on the automatic improvement of software artefacts. In this paper, we discuss why we believe that DevOps offers an excellent technical context for easing the adoption of GI techniques by software developers. We also discuss A/B testing as a prominent and clear example of GI taking place in the wild today, albeit one with human-supervised fitness and mutation operators. %K genetic algorithms, genetic programming, genetic improvement, continuous integration, DevOps %R 10.1145/3194810.3194818 %U http://www.shinhwei.com/devop-gi.pdf %U http://dx.doi.org/10.1145/3194810.3194818 %P 35-37 %0 Book Section %T Evolving Efficient Algorithms by Genetic Programming: A Case Study in Sorting %A Bauer, Eric T. %E Koza, John R. %B Genetic Algorithms and Genetic Programming at Stanford 1995 %D 1995 %8 November %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-195720-5 %F bauer:1995:EEAGPACSS %K genetic algorithms, genetic programming %P 1-10 %0 Conference Proceedings %T Network Management Practices and Sector Performance - A Genetic Programming Approach %A Bauer, Johannes M. %A DeMaagd, Kurt %Y Mateja, Elizabeth %S 36th Research Conference on Communications, Information, and Internet Policy %D 2008 %8 sep 27 %C Arlington, VA, USA %F Bauer:2008:TREC %X Introduction The migration to next-generation network architectures, in which platform and application/content layers are relatively distinct, has unleashed a very important and possibly far-reaching policy debate as to the rules that should govern the interaction of players operating on one or both of these layers. Started as a discussion on network neutrality, the debate recently shifted focus to delineating reasonable from unreasonable forms of network management. Legislation to strengthen regulatory powers (Markey Bill) or antitrust enforcement (Conyers Bill) is pending in Congress. The Federal Communications Commission has reasserted its willingness to enforce an open internet in its Comcast Decision in August 2008. %K genetic algorithms, genetic programming %U http://www.tprcweb.com/images/stories/2008/Bauer-DeMaagd-Network-Management-2008-TPRC-fin.pdf %0 Report %T Toward Code Evolution By Artificial Economies %A Baum, Eric B. %A Durdanovic, Igor %D 1998 %8 October %I NEC Research Institute %C 4 Independence Way, Princeto, NJ 08540, USA %F baum:1998:tceaeTR %K genetic algorithms, genetic programming %0 Conference Proceedings %T Toward Code Evolution By Artificial Economies %A Baum, Eric B. %A Durdanovic, Igor %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 baum:1998:tceae %X We have begun exploring code evolution by artificial economies. We implemented a reinforcement learning machine called Hayek2 consisting of agents, written in a machine language inspired by Ray’s Tierra, that interact economically. The economic structure of Hayek2 addresses credit assignment at both the agent and meta levels. Hayek2 succeeds in evolving code to solve Blocks World problems, and has been more effective at this than our hillclimbing program and our genetic program. Our hill climber and our GP also performed well, learning algorithms as strong as a simple search program that incorporates hand-coded domain knowledge. We made efforts to optimise our hillclimbing program and it has features that may be of independent interest. Our genetic program using crossover performed far better than a version using other macro-mutations or our hillclimber, bearing on a controversy in the Genetic Programming literature %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.7596.pdf %P 14-22 %0 Conference Proceedings %T Toward Code Evolution By Artificial Economies (Extended Abstract) %A Baum, Eric B. %A Durdanovic, Igor %Y Landweber, Laura F. %Y Winfree, Erik %S Evolution as Computation, DIMACS Workshop, Princeton, January 1999 %S Natural Computing Series %D 2001 %8 November 12 jan %I Springer-Verlag %C Princeton University %@ 3-540-66709-1 %F oai:CiteSeerPSU:5199 %X We have begun exploring code evolution by artificial economies. We implemented a reinforcement learning machine called Hayek2 consisting of agents, written in a machine language inspired by Ray’s Tierra, that interact economically. The economic structure of Hayek2 addresses credit assignment at both the agent and meta levels. Hayek2 succeeds in evolving code to solve Blocks World problems, and has been more effective at this than our hillclimbing program and our genetic program. Our hillclimber and our GP also performed well, learning algorithms as strong as a simple search program that incorporates hand-coded domain knowledge. We made efforts to optimize our hillclimbing program and it has features that may be of independent interest. Our genetic program using crossover performed far better than a version using other macro-mutations or our hillclimber, bearing on a controversy in the Genetic Programming literature. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-55606-7_16 %U http://citeseer.ist.psu.edu/5199.html %U http://dx.doi.org/10.1007/978-3-642-55606-7_16 %P 314-332 %0 Journal Article %T Evolution of Cooperative Problem Solving in an Artificial Economy %A Baum, Eric B. %A Durdanovic, Igor %J Neural Computation %D 2000 %8 dec %V 12 %N 12 %@ 0899-7667 %F baum:2000:NeurComp %X We address the problem of how to reinforce learning in ultracomplex environments, with huge state-spaces, where one must learn to exploit a compact structure of the problem domain. The approach we propose is to simulate the evolution of an artificial economy of computer programs. The economy is constructed based on two simple principles so as to assign credit to the individual programs for collaborating on problem solutions. We find empirically that starting from programs that are random computer code, we can develop systems that solve hard problems. In particular, our economy learned to solve almost all random Blocks World problems with goal stacks that are 200 blocks high. Competing methods solve such problems only up to goal stacks of at most 8 blocks. Our economy has also learned to unscramble about half a randomly scrambled Rubik’s cube and to solve several commercially sold puzzles. %K genetic algorithms, genetic programming, STGP %9 journal article %R 10.1162/089976600300014700 %U https://www.eecs.harvard.edu/cs286r/courses/spring06/papers/baum_nc00.pdf %U http://dx.doi.org/10.1162/089976600300014700 %P 2743-2775 %0 Journal Article %T Examination of genetic programming paradigm for high-throughput experimentation and heterogeneous catalysis %A Baumes, Laurent A. %A Collet, Pierre %J Computational Materials Science %D 2009 %8 mar %V 45 %N 1 %@ 0927-0256 %F Baumes2008 %X The strong feature dependencies that exist in catalyst description do not permit using common algorithms while not loosing crucial information. Data treatments are restricted by the form of input data making the full use of the experimental information impossible, confining the experimentation studies, and reducing one of the primary goals of HTE: to enlarge the search space. Consequently, an advanced representation of the catalytic data supporting the intrinsic complexity of heterogeneous catalyst data structure is proposed. Likewise, an optimization strategy that can manipulate efficiently such data type, permitting a valuable connection between algorithms, high-throughput (HT) apparatus, and databases, is depicted. Such a new methodology enables the integration of domain knowledge through its configuration considering the study to be investigated. For the first time in heterogeneous catalysis, a conceptual examination of genetic programming (GP) is achieved. %K genetic algorithms, genetic programming, Heterogeneous catalysis, High-throughput, Materials, Combinatorial, Representation, Data structure %9 journal article %R 10.1016/j.commatsci.2008.03.051 %U http://www.sciencedirect.com/science/article/B6TWM-4T4J19Y-1/2/809324138cc0b8f49634eae7f22e995f %U http://dx.doi.org/10.1016/j.commatsci.2008.03.051 %P 27-40 %0 Journal Article %T Using Genetic Programming for an Advanced Performance Assessment of Industrially Relevant Heterogeneous Catalysts %A Baumes, L. A. %A Blansche, A. %A Serna, P. %A Tchougang, A. %A Lachiche, N. %A Collet, P. %A Corma, A. %J Materials and Manufacturing Processes %D 2009 %8 mar %V 24 %N 3 %I Taylor and Francis %@ 1042-6914 %F BBSTLCC09 %X Beside the ease and speed brought by automated synthesis stations and reactors technologies in materials science, adapted informatics tools must be further developed in order to handle the increase of throughput and data volume, and not to slow down the whole process. This article reports the use of genetic programming (GP) in heterogeneous catalysis. Despite the fact that GP has received only little attention in this domain, it is shown how such an approach can be turned into a very singular and powerful tool for solid optimization, discovery, and monitoring. Jointly with neural networks, the GP paradigm is employed in order to accurately and automatically estimate the whole curve conversion vs. time in the epoxidation of large olefins using titanosilicates, Ti-MCM-41 and Ti-ITQ-2, as catalysts. In contrast to previous studies in combinatorial materials science and high-throughput screening, it was possible to estimate the entire evolution of the catalytic reaction for unsynthesized catalysts. Consequently, the evaluation of the performance of virtual solids is not reduced to a single point (e.g., the conversion level at only one given reaction time or the initial reaction rate). The methodology is thoroughly detailed, while stressing on the comparison between the recently proposed Context Aware Crossover (CAX) and the traditional crossover operator. %K genetic algorithms, genetic programming, Data mining, Heterogeneous catalysis, High-throughput, Materials science %9 journal article %R 10.1080/10426910802679196 %U http://lsiit.u-strasbg.fr/Publications/2009/BBSTLCC09 %U http://dx.doi.org/10.1080/10426910802679196 %P 282-292 %0 Journal Article %T EASEA: a generic optimization tool for GPU machines in asynchronous island model %A Baumes, Laurent A. %A Kruger, Frederic %A Collet, Pierre %J Computer Methods in Materials Science %D 2011 %V 11 %N 3 %I The AGH University of Science and Technology Press, Open Access %@ 1641-8581 %F krueg11ease %X Very recently, we presented an efficient implementation of Evolutionary Algorithms (EAs) using Graphics Processing Units (GPU) for solving microporous crystal structures. Because of both the inherent complexity of zeolitic materials and the constant pressure to accelerate R and D solutions, an asynchronous island model running on clusters of machines equipped with GPU cards, i.e. the current trend for super-computers and cloud computing, is presented. This last improvement of the EASEA platform allows an effortless exploitation of hierarchical massively parallel systems. It is demonstrated that supra-linear speedup over one machine and linear speedup considering clusters of different sizes are obtained. Such an island implementation over several potentially heterogeneous machines opens new horizon for various domains of application where computation time for optimisation remains the principal bottleneck. %K genetic algorithms, genetic programming, GPGPU, Evolutionary Algorithms, Island Model, Parallelism, Zeolite Materials %9 journal article %U http://icube-publis.unistra.fr/docs/7407/baumes.pdf %P 489-499 %0 Journal Article %T Boosting theoretical zeolitic framework generation for the determination of new materials structures using GPU programming %A Baumes, Laurent A. %A Kruger, Frederic %A Jimenez, Santiago %A Collet, Pierre %A Corma, Avelino %J Physical Chemistry Chemical Physics %D 2011 %V 13 %N 10 %I The Royal Society of Chemistry %@ 1463-9076 %F Baumes:2011:PCCP %X Evolutionary algorithms have proved to be efficient for solving complicated optimization problems. On the other hand, the many-core architecture in graphical cards General Purpose Graphic Processing Unit (GPGPU) offers one of the most attractive cost/performance ratio. Using such hardware, the manuscript shows how an efficiently implemented genetic algorithm with a simple fitness function allows boosting the determination of zeolite structures. A case study is presented. %K genetic algorithms, genetic programming, memetic genetic algorithm, EASEA, GPU, GPGPU, nVidia GTX 295, CUDA %9 journal article %R 10.1039/C0CP02833A %U http://dx.doi.org/10.1039/C0CP02833A %P 4674-4678 %0 Conference Proceedings %T Hybrid Stochastic Genetic Evolution-Based Prediction Model of Received Input Voltage for Underground Imaging Applications %A Baun, Jonah Jahara %A Janairo, Adrian Genevie %A Concepcion, Ronnie %A Francisco, Kate %A Enriquez, Mike Louie %A Relano, R-Jay %A de Leon, Joseph Aristotle %A Bandala, Argel %A Vicerra, Ryan Rhay %A Dungca, Jonathan %S 2023 8th International Conference on Business and Industrial Research (ICBIR) %D 2023 %8 may %F Baun:2023:ICBIR %X The capacitive resistivity technique in underground object detection comprises configured transmitter and receiver antennas that are capacitively coupled to the ground. However, underground imaging lacks a basis for determining the received voltage for precise data analysis. This study aimed to develop a prediction model of the received input voltage signal amplitude from the ground of a single-pair antenna underground imaging system. The receiver antenna circuit for this application is designed and simulated in Proteus Software. Genetic Programming (GP) is applied to predict the received input signal based on the shape of the received waveform signal, operating frequency, resistance of the waveform shaping circuit, and buffer amplifier output signal. The resulting fitness function of GP (4) is acceptable as it scored an R2 of 99.38percent with a negligible MSE of 0.0059 and an MAE of 12.3423. Then, the GP fitness function is optimised through Genetic Algorithm (GA), Differential Evolution (DE), and Evolutionary Strategy (ES) in which the GP-GA model outperformed the two hybrid models providing fast convergence and 2.49e-8 best fitness value. This study proved that GP can be effectively combined with stochastic genetic evolution algorithms to avoid lengthy mathematical calculations and accurately estimate the natural voltage received from the ground. %K genetic algorithms, genetic programming, Resistance, Imaging, Stochastic processes, Receiving antennas, Voltage, Predictive models, Conductivity, capacitive resistivity technique, underground imaging, voltage prediction, stochastic genetic evolution %R 10.1109/ICBIR57571.2023.10147464 %U http://dx.doi.org/10.1109/ICBIR57571.2023.10147464 %P 549-555 %0 Conference Proceedings %T Single-pair Equatorial Dipole-Dipole Underground Imaging Antenna Capacitance Minimization Using Grey Wolf Optimization Algorithm %A Baun, Jonah Jahara %A Janairo, Adrian Genevie %A Relano, R-Jay %A Concepcion, Ronnie %A Bandala, Argel %A Vicerra, Ryan Rhay %A Mirjalili, Seyedali %S 2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) %D 2023 %8 nov %F Baun:2023:HNICEM %X Roads using capacitive resistivity underground imaging at very low frequencies are vulnerable to capacitance dynamics characteristic of the antenna, which can lead to unwanted signal reflection, coupling, and an adverse influence on the sensitivity of the reception. Conventional mathematical antenna models are time-consuming, repetitive, prone to error by humans, and produce inconsistent results. A novel method for optimising plate-wire antenna capacitance by equatorial dipole-dipole antenna geometry modelling has been proposed in this paper to tackle this new problem. It involves the use of Genetic programming (GP) optimised through the Grey Wolf Optimisation (GWO) algorithm. Based on 241 permutations of antenna wire radius and elevation along with the width, length, thickness, and elevation of dipole plates, GP was used to develop the fitness function of the capacitance of the antenna. The GP-GWO was used to minimise the capacitance (almost 1 nF) in order to achieve quasi-static conditions. The developed GP-GWO model was compared to the electrical properties of the default antenna and other improved versions through 3D modelling using Altair Feko. In terms of sensitivity, the suggested model outperformed the existing models to the point where it could detect the presence of the pipe that provided the lowest received voltage of 0.080V clearly. %K genetic algorithms, genetic programming, Geometry, Sensitivity, Dipole antennas, Imaging, Receiving antennas, Conductivity, advanced metaheuristics, antenna geometry optimisation, bio-inspired optimisation, capacitance modelling, subsurface imaging %R 10.1109/HNICEM60674.2023.10589146 %U http://dx.doi.org/10.1109/HNICEM60674.2023.10589146 %0 Conference Proceedings %T Meteorological Data Analysis and Prediction by Means of Genetic Programming %A Bautu, Andrei %A Bautu, Elena %S Proceedings of the Fifth Workshop on Mathematical Modelling of Environmental and Life Sciences Problems %D 2006 %8 sep %C Constanta, Romania %G en %F Bautu:2006:mmelsp %X Weather systems use extremely complex combinations of mathematical tools for analysis and forecasting. Unfortunately, due to phenomena in the world climate, such as the greenhouse effect, classical models may become inadequate mostly because they lack adaptation. Therefore, the weather prediction problem is suited for heuristic approaches, such as Evolutionary Algorithms. Experimentation with heuristic methods like Genetic Programming (GP) can lead to the development of new insights or promising models that can be fine tuned with more focused techniques. This paper describes a GP approach for analysis and prediction of data and provides experimental results of the afore mentioned method on real-world meteorological time series. %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.613.3355 %P 35-42 %0 Journal Article %T Quantum Circuit Design By Means Of Genetic Programming %A Bautu, Andrei %A Bautu, Elena %J Romanian Journal of Physics %D 2007 %V 52 %N 5-7 %I Romanian Academy Publishing House %C Bucharest, Romania %@ 1221-146X %F Bautu20071q %X Research in quantum technology has shown that quantum computers can provide dramatic advantages over classical computers for some problems. The efficiency of quantum computing is considered to become so significant that the study of quantum algorithms has attracted widespread interest. Development of quantum algorithms and circuits is difficult for a human researcher, so automatic induction of computer programs by means of genetic programming, which uses almost no auxiliary information on the search space, proved to be useful in generating new quantum algorithms. This approach takes advantage of the intrinsic parallelism of the genetic algorithm and quantum computing parallelism. The paper begins with a brief review on some basic concepts in genetic algorithms and quantum computation. Next, it describes an application of genetic programming for evolving quantum computing circuits. %K genetic algorithms, genetic programming, quantum gates %9 journal article %U https://rjp.nipne.ro/2007_52_5-7.html %P 697-704 %0 Conference Proceedings %T A GEP-based approach for solving Fredholm first kind integral equations %A Bautu, Elena %A Bautu, Andrei %A Luchian, Henri %S Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2005 %D 2005 %8 sep %I IEEE Computer Society %@ 0-7695-2453-2 %F conf:synasc:bautu2005 %X Evolutionary techniques have been widely accepted as an effective meta-heuristic for a wide variety of problems in different domains. The main purpose of this paper is to present a symbolic technique based on the evolutionary paradigm gene expression programming (GEP) for solving Fredholm first type integral equations. We present the main traits of the gene expression algorithm (GEA), and our implementation for solving first kind integral equations of Fredholm type. The results obtained on four model problems using the symbolic technique described in this paper prove it to be suitable to handle this class of problems. %K genetic algorithms, genetic programming, gene expression programming, Fredholm integral equations, GEA, GEP approach, evolutionary techniques, first kind integral equations, gene expression algorithm, symbolic technique, Fredholm integral equations, evolutionary computation, symbol manipulation %R 10.1109/SYNASC.2005.6 %U http://dx.doi.org/10.1109/SYNASC.2005.6 %P 325 %0 Conference Proceedings %T Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming %A Bautu, Elena %A Bautu, Andrei %A Luchian, Henri %S Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC’05) %D 2005 %F Bautu:2005:SYNASC %X regression on a finite sample of noisy data. The purpose is to obtain a mathematical model for data which is both reliable and valid, yet the analytical expression is not restricted to any particular form. To obtain a statistical model of the noisy data set we use symbolic regression with pseudo-random number generators. We begin by describing symbolic regression and our implementation of this technique using genetic programming (GP) and gene expression programming (GEP). We present some results for symbolic regression on computer generated and real financial data sets in the final part of this paper. %K genetic algorithms, genetic programming, Gene Expression Programming %R 10.1109/SYNASC.2005.70 %U http://dx.doi.org/10.1109/SYNASC.2005.70 %P 321-324 %0 Conference Proceedings %T AdaGEP - An Adaptive Gene Expression Programming Algorithm %A Bautu, Elena %A Bautu, Andrei %A Luchian, Henri %Y Negru, Viorel %Y Jebelean, Tudor %Y Petcu, Dana %Y Zaharie, Daniela %S Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2007 %D 2007 %8 sep 26 29 %I IEEE Computer Society %C Timisoara, Romania %F conf/synasc/BautuBL07 %X Many papers focused on fine-tuning the gene expression programming (GEP) operators or their application rates in order to improve the performances of the algorithm. Much less work was done on optimizing the structural parameters of the chromosomes (i.e. number of genes and gene size). This is probably due to the fact that the no free lunch theorem states that no fixed values for these parameters will ever suit all problems. To counteract this fact, this paper presents a modified GEP algorithm, called AdaGEP, which automatically adapts the number of genes used by the chromosome. The adaptation process takes place at chromosome level, allowing chromosomes in the population to evolve with different number of genes. %K genetic algorithms, genetic programming, gene expression programming %R 10.1109/SYNASC.2007.51 %U http://dx.doi.org/10.1109/SYNASC.2007.51 %P 403-406 %0 Journal Article %T Numerical Solution For Fredholm First Kind Integral Equations Occurring In Synthesis of Electromagnetic Fields %A Bautu, Elena %A Pelican, Elena %J Romanian Journal of Physics %D 2007 %V 52 %N 3-4 %I Romanian Academy Publishing House %@ 1221-146X %F Bautu20071 %X It is known that Fredholm integral equations of the first kind with the kernel occur when solving with problems of synthesis of electrostatic and magnetic fields (m, n nonnegative rational numbers). This paper presents two approaches for solving such an equation. The first one involves discretisation by a collocation method and numerical solution using an approximate orthogonalisation algorithm. The second method is based on a nature inspired heuristic, namely genetic programming. It applies genetically-inspired operators to populations of potential solutions in the form of program trees, in an iterative fashion, creating new populations while searching for an optimal or near-optimal solution to the problem at hand. Results obtained in experiments are presented for both approaches. %K genetic algorithms, genetic programming, Gene Expression Programming, EM fields, Fredholm integral equations of the first kind, inverse problems %9 journal article %U http://www.nipne.ro/rjp/2007_52_3-4.html %P 245-256 %0 Conference Proceedings %T An Evolutionary Approach for Modeling Time Series %A Bautu, Elena %A Bautu, Andrei %A Luchian, Henri %S 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC ’08 %D 2008 %8 sep %F Bautu:2008:SYNASC %X Change points in time series appear due to variations in the data generation process. We consider the problem of modeling time series generated by dynamic processes, and we focus on finding the change points using a specially tailored genetic algorithm. The algorithm employs a new representation, described in detail in the paper. Suitable genetic operators are also defined and explained. The results obtained on computer generated time series provide evidence that the approach can be used for change point detection, and has good potential for time series modeling. %K genetic algorithms, genetic programming, change point detection, data generation process, evolutionary approach, genetic operator, time series modeling, time series %R 10.1109/SYNASC.2008.63 %U http://dx.doi.org/10.1109/SYNASC.2008.63 %P 507-513 %0 Journal Article %T Symbolic approach for the generalized airfoil equation %A Bautu, Elena %A Pelican, Elena %J Creative Mathematics and Informatics %D 2008 %V 17 %N 2 %@ 1584-286X %F Bautu20081 %X The generalised airfoil equation governs the pressure across an airfoil oscillating in a wind tunnel. In this paper we analyse the problem for an airfoil with a flap, by means of Gene Expression Programming (GEP). We present the main traits of the GEP metaheuristic and then we define its elements in order to be used for integral equations of the first kind. The results obtained by our symbolic approach confirm the suitability of this method for problems modelled by Fredholm first kind integral equations. %K genetic algorithms, genetic programming, Gene Expression Programming, Generalised airfoil equation, Fredholm integral equation of the first kind, airfoil equation %9 journal article %U https://www.creative-mathematics.cunbm.utcluj.ro/article/symbolic-approach-for-the-generalized-airfoil-equation/ %P 52-60 %0 Conference Proceedings %T Evolving Gene Expression Programming Classifiers for Ensemble Prediction of Movements on the Stock Market %A Bautu, Elena %A Bautu, Andrei %A Luchian, Henri %Y Barolli, Leonard %Y Xhafa, Fatos %Y Vitabile, Salvatore %Y Hsu, Hui-Huang %S The Fourth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2010) %D 2010 %8 15 18 feb %I IEEE Computer Society %C Krakow, Poland %F conf/cisis/BautuBL10 %K genetic algorithms, genetic programming, gene expression programming %R 10.1109/CISIS.2010.101 %U http://dx.doi.org/10.1109/CISIS.2010.101 %P 108-115 %0 Thesis %T Intelligent Techniques for Data Modeling Problems %A Bautu, Elena %D 2010 %8 jun %C Iasi, Romania %C Al. I. Cuza University %F bautu:thesis %O Romanian subtitle is Programare genetica pentru probleme de optimizare in Inteligenta artificiala %X Supervised learning deals with the problem of discovering models from data as relationships between input and output attributes. Two types of models are distinguished: regression models for continuous output and classification models (classiffiers) for discrete output. This thesis addresses both regression and classiffication problems, with an emphasis on new applications and on proposing new evolutionary techniques. First, we address the regression domain. Symbolic regression by means of evolutionary techniques is recommended when there is little or no a priori information on the modelled process. It relies on a set of input-output observations to infer mathematical models, posing no constraints on the structure, the coefficients or the size of the model. We introduce inverse problems modeled by Fredholm integral equations of the first kind and the inverse problem of log synthesis to be modelled by symbolic regression by means of gene expression programming. A new genetic programming scheme is formulated for the problem of automatically designing quantum circuits. An adaptive version of the gene expression programming algorithm is presented, which automatically tunes the complexity of the model by a gene (de)activation mechanism. For modelling time series produced by dynamic processes, we propose an evolutionary approach that uses a novel representation (and suitable genetic operators) to partition the time series based on change points. Empirical results prove the approach to be promising. Research on building classifiers for a given problem is also extensive, since there exists no best classifier at all tasks. The problem of predicting the direction of change of stock price on the market can be formulated as the search for a classifier that links past evolution to an increase or decrease. We explore new techniques for classification, in the context of predicting the direction of change of stock price, formulated as a binary classification %K genetic algorithms, genetic programming, gene expression programming, inverse problems, financial forecasting, data analysis, hypernetwork, hybridization %9 Ph.D. thesis %U https://sites.google.com/site/ebautu/home/publications/thesis/thesis_elena_bautu.pdf %0 Book %T Intelligent Techniques for Data Modeling Problems: Nature inspired meta-heuristics and learning models applied to time series modeling and forecasting %A Bautu, Elena %D 2012 %8 20 mar %I Lambert Academic Publishing %C Moldova %F Bautu:book %X Supervised learning deals with the problem of discovering models from data as relationships between input and output attributes. Two types of models are distinguished: regression models (for continuous output attributes) and classification models (for discrete output attributes). This thesis addresses both regression and classification problems with an emphasis on new applications and on presenting improved evolutionary techniques. Such techniques include Gene Expression Programming (classical and its adaptive version), Genetic Programming, and the hypernetwork model of learning (classical and its evolutionary version). Such methods can be successfully applied to many problems from various domains. This thesis presents applications for symbolic regression for inverse problems, quantum circuit design, modeling of dynamic processes, and forecasting price movement. %K genetic algorithms, genetic programming, Gene Expression Programming %U https://www.amazon.com/Intelligent-Techniques-Data-Modeling-Problems/dp/3848434792 %0 Journal Article %T A Hybrid Approach for Modelling Financial Time Series %A Bautu, Elena %A Barbulescu, Alina %J The International Arab Journal of Information Technology (IAJIT) %D 2012 %8 jul %V 9 %N 4 %@ 1683-3198 %F Bautu2012 %X The problem we tackle concerns forecasting time series in financial markets. AutoRegressive Moving-Average (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced models. Time series from financial domains often encapsulate different linear and non-linear patterns. ARMA models, although flexible, assume a linear form for the models. GEP evolves models adapting to the data without any restrictions with respect to the form of the model or its coefficients. Our approach benefits from the capability of ARMA to identify linear trends as well as GEP’s ability to obtain models that capture nonlinear patterns from data. Investigations are performed on real data sets. They show a definite improvement in the accuracy of forecasts of the hybrid method over pure ARMA and GEP used separately. Experimental results are analysed and discussed. Conclusions and some directions for further research end the paper. %K genetic algorithms, genetic programming, Gene Expression Programming, Financial time series, forecasting, ARMA, GEP, and hybrid methodolog %9 journal article %U http://www.ccis2k.org/iajit/PDF/vol.9,no.4/2806-5.pdf %P 327-335 %0 Conference Proceedings %T Genetic Improvement for DNN Security %A Baxter, Hunter %A Huang, Yu %A Leach, Kevin %S "13th International Workshop on Genetic Improvement %F Baxter:2024:GI %0 Journal Article %D 2024 %8 16 apr %I ACM %C Lisbon %F 2024"b %O Best Presentation %X Genetic improvement (GI) in Deep Neural Networks (DNNs) has traditionally enhanced neural architecture and trained DNN parameters. Recently, GI has supported large language models by optimising DNN operator scheduling on accelerator clusters. However, with the rise of adversarial AI, particularly model extraction attacks, there is an unexplored potential for GI in fortifying Machine Learning as a Service (MLaaS) models. We suggest a novel application of GI, not to improve model performance, but to diversify operator parallelism for the purpose of a moving target defence against model extraction attacks. We discuss an application of GI to create a DNN model defense strategy that uses probabilistic isolation, offering unique benefits not present in current DNN defense systems. %K genetic algorithms, genetic programming, Genetic Improvement, Computer Security, ANN %9 journal article %R 10.1145/3643692.3648261 %U http://gpbib.cs.ucl.ac.uk/gi2024/Genetic_Improvement_for_DNN_Security.pdf %U http://dx.doi.org/10.1145/3643692.3648261 %P 11-12 %0 Journal Article %T A Semi-empirical Approach Based on Genetic Programming for the Study of Biophysical Controls on Diameter-Growth of Fagus orientalis in Northern Iran %A Bayat, Mahmoud %A Noi, Phan Thanh %A Zare, Rozita %A Bui, Dieu Tien %J Remote. Sens. %D 2019 %V 11 %N 14 %F DBLP:journals/remotesensing/BayatNZB19 %K genetic algorithms, genetic programming %9 journal article %R 10.3390/rs11141680 %U https://doi.org/10.3390/rs11141680 %U http://dx.doi.org/10.3390/rs11141680 %P 1680 %0 Journal Article %T Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete %A Bayazidi, Alireza Mohammadi %A Wang, Gai-Ge %A Bolandi, Hamed %A Alavi, Amir H. %A Gandomi, Amir H. %J Mathematical Problems in Engineering %D 2014 %I Hindawi %F Bayazidi:2014:MPiE %X This paper presents a new multigene genetic programming (MGGP) approach for estimation of elastic modulus of concrete. The MGGP technique models the elastic modulus behaviour by integrating the capabilities of standard genetic programming and classical regression. The main aim is to derive precise relationships between the tangent elastic moduli of normal and high strength concrete and the corresponding compressive strength values. Another important contribution of this study is to develop a generalised prediction model for the elastic moduli of both normal and high strength concrete. Numerous concrete compressive strength test results are obtained from the literature to develop the models. A comprehensive comparative study is conducted to verify the performance of the models. The proposed models perform superior to the existing traditional models, as well as those derived using other powerful soft computing tools. %K genetic algorithms, genetic programming %9 journal article %R 10.1155/2014/474289 %U http://dx.doi.org/10.1155/2014/474289 %0 Conference Proceedings %T A Genetic Programming Framework for Error Recovery in Robotic Assembly Systems %A Baydar, Cem M. %A Saitou, Kazuhiro %Y Whitley, Darrell %Y Goldberg, David %Y Cantu-Paz, Erick %Y Spector, Lee %Y Parmee, Ian %Y Beyer, Hans-Georg %S Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000) %D 2000 %8 October 12 jul %I Morgan Kaufmann %C Las Vegas, Nevada, USA %@ 1-55860-708-0 %F Baydar:2000:GECCO %X The advantages and performance of genetic programming in the use of error recovery planning in robotic assembly systems are presented. A framework is developed and coupled with a 3D robotic simulation software for the generation of error recovery logic in assembly systems to generate robust recovery programs in robot language itself. Performance of the system is evaluated with the simulations made on a three dimensionally modeled automated assembly line. The obtained results showed that the system is efficient of generating robust recovery plans for different error states. %K genetic algorithms, genetic programming, Poster %U http://gpbib.cs.ucl.ac.uk/gecco2000/RW036.pdf %P 756 %0 Conference Proceedings %T Off-Line Error Recovery Logic Synthesis in Automated Assembly Lines by using Genetic Programming %A Baydar, Cem M. %A Saitou, Kazuhiro %Y Liang, Steven Y. %Y Arai, Tatsuo %S Proceedings Of The 2000 Japan/USA Symposium On Flexible Automation %D 2000 %8 23 26 jul %C Ann Arbor, MI, USA %@ 0-7918-1998-1 %F oai:CiteSeerPSU:538284 %X Unexpected failures are one of the most important problems, which cause costly shutdowns in an assembly line. Generally the recovery process is done by the experts or automated error recovery logic controllers embedded in the system. The previous work in the literature is focused on the on-line recovery of the assembly lines which makes the process, time and money consuming. Therefore a novel approach is necessary which requires less time and hardware effort for the generation of error recovery logic. The proposed approach is based on three-dimensional geometric modelling of the assembly line coupled with the evolutionary computation techniques to generate error recovery logic in an off-line manner. The scope of this work is focused on finding an error recovery algorithm from a predefined error case. An automated assembly line is virtually modeled and the validity of the recovery algorithm is evaluated in a generate and test fashion by using a commercial software package. The obtained results showed that the developed framework is capable of generating recovery algorithms from an arbitrary part positioning error case. It is aimed that this approach will be coupled with the error generation in the future, providing efficient ways for the study of error recovery in automated assembly lines. %K genetic algorithms, genetic programming, Error Recovery Synthesis, Off-line Programming, Automated Assembly Lines %U http://citeseer.ist.psu.edu/538284.html %0 Conference Proceedings %T Generation of Robust Recovery Logic in Assembly Systems using Multi-Level Optimization and Genetic Programming %A Baydar, Cem M. %A Saitou, Kazuhiro %S Proceedings of DETC-00 ASME 2000 Design Engineering Technical Conferences and Computers and Information in Engineering Conference %D 2000 %8 October 13 sep %C Baltimore, Maryland, USA %G en %F oai:CiteSeerPSU:535775 %X Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, these errors are handled by human experts or logic controllers. However, these controller codes are based on anticipated error scenarios and are deficient in dealing with unforeseen situations. In our previous work (Baydar and Saitou, 2000a), an approach for the automated generation of error recovery logic was discussed. The method is based on three-dimensional geometric modeling of the assembly line to generate error recovery logic in an ’off-line’ manner using Genetic Programming. The scope of our previous work was focused on finding an error recovery algorithm from a predefined error case. However due to the geometrical features of the assembly lines, there may be cases which can be detected as the same type of error by the sensors. Therefore robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence. In this paper, an extension of our previous study is presented to overcome this problera An assembly line is modeled and from the given error cases optimum way of error recovery is investigated using multi-level optimization. The obtained results showed that the infrastructure is capable of finding robust error recovery algorithms and multi-level optimization procedure improved the process. It is expected that the results of this study will be combined with the automatic error generation, resulting in efficient ways to automated error recovery logic synthesis. %K genetic algorithms, genetic programming %U http://citeseer.ist.psu.edu/535775.html %0 Conference Proceedings %T Off-line error prediction, diagnosis and recovery using virtual assembly systems %A Baydar, Cem M. %A Saitou, Kazuhiro %S Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2001 %D 2001 %8 21 26 may %V 1 %I IEEE %C Seoul, Korea %@ 0-7803-6576-3 %F Baydar:2001:ICRA %X Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of many parameters, it is difficult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focusing on online diagnosing and recovering the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the possible errors and they are deficient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the 3D model of the assembly line to predict the possible errors in an offline manner. After that, these predicted errors can be diagnosed and recovered using Bayesian reasoning and genetic programming. A case study composed of a peg-in-hole assembly was performed and the results are discussed. It is expected that with this new approach, errors can be diagnosed and recovered accurately and costly downtime of robotic assembly systems will be reduced. %K genetic algorithms, genetic programming, 3D model, Bayesian reasoning, Monte Carlo simulation, assembly line, automated assembly systems, error scenarios, peg-in-hole assembly, unexpected failures, virtual assembly systems, Bayes methods, Monte Carlo methods, assembling, fault diagnosis, industrial robots, inference mechanisms, robot programming %R 10.1109/ROBOT.2001.932651 %U http://dx.doi.org/10.1109/ROBOT.2001.932651 %P 818-823 %0 Thesis %T Off-line Error Prediction and Recovery Logic Synthesis using Virtual Assembly Systems %A Baydar, Cem Mehmet %D 2001 %C USA %C The University of Michigan %F Baydar:thesis %X The advent of industrial robots has enabled large-scale automation in assembly lines with high productivity and minimum human intervention. However, growing complexity of robotic assembly systems makes them vulnerable to perturbations in process parameters, causing unexpected failures . Generally, the recovery process from this type of failures is carried out in a limited way by human experts or automated error recovery logic controllers embedded in the system. It is not possible to predict all failures and previous work in the literature focused on ’on-line’ recovery of assembly lines when a failure occurs. Extensive downtime of a production system is costly and a failure recovery process that requires less time and hardware effort would be valuable. This dissertation offers a new approach for error prediction, diagnosis and recovery in assembly systems. It combines three-dimensional geometric model of assembly system with statistical distributions of process parameters and uses Monte Carlo simulation to predict possible failures, which may not be foreseen by human experts. The calculation of the likelihood of occurrence of each failure for a detected sensory symptom is achieved by Bayesian Reasoning and Genetic Programming is used to generate the requisite error recovery codes in an ’off-line’ manner. The proposed approach is implemented and its validity is demonstrated in several case studies. Although main disadvantage was identified as costly computation time because of Monte Carlo simulation and Genetic Programming, two major advantages are expected to be achieved by this approach: Reducing lengthy ramp-up time for new systems (since most of pre-launch testing is debugging error recovery codes), and diagnosing and recovering unexpected errors accurately so that costly downtimes are reduced. Future work is suggested on the application of this method to manufacturing systems and exploration of a sampling algorithm which reduces the costly computation time of Monte Carlo simulation. %K genetic algorithms, genetic programming, Applied sciences, Robotic assembly, Failure diagnosis, Off-line, Error prediction, Recovery logic, Virtual assembly %9 Ph.D. thesis %U http://mirlyn.lib.umich.edu/Record/004198436 %0 Journal Article %T Automated generation of robust error recovery logic in assembly systems using genetic programming %A Baydar, Cem M. %A Saitou, Kazuhiro %J Journal of Manufacturing Systems %D 2001 %V 20 %N 1 %@ 0278-6125 %F Baydar200155 %X Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, the recovery process is done ’on-line’ by human experts or automated error recovery logic controllers embedded in the system. However, these controller codes are programmed based on anticipated error scenarios and, due to the geometrical features of the assembly lines, there may be error cases that belong to the same anticipated type but are present in different positions, each requiring a different way to recover. Therefore, robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence. The proposed approach is based on three-dimensional geometric modeling of the assembly line coupled with the genetic programming and multi-level optimization techniques to generate robust error recovery logic in an ’off-line’ manner. The approach uses genetic programming’s flexibility to generate recovery plans in the robot language itself. An assembly line is modeled and from the given error cases an optimum way of error recovery is investigated using multi-level optimization in a ’generate and test’ fashion. The obtained results showed that with the improved convergence gained by using multi-level optimisation, the infrastructure is capable of finding robust error recovery algorithms. It is expected that this approach will require less time for the generation of robust error recovery logic. %K genetic algorithms, genetic programming, robotics, Automated Assembly Systems, Error Recovery, Multi-Level Optimization %9 journal article %R 10.1016/S0278-6125(01)80020-0 %U http://www.sciencedirect.com/science/article/B6VJD-441R1H8-6/2/cdebaddb30a67a67dc7cb6dd41fabf9f %U http://dx.doi.org/10.1016/S0278-6125(01)80020-0 %P 55-68 %0 Journal Article %T Off-line error prediction, diagnosis and recovery using virtual assembly systems %A Baydar, Cem %A Saitou, Kazuhiro %J Journal of Intelligent Manufacturing %D 2004 %8 oct %V 15 %N 5 %I Springer %@ 0956-5515 %F Baydar:2004:JIM %X Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of many parameters, it is difficult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focusing on on-line diagnosing and recovery of the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the possible errors and they are deficient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the 3-D model of the assembly line to predict the possible errors in an off-line manner. After that, these predicted errors are diagnosed and recovered using Bayesian reasoning and genetic algorithms. Several case studies are performed on single-station and multi-station assembly systems and the results are discussed. It is expected that with this new approach, errors can be diagnosed and recovered accurately and costly down times of robotic assembly systems will be reduced. %K genetic algorithms, genetic programming, Off-line programming, robotic assembly systems, virtual factories, error diagnosis and recovery %9 journal article %R 10.1023/B:JIMS.0000037716.69868.d0 %U http://dx.doi.org/10.1023/B:JIMS.0000037716.69868.d0 %P 679-692 %0 Conference Proceedings %T Accelerating Tangled Program Graph Evolution under Visual Reinforcement Learning Tasks with Mutation and Multi-actions %A Bayer, Caleidgh %A Amaral, Ryan %A Smith, Robert %A Ianta, Alexandru %A Heywood, Malcolm %Y Banzhaf, Wolfgang %Y Trujillo, Leonardo %Y Winkler, Stephan %Y Worzel, Bill %S Genetic Programming Theory and Practice XVIII %S Genetic and Evolutionary Computation %D 2021 %8 19 21 may %I Springer %C East Lansing, USA %F Bayer:2021:GPTP %X Tangled Program Graphs (TPG) represents a genetic programming framework in which emergent modularity incrementally composes programs into teams of programs into graphs of teams of programs. To date, the framework has been demonstrated on reinforcement learning tasks with stochastic partially observable state spaces or time series prediction. However, evolving solutions to reinforcement tasks often requires agents to demonstrate/ juggle multiple properties simultaneously. Hence, we are interesting in maintaining a population of diverse agents. Specifically, agent performance on a reinforcement learning task controls how much of the task they are exposed to. Premature convergence might therefore preclude solving aspects of a task that the agent only later encounters. Moreover, pointless complexity may also result in which graphs largely consist of hitchhikers. In this research we benchmark the use of rampant mutation (multiple mutations applied simultaneously for offspring creation) and action programs (multiple actions per state). Several parameterizations are also introduced that potentially penalize the introduction of hitchhikers. Benchmarking over five VizDoom tasks demonstrates that rampant mutation reduces the likelihood of encountering pathologically bad offspring while action programs appears to improve performance in four out of five tasks. Finally, use of TPG parameterizations that actively limit the complexity of solutions appears to result in very efficient low dimensional solutions that generalize best across all combinations of 3, 4 and 5 VizDoom tasks. %K genetic algorithms, genetic programming %R 10.1007/978-981-16-8113-4_1 %U http://dx.doi.org/10.1007/978-981-16-8113-4_1 %P 1-19 %0 Conference Proceedings %T Emergent Braitenberg-style Behaviours for Navigating the ViZDoom ’My Way Home’ Labyrinth %A Bayer, Caleidgh Grace %A Smith, Robert J. %A Heywood, Malcolm I. %Y Dolson, Emily %Y Heinrich, Mary Katherine %S Proceedings of the 2025 Genetic and Evolutionary Computation Conference %S GECCO ’25 %D 2025 %8 14 18 jul %I Association for Computing Machinery %C Malaga, Spain %F bayer:2025:GECCO %X The navigation of complex labyrinths under partially observable visual state is typically addressed using complex recurrent, convolutional learning architectures (i.e. deep reinforcement learning). Conversely, in this work, we show that navigation can be achieved through the emergent evolution of a simple Braitentberg-style vehicle. We demonstrate that the interaction between agent and labyrinth is sufficient to learn a complex navigation behaviour from simple heuristics. To do so, the approach of tangled program graphs is assumed in which programs cooperatively coevolve to develop a modular indexing scheme that employs < 2.5 percent of state space. We attribute this simplicity to several biases implicit in the representation, such as: (1) the use of pixel indexing as opposed to deploying a convolutional kernel or image processing operators, and; (2) extensive support for modularity in which behaviours are always decomposed into contexts and corresponding actions. %K genetic programming, navigation, tangled program graphs, Complex Systems %R 10.1145/3712256.3726331 %U https://doi.org/10.1145/3712256.3726331 %U http://dx.doi.org/10.1145/3712256.3726331 %P 113-121 %0 Journal Article %T Prediction of cement strength using soft computing techniques %A Baykasoglu, Adil %A Dereli, Turkay %A Tanis, Serkan %J Cement and Concrete Research %D 2004 %8 nov %V 34 %N 11 %F Baykasoglu:2004:CCR %X we aim to propose prediction approaches for the 28-day compressive strength of Portland composite cement (PCC) by using soft computing techniques. Gene expression programming (GEP) and neural networks (NNs) are the soft computing techniques that are used for the prediction of compressive cement strength (CCS). In addition to these methods, stepwise regression analysis is also used to have an idea about the predictive power of the soft computing techniques in comparison to classical statistical approach. The application of the genetic programming (GP) technique GEP to the cement strength prediction is shown for the first time in this paper. The results obtained from the computational tests have shown that GEP is a promising technique for the prediction of cement strength. %K genetic algorithms, genetic programming, Gene expression programming, Modelling, Compressive strength, Cement manufacture %9 journal article %R 10.1016/j.cemconres.2004.03.028 %U http://www.sciencedirect.com/science/article/B6TWG-4CBVDJS-1/2/46a55d4141904806cf09f3c92f56beb4 %U http://dx.doi.org/10.1016/j.cemconres.2004.03.028 %P 2083-2090 %0 Conference Proceedings %T Soft computing approaches to production line design %A Baykasoglu, Adil %Y Gindy, Nabil %S ICRM’2005 3rd International Conference on Responsive Manufacturing %D 2005 %8 December 14 sep %C Guangzhou, China %F Baykasoglu:2005:ICRM %X Gene Expression Programming (GEP) is used to develop a meta-model for the multiobjective design of a hypothetical production line. The developed meta-model is used to optimize production line design with Multiple Objective Tabu Search algorithm (MOTS). It is found out that GEP and MOTS can be effectively used to solve production line design problems which are known as complex design problems. %K genetic algorithms, genetic programming, Gene Expression Programming, Manufacturing system design, soft computing %U http://delta.cs.cinvestav.mx/~ccoello/EMOO/baykasoglu05a.pdf.gz %P 273-279 %0 Journal Article %T MEPAR-miner: Multi-expression programming for classification rule mining %A Baykasoglu, Adil %A Ozbakir, Lale %J European Journal of Operational Research %D 2007 %V 183 %N 2 %@ 0377-2217 %F Baykasoglu2007767 %X Classification and rule induction are two important tasks to extract knowledge from data. In rule induction, the representation of knowledge is defined as IF-THEN rules which are easily understandable and applicable by problem-domain experts. In this paper, a new chromosome representation and solution technique based on Multi-Expression Programming (MEP) which is named as MEPAR-miner (Multi-Expression Programming for Association Rule Mining) for rule induction is proposed. Multi-Expression Programming (MEP) is a relatively new technique in evolutionary programming that is first introduced in 2002 by Oltean and Dumitrescu. MEP uses linear chromosome structure. In MEP, multiple logical expressions which have different sizes are used to represent different logical rules. MEP expressions can be encoded and implemented in a flexible and efficient manner. MEP is generally applied to prediction problems; in this paper a new algorithm is presented which enables MEP to discover classification rules. The performance of the developed algorithm is tested on nine publicly available binary and n-ary classification data sets. Extensive experiments are performed to demonstrate that MEPAR-miner can discover effective classification rules that are as good as (or better than) the ones obtained by the traditional rule induction methods. It is also shown that effective gene encoding structure directly improves the predictive accuracy of logical IF-THEN rules. %K genetic algorithms, genetic programming, Data mining, Classification rules, Multi-expression programming, Evolutionary programming %9 journal article %R 10.1016/j.ejor.2006.10.015 %U http://www.sciencedirect.com/science/article/B6VCT-4MJS038-M/2/f780e675b2900eb28473dcbf6cfa03fb %U http://dx.doi.org/10.1016/j.ejor.2006.10.015 %P 767-784 %0 Journal Article %T Prediction of compressive and tensile strength of limestone via genetic programming %A Baykasoglu, Adil %A Gullu, Hamza %A Canakci, Hanifi %A Ozbakir, Lale %J Expert Systems with Applications %D 2008 %8 jul aug %V 35 %N 1-2 %@ 0957-4174 %F Baykasoglu2008111 %X Accurate determination of compressive and tensile strength of limestone is an important subject for the design of geotechnical structures. Although there are several classical approaches in the literature for strength prediction their predictive accuracy is generally not satisfactory. The trend in the literature is to apply artificial intelligence based soft computing techniques for complex prediction problems. Artificial neural networks which are a member of soft computing techniques were applied to strength prediction of several types of rocks in the literature with considerable success. Although artificial neural networks are successful in prediction, their inability to explicitly produce prediction equations can create difficulty in practical circumstances. Another member of soft computing family which is known as genetic programming can be a very useful candidate to overcome this problem. Genetic programming based approaches are not yet applied to the strength prediction of limestone. This paper makes an attempt to apply a promising set of genetic programming techniques which are known as multi expression programming (MEP), gene expression programming (GEP) and linear genetic programming (LGP) to the uniaxial compressive strength (UCS) and tensile strength prediction of chalky and clayey soft limestone. The data for strength prediction were generated experimentally in the University of Gaziantep civil engineering laboratories by using limestone samples collected from Gaziantep region of Turkey. %K genetic algorithms, genetic programming, multi expression programming, gene expression programming, Prediction, Limestone, Strength of materials %9 journal article %R 10.1016/j.eswa.2007.06.006 %U http://dx.doi.org/10.1016/j.eswa.2007.06.006 %P 111-123 %0 Journal Article %T Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches %A Baykasoglu, Adil %A Oztas, Ahmet %A Ozbay, Erdogan %J Expert Systems with Applications %D 2009 %8 apr %V 36 %N 3 %@ 0957-4174 %F Baykasoglu2008 %X The optimization of composite materials such as concrete deals with the problem of selecting the values of several variables which determine composition, compressive stress, workability and cost etc. This study presents multi-objective optimization (MOO) of high-strength concretes (HSCs). One of the main problems in the optimization of HSCs is to obtain mathematical equations that represents concrete characteristic in terms of its constitutions. In order to solve this problem, a two step approach is used in this study. In the first step, the prediction of HSCs parameters is performed by using regression analysis, neural networks and Gen Expression Programming (GEP). The output of the first step is the equations that can be used to predict HSCs properties (i.e. compressive stress, cost and workability). In order to derive these equations the data set which contains many different mix proportions of HSCs is gathered from the literature. In the second step, a MOO model is developed by making use of the equations developed in the first step. The resulting MOO model is solved by using a Genetic Algorithm (GA). GA employs weighted and hierarchical method in order to handle multiple objectives. The performances of the prediction and optimization methods are also compared in the paper. %K genetic algorithms, genetic programming, gene expression programming, Multiple objective optimization, Meta-heuristics, Prediction, High-strength concrete %9 journal article %R 10.1016/j.eswa.2008.07.017 %U http://www.sciencedirect.com/science/article/B6V03-4T0WJSK-G/2/2dd2cbea4bb9a919e91f3953aecaaa06 %U http://dx.doi.org/10.1016/j.eswa.2008.07.017 %P 6145-6155 %0 Journal Article %T Gene expression programming based due date assignment in a simulated job shop %A Baykasoglu, Adil %A Gocken, Mustafa %J Expert Systems with Applications %D 2009 %V 36 %N 10 %@ 0957-4174 %F Baykasoglu:2009:ESA %X In this paper, a new approach for due date assignment in a multi-stage job shop is proposed and evaluated. The proposed approach is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is a relatively new member of the genetic programming family. The primary objective of this research is to compare the performance of the proposed due date assignment model with several previously proposed conventional due date assignment models. For this purpose, simulation models are developed and comparisons of the due date assignment models are made mainly in terms of the mean absolute percent error (MAPE), mean percent error (MPE) and mean tardiness (MT). Some additional performance measurements are also given. Simulation experiments revealed that for many test conditions the proposed due date assignment method dominates all other compared due date assignment methods. %K genetic algorithms, genetic programming, Gene expression programming, Due date assignment %9 journal article %R 10.1016/j.eswa.2009.03.061 %U http://www.sciencedirect.com/science/article/B6V03-4VY2C6B-1/2/d174ebf2e7f0566d9c964be7d6f4f2ab %U http://dx.doi.org/10.1016/j.eswa.2009.03.061 %P 12143-12150 %0 Journal Article %T Genetic Programming Based Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop %A Baykasoglu, Adil %A Gocken, Mustafa %A Ozbakir, Lale %J Simulation %D 2010 %V 86 %N 12 %F Baykasoglu:2010:S %X In this paper, a genetic programming based data mining approach is proposed to select dispatching rules which will result in competitive shop performance under a given set of shop parameters (e.g. interarrival times, pre-shop pool length). The main purpose is to select the most appropriate conventional dispatching rule set according to the current shop parameters. In order to achieve this, full factorial experiments are carried out to determine the effect of input parameters on predetermined performance measures. Afterwards, a genetic programming based data mining tool that is known as MEPAR-miner (multi-expression programming for classification rule mining) is employed to extract knowledge on the selection of best possible conventional dispatching rule set according to the current shop status. The obtained results have shown that the selected dispatching rules are appropriate ones according to the current shop parameters. All of the results are illustrated via numerical examples and experiments on simulated data. %K genetic algorithms, genetic programming, data mining, dispatching rules %9 journal article %R 10.1177/0037549709346561 %U http://dx.doi.org/10.1177/0037549709346561 %P 715-728 %0 Journal Article %T Fuzzy functions via genetic programming %A Baykasoglu, Adil %A Maral, Sultan %J Journal of Intelligent and Fuzzy Systems %D 2014 %V 27 %N 5 %F journals/jifs/BaykasogluM14 %K genetic algorithms, genetic programming %9 journal article %U http://dx.doi.org/10.3233/IFS-141205 %P 2355-2364 %0 Journal Article %T Discovering task assignment rules for assembly line balancing via genetic programming %A Baykasoglu, Adil %A Ozbakir, Lale %J The International Journal of Advanced Manufacturing Technology %D 2015 %V 76 %N 1-4 %F baykasoglu:2015:IJAMT %X Assembly line is one of the most commonly used manufacturing processes to produce final products in a flow line. Design of efficient assembly lines has considerable importance for the production of high-quantity standardized products. Several solution approaches such as exact, heuristic, and metaheuristics have been developed since the problem is first formulated. In this study, a new approach based on genetic programming so as to generate composite task assignment rules is proposed for balancing simple assembly lines. The proposed approach can also be applied to other types of line balancing problems. The present method makes use of genetic programming to discover task assignment rules which can be used within a single-pass constructive heuristic in order to balance a given assembly line quickly and effectively. Suitable parameters affecting the balance of the assembly line are evaluated and employed to discover highly efficient composite task assignment rules. Extensive computational results and comparisons proved the efficiency of the proposed approach in producing generic composite task assignment rules for balancing assembly lines. %K genetic algorithms, genetic programming, Assembly line balancing, Automatic rule generation, Evolutionary intelligence %9 journal article %R 10.1007/s00170-014-6295-4 %U http://link.springer.com/article/10.1007/s00170-014-6295-4 %U http://dx.doi.org/10.1007/s00170-014-6295-4 %P 417-434 %0 Journal Article %T Vive l’evolution %A Bayne, Michael D. %J Deep Magic %D 1997 %8 December %F bayne:1997:ve %O www page %X Evolutionary computing is a blanket term encompassing a host of methodologies and philosophies, all based upon the premise that mother nature is darned good at solving problems. The world is literally crawling with problem solvers of infinite variety. Although Charles Darwin planted the idea in 1859 with the publication of The Origin of Species, the concept of mimicking mother nature’s problem solving techniques didn’t start to flower until the mid-1960s, when the computing power to actually investigate such techniques was readily available. %K genetic algorithms, genetic programming, Java, www %9 journal article %U http://samskivert.com/internet/deep/1997/02/12/ %0 Journal Article %T Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: An evolutionary approach %A Baziar, Mohammad H. %A Jafarian, Yaser %A Shahnazari, Habib %A Movahed, Vahid %A Tutunchian, Mohammad Amin %J Computer & Geosciences %D 2011 %V 37 %N 11 %@ 0098-3004 %F Baziar2011 %X Liquefaction is a catastrophic type of ground failure, which usually occurs in loose saturated soil deposits under earthquake excitations. A new predictive model is presented in this study to estimate the amount of strain energy density, which is required for the liquefaction triggering of sand-silt mixtures. A wide-ranging database containing the results of cyclic tests on sand-silt mixtures was first gathered from previously published studies. Input variables of the model were chosen from the available understandings evolved from the previous studies on the strain energy-based liquefaction potential assessment. In order to avoid over training, two sets of validation data were employed and a particular monitoring was made on the behaviour of the evolved models. Results of a comprehensive parametric study on the proposed model are in accord with the previously published experimental observations. Accordingly, the amount of strain energy required for liquefaction onset increases with increase in initial effective overburden pressure, relative density, and mean grain size. The effect of nonplastic fines on strain energy-based liquefaction resistance shows a more complicated behavior. Accordingly, liquefaction resistance increases with increase in fines up to about 10-15percent and then starts to decline for a higher increase in fines content. Further verifications of the model were carried out using the valuable results of some down hole array data as well as centrifuge model tests. These verifications confirm that the proposed model, which was derived from laboratory data, can be successfully used under field conditions. %K genetic algorithms, genetic programming, Liquefaction, Capacity energy, Sand, Silt, Wildlife %9 journal article %R 10.1016/j.cageo.2011.04.008 %U http://www.sciencedirect.com/science/article/B6V7D-52R9DF5-2/2/08fa46566f649fc2348af34aa83ebbb2 %U http://dx.doi.org/10.1016/j.cageo.2011.04.008 %P 1883-1893 %0 Journal Article %T Evolutionary feature selection approaches for insolvency business prediction with genetic programming %A Beade, Angel %A Rodriguez, Manuel %A Santos, Jose %J Natural Computing %D 2023 %V 22 %N 4 %F beade:2023:NC %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s11047-023-09951-4 %U http://link.springer.com/article/10.1007/s11047-023-09951-4 %U http://dx.doi.org/10.1007/s11047-023-09951-4 %0 Journal Article %T Variable selection in the prediction of business failure using genetic programming %A Beade, Angel %A Rodriguez, Manuel %A Santos, Jose %J Knowledge-Based Systems %D 2024 %V 289 %@ 0950-7051 %F BEADE:2024:knosys %X This study focuses on dimensionality reduction by variable selection in business failure prediction models. A new method of dimensionality reduction by variable selection using Genetic Programming is proposed, which takes into account the relative frequency of occurrence of the explanatory variables in the evolved solutions, as well as the statistical relevance of that frequency. For a better evaluation of the proposed method and its comparison with other well-tested and widely used variable selection methods, the prediction of business failure in three temporal horizons (1, 5 and 9 years prior to failure) is considered. Additionally, a comparison of the sets of variables selected with different feature selection methods is performed, also considering different classifiers in the comparison, among which Genetic Programming is included as a classifier. The results indicate that the proposed method (using Genetic Programming as a variable selection method) is superior to the most tested and widely used methods analyzed, and this superiority increases if Genetic Programming is also used as a classification method %K genetic algorithms, genetic programming, Business failure, Dimensionality reduction, Feature selection, Evolutionary computation %9 journal article %R 10.1016/j.knosys.2024.111529 %U https://www.sciencedirect.com/science/article/pii/S0950705124001643 %U http://dx.doi.org/10.1016/j.knosys.2024.111529 %P 111529 %0 Conference Proceedings %T Genetic programming for feature selection in business failure prediction. Comparison of the use of financial variables and economic environment variables %A Beade, Angel %A Rodriguez, Manuel %A Santos, Jose %S 2024 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) %D 2024 %8 sep %F Beade:2024:INISTA %X In this work we have experimented with the use of genetic programming as a feature selection method as well as a classifier to obtain business failure prediction models with different prediction temporal horizons. In the prediction models, a wide set of explanatory variables has been used, all of them based on the annual accounts of the company. In addition, an extended set of explanatory variables incorporating variables from the economic environment has been considered. Comparison of the prediction results between these alternatives shows a trend towards better results using the feature selection process, while there is no trend towards better results using economic environment variables. %K genetic algorithms, genetic programming, Economics, Technological innovation, Biological system modelling, Companies, Predictive models, Feature extraction, feature selection, business failure %R 10.1109/INISTA62901.2024.10683824 %U http://dx.doi.org/10.1109/INISTA62901.2024.10683824 %0 Journal Article %T Business failure prediction models with high and stable predictive power over time using genetic programming %A Beade, Angel %A Rodriguez, Manuel %A Santos, Jose %J Operational Research %D 2024 %F beade:2024:OR %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s12351-024-00852-7 %U https://link.springer.com/article/10.1007/s12351-024-00852-7 %U http://dx.doi.org/10.1007/s12351-024-00852-7 %0 Conference Proceedings %T Semantically Driven Crossover in Genetic Programming %A Beadle, Lawrence %A Johnson, Colin %Y Wang, Jun %S Proceedings of the IEEE World Congress on Computational Intelligence %D 2008 %8 January 6 jun %I IEEE Press %C Hong Kong %F Beadle:2008:CEC %X Crossover forms one of the core operations in genetic programming and has been the subject of many different investigations. We present a novel technique, based on semantic analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space. We demonstrate how this technique results in better performance and smaller solutions in two separate genetic programming experiments. %K genetic algorithms, genetic programming, Program Semantics, Crossover, Reduced Ordered Binary Decision Diagrams %R 10.1109/CEC.2008.4630784 %U http://results.ref.ac.uk/Submissions/Output/1423275 %U EC0044.pdf %U http://dx.doi.org/10.1109/CEC.2008.4630784 %P 111-116 %0 Journal Article %T Semantic Analysis of Program Initialisation in Genetic Programming %A Beadle, Lawrence %A Johnson, Colin G. %J Genetic Programming and Evolvable Machines %D 2009 %8 sep %V 10 %N 3 %@ 1389-2576 %F Beadle:2009:GPEM %X Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this paper we perform analyses of behavioural diversity, the size and shape of starting populations, the effects of purely semantic program initialisation and the importance of tree shape in the context of program initialisation. To achieve this, we create four different algorithms, in addition to using the traditional ramped half and half technique, applied to seven genetic programming problems. We present results to show that varying the choice and design of program initialisation can dramatically influence the performance of genetic programming. In particular, program behaviour and evolvable tree shape can have dramatic effects on the performance of genetic programming. The four algorithms we present have different rates of success on different problems. %K genetic algorithms, genetic programming, Program initialisation, Program semantics, Program structure %9 journal article %R 10.1007/s10710-009-9082-5 %U http://dx.doi.org/10.1007/s10710-009-9082-5 %P 307-337 %0 Conference Proceedings %T Semantically Driven Mutation in Genetic Programming %A Beadle, Lawrence %A Johnson, Colin G. %Y Tyrrell, Andy %S 2009 IEEE Congress on Evolutionary Computation %D 2009 %8 18 21 may %I IEEE Press %C Trondheim, Norway %F Beadle:2009:cec %X Using semantic analysis, we present a technique known as semantically driven mutation which can explicitly detect and apply behavioural changes caused by the syntactic changes in programs that result from the mutation operation. Using semantically driven mutation, we demonstrate increased performance in genetic programming on seven benchmark genetic programming problems over two different domains. %K genetic algorithms, genetic programming, Genetic programming, program semantics, semantically driven mutation, reduced ordered binary decision diagrams. %R 10.1109/CEC.2009.4983099 %U P009.pdf %U http://dx.doi.org/10.1109/CEC.2009.4983099 %P 1336-1342 %0 Thesis %T Semantic and Structural Analysis of Genetic Programming %A Beadle, Lawrence Charles John %D 2009 %8 jul %C Canterbury, UK %C School of Computing, University of Kent %F Beadle:thesis %X Genetic programming (GP) is a subset of evolutionary computation where candidate solutions are evaluated through execution or interpreted execution. The candidate solutions generated by GP are in the form of computer programs, which are evolved to achieve a stated objective. Darwinian evolutionary theory inspires the processes that make up GP which include crossover, mutation and selection. During a GP run, crossover, mutation and selection are performed iteratively until a program that satisfies the stated objectives is produced or a certain number of time steps have elapsed. The objectives of this thesis are to empirically analyse three different aspects of these evolved programs. These three aspects are diversity, efficient representation and the changing structure of programs during evolution. In addition to these analyses, novel algorithms are presented in order to test theories, improve the overall performance of GP and reduce program size. This thesis makes three contributions to the field of GP. Firstly, a detailed analysis is performed of the process of initialisation (generating random programs to start evolution) using four novel algorithms to empirically evaluate specific traits of starting populations of programs. It is shown how two factors simultaneously effect how strong the performance of starting population will be after a GP run. Secondly, semantically based operators are applied during evolution to encourage behavioural diversity and reduce the size of programs by removing inefficient segments of code during evolution. It is demonstrated how these specialist operators can be effective individually and when combined in a series of experiments. Finally, the role of the structure of programs is considered during evolution under different evolutionary parameters considering different problem domains. This analysis reveals some interesting effects of evolution on program structure as well as offering evidence to support the success of the specialist operators. %K genetic algorithms, genetic programming, determinacy analysis, Craig interpolants %9 Ph.D. thesis %U http://www.beadle.me/Me/LBeadle_PhD_Thesis.pdf %0 Conference Proceedings %T Traffic Data: Less is More %A Beale, Stuart %S Road Transport Information and Control %D 2002 %8 19 21 mar %C Savoy Place, London, UK %F beale:2002:RTIC %X In support of the Governments 10 Year Transport Plan the Highways Agency has an ambitious programme to roll-out traffic systems on the English motorway network. The control methodologies within these systems can be further developed which will help meet the Government’s targets to reduce congestion and accidents. This paper describes three innovative projects being undertaken by the Highways Agency. The approach to these projects departs from the traditional engineering approach, instead we have used mathematical techniques to evolve control functions that learn and operate on the available traffic data. %K genetic algorithms, genetic programming %R 10.1049/cp:20020233 %U http://dx.doi.org/10.1049/cp:20020233 %0 Journal Article %T The joy of genetic programming %A Beard, Nick %J Personal Computer World %D 1993 %8 jun %F ga:Beard93a %K genetic algorithms, genetic programming %9 journal article %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ga_beard93a.pdf %P 471-472 %0 Conference Proceedings %T Short term memory in genetic programming %A Bearpark, K. %A Keane, A. J. %Y Parmee, I. C. %S Fourth International Conference on Adaptive Computing in Design and Manufacture, ACDM ’00 %D 2000 %I Springer-Verlag %C University of Plymouth, Devon, UK %F Bearpark:2000:ACDM %X The recognition of useful information, its retention in memory, and subsequent use plays an important part in the behaviour of many biological species. Information gained by experience in one generation can be propagated to subsequent generations by some form of teaching. Each generation can then supplement its taught learning by its own experience. In this paper we explore the role of memorised information in the performance of a Genetic Programming (GP) system that uses a tree structure as its representation. Memory is implemented in the form of a set of subtrees derived from successful members of each generation. The memory is used by a genetic operator similar to the mutation operator but with the following difference. In a tree-structured system the mutation operator replaces randomly selected sub-trees by new randomly-generated sub-trees. The memory operator replaces randomly selected sub-trees by sub-trees randomly randomly selected from the memory. To study the memory operator’s impact a GP system is used to evolve a well-known expression from classical kinetics using fitness-based selection. The memory operator is used together with the common crossover and mutation operators. It is shown that the addition of a memory operator increases the probability of a successful evolution for this particular problem. At this stage we make no claim for its impact on other problems that have been successfully addressed by Genetic Programming %K genetic algorithms, genetic programming %R 10.1007/978-1-4471-0519-0_25 %U http://eprints.soton.ac.uk/21399/1/bear_00.pdf %U http://dx.doi.org/10.1007/978-1-4471-0519-0_25 %P 309-320 %0 Thesis %T Learning and memory in genetic programming %A Bearpark, Keith %D 2000 %C UK %C School of Engineering Sciences, University of Southampton %F Bearpark:thesis %X Genetic Programming is a form of Evolutionary Computation in which computer programs are evolved by methods based on simulating the natural evolution of biological species. A new generation of a species acquires the characteristics of previous generations through the inheritance of genes by sexual reproduction and through random changes in alleles by random mutation. The new generation may enhance its ability to survive by the acquisition of cultural knowledge through learning processes. This thesis combines the transfer of knowledge by genetic means with the transfer of knowledge by cultural means. In particular, it introduces a new evolutionary operator, memory operator. In conventional genetic programming systems, a new generation is formed from a mating pool whose members are selected from the fittest members of previous generation. The new generation is produced by the exchange of genes between members of the mating pool and the random replacement of genes in the offspring. The new generation may or may not be able to survive better than its predecessor in a given environment. The memory operator augments the evolutionary process by inserting into new chromosomes genetic material known to often result in fitness improvements. This material is acquired through a learning process in which the system is required to evolve generations that survive in a less demanding environment. The cultural knowledge acquired in this learning process is applied as an intelligent form of mutation to aid survival in a more demanding environment. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://eprints.soton.ac.uk/45930/ %0 Conference Proceedings %T Lens System Design And Re-engineering With Evolutionary Algorithms %A Beaulieu, Julie %A Gagné, Christian %A Parizeau, Marc %Y Langdon, W. B. %Y Cantú-Paz, E. %Y Mathias, K. %Y Roy, R. %Y Davis, D. %Y Poli, R. %Y Balakrishnan, K. %Y Honavar, V. %Y Rudolph, G. %Y Wegener, J. %Y Bull, L. %Y Potter, M. A. %Y Schultz, A. C. %Y Miller, J. F. %Y Burke, E. %Y Jonoska, N. %S GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %D 2002 %8 September 13 jul %I Morgan Kaufmann Publishers %C New York %@ 1-55860-878-8 %F beaulieu:2002:gecco %X presents some lens system design and re-engineering experimentations with genetic algorithms and genetic programming. These Evolutionary Algorithms (EA) were successfully applied to a design problem that was previously presented to expert participants of an international lens design conference. Comparative results demonstrate that the use of EA for lens system design is very much human-competitive. %K genetic algorithms, genetic programming, evolvable hardware, evolutionary reengineering, evolvable optics, lens system design %U http://vision.gel.ulaval.ca/~parizeau/Publications/gecco02-lens.pdf %P 155-162 %0 Conference Proceedings %T Grammatical Evolution of L-systems %A Beaumont, Darren %A Stepney, Susan %Y Tyrrell, Andy %S 2009 IEEE Congress on Evolutionary Computation %D 2009 %8 18 21 may %I IEEE Press %C Trondheim, Norway %F Beaumont:2009:cec %X L-systems are parallel generative grammars that can model branching structures. Taking a graphical object and attempting to derive an L-system describing it is a hard problem. Grammatical Evolution (GE) is an evolutionary technique aimed at creating grammars describing the legal structures an object can take. We use GE to evolve L-systems, and investigate the effect of elitism, and the form of the underlying grammar. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1109/CEC.2009.4983247 %U P007.pdf %U http://dx.doi.org/10.1109/CEC.2009.4983247 %P 2446-2453 %0 Conference Proceedings %T From Binary to Continuous Gates - and Back Again %A Bechmann, Matthias %A Sebald, Angelika %A Stepney, Susan %Y Tempesti, Gianluca %Y Tyrrell, Andy M. %Y Miller, Julian F. %S Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010 %S Lecture Notes in Computer Science %D 2010 %8 sep 6 8 %V 6274 %I Springer %C York %G en %F Bechmann:2010:ICES %X We describe how nuclear magnetic resonance (NMR) spectroscopy can serve as a substrate for the implementation of classical logic gates. The approach exploits the inherently continuous nature of the NMR parameter space. We show how simple continuous NAND gates with sin/sin and sin/sinc characteristics arise from the NMR parameter space. We use these simple continuous NAND gates as starting points to obtain optimised target NAND circuits with robust, error-tolerant properties. We use Cartesian Genetic Programming (CGP) as our optimisation tool. The various evolved circuits display patterns relating to the symmetry properties of the initial simple continuous gates. Other circuits, such as a robust XOR circuit built from simple NAND gates, are obtained using similar strategies. We briefly mention the possibility to include other target objective functions, for example other continuous functions. Simple continuous NAND gates with sin/sin characteristics are a good starting point for the creation of error-tolerant circuits whereas the more complicated sin/sinc gate characteristics offer potential for the implementation of complicated functions by choosing some straightforward, experimentally controllable parameters appropriately. %K genetic algorithms, genetic programming, cartesian genetic programming %R 10.1007/978-3-642-15323-5_29 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.7390 %U http://dx.doi.org/10.1007/978-3-642-15323-5_29 %P 335-347 %0 Journal Article %T Machine Learning Techniques Accurately Classify Microbial Communities by Bacterial Vaginosis Characteristics %A Beck, Daniel %A Foster, James A. %J PLoS ONE %D 2014 %8 feb 3 %V 9 %N 2 %I Public Library of Science %F Beck:2014:PLoSONE %X Microbial communities are important to human health. Bacterial vaginosis (BV) is a disease associated with the vagina microbiome. While the causes of BV are unknown, the microbial community in the vagina appears to play a role. We use three different machine-learning techniques to classify microbial communities into BV categories. These three techniques include genetic programming (GP), random forests (RF), and logistic regression (LR). We evaluate the classification accuracy of each of these techniques on two different datasets. We then deconstruct the classification models to identify important features of the microbial community. We found that the classification models produced by the machine learning techniques obtained accuracies above 90percent for Nugent score BV and above 80percent for Amsel criteria BV. While the classification models identify largely different sets of important features, the shared features often agree with past research. %K genetic algorithms, genetic programming, Bacterial vaginosis, Microbiome, Lactobacillus, Vagina, Community ecology, Machine learning algorithms %9 journal article %R 10.1371/journal.pone.0087830 %U http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912131 %U http://dx.doi.org/10.1371/journal.pone.0087830 %P e87830 %0 Thesis %T Investigating the use of classification models to study microbial community associations with bacterial vaginosis %A Beck, Daniel %D 2014 %8 may %C USA %C University of Idaho %F Beck:thesis %X Microbial communities are highly complex, often composed of hundreds or thousands of different microbe types. They are found nearly everywhere; in soil, water, and in close association with other organisms. Microbial communities are difficult to study. Many microbes are not easily grown in laboratory conditions. Interactions between microbes may limit the applicability of observations collected using isolated taxa. However, new sequencing technology is allowing researchers to study microbial communities in novel ways. Among these new techniques is 16S rRNA fingerprinting, which enables researchers to estimate the relative abundance of most microbes in the community. These techniques are often used to study microbial communities living on or in the human body. These microbiomes are found at many different body sites and have been linked to the health of their human host. In particular, the vagina microbiome has been linked to bacterial vaginosis (BV). BV is highly prevalent with symptoms including odour, discharge, and irritation. While no single microbe has been shown to cause BV, the structure of the microbial community as a whole is associated with BV. In this thesis, I explore methods that may be used to discover associations between microbial communities and phenotypes of those communities. I focus on associations between the vagina microbiome and BV. The first two chapters of this thesis describe software tools used to explore and visualise ecological datasets. In the last two chapters, I explore the use of machine learning techniques to model the relationships between the vagina microbiome and BV. Machine learning techniques are able to produce complex models that classify microbial communities by BV characteristics. These models may capture interactions that simpler models miss. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, grammatical evolution, Linear GP, Push, Bioinformatics, Biology %9 Ph.D. thesis %U https://www.lib.uidaho.edu/digital/etd/items/beck_idaho_0089e_10212.html %0 Journal Article %T Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis %A Beck, Daniel %A Foster, James A. %J BioData Mining %D 2015 %8 December %V 8 %N 23 %F Beck:2015:BDM %X Background: Bacterial vaginosis (BV) is a disease associated with the vagina microbiome. It is highly prevalent and is characterized by symptoms including odour, discharge and irritation. No single microbe has been found to cause BV. In this paper we use random forests and logistic regression classifiers to model the relationship between the microbial community and BV. We use subsets of the microbial community features in order to determine which features are important to the classification models. Results: We find that models generated using logistic regression and random forests perform nearly identically and identify largely similar important features. Only a few features are necessary to obtain high BV classification accuracy. Additionally, there appears to be substantial redundancy between the microbial community features. Conclusions: These results are in contrast to a previous study in which the important features identified by the classifiers were dissimilar. This difference appears to be the result of using different feature importance measures. It is not clear whether machine learning classifiers are capturing patterns different from simple correlations. %K genetic algorithms, genetic programming %9 journal article %R 10.1186/s13040-015-0055-3 %U http://dx.doi.org/10.1186/s13040-015-0055-3 %0 Conference Proceedings %T Extending the bounds of the search space: A Multi-Population approach %A Beck, M. A. %A Parmee, I. C. %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 beck:1999:EAM %K real world applications %U http://gpbib.cs.ucl.ac.uk/gecco1999/RW-762.pdf %P 1469-1476 %0 Generic %T Thinking, Learning, and Autonomous Problem Solving %A Becker, Joerg D. %D 2002 %8 dec 10 %F oai:arXiv.org:cs/0212019 %O Comment: 9 pages, 4 figures %X Ever increasing computational power will require methods for automatic programming. We present an alternative to genetic programming, based on a general model of thinking and learning. The advantage is that evolution takes place in the space of constructs and can thus exploit the mathematical structures of this space. The model is formalized, and a macro language is presented which allows for a formal yet intuitive description of the problem under consideration. A prototype has been developed to implement the scheme in PERL. This method will lead to a concentration on the analysis of problems, to a more rapid prototyping, to the treatment of new problem classes, and to the investigation of philosophical problems. We see fields of application in nonlinear differential equations, pattern recognition, robotics, model building, and animated pictures. %U http://arxiv.org/abs/cs/0212019 %0 Conference Proceedings %T AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms %A Becker, Kory %A Gottschlich, Justin %S Proceedings of the Genetic and Evolutionary Computation Conference Companion %S GECCO ’21 %D 2021 %8 jul 10 14 %I Association for Computing Machinery %C internet %F Becker:2021:GECCOcomp %X we present AI Programmer, a machine learning (ML) system that can automatically generate full software programs, while requiring only minimal human guidance. At its core, AI Programmer uses a genetic algorithm (GA), coupled with a tightly constrained programming language that minimizes the overhead of its ML search space. Part of AI Programmer’s novelty stems from (i) its unique system design, including an embedded, hand-crafted interpreter for efficiency and security and (ii) its augmentation of classic GA to include instruction-gene randomization bindings and programming language-specific genome construction and elimination techniques. We provide a detailed examination of AI Programmer’s system design, several examples detailing how the system works, and experimental data demonstrating its software generation capabilities and performance using only mainstream CPUs. %K genetic algorithms, genetic programming, machine learning, evolutionary computation, artificial intelligence, genetic algorithm, program synthesis, code generation and optimization, programming languages, Brain-dead %R 10.1145/3449726.3463125 %U http://dx.doi.org/10.1145/3449726.3463125 %P 1513-1521 %0 Report %T Comprehensibility and Overfitting Avoidance in Genetic Programming for Technical Trading Rules %A Becker, Lee A. %A Seshadri, Mukund %D 2003 %8 may %I Worcester Polytechnic Institute %F becker:2003-09 %X This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the search, can express complexity while retaining comprehensibility. Several of the learned technical trading rules outperform a buy and hold strategy for the S&P500 on the testing period from 1990-2002, even taking into account transaction costs. %K genetic algorithms, genetic programming, comprehensibility , Occam’s razor, overfitting, complexity penalising, S&P500, technical analysis, market timing %U ftp://ftp.cs.wpi.edu/pub/techreports/pdf/03-09.pdf %0 Report %T Cooperative Coevolution of Technical Trading Rules %A Becker, Lee A. %A Seshadri, Mukund %D 2003 %8 may %I Worcester Polytechnic Institute %F becker:2003-15 %X This paper describes how cooperative coevolution can be used for GP of technical trading rules. A number of different methods of choosing collaborators for fitness evaluation are investigated. Several of the methods outperformed, at a statistically significant level, a buy-and-hold strategy for the S&P500 on the testing period from 1990-2002, even taking into account transaction costs. %K genetic algorithms, genetic programming %U ftp://ftp.cs.wpi.edu/pub/techreports/pdf/03-15.pdf %0 Conference Proceedings %T GP-evolved Technical Trading Rules Can Outperform Buy and Hold %A Becker, Lee A. %A Seshadri, Mukund %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 becker:2003:CINC %X This paper presents a number of experiments in which GP-evolved technical trading rules outperform a buy-and-hold strategy on the S&P500, even taking into account transaction costs. Several methodology changes from previous work are discussed and tested. These include a complexity-penalising factor, a fitness function that considers consistency of performance, and coevolution of a separate buy and sell rule. %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/W.Yan/gp-evolved-technical-trading.pdf %0 Generic %T Faster AutoML with TPOT and RAPIDS %A Becker, Nick %A Dessavre, Dante Gama %A Zedlewski, John %D 2020 %8 nov 5 %I www blog %F Becker:2020:TPOT %K genetic algorithms, genetic programming, TPOT, AutoML, GPU, Data Science, Machine Learning, Artificial Intelligence, AI, Big Data, Python, Higgs Boson, Airline delays %U https://medium.com/rapids-ai/faster-automl-with-tpot-and-rapids-758455cd89e5 %0 Book Section %T Stock Selection : An Innovative Application of Genetic Programming Methodology %A Becker, Ying %A Fei, Peng %A Lester, Anna M. %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 Becker:2006:GPTP %X One of the major challenges in an information-rich financial market is how effectively to derive an optimum investment solution among vast amounts of available information. The most efficacious combination of factors or information signals can be found by evaluating millions of possibilities, which is a task well beyond the scope of manual efforts. Given the limitations of the manual approach, factor combinations are typically linear. However, the linear combination of factors might be too simple to reflect market complexities and thus fully capture the predictive power of the factors. A genetic programming process can easily explore both linear and non-linear formulae. In addition, the ease of evaluation facilitates the consideration of broader factor candidates for a stock selection model. Based upon SSgA’s previous research on using genetic programming techniques to develop quantitative investment strategies, we extend our application to develop stock selection models in a large investable stock universe, the S&P 500 index. Two different fitness functions are designed to derive GP models that accommodate different investment objectives. First, we demonstrate that the GP process can generate a stock selection model for an low active risk investment style. Compared to a traditional model, the GP model has significantly enhanced future stock return ranking capability. Second, to suit an active investment style, we also use the GP process to generate a model that identifies the stocks with future returns lying in the fat tails of the return distribution. A portfolio constructed based on this model aims to aggressively generate the highest returns possible compared to an index following portfolio. Our tests show that the stock selection power of the GP models is statistically significant. Historical backtest results indicate that portfolios based on GP models outperform the benchmark and the portfolio based on the traditional model. Further, we demonstrate that GP models are more robust in accommodating various market regimes and have more consistent performance than the traditional model. %K genetic algorithms, genetic programming, equity market, stock selection, quantitative asset management Capital Asset Pricing Model, Arbitrage Pricing Model, Technical trading rules, S&P 500, Stock selection models, Information ratio, Information coefficient, Quantitative asset management %R 10.1007/978-0-387-49650-4_19 %U http://dx.doi.org/10.1007/978-0-387-49650-4_19 %P 315-334 %0 Book Section %T An Empirical Study of Multi-Objective Algorithms for Stock Ranking %A Becker, Ying L. %A Fox, Harold %A Fei, Peng %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 Becker:2007:GPTP %X Quantitative models for stock selection and portfolio management face the challenge of determining the most efficacious factors, and how they interact, from large amounts of financial data. Genetic programming using simple objective fitness functions has been shown to be an effective technique for selecting factors and constructing multi-factor models for ranking stocks, but the resulting models can be somewhat unbalanced in satisfying the multiple objectives that portfolio managers seek: large excess returns that are consistent across time and the cross-sectional dimensions of the investment universe. In this study, we implement and evaluate three multi-objective algorithms to simultaneously optimise the information ratio, information coefficient, and intra-fractile hit rate of a portfolio. These algorithms the constrained fitness function, sequential algorithm, and parallel algorithm take widely different approaches to combine these different portfolio metrics. The results show that the multi-objective algorithms do produce well-balanced portfolio performance, with the constrained fitness function performing much better than the sequential and parallel multi-objective algorithms. Moreover, this algorithm generalises to the held-out test data set much better than any of the single fitness algorithms. %K genetic algorithms, genetic programming %R 10.1007/978-0-387-76308-8_14 %U http://dx.doi.org/10.1007/978-0-387-76308-8_14 %P 239-259 %0 Conference Proceedings %T Genetic programming for quantitative stock selection %A Becker, Ying L. %A O’Reilly, Una-May %Y Xu, Lihong %Y Goodman, Erik D. %Y Chen, Guoliang %Y Whitley, Darrell %Y Ding, Yongsheng %S GEC ’09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation %D 2009 %8 jun 12 14 %I ACM %C Shanghai, China %F BeckerO:2009:GEC %X We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by experts. We describe the multi-stage training, testing and validation process that we have integrated with GP selection to be appropriate for financial panel data and how the GP solutions are situated within a portfolio selection strategy. We share our experience with the pros and cons of evolved linear and non-linear models, and outline how we have used GP extensions to balance different objectives of portfolio managers and control the complexity of evolved models. %K genetic algorithms, genetic programming %R 10.1145/1543834.1543837 %U http://dx.doi.org/10.1145/1543834.1543837 %P 9-16 %0 Book Section %T Evolving Light Cycle Algorithms %A Bedner, Ilja %E Koza, John R. %B Genetic Algorithms and Genetic Programming at Stanford 1997 %D 1997 %8 17 mar %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-205981-2 %F Bedner:1997:elca %X Evolution of autonomous agents that must compete for survival in the light-cycle game as seen in the movie tron %K genetic algorithms, genetic programming, games %0 Conference Proceedings %T A genetic programming approach to solve scheduling problems with parallel simulation %A Beham, Andreas %A Winkler, Stephan %A Wagner, Stefan %A Affenzeller, Michael %S IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2008 %D 2008 %8 apr %F Beham:2008:ieeeIPDPS %X Scheduling and dispatching are two ways of solving production planning problems. In this work, based on preceding works, it is explained how these two approaches can be combined by the means of an automated rule generation procedure and simulation. Genetic programming is applied as the creator and optimizer of the rules. A simulator is used for the fitness evaluation and distributed over a number of machines. Some example results suggest that the approach could be successfully applied in the real world as the results are more than human competitive. %K genetic algorithms, genetic programming, dispatching, fitness evaluation, parallel simulation, production planning, scheduling problem, dispatching, production planning, scheduling %R 10.1109/IPDPS.2008.4536379 %U http://dx.doi.org/10.1109/IPDPS.2008.4536379 %P 1-5 %0 Conference Proceedings %T Fitness Landscape based Parameter Estimation for Robust Taboo Search %A Beham, Andreas %A Pitzer, Erik %A Affenzeller, Michael %Y Moreno-Diaz, Roberto %Y Pichler, Franz %Y Quesada-Arencibia, Alexis %S Computer Aided Systems Theory, Eurocast 2013 %S LNCS %D 2013 %8 October 15 feb %V 8111 %I Springer %C Las Palmas, Spain %F 3360 %X Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the effort that these algorithms are allowed to make and also the strategy and search behaviour [1]. Adjusting these parameters allows to increase the algorithms performances with respect to different problem- and problem instance characteristics. %K genetic algorithms, genetic programming, Problem Instance, Problem Size, Fitness Landscape, Quadratic Assignment Problem, Large Problem Size %R 10.1007/978-3-642-53856-8_37 %U https://link.springer.com/chapter/10.1007/978-3-642-53856-8_37 %U http://dx.doi.org/10.1007/978-3-642-53856-8_37 %P 292-299 %0 Conference Proceedings %T Optimization Knowledge Center: A Decision Support System for Heuristic Optimization %A Beham, Andreas %A Wagner, Stefan %A Affenzeller, Michael %S Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion %S GECCO ’16 Companion %D 2016 %I ACM %C New York, NY, USA %F Beham:2016:OKC:2908961.2931724 %K genetic algorithms, genetic programming, decision-support-system, heuristic optimization, knowledge base %R 10.1145/2908961.2931724 %U http://doi.acm.org/10.1145/2908961.2931724 %U http://dx.doi.org/10.1145/2908961.2931724 %P 1331-1338 %0 Journal Article %T Predictive model of modified asphalt mixtures with nano hydrated lime to increase resistance to moisture and fatigue damages by the use of deicing agents %A Behbahani, Hamid %A Hamedi, Gholam Hossein %A Najafi Moghaddam Gilani, Vahid %J Construction and Building Materials %D 2020 %V 265 %@ 0950-0618 %F BEHBAHANI:2020:CBM %X Deicing agents are used to dissolving the frost on road surfaces in winter and cold areas. Researchers have evaluated the impact of different deicing agents on the moisture susceptibility performance of asphalt mixtures, but they have not investigated the effect of these agents on fatigue failure and thermodynamic parameters of asphalt mixtures. Therefore, in this research, by investigating the effect of two new deicing agents of calcium magnesium acetate (CMA) and potassium acetate (PA) as well as sodium chloride (NaCl) traditional agent on moisture and fatigue performances of asphalt mixtures, predictive model of the tensile strength ratio (TSR) and the fatigue life ratio (NFR) using genetic programming (GP) based on the surface free energy (SFE) components and other properties of asphalt mixtures were presented. Nano hydrated lime (NHL) was applied as an asphalt binder modifier and an anti-stripping agent to improve the strength of asphalt mixtures. The results indicated that the saturated mixtures in CMA had the highest indirect tensile strength (ITS) and fatigue life in lower freeze-thaw cycles, while the NaCl-saturated samples had more ITS and fatigue life in higher cycles. The CMA-saturated samples had the greatest TSR and NFR. Using NHL in all saturated samples resulted in increasing TSR and NFR values. Results of SFE method showed that using NHL increased the polar, non-polar and basic components of asphalt binders and decreased their acidic components. Also, using NHL increased the total SFE amount of asphalt binder, enhancing the adhesion of aggregate and asphalt binder and cohesion in asphalt binder membrane, and as a result, improving the moisture resistance in asphalt mixtures. Using 1.5percent NHL had the greatest effect on improving adhesion free energy (AFE), cohesion free energy (CFE) and detachment energy (DE). Among deicing solutions, CMA had the highest CFE, in general, and NaCl had the best DE values. PA-saturated samples had the greatest permeability of asphalt mixture (PAM) values. GP model had a high R2 96.4percent and 98.3percent for TSR and NFR, respectively. Using GP model to achieve the maximum TSR and NFR, the Pareto curve showed that 1.32percent NHL was the optimum value for simultaneously increasing moisture resistance and fatigue life %K genetic algorithms, genetic programming, Asphalt mixture, Moisture susceptibility, Fatigue life, Surface free energy, Deicing agents, Nano hydrated lime, Two-objective optimization %9 journal article %R 10.1016/j.conbuildmat.2020.120353 %U http://www.sciencedirect.com/science/article/pii/S0950061820323588 %U http://dx.doi.org/10.1016/j.conbuildmat.2020.120353 %P 120353 %0 Journal Article %T Mechatronic Design Evolution Using Bond Graphs and Hybrid Genetic Algorithm With Genetic Programming %A Behbahani, Saeed %A de Silva, Clarence W. %J IEEE/ASME Transactions on Mechatronics %D 2013 %8 feb %V 18 %N 1 %@ 1083-4435 %F Behbahani:2012:transMechtron %X A typical mechatronic problem (modelling, identification, and design) entails finding the best system topology as well as the associated parameter values. The solution requires concurrent and integrated methodologies and tools based on the latest theories. The experience on natural evolution of an engineering system indicates that the system topology evolves at a much slower rate than the parametric values. This paper proposes a two-loop evolutionary tool, using a hybrid of genetic algorithm (GA) and genetic programming (GP) for design optimisation of a mechatronic system. Specifically, GP is used for topology optimization, while GA is responsible for finding the elite solution within each topology proposed by GP. A memory feature is incorporated with the GP process to avoid the generation of repeated topologies, a common drawback of GP topology exploration. The synergic integration of GA with GP, along with the memory feature, provides a powerful search ability, which has been integrated with bond graphs (BG) for mechatronic model exploration. The software developed using this approach provides a unified tool for concurrent, integrated, and autonomous topological realisation of a mechatronic problem. It finds the best solution (topology and parameters) starting from an abstract statement of the problem. It is able to carry out the process of system configuration realization, which is normally performed by human experts. The performance of the software tool is validated by applying it to mechatronic design problems. %K genetic algorithms, genetic programming, Bond graphs, electrohydraulic systems %9 journal article %R 10.1109/TMECH.2011.2165958 %U http://dx.doi.org/10.1109/TMECH.2011.2165958 %P 190-199 %0 Journal Article %T Niching Genetic Scheme With Bond Graphs for Topology and Parameter Optimization of a Mechatronic System %A Behbahani, Saeed %A de Silva, Clarence W. %J IEEE/ASME Transactions on Mechatronics %D 2014 %8 feb %V 19 %N 1 %@ 1083-4435 %F Behbahani:2013:Mechatronics %X This paper presents a novel multimodal evolutionary optimisation algorithm for the complex problem of concurrent and integrated design of a mechatronic system, with the objective of realising the best topology and the best parameters from a multicriteria viewpoint and with different preferences. The associated search space can be large and complex due to the existence of different classes of configurations, possible topologies, and the parameter values of the elements. The proposed algorithm efficiently explores the search space to find several elite configurations for different preferences, with more detailed competition by incorporating the domain knowledge of experts and considering some criteria that are not included in the course of regular evolutionary optimisation. The developed approach consists of a two-loop optimisation. For each topology, a genetic algorithm-based optimisation is performed to find an elite representative of the topology. The elites will compete with each other to become the best design. A strategy of restricted competition selection is employed in the competition of topologies, with the aim of finding alternative elites from which the one that best satisfies the customer preference may be chosen. The designer may incorporate a higher level competition between elites in order to obtain the global optimum. %K genetic algorithms, genetic programming, Bond graphs, car suspension, genetic algorithms (GAs), genetic programming (GP), niching genetic schemes, skyhook %9 journal article %R 10.1109/TMECH.2012.2230013 %U http://dx.doi.org/10.1109/TMECH.2012.2230013 %P 269-277 %0 Book Section %T Using Genetic Algorithms and Convolution to Find Optimal Strategies in Games without Perfect Information %A Beheler, Joey %E Koza, John R. %B Genetic Algorithms and Genetic Programming at Stanford 1995 %D 1995 %8 November %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-195720-5 %F beheler:1995:UGACFOSGPI %K genetic algorithms %P 11-18 %0 Journal Article %T An Application of Genetic Programming for Power System Planning and Operation %A Behera, R. %A Pati, B. B. %A Panigrahi, B. P. %A Misra, S. %J ACEEE International Journal on Control System and Instrumentation %D 2012 %8 mar %V 3 %N 2 %@ 2158-0006 %G ENG %F Behera:2012:ACEEijcsi %O Special Issue %X This work incorporates the identification of model in functional form using curve fitting and genetic programming technique which can forecast present and future load requirement. Approximating an unknown function with sample data is an important practical problem. In order to forecast an unknown function using a finite set of sample data, a function is constructed to fit sample data points. This process is called curve fitting. There are several methods of curve fitting. Interpolation is a special case of curve fitting where an exact fit of the existing data points is expected. Once a model is generated, acceptability of the model must be tested. There are several measures to test the goodness of a model. Sum of absolute difference, mean absolute error, mean absolute percentage error, sum of squares due to error (SSE), mean squared error and root mean squared errors can be used to evaluate models. Minimising the squares of vertical distance of the points in a curve (SSE) is one of the most widely used method .Two of the methods has been presented namely Curve fitting technique and Genetic Programming and they have been compared based on (SSE)sum of squares due to error. %K genetic algorithms, genetic programming Computer Aided Engineering, Mutation, Fitness Function %9 journal article %U http://hal.archives-ouvertes.fr/docs/00/74/16/55/PDF/59.pdf %P 15-20 %0 Journal Article %T Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network %A Beiki, Morteza %A Bashari, Ali %A Majdi, Abbas %J International Journal of Rock Mechanics and Mining Sciences %D 2010 %V 47 %N 7 %@ 1365-1609 %F Beiki20101091 %X We use genetic programming (GP) to determine the deformation modulus of rock masses. A database of 150 data sets, including modulus of elasticity of intact rock (Ei), uniaxial compressive strength (UCS), rock mass quality designation (RQD), the number of joint per meter (J/m), porosity, and dry density for possible input parameters, and the modulus deformation of the rock mass determined by a plate loading test for output, was established. The values of geological strength index (GSI) system were also determined for all sites and considered as another input parameter. Sensitivity analyses are considered to find out the important parameters for predicting of the deformation modulus of rock mass. Two approaches of sensitivity analyses, based on statistical analysis of RSE values and sensitivity analysis about the mean, are performed. Evolution of the sensitivity analyses results establish the fact that variable of UCS, GSI, and RQD play more prominent roles for predicting modulus of the rock mass, and so those are considered as the predictors to design the GP model. Finally, two equations were achieved by GP. The statistical measures of root mean square error (RMSE) and variance account for (VAF) have been used to compare GP models with the well-known existing empirical equations proposed for predicting the deformation modulus. These performance criteria proved that the GP models give higher predictions over existing empirical models. %K genetic algorithms, genetic programming, Deformation modulus of rock mass, Relative strength of effect (RSE), Sensitivity analysis about the mean %9 journal article %R 10.1016/j.ijrmms.2010.07.007 %U http://www.sciencedirect.com/science/article/B6V4W-50RFN0V-1/2/fa0de8195c17e39f39b1ecead4df4da4 %U http://dx.doi.org/10.1016/j.ijrmms.2010.07.007 %P 1091-1103 %0 Journal Article %T Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks %A Beiki, Morteza %A Majdi, Abbas %A Givshad, Ali Dadi %J International Journal of Rock Mechanics and Mining Sciences %D 2013 %V 63 %@ 1365-1609 %F Beiki:2013:IJRMMS %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.ijrmms.2013.08.004 %U http://www.sciencedirect.com/science/article/pii/S1365160913001196 %U http://dx.doi.org/10.1016/j.ijrmms.2013.08.004 %P 159-169 %0 Conference Proceedings %T GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts %A Bekavac, Marko %A Snajder, Jan %Y Piskorski, Jakub %Y Pivovarova, Lidia %Y Tanev, Hristo %Y Yangarber, Roman %S Proceedings of the 4th Biennial International Workshop on Balto-Slavic Natural Language Processing %D 2013 %8 August 9 aug %I Association for Computational Linguistics %C Sofia, Bulgaria %G en %F bekavac-vsnajder:2013:BSNLP %X We describe GPKEX, a key-phrase extraction method based on genetic programming. We represent Keyphrase scoring measures as syntax trees and evolve them to produce rankings for key phrase candidates extracted from text. We apply and evaluate GPKEX on Croatian newspaper articles. We show that GPKEX can evolve simple and interpretable key-phrase scoring measures that perform comparably to more complex machine learning methods previously developed for Croatian. %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.397.588 %P 43-47 %0 Journal Article %T Correcting Instruction Expression Logic Errors with GenExp: A Genetic Programming Solution %A Bekkouche, Mohammed %J Computer Science Journal of Moldova %D 2023 %V 31 %N 2 %F DBLP:journals/csjm/Bekkouche23 %X Correcting logical errors in a program is not simple even with the availability of an error locating tool. we introduce GenExp, a genetic programming approach to automate the task of repairing instruction expressions from logical errors. Starting from an error location specified by the programmer, we search for a replacement instruction that passes all test cases. Specifically, we generate expressions that will substitute the selected instruction expression until we obtain one that corrects the input program. The search space is exponentially large, making exhaustive methods inefficient. Therefore, we use a genetic programming meta-heuristic that organises the search process into stages, with each stage producing a group of individuals. The results showed that our approach can find at least one plausible patch for almost all cases considered in experiments and outperforms a notable state-of-the-art error repair approach like ASTOR. Although our tool is slower than ASTOR, it provides greater precision in detecting plausible repairs, making it a suitable option for users who prioritise accuracy over speed. %K genetic algorithms, genetic programming, genetic improvement, APR, error correction, instruction expression, plausible patch, crossover, mutation %9 journal article %R 10.56415/csjm.v31.12 %U http://www.math.md/publications/csjm/issues/v31-n2/13785/ %U http://dx.doi.org/10.56415/csjm.v31.12 %P 217-247 %0 Conference Proceedings %T Embedding-Based Selection Operators for Genetic Programming %A Bel Moudden, Oumaima %A Guibadj, Rym %A Robilliard, Denis %A Fonlupt, Cyril %A Kadrani, Abdeslam %Y Ekart, Aniko %Y Pillay, Nelishia %S Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion %S GECCO ’25 Companion %D 2025 %8 14 18 jul %I Association for Computing Machinery %C Malaga, Spain %F bel-moudden:2025:GECCOcomp %X The field of automatic programming has made significant advances in recent years, particularly through the development of semantic-aware algorithms in Genetic Programming (GP) and the emergence of Large Language Models (LLMs) designed for code. This raises a natural question: can these two approaches be combined to achieve synergistic effects? In this paper, we explore whether the semantic information embedded in vector representations of source code, generated by dedicated LLMs, can enhance GP performance. Specifically, we integrate code representation models expected to capture both syntactic and semantic attributes of GP-generated programs into their embeddings, and propose informed selection operators that leverage these embedded attributes. Experimental results on a diverse set of benchmark problems demonstrate that embedding-based selection methods significantly improve convergence speed in GP. This integration of code embeddings into the selection process offers a promising direction for enhancing the efficiency of GP evolutionary search. %K genetic algorithms, genetic programming, code embedding, selection operator, source code representation, convergence optimization: Poster %R 10.1145/3712255.3726679 %U https://doi.org/10.1145/3712255.3726679 %U http://dx.doi.org/10.1145/3712255.3726679 %P 591-594 %0 Journal Article %T Strategy creation, decomposition and distribution in particle navigation %A Beldek, Ulas %A Leblebicioglu, Kemal %J Information Sciences %D 2007 %8 January %V 177 %N 3 %F Beldek:2007:IS %X Strategy planning is crucial to control a group to achieve a number of tasks in a closed area full of obstacles. In this study, genetic programming has been used to evolve rule-based hierarchical structures to move the particles in a grid region to accomplish navigation tasks. Communications operations such as receiving and sending commands between particles are also provided to develop improved strategies. In order to produce more capable strategies, a task decomposition procedure is proposed. In addition, a conflict module is constructed to handle the challenging situations and conflicts such as blockage of a particle’s pathway to destination by other particles. %K genetic algorithms, genetic programming, Rule-base, Strategy planning, Robot navigation, Maze solving, Optimization, Multi-agent systems %9 journal article %R 10.1016/j.ins.2006.07.008 %U http://dx.doi.org/10.1016/j.ins.2006.07.008 %P 755-770 %0 Journal Article %T Personalized and object-centered tag recommendation methods for Web 2.0 applications %A Belem, Fabiano M. %A Martins, Eder F. %A Almeida, Jussara M. %A Goncalves, Marcos A. %J Information Processing & Management %D 2014 %V 50 %N 4 %@ 0306-4573 %F Belem:2014:IPM %K genetic algorithms, genetic programming, Tag recommendation, Relevance metrics, Personalisation %9 journal article %R 10.1016/j.ipm.2014.03.002 %U http://www.sciencedirect.com/science/article/pii/S0306457314000181 %U http://dx.doi.org/10.1016/j.ipm.2014.03.002 %P 524-553 %0 Conference Proceedings %T Extrinsic Evolution of Finite State Machine %A Belgasem, A. %A Kalganova, T. %A Almaini, A. %Y Parmee, I. C. %S Proc. of ACDM2002 %D 2002 %8 apr 16 18 %I Springer %F Belgasem:2002:ACDM %X extrinsic evolvable hardware approach to evolve finite state machines (FSM). Both the genetic algorithm (GA) and Evolvable Hardware (EHW) are combined together to produce optimal logic circuit. GA is used to optimise the state assignment problem. EHW is used to design the combinational parts of the desired circuit. The approach is tested on a number of finite state machines from MCNC benchmark set. These circuits have been evolved using different functional sets of logic gates and GA parameters. The results show promise for the use of this approach as a design method for sequential logic circuits. %K genetic algorithms, genetic programming, evolvable hardware %U http://bura.brunel.ac.uk/handle/2438/2514 %P 157-168 %0 Conference Proceedings %T Intrusion detection based on genetic fuzzy classification system %A Belhor, M. %A Jemili, F. %S 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA) %D 2016 %8 nov %F Belhor:2016:AICCSA %X Information system is vital for any company. However, the opening to the outside world makes the computer system more vulnerable to attack. It is essential to protect it. Intrusion Detection System (IDS) is an auditing mechanism that analyses the traffic system and applications to identify normal use of the system and an intrusion attempt and also it prevent security managers. Despite the advantages of IDS, they suffer from a few problems. The major problem in the field of intrusion detection is the classification problem. Genetic Fuzzy System (GFS) are models capable of integrating accuracy and high comprehensibility in their results. They have been widely employed to solve classification problems. In this paper, we use a new GFS model called Genetic Programming Fuzzy Inference System for Classification (GPFIS-Class). It based on Multi-Gene Genetic Programming (MGGP). This model is not used in the intrusion detection area. We use an efficient feature selection method to eliminate data redundancy and irrelevant features in order to analyse the huge data namely the NSL-KDD data set. %K genetic algorithms, genetic programming %R 10.1109/AICCSA.2016.7945690 %U http://dx.doi.org/10.1109/AICCSA.2016.7945690 %0 Conference Proceedings %T Evolutionary Multimodel Partitioning Filters for Nonlinear Systems %A Beligiannis, G. N. %A Demiris, E. N. %A Likothanassis, S. D. %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 beligiannis:1999:EMPFNS %K genetic algorithms, EHW, evolvable hardware, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/GP-452.ps %P 1227 %0 Journal Article %T Nonlinear model structure identification of complex biomedical data using a genetic-programming-based technique %A Beligiannis, Grigorios N. %A Skarlas, Lambros V. %A Likothanassis, Spiridon D. %A Perdikouri, Katerina G. %J IEEE Transactions on Instrumentation and Measurement %D 2005 %8 dec %V 54 %N 6 %@ 0018-9456 %F Beligiannis:2005:tIM %X In this contribution, a genetic programming (GP)-based technique, which combines the ability of GP to explore both automatically and effectively, the whole set of candidate model structures and the robustness of evolutionary multimodel partitioning filters, is presented. The method is applied to the nonlinear system identification problem of complex biomedical data. Simulation results show that the algorithm identifies the true model and the true values of the unknown parameters for each different model structure, thus assisting the GP technique to converge more quickly to the (near) optimal model structure. The method has all the known advantages of the evolutionary multi model partitioning filters, that is, it is not restricted to the Gaussian case; it is applicable to on-line/adaptive operation and is computationally efficient. Furthermore, it can be realized in a parallel processing fashion, a fact which makes it amenable to very large scale integration implementation. %K genetic algorithms, genetic programming, medical signal processing, nonlinear dynamical systems complex biomedical data identification, evolutionary multimodel partitioning filters, nonlinear model structure %9 journal article %R 10.1109/TIM.2005.858573 %U http://dx.doi.org/10.1109/TIM.2005.858573 %P 2184-2190 %0 Book Section %T Evolving the Structure and Weights of Recurrent Neural Network though Genetic Algorithms %A Bell, Matt %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 bell:1999:ESWRNNGA %K genetic algorithms, genetic programming %P 11-20 %0 Conference Proceedings %T A One-Vs-One Approach to Improve Tangled Program Graph Performance on Classification Tasks %A Bellanger, Thibaut %A Le Berre, Matthieu %A Clergue, Manuel %A Hao, Jin-Kao %Y van Stein, Niki %Y Marcelloni, Francesco %Y Lam, H. K. %Y Cottrell, Marie %Y Filipe, Joaquim %S Proceedings of the 15th International Joint Conference on Computational Intelligence, IJCCI 2023 %D 2023 %8 nov 13 15 %I SCITEPRESS %C Rome, Italy %F DBLP:conf/ijcci/BellangerBCH23 %X We propose an approach to improve the classification performance of the Tangled Programs Graph (TPG). TPG is a genetic programming method that aims to discover Directed Acyclic Graphs (DAGs) through an evolutionary process, where the edges carry programs that allow nodes to create a route from the root to a leaf, and the leaves represent actions or labels in classification. Despite notable successes in reinforcement learning tasks, TPG performance in classification appears to be limited in its basic version, as evidenced by the scores obtained on the MNIST dataset. However, the advantage of TPG compared to neural networks is to obtain, like decision trees, a global decision that is decomposable into simple atomic decisions and thus more easily explainable. Compared to decision trees, TPG has the advantage that atomic decisions benefit from the expressiveness of a pseudo register-based programming language, and the graph evolutionary construction prevents the emergence of overfitting. Our approach consists of decomposing the multi-class problem into a set of one-vs-one binary problems, training a set of TPG for each of them, and then combining the results of the TPGs to obtain a global decision, after selecting the best ones by a genetic algorithm. We test our approach on several benchmark datasets, and the results obtained are promising and tend to validate the proposed method. %K genetic algorithms, genetic programming, Classification, Tangled Program Graph, Ensemble Learning, Evolutionary Machine Learning, Evolutionary Search and Meta-Heuristics %R 10.5220/0012167700003595 %U https://www.insticc.org/node/TechnicalProgram/ijcci/2023/presentationDetails/121677 %U http://dx.doi.org/10.5220/0012167700003595 %P 53-63 %0 Conference Proceedings %T Directed Acyclic Program Graph Applied to Supervised Classification %A Bellanger, Thibaut %A Le Berre, Matthieu %A Clergue, Manuel %A Hao, Jin-Kao %Y Wilson, Dennis G. %Y Kalkreuth, Roman %Y Medvet, Eric %Y Nadizar, Giorgia %Y Squillero, Giovanni %Y Tonda, Alberto %Y Lavinas, Yuri %S 2nd GECCO workshop on Graph-based Genetic Programming %S GECCO ’24 %D 2024 %8 14 jul %I Association for Computing Machinery %C Melbourne, Australia %F Bellanger:2024:GGP %X In the realm of Machine Learning, the pursuit of simpler yet effective models has led to increased interest in decision trees due to their interpretability and efficiency. However, their inherent simplicity often limits their ability to handle intricate patterns in data. This paper introduces a novel approach termed Directed Acyclic Graphs of Programs, inspired by evolutionary strategies, to address this challenge. By iteratively constructing program graphs from binary decision makers, our method offers a balance of simplicity and performance for classification tasks. Notably, we emphasize the preservation of model interpretability and expressiveness, avoiding the use of ensemble techniques like voting. Experimental evaluations demonstrate the superiority of our approach over existing methods in terms of both effectiveness and interpretability. %K genetic algorithms, genetic programming, evolutionary algorithm, local search, supervised classification %R 10.1145/3638530.3664115 %U http://dx.doi.org/10.1145/3638530.3664115 %P 1676-1680 %0 Thesis %T Theoretical Studies of Excited State 1,3 Dipolar Cycloadditions %A Bellucci, Michael Anthony %D 2012 %C USA %C Boston University %F Bellucci:thesis %X The 1,3 dipolar photocycloaddition reaction between 3-hydroxy-4’,5,7-trimethoxyflavone (3-HTMF) and methyl cinnamate is investigated in this work. Since its inception in 2004 [JACS, 124, 13260 (2004)], this reaction remains at the forefront in the synthetic design of the rocaglamide natural products. The reaction is multi-faceted in that it involves multiple excited states and is contingent upon excited state intramolecular proton transfer (ESIPT) in 3-HTMF. Given the complexity of the reaction, there remain many questions regarding the underlying mechanism. Consequently, throughout this work we investigate the mechanism of the reaction along with a number of other properties that directly influence it. To investigate the photocycloaddition reaction, we began by studying the effects of different solvent environments on the ESIPT reaction in 3-hydroxyflavone since this underlying reaction is sensitive to the solvent environment and directly influences the cycloaddition. To study the ESIPT reaction, we developed a parallel multi-level genetic program to fit accurate empirical valence bond (EVB) potentials to ab initio data. We found that simulations with our EVB potentials accurately reproduced experimentally determined reaction rates, fluorescence spectra, and vibrational frequency spectra in all solvents. Furthermore, we found that the ultrafast ESIPT process results from a combination of ballistic transfer and intramolecular vibrational redistribution. To investigate the cycloaddition reaction mechanism, we used the string method to obtain minimum energy paths on the ab initio potential. These calculations demonstrated that the reaction can proceed through formation of an exciplex in the S1 state, followed by a non-adiabatic transition to the ground state. In addition, we investigated the enantioselective catalysis of the reaction using alpha,alpha,alpha’,alpha’-tetraaryl-1,3-dioxolan-4,5-dimethanol alcohol (TADDOL). We found that TADDOL lowered the energy barrier by 10-12 kcal/mol through stabilizing hydrogen bond interactions. Using temperature accelerated molecular dynamics, we obtained the potential of mean force (PMF) associated with 3-HTMF attacking the TADDOL/methyl cinnamate complex. We found that the exo reaction is inhibited through steric interactions with the aryl substituents on TADDOL. Furthermore, we found that the exo configuration breaks the intramolecular hydrogen bond in TADDOL, which stabilizes the individual reactants apart from each other. The role of the T1 state is also discussed. %K genetic algorithms, genetic programming, Chemistry, Dipolar cycloaddition, Excited state, Hydroxy flavone, Methyl cinnamate, Physical chemistry, Proton transfer, Pure science, Quantum physics %9 Ph.D. thesis %U http://www.bu.edu/phpbin/calendar/event.php?id=127428&cid=17 %0 Thesis %T MotifGP: DNA Motif Discovery Using Multiobjective Evolution %A Belmadani, Manuel %D 2016 %C Canada %C School of Electrical Engineering and Computer Science, University of Ottawa %F Belmadani2016-bn %X The motif discovery problem is becoming increasingly important for molecular biologists as new sequencing technologies are producing large amounts of data, at rates which are unprecedented. The solution space for DNA motifs is too large to search with naive methods, meaning there is a need for fast and accurate motif detection tools. We propose MotifGP, a multiobjective motif discovery tool evolving regular expressions that characterize overrepresented motifs in a given input dataset. This thesis describes and evaluates a multiobjective strongly typed genetic programming algorithm for the discovery of network expressions in DNA sequences. Using 13 realistic data sets, we compare the results of our tool, MotifGP, to that of DREME, a state-of-art program. MotifGP outperforms DREME when the motifs to be sought are long, and the specificity is distributed over the length of the motif. For shorter motifs, the performance of MotifGP compares favourably with the state-of-the-art method. Finally, we discuss the advantages of multi-objective optimization in the context of this specific motif discovery problem. %K genetic algorithms, genetic programming %9 Master degree in Computer Science Specialization in Bioinformatics %9 Masters thesis %R 10.20381/ruor-5077 %U http://hdl.handle.net/10393/34213 %U http://dx.doi.org/10.20381/ruor-5077 %0 Conference Proceedings %T MotifGP: Using multi-objective evolutionary computing for mining network expressions in DNA sequences %A Belmadani, Manuel %A Turcotte, Marcel %S 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) %D 2016 %8 oct %F Belmadani:2016:CIBCB %X This paper describes and evaluates a multi-objective strongly typed genetic programming algorithm for the discovery of network expressions in DNA sequences. Using 13 realistic data sets, we compare the results of our tool, MotifGP, to that of DREME, a state-of-the-art program. MotifGP outperforms DREME when the motifs to be sought are long, and the specificity is distributed over the length of the motif. For shorter motifs, the performance of MotifGP compares favourably with the state-of-the-art method. Finally, we discuss the advantages of multi-objective optimisation in the context of this specific motif discovery problem. %K genetic algorithms, genetic programming %R 10.1109/CIBCB.2016.7758133 %U http://dx.doi.org/10.1109/CIBCB.2016.7758133 %0 Conference Proceedings %T Evolution of Visual Feature Detectors %A Belpaeme, Tony %Y Poli, Riccardo %Y Cagnoni, Stefano %Y Voigt, Hans-Michael %Y Fogarty, Terry %Y Nordin, Peter %S Late Breaking Papers at EvoISAP’99: the First European Workshop on Evolutionary Computation in Image Analysis and Signal Processing %D 1999 %8 28 may %C Goteborg, Sweden %F belpaeme:1999:evfd %X This paper describes how sets of visual feature detectors are evolved starting from simple primitives. The primitives, of which some are inspired on visual processing observed in mammalian visual pathways, are combined using genetic programming to form a feed-forward feature-extraction hierarchy. Input to the feature detectors consists of a series of real-world images, containing objects or faces. The results show how each set of feature detectors self-organizes into a set which is capable of returning feature vectors for discriminating the input images. We discuss the influence of different settings on the evolution of the feature detectors and explain some phenomena. %K genetic algorithms, genetic programming %U http://arti.vub.ac.be/~tony/papers/EvoIASP99.ps.gz %P 1-10 %0 Journal Article %T Evaluation of Real-Time Requirements by Simulation Based Analysis %A Belschner, R. %J IFAC Proceedings Volumes %D 1996 %8 nov %V 29 %N 6 %@ 1474-6670 %F BELSCHNER199619 %O 20th IFAC/IFIP Workshop on Real Time Programming 1995 (WRTP ’95), Fort Lauderdale, USA, 6-10 November %X This paper addresses issues related to the software design in distributed real-time automation systems. Beside the influence of the real-time operating system and the behaviour of the technical process, the properties of the communication network play an important part for the temporal correctness of the software design. In consideration of the enormous complexity, an attempt to improve the software correctness concerning the temporal behaviour by a three layered Simulation Based Analysis (SBA) System is presented. The SBA system automatically evaluates timing requirements, which can be specified by a problem adapted language, and estimates the system’s behaviour in both the best case and especially the worst case by using evolutionary strategies. The SBA system is currently implemented into a prototype. %K genetic algorithms, genetic programming, SBSE, EPOSIX, event driven simulation, distributed real-time systems, timing requirements, worst case analysis, evolutionary strategies %9 journal article %R 10.1016/S1474-6670(17)43741-4 %U http://www.sciencedirect.com/science/article/pii/S1474667017437414 %U http://dx.doi.org/10.1016/S1474-6670(17)43741-4 %P 19-26 %0 Conference Proceedings %T CORE: Constrained Optimization by Random Evolution %A Belur, Sheela V. %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 Belur:1997:CORElb %K genetic algorithms %P 280-286 %0 Journal Article %T Active Contour Extension Basing on Haralick Texture Features, Multi-gene Genetic Programming, and Block Matching to Segment Thyroid in 3D Ultrasound Images %A Benabdallah, Fatma Zohra %A Djerou, Leila %J Arabian Journal for Science and Engineering %D 2023 %V 48 %N 2 %F benabdallah:2023:AJSE %X The segmentation and estimation of thyroid volume in 3D ultrasound images have attracted the research community’s attention because of their great importance in clinical diagnosis. Usually, thyroid volume estimation is based on the segmentation of 3D ultrasound images, which is difficult due to various disorders, including non-homogeneous texture distribution within the thyroid region, artifacts, speckles, and the nature of the thyroid shape. This paper presents an approach to segmenting all individual slices and then reconstructing them into a 3D object to overcome these difficulties. The process involves four techniques. The VOI initialization encompasses the probable thyroid gland; it greatly affects the segmentation results. Multi-gene genetic programming determines the appropriate textural features. The block-matching technique estimates the thyroid gland’s change in size and location from slice to slice. Finally, the ITKSNAP software reconstructs the 3D volume. The proposed method is compared with state-of-the-art methods to prove its effectiveness in medical image analysis. Sixteen 3D images from an ultrasound thyroid image dataset were used for the experiments. The analysis of the results based on performance evaluation metrics shows that the proposed method is more efficient than the state-of-the-art methods %K genetic algorithms, genetic programming, multi-gene genetic programming (MGGP), GPTIPS 2, segmentation, Ultrasound images, Thyroid gland, Volume estimation %9 journal article %R 10.1007/s13369-022-07286-3 %U http://link.springer.com/article/10.1007/s13369-022-07286-3 %U http://dx.doi.org/10.1007/s13369-022-07286-3 %P 2429-2440 %0 Conference Proceedings %T Evolving Lose-Checkers Players using Genetic Programming %A Benbassat, Amit %A Sipper, Moshe %S IEEE Conference on Computational Intelligence and Game %D 2010 %8 18 21 aug %C IT University of Copenhagen, Denmark %F Benbassat:2010:CIGPU %X We present the application of genetic programming (GP) to the zero-sum, deterministic, full-knowledge board game of Lose Checkers. Our system implements strongly typed GP trees, explicitly defined introns, local mutations, and multitree individuals. Explicitly defined introns in the genome allow for information selected out of the population to be kept as a reservoir for possible future use. Multi-tree individuals are implemented by a method inspired by structural genes in living organisms, whereby we take a single tree describing a state evaluator and split it. %K genetic algorithms, genetic programming, explicitly defined intron, full knowledge board game, genetic programming tree, local mutation, lose checker player, multitree individual, state evaluator, computer games, trees (mathematics) %R 10.1109/ITW.2010.5593376 %U http://game.itu.dk/cig2010/proceedings/papers/cig10_005_011.pdf %U http://dx.doi.org/10.1109/ITW.2010.5593376 %P 30-37 %0 Conference Proceedings %T Evolving board-game players with genetic programming %A Benbassat, Amit %A Sipper, Moshe %Y Nicolau, Miguel %S GECCO 2011 Graduate students workshop %D 2011 %8 December 16 jul %I ACM %C Dublin, Ireland %F Benbassat:2011:GECCOcomp %X We present the application of genetic programming (GP) to zero-sum, deterministic, full-knowledge board games. Our work expands previous results in evolving board-state evaluation functions for Lose Checkers to a 10x10 variant of Checkers, as well as Reversi. Our system implements strongly typed GP trees, explicitly defined introns, and a selective directional crossover method. %K genetic algorithms, genetic programming, STGP, Design, games, board games, alpha-beta search, Explicitly Defined Introns %R 10.1145/2001858.2002080 %U https://www.moshesipper.com/pubs/board_game.pdf %U http://dx.doi.org/10.1145/2001858.2002080 %P 739-742 %0 Book Section %T More or Less? Two Approaches to Evolving Game-Playing Strategies %A Benbassat, Amit %A Elyasaf, Achiya %A Sipper, Moshe %E Riolo, Rick %E Vladislavleva, Ekaterina %E Ritchie, Marylyn D. %E Moore, Jason H. %B Genetic Programming Theory and Practice X %S Genetic and Evolutionary Computation %D 2012 %8 December 14 may %I Springer %C Ann Arbor, USA %F Benbassat:2012:GPTP %X We present two opposing approaches to the evolution of game strategies, one wherein a minimal amount of domain expertise is injected into the process, the other infusing the evolutionary setup with expertise in the form of domain heuristics. We show that the first approach works well for several popular board games, while the second produces top-notch solvers for the hard game of FreeCell. %K genetic algorithms, genetic programming, alpha-beta search, Checkers, Dodgem, Freecell, Hyper heuristic, Reversi %R 10.1007/978-1-4614-6846-2_12 %U http://dx.doi.org/10.1007/978-1-4614-6846-2_12 %P 171-185 %0 Conference Proceedings %T Evolving players that use selective game-tree search with genetic programming %A Benbassat, Amit %A Sipper, Moshe %Y Rodriguez, Katya %Y Blum, Christian %S GECCO 2012 Late breaking abstracts workshop %D 2012 %8 July 11 jul %I ACM %C Philadelphia, Pennsylvania, USA %F Benbassat:2012:GECCOcomp %X We present the application of genetic programming (GP) to evolving game-tree search in board games. Our work expands previous results in evolving board-state evaluation functions for multiple board games, now evolving a search-guiding evaluation function alongside it. Our system implements strongly typed GP trees, explicitly defined introns, and a selective directional crossover method. %K genetic algorithms, genetic programming %R 10.1145/2330784.2330894 %U http://dx.doi.org/10.1145/2330784.2330894 %P 631-632 %0 Conference Proceedings %T Evolving both search and strategy for Reversi players using genetic programming %A Benbassat, Amit %A Sipper, Moshe %S IEEE Conference on Computational Intelligence and Games, CIG 2012 %D 2012 %8 November 14 sep %I IEEE %C Granada %F Benbassat:2012:CIG %X We present the application of genetic programming to the zero-sum, deterministic, full-knowledge board game of Reversi. Expanding on our previous work on evolving boardstate evaluation functions, we now evolve the search algorithm as well, by allowing evolved programs control of game-tree pruning. We use strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. We show that our system regularly churns out highly competent players and our results prove easy to scale. %K genetic algorithms, genetic programming, computer games, search problems, trees (mathematics), Reversi players, deterministic board game, full-knowledge board game, game-tree pruning, search algorithm, selective directional crossover method, zero-sum board game, Games, Humans, Receivers, Sociology, Statistics %R 10.1109/CIG.2012.6374137 %U https://bibtex.github.io/CIG-2012-BenbassatS.html %U http://dx.doi.org/10.1109/CIG.2012.6374137 %P 47-54 %0 Conference Proceedings %T EvoMCTS: Enhancing MCTS-based players through genetic programming %A Benbassat, Amit %A Sipper, Moshe %S IEEE Conference on Computational Intelligence in Games (CIG 2013) %D 2013 %8 November 13 aug %C Niagara Falls, Canada %F Benbassat:2013:CIG %X We present EvoMCTS, a genetic programming method for enhancing level of play in games. Our work focuses on the zero-sum, deterministic, perfect-information board game of Reversi. Expanding on our previous work on evolving board-state evaluation functions for alpha-beta search algorithm variants, we now evolve evaluation functions that augment the MTCS algorithm. We use strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. Our system regularly evolves players that outperform MCTS players that use the same amount of search. Our results prove scalable and EvoMCTS players whose search is increased offline still outperform MCTS counterparts. To demonstrate the generality of our method we apply EvoMCTS successfully to the game of Dodgem. %K genetic algorithms, genetic programming, Coevolution, Monte Carlo Tree Search, Upper Confidence Bounds applied to Trees, UCT, CoPlayNum, playoutBranchingFactor %R 10.1109/CIG.2013.6633631 %U https://www.moshesipper.com/pubs/evomcts_players.pdf %U http://dx.doi.org/10.1109/CIG.2013.6633631 %0 Thesis %T Finding Methods for Evolving Competent Agents in Multiple Domains %A Benbassat, Amit %D 2014 %8 sep %C Israel %C Ben-Gurion University of the Negev %F BenbassatDissertation %X We present the application of genetic programming (GP) to search in zero-sum, deterministic, full-knowledge board games. We use multiple board games and multiple search algorithms as test cases in order to exhibit the flexibility of our system. We conduct experiments evolving players for variants of Checkers, Reversi, Dodgem, Nine Men’s Morris and Hex, evolving them in conjunction with the Alpha-Beta search and Monte Carlo Tree Search (MCTS) algorithms. Throughout our research we rely on modern neo-Darwinian theory specifically, the gene-centred view of evolution to guide the design of our setup. Our evolutionary system implements strongly typed GP trees, explicitly defined introns, various mutation operators, a novel selective crossover operator, and multi-tree individuals. Explicitly defined introns in the genome allow for information selected out of the population to be kept as a reserve for possible future use. Selective genetic operators allow us to apply additional selection pressure during the procreation stage. Multi-tree individuals allow us to evolve software components that can be integrated into existing search algorithms where they improve play level over hand-crafted baseline players. Our results demonstrate patent improvement in play level for every game, clearly showing that GP is applicable to evolving search in board games. Results show differing levels of scalability, with the best scalability shown when using the MCTS algorithm. We also present our highly scalable EvoMCTS system designed as a scalable, easy-to-use, quick learning tool to improve the play level in games without need for any expert domain knowledge. Pursuing the goal of general game playing (GGP) we present a system that can serve as a stepping stone on the way to general game learning (GGL), where a system can learn a game upon getting its rule set, and the human developer can improve the resulting players by supplying the learning system with relevant information about the game. %K genetic algorithms, genetic programming, MTCS %9 Ph.D. thesis %U https://dl.dropboxusercontent.com/u/36726425/ThesisFinalSubmissionWithTitle.pdf %0 Journal Article %T EvoMCTS: A Scalable Approach for General Game Learning %A Benbassat, Amit %A Sipper, Moshe %J IEEE Transactions on Computational Intelligence and AI in Games %D 2014 %8 dec %V 6 %N 4 %@ 1943-068X %F Benbassat:2014:ieeegames %X We present the application of genetic programming as a generic game learning approach to zero-sum, deterministic, full knowledge board games by evolving board-state evaluation functions to be used in conjunction with Monte Carlo Tree Search (MCTS). Our method involves evolving board-evaluation functions that are then used to guide the MCTS play out strategy. We examine several variants of Reversi, Dodgem, and Hex using strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. Our results show a proficiency that surpasses that of baseline handcrafted players using equal and in some cases a greater amount of search, with little domain knowledge and no expert domain knowledge. Moreover, our results exhibit scalability. %K genetic algorithms, genetic programming, STGP, MCTS, Board Games, Monte Carlo Methods, Search %9 journal article %R 10.1109/TCIAIG.2014.2306914 %U http://dx.doi.org/10.1109/TCIAIG.2014.2306914 %P 382-394 %0 Journal Article %T Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils %A Amin Benbouras, Mohammed %A Petrisor, Alexandru-Ionut %J Applied Sciences %D 2021 %V 11 %N 2 %@ 2076-3417 %F benbouras:2021:AS %X Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In order to overcome this limitation, the current study aims to elaborate an alternative model for estimating the swelling index from geotechnical physical parameters. The reliability of the approach is tested through several advanced machine learning methods like Extreme Learning Machine, Deep Neural Network, Support Vector Regression, Random Forest, LASSO regression, Partial Least Square Regression, Ridge Regression, Kernel Ridge, Stepwise Regression, Least Square Regression, and genetic Programing. These methods have been applied for modelling samples consisting of 875 Oedometer tests. Firstly, principal component analysis, Gamma test, and forward selection are used to reduce the input variable numbers. Afterward, the advanced techniques have been applied for modelling the proposed optimal inputs, and their accuracy models were evaluated through six statistical indicators and using K-fold cross validation approach. The comparative study shows the efficiency of FS-RF model. This elaborated model provided the most appropriate prediction, closest to the experimental values compared with other models and formulae proposed by the previous studies. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/app11020536 %U https://www.mdpi.com/2076-3417/11/2/536 %U http://dx.doi.org/10.3390/app11020536 %0 Journal Article %T Genetic programming based symbolic regression for shear capacity prediction of SFRC beams %A Ben Chaabene, Wassim %A Nehdi, Moncef L. %J Construction and Building Materials %D 2021 %V 280 %@ 0950-0618 %F BENCHAABENE:2021:CBM %X The complexity of shear transfer mechanisms in steel fiber-reinforced concrete (SFRC) has motivated researchers to develop diverse empirical and soft-computing models for predicting the shear capacity of SFRC beams. Yet, such existing methods have been developed based on limited experimental databases, which makes their generalization capability uncertain. To account for the limited experimental data available, this study pioneers a novel approach based on tabular generative adversarial networks (TGAN) to generate 2000 synthetic data examples. A ’train on synthetic - test on real’ philosophy was adopted. Accordingly, the entire 2000 synthetic data were used for training a genetic programming-based symbolic regression (GP-SR) model to develop a shear strength equation for SFRC beams without stirrups. The model accuracy was then tested on the entire set of 309 real experimental data examples, which thus far are unknown to the model. Results show that the novel GP-SR model achieved superior predictive accuracy, outperforming eleven existing equations. Sensitivity analysis revealed that the shear-span-to-depth ratio was the most influential parameter in the proposed equation. The present study provides an enhanced predictive model for the shear capacity of SFRC beams, which should motivate further research to effectively train evolutionary algorithms using synthetic data when acquiring large and comprehensive experimental datasets is not feasible %K genetic algorithms, genetic programming, Steel fiber, Concrete, Beam, Shear strength, Symbolic regression, Generative adversarial network, Synthetic data %9 journal article %R 10.1016/j.conbuildmat.2021.122523 %U https://www.sciencedirect.com/science/article/pii/S095006182100283X %U http://dx.doi.org/10.1016/j.conbuildmat.2021.122523 %P 122523 %0 Conference Proceedings %T Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for Credit Card Fraud Detection %A Benchaji, Ibtissam %A Douzi, Samira %A El Ouahidi, Bouabid %S 2018 2nd Cyber Security in Networking Conference (CSNet) %D 2018 %8 24 26 oct %C Paris %F Benchaji:2018:CSNet %X With the growing usage of credit card transactions, financial fraud crimes have also been drastically increased leading to the loss of huge amounts in the finance industry. Having an efficient fraud detection method has become a necessity for all banks in order to minimize such losses. In fact, credit card fraud detection system involves a major challenge: the credit card fraud data sets are highly imbalanced since the number of fraudulent transactions is much smaller than the legitimate ones. Thus, many of traditional classifiers often fail to detect minority class objects for these skewed data sets. This paper aims first: to enhance classified performance of the minority of credit card fraud instances in the imbalanced data set, for that we propose a sampling method based on the K-means clustering and the genetic algorithm. We used K-means algorithm to cluster and group the minority kind of sample, and in each cluster we use the genetic algorithm to gain the new samples and construct an accurate fraud detection classifier. %K genetic algorithms, genetic programming, Credit cards, Sociology, Statistics, Biological cells, Clustering algorithms, Training, Fraud Detection, Imbalanced dataset, K-means clustering, Autoencoder %R 10.1109/CSNET.2018.8602972 %U http://dx.doi.org/10.1109/CSNET.2018.8602972 %0 Journal Article %T Automatically designed machine vision system for the localization of CCA transverse section in ultrasound images %A Benes, Radek %A Karasek, Jan %A Burget, Radim %A Riha, Kamil %J Computer Methods and Programs in Biomedicine %D 2013 %V 109 %N 1 %@ 0169-2607 %F Benes201392 %X The common carotid artery (CCA) is a source of important information that doctors can use to evaluate the patients’ health. The most often measured parameters are arterial stiffness, lumen diameter, wall thickness, and other parameters where variation with time is usually measured. Unfortunately, the manual measurement of dynamic parameters of the CCA is time consuming, and therefore, for practical reasons, the only alternative is automatic approach. The initial localisation of artery is important and must precede the main measurement. This article describes a novel method for the localization of CCA in the transverse section of a B-mode ultrasound image. The novel method was designed automatically by using the grammar-guided genetic programming (GGGP). The GGGP searches for the best possible combination of simple image processing tasks (independent building blocks). The best possible solution is represented with the highest detection precision. The method is tested on a validation database of CCA images that was specially created for this purpose and released for use by other scientists. The resulting success of the proposed solution was 82.7percent, which exceeded the current state of the art by 4percent while the computation time requirements were acceptable. The paper also describes an automatic method that was used in designing the proposed solution. This automatic method provides a universal approach to designing complex solutions with the support of evolutionary algorithms. %K genetic algorithms, genetic programming, Common carotid artery, Localisation, Machine vision system %9 journal article %R 10.1016/j.cmpb.2012.08.014 %U http://www.sciencedirect.com/science/article/pii/S0169260712001964 %U http://dx.doi.org/10.1016/j.cmpb.2012.08.014 %P 92-103 %0 Conference Proceedings %T Use of genetic programming for the search of a new learning rule for neutral networks %A Bengio, Samy %A Bengio, Yoshua %A Cloutier, Jocelyn %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 Bengio:1994:GPslrNN %X In previous work ([1, 2, 3]) we explained how to use standard optimization methods such as simulated annealing, gradient descent and genetic algorithms to optimize a parametric function which could be used as a learning rule for neural networks. To use these methods, we had to choose a fixed number of parameters and a rigid form for the learning rule. In this article, we propose to use genetic programming to find not only the values of rule parameters but also the optimal number of parameters and the form of the rule. Experiments on classification tasks suggest genetic programming finds better learning rules than other optimization methods. Furthermore, the best rule found with genetic programming outperformed the well-known backpropagation algorithm for a given set of tasks %K genetic algorithms, genetic programming, ANN %R 10.1109/ICEC.1994.349932 %U http://www.idiap.ch/~bengio/cv/publications/ps/bengio_1994_wcci.ps.gz %U http://dx.doi.org/10.1109/ICEC.1994.349932 %P 324-327 %0 Journal Article %T On the Search for New Learning Rules for ANNs %A Bengio, Samy %A Bengio, Yoshua %A Cloutier, Jocelyn %J Neural Processing Letters %D 1995 %V 2 %N 4 %@ 1370-4621 %F bengio:1995:npl %X we present a framework where a learning rule can be optimized within a parametric learning rule space. We define what we call parametric learning rules and present a theoretical study of their generalization properties when estimated from a set of learning tasks and tested over another set of tasks. We corroborate the results of this study with practical experiments. %K genetic algorithms, genetic programming, ANN, Neural Network, Artificial Intelligence, Complex System, Nonlinear Dynamics, Practical Experiment %9 journal article %R 10.1007/BF02279935 %U http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1995_npl.pdf %U http://dx.doi.org/10.1007/BF02279935 %P 26-30 %0 Thesis %T Evolutionary Algorithms: Handling Constraints and Real-World Application %A Ben Hamida, Sana %D 2001 %8 mar %C Paris, France %C Ecole Polytechnique %F BenHamid:thesis %X The present work is a heuristic and experimental study in the evolutionary computation domain, and starts with an introduction to the artificial evolution with a synthesis of the principal approaches. The first part is a heuristic study devoted to constraint handling in evolutionary computation. It presents an extensive review of previous constraint handling methods in the literature and their limitations. Two solutions are then proposed. The first idea is to improve genetic operator exploration capacity for constrained optimisation problems. The logarithmic mutation operator is conceived to explore both locally and globally the search space. The second solution introduces the original Adaptive Segregational Constraint Handling Evolutionary Algorithm (ASCHEA), the main idea of which is to maintain population diversity. In order to achieve this goal, three main ingredients are used: An original adaptive penalty method, a constraint-driven recombination, and a segregation selection that distinguishes between feasible and infeasible individuals to enhance the chances of survival of the feasible ones. Moreover, a niching method with an adaptive radius is added to ASCHEA in order to handle multimodal functions. Finally, to complete the ASCHEA system, a new equality constraint handling strategy is introduced, that reduces progressively the feasible domain in order to approach the actual null-measured domain as close as possible at the end of the evolution. The second part is a case study tackling a real-world problem. The goal is to design the 2-dimensional profile of an optical lens (phase plate) in order to control focal-plane irradiance of some laser beam. The aim is to design the phase plate such that a small circular target on the focal plane is uniformly illuminated without energy loss. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://www.cmap.polytechnique.fr/~sana/these.ps.gz %0 Generic %T Nested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatility %A Ben Hamida, Sana %A Cazenave, Tristan %D 2020 %8 feb 24 %I HAL %G en %F oai:HAL:hal-02489115v1 %O working paper or preprint %X We are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learning from financial time series to generate nonlinear functions for market volatility prediction. The input data, that is a series of daily prices of European S&P500 index, is filtered and sampled in order to improve the training process. Using some assessment metrics, the best generated models given by both approaches for each training sub-sample, are evaluated and compared. Results show that Nested Monte Carlo is able to generate better forecasting models than Genetic Programming for the majority of learning samples. %K genetic algorithms, genetic programming, computer science, artificial intelligence, AI %U https://hal-univ-paris10.archives-ouvertes.fr/hal-02489115 %0 Book Section %T Genetic Fitting: Evolutionary Search of Optimal Approximations for Discrete Functions %A Benini, Luca %E Koza, John R. %B Genetic Algorithms and Genetic Programming at Stanford 1995 %D 1995 %8 November %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-195720-5 %F benini:1995:GFESOADF %K genetic algorithms, genetic programming %P 19-28 %0 Conference Proceedings %T Gene Expression Programming for Evolving Two-Dimensional Cellular Automata in a Distributed Environment %A Benitez, Cesar Manuel Vargas %A Weinert, Wagner Rodrigo %A Lopes, Heitor Silverio %Y Camacho, David %Y Braubach, Lars %Y Venticinque, Salvatore %Y Badica, Costin %S IDC %S Studies in Computational Intelligence %D 2014 %V 570 %I Springer %F conf/idc/BenitezWL14 %K genetic algorithms, genetic programming %U http://dx.doi.org/10.1007/978-3-319-10422-5 %P 107-117 %0 Conference Proceedings %T Evolutionary Route to Computation in Self-Assembled Nanoarrays %A Benjamin, Simon 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 Benjamin:2008:cec %X Ordered nanoarrays, i.e. regular patterns of quantum structures at the nanometre scale, can now be synthesised in a range of systems. In this paper I study a form of array computation where the internal dynamics are driven by intrinsic cell-cell interactions and global optical pulses addressing entire structure indiscriminately. The array would need to be ’ wired’ to conventional technologies only at its boundary. Any self assembled array would have a unique set of defects, therefore I employ an ab initio evolutionary process to subsume such flaws without any need to determine their location or nature. The approach succeeds for various forms of physical interaction within the array. %K genetic algorithms, genetic programming %R 10.1109/CEC.2008.4631216 %U EC0685.pdf %U http://dx.doi.org/10.1109/CEC.2008.4631216 %P 3094-3101 %0 Conference Proceedings %T Design Innovation for Real World Applications, Using Evolutionary Algorithms %A Benkhelifa, E. %A Dragffy, G. %A Pipe, A. G. %A Nibouche, M. %Y Tyrrell, Andy %S 2009 IEEE Congress on Evolutionary Computation %D 2009 %8 18 21 may %I IEEE Press %C Trondheim, Norway %F Benkhelifa:2009:cec %X This paper discusses two important features of electronic design through evolutionary processes; creativity and innovation. Hence, conventional design methodologies are discussed and compared with their counterparts via evolutionary processes. An evolutionary search is used as an engine for discovering new designs for a real world application. Attempts to extract some useful principles from the evolved designs are presented and results are compared to conventional design topologies for the same problems. %K genetic algorithms, genetic programming %R 10.1109/CEC.2009.4983043 %U P692.pdf %U http://dx.doi.org/10.1109/CEC.2009.4983043 %P 918-924 %0 Conference Proceedings %T Evolutionary design optimisation of a 32-Step Traffic Lights Controller %A Benkhelifa, Elhadj %A Tiwari, Ashutosh %A Pipe, Anthony %S IEEE Congress on Evolutionary Computation (CEC 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Benkhelifa:2010:cec %X This paper shows a successful application of evolutionary algorithms for the design and optimisation of complex real world digital circuit that is a 32-Step Traffic Lights Controller. It discusses two important features of electronic design through evolutionary processes; creativity and innovation. Results are compared to conventional design topologies; and attempt to analyse the evolved designs is presented. %K genetic algorithms, genetic programming, EHW %R 10.1109/CEC.2010.5586108 %U http://dx.doi.org/10.1109/CEC.2010.5586108 %0 Conference Proceedings %T Learning Sets of Sub-Models for Spatio-Temporal Prediction %A Bennett, Andrew %A Magee, Derek %Y Bramer, Max %Y Ellis, Richard %S AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence %D 2007 %8 October 12 dec %C Cambridge, UK %F Bennett:2007:SGAI %X In this paper we describe a novel technique which implements a spatio-temporal model as a set of sub-models based on first order logic. These sub-models model different, typically independent, parts of the dataset; for example different spatio or temporal contexts. To decide which sub-models to use in different situations a context chooser is used. By separating the sub-models from where they are applied allows greater flexibility for the overall model. The sub-models are learnt using an evolutionary technique called Genetic Programming. The method has been applied to spatio-temporal data. This includes learning the rules of snap by observation, learning the rules of a traffic light sequence, and finally predicting a person’s course through a network of CCTV cameras. %K genetic algorithms, genetic programming, card game playing %U http://www.bcs-sgai.org/ai2007/admin/papers2.php?f=techpapers %0 Conference Proceedings %T Using Genetic Programming to Learn Models Containing Temporal Relations from Spatio-Temporal Data %A Bennett, Andrew %A Magee, Derek %Y Hatzilygeroudis, Ioannis %Y Koutsojannis, Constantinos %Y Palade, Vasile %S Proceedings of the 1st International Workshop on Combinations of Intelligent Methods and Applications %D 2008 %8 jul 22 %C Patras, Greece %G en %F Bennett:2008:CIMA %X In this paper we describe a novel technique for learning predictive models from non-deterministic spatio-temporal data. Our technique learns a set of sub-models that model different, typically independent, aspects of the data. By using temporal relations, and implicit feature selection, based on the use of 1st order logic expressions, we make the sub-models general, and robust to irrelevant variations in the data.We use Allen’s intervals [1], plus a set of four novel temporal state relations, which relate temporal intervals to the current time. These are added to the system as background knowledge in the form of functions. To combine the sub-models into a single model a context chooser is used. This probabilistically picks the most appropriate set of sub-models to predict in a certain context, and allows the system to predict in non-deterministic situations. The models are learnt using an evolutionary technique called Genetic Programming. The method has been applied to learning the rules of snap, and uno by observation; and predicting a person’s course through a network of CCTV cameras. %K genetic algorithms, genetic programming %U http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/paper2.pdf %0 Thesis %T Using genetic programming to learn predictive models from spatio-temporal data %A Bennett, Andrew David %D 2010 %8 jul %C UK %C School of Computing, University of Leeds %F bennett_a %X This thesis describes a novel technique for learning predictive models from nondeterministic spatio-temporal data. The prediction models are represented as a production system, which requires two parts: a set of production rules, and a conflict resolver. The production rules model different, typically independent, aspects of the spatio-temporal data. The conflict resolver is used to decide which sub-set of enabled production rules should be fired to produce a prediction. The conflict resolver in this thesis can probabilistically decide which set of production rules to fire, and allows the system to predict in non-deterministic situations. The predictive models are learnt by a novel technique called Spatio-Temporal Genetic Programming (STGP). STGP has been compared against the following methods: an Inductive Logic Programming system (Progol), Stochastic Logic Programs, Neural Networks, Bayesian Networks and C4.5, on learning the rules of card games, and predicting a person’s course through a network of CCTV cameras. This thesis also describes the incorporation of qualitative temporal relations within these methods. Allen’s intervals [1], plus a set of four novel temporal state relations, which relate temporal intervals to the current time are used. The methods are evaluated on the card game Uno, and predicting a person’s course through a network of CCTV cameras. This work is then extended to allow the methods to use qualitative spatial relations. The methods are evaluated on predicting a person’s course through a network of CCTV cameras, aircraft turnarounds, and the game of Tic Tac Toe. Finally, an adaptive bloat control method is shown. This looks at adapting the amount of bloat control used during a run of STGP, based on the ratio of the fitness of the current best predictive model to the initial fitness of the best predictive model. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://etheses.whiterose.ac.uk/1376/ %0 Conference Proceedings %T Automatic Creation of an Efficient Multi-Agent Architecture Using Genetic Programming with Architecture-Altering Operations %A Bennett III, Forrest H. %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 bennett:1996:emaa %K genetic algorithms, genetic programming %U http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap4.pdf %P 30-38 %0 Conference Proceedings %T Emergence of a Multi-Agent Architecture and New Tactics For the Ant Colony Foraging Problem Using Genetic Programming %A Bennett III, Forrest H. %Y Maes, Pattie %Y Mataric, Maja J. %Y Meyer, Jean-Arcady %Y Pollack, Jordan %Y Wilson, Stewart W. %S Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior: From animals to animats 4 %D 1996 %8 September 13 sep %I MIT Press %C Cape Code, USA %@ 0-262-63178-4 %F bennett:1996:emaant %X Previous work in multi-agent systems has required the human designer to make up-front decisions about the multi-agent architecture, including the number of agents to employ and the specific tasks to be performed by each agent. This paper describes the automatic evolution of these decisions during a run of genetic programming using architecture-altering operations.Genetic programming is extended to the discovery of multi-agent solutions for a central-place foraging problem for an ant colony. In this problem each individual ant is controlled by a set of agents, where agent is used in the sense of Minsky’s Society of Mind.Two new tactics for the central-place food foraging problem that were discovered by genetic programming are presented in this paper.Genetic programming was able to evolve time-efficient solutions to this problem by distributing the functions and terminals across successively more agents in such a way as to reduce the maximum number of functions executed per agent. The other source of time-efficiency in the evolved solution was the cooperation that emerged among the ants in the ant colony. %K genetic algorithms, genetic programming %R 10.7551/mitpress/3118.003.0044 %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6291906 %U http://dx.doi.org/10.7551/mitpress/3118.003.0044 %P 430-439 %0 Conference Proceedings %T Evolution of a 60 Decibel op amp using genetic programming %A Bennett III, Forrest H. %A Koza, John R. %A Andre, David %A Keane, Martin A. %Y Higuchi, Tetsuya %Y Masaya, Iwata %Y Liu, Weixin %S Proceedings of International Conference on Evolvable Systems: From Biology to Hardware (ICES-96) %S Lecture Notes in Computer Science %D 1996 %8 July 8 oct %V 1259 %I Springer-Verlag %C Tsukuba, Japan %@ 3-540-63173-9 %F bennet:1996:ices60db %X Genetic programming was used to evolve both the topology and sizing (numerical values) for each component of a low-distortion, low-bias 60 decibel (1000-to-1) amplifier with good frequency generalization. %K genetic algorithms, genetic programming %U http://www.genetic-programming.com/jkpdf/ices1996fhbamplifier60.pdf %0 Conference Proceedings %T A Multi-Skilled Robot that Recognizes and Responds to Different Problem Environments %A Bennett III, Forrest H. %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 bennet:1997:msrrrdpe %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/bennet_1997_msrrrdpe.pdf %P 44-51 %0 Conference Proceedings %T Darwinian Programming and Engineering Design using Genetic Programming %A Bennett III, Forrest H. %A Koza, John R. %A Keane, Martin A. %A Andre, David %Y Ryan, Conor %Y Buckley, Jim %S Proceedings of the 1st International Workshop on Soft Computing Applied to Software Engineering %D 1999 %8 December 14 apr %I Limerick University Press %C University of Limerick, Ireland %@ 1-874653-52-6 %F bennett:1999:SCASE %X One of the central challenges of computer science is to build a system that can automatically create computer programs that are competitive with those produced by humans. This paper presents a candidate set of criteria that identify when a machine-created solution is competitive with a human-produced result. We argue that the field of design is a useful testbed for determining whether an automated technique can produce results that are competitive with human-produced results. We present several results that are competitive with the products of human creativity and inventiveness. This claim is supported by the fact that each of the results infringe on previously issued patents. %K genetic algorithms, genetic programming %U http://www.genetic-programming.com/jkpdf/scase1999.pdf %P 31-40 %0 Conference Proceedings %T Automatic Synthesis of the Topology and Sizing for Analog Electrical Circuits Using Genetic Programming %A Bennett III, Forrest H. %A Keane, Martin A. %A Andre, David %A Koza, John R. %Y Miettinen, Kaisa %Y Makela, Marko M. %Y Neittaanmaki, Pekka %Y Periaux, Jacques %S Evolutionary Algorithms in Engineering and Computer Science %D 1999 %8 30 may 3 jun %I John Wiley & Sons %C Jyvaskyla, Finland %@ 0-471-99902-4 %F bennet:1999:astsaecGP %X The design (synthesis) of an analog electrical circuit entails the creation of both the topology and sizing (numerical values) of all of the circuit’s components. There has previously been no general automated technique for automatically creating the design for an analog electrical circuit from a high-level statement of the circuit’s desired behavior. We have demonstrated how genetic programming can be used to automate the design of seven prototypical analog circuits, including a lowpass filter, a highpass filter, a passband filter, a bandpass filter, a frequency-measuring circuit, a 60 dB amplifier, a differential amplifier, a computational circuit for the square root function, and a time-optimal robot controller circuit. All seven of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require human intelligence. The approach described herein can be directly applied to many other problems of analog circuit synthesis. %K genetic algorithms, genetic programming %U http://www.genetic-programming.com/jkpdf/eurogen1999circuits.pdf %P 199-229 %0 Conference Proceedings %T Genetic programming: Biologically inspired computation that exhibits creativity in solving non-trivial problems %A Bennett III, Forrest H. %A Koza, John R. %A Keane, Martin A. %A Andre, David %S Proceedings of the AISB’99 Symposium on Scientific Creativity %D 1999 %8 August 9 apr %C Edingburgh %F bennett:1999:AISB %X This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems. We argue that the field of design is a useful testbed for determining whether an automated technique can produce results that are competitive with human-produced results. We present several results that are competitive with the products of human creativity and inventiveness. This claim is supported by the fact that each of the results infringe on previously issued patents. This paper presents a candidate set of criteria that identify when a machine-created solution to a problem is competitive with a human-produced result. %K genetic algorithms, genetic programming %U http://www.genetic-programming.com/jkpdf/aisb1999.pdf %P 29-38 %0 Conference Proceedings %T Building a Parallel Computer System for $18,000 that Performs a Half Peta-Flop per Day %A Bennett III, Forrest H. %A Koza, John R. %A Shipman, James %A Stiffelman, Oscar %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 bennett:1999:BPCSPHPD %X Techniques of evolutionary computation generally require significant computational resources to solve non-trivial problems of interest. Increases in computing power can be realized either by using a faster computer or by parallelizing the application. Techniques of evolutionary computation are especially amenable to parallelization. This paper describes how to build a 10-node Beowulf-style parallel computer system for $18,000 that delivers about a half peta-flop (1015 floating-point operations) per day on runs of genetic programming. Each of the 10 nodes of the system contains a 533 MHz Alpha processor and runs with the Linux operating system. This amount of computational power is sufficient to yield solutions (within a couple of days per problem) to 14 published problems where genetic programming has produced results that are competitive with human-produced results. %K genetic algorithms, genetic programming, real world applications, parallel computing %U http://www.genetic-programming.com/jkpdf/gecco1999beowulf.pdf %P 1484-1490 %0 Conference Proceedings %T Evolution by Means of Genetic Programming of Analog Circuits that Perform Digital Functions %A Bennett III, Forrest H. %A Koza, John R. %A Keane, Martin A. %A Yu, Jessen %A Mydlowec, William %A Stiffelman, Oscar %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 bennett:1999:EMGPACPDF %X This paper demonstrates the ability of genetic programming to evolve analog circuits that perform digital functions and mixed analog-digital circuits. The evolved circuits include two purely digital circuits (a 100 nano-second NAND circuit and a two-instruction arithmetic logic unit circuit) and one mixed-signal circuit, namely a three-input digital-to-analog converter. %K genetic algorithms, genetic programming, real world applications %U http://www.genetic-programming.com/jkpdf/gecco1999analog.pdf %P 1477-1483 %0 Conference Proceedings %T Automatic synthesis, placement, and routing of an amplifier circuit by means of genetic programming %A Bennett III, Forrest H. %A Koza, John R. %A Yu, Jessen %A Mydlowec, William %Y Miller, Julian %Y Thompson, Adrian %Y Thomson, Peter %Y Fogarty, Terence C. %S Evolvable Systems: From Biology to Hardware Third International Conference, ICES 2000 %S LNCS %D 2000 %8 17 19 apr %V 1801 %I Springer-Verlag %C Edinburgh, Scotland, UK %@ 3-540-67338-5 %F bennett:2000:ICES %X The complete design of a circuit typically includes the tasks of creating the circuit’s placement and routing as well as creating its topology and component sizing. Design engineers perform these four tasks sequentially. Each of these four tasks is, by itself, either vexatious or computationally intractable. This paper describes an automatic approach in which genetic programming starts with a high-level statement of the requirements for the desired circuit and simultaneously creates the circuit’s topology, component sizing, placement, and routing as part of a single integrated design process. The approach is illustrated using the problem of designing a 60 decibel amplifier. The fitness measure considers the gain, bias, and distortion of the candidate circuit as well as the area occupied by the circuit after the automatic placement and routing. %K genetic algorithms, genetic programming %U http://www.genetic-programming.com/jkpdf/ices2000.pdf %P 1-10 %0 Conference Proceedings %T Using Genetic Programming to Design Decentralized Controllers for Self-Reconfigurable Modular Robots %A Bennett III, Forrest H. %A Rieffel, Eleanor G. %Y Whitley, Darrell %S Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference %D 2000 %8 August %C Las Vegas, Nevada, USA %F Bennett:2000:GECCOlb %K genetic algorithms, genetic programming %P 35-42 %0 Conference Proceedings %T Design of Decentralized Controllers for Self-Reconfigurable Modular Robots Using Genetic Programming %A Bennett III, Forrest H. %A Rieffel, Eleanor G. %S Proceedings of the Second NASA / DoD Workshop on Evolvable Hardware %D 2000 %8 jul 13 15 %I IEEE Computer Society %C Palo Alto, California %@ 0-7695-0762-X %F bennett:2000:EH %X Advantages of self-reconfigurable modular robots over conventional robots include physical adaptability, robustness in the presence of failures, and economies of scale. Creating control software for modular robots is one of the central challenges to realizing their potential advantages. Modular robots differ enough from traditional robots that new techniques must be found to create software to control them. The novel difficulties are due to the fact that modular robots are ideally controlled in a decentralized manner, dynamically change their connectivity topology, may contain hundreds or thousands of modules, and are expected to perform tasks properly even when some modules fail. We demonstrate the use of genetic programming to automatically create distributed controllers for self-reconfigurable modular robots. . %K genetic algorithms, genetic programming, connectivity topology, control software, decentralized controllers, distributed controllers, physical adaptability, robustness, self-reconfigurable modular robots, control engineering computing, controllers, decentralised control %R 10.1109/EH.2000.869341 %U http://dx.doi.org/10.1109/EH.2000.869341 %P 43-52 %0 Conference Proceedings %T Programmable Smart Membranes: Using Genetic Programming to Evolve Scalable Distributed Controllers for a Novel Self-Reconfigurable Modular Robotic Application %A Bennett III, Forrest H. %A Dolin, Brad %A Rieffel, Eleanor G. %Y Miller, Julian F. %Y Tomassini, Marco %Y Lanzi, Pier Luca %Y Ryan, Conor %Y Tettamanzi, Andrea G. B. %Y Langdon, William B. %S Genetic Programming, Proceedings of EuroGP’2001 %S LNCS %D 2001 %8 18 20 apr %V 2038 %I Springer-Verlag %C Lake Como, Italy %@ 3-540-41899-7 %F bennett:2001:EuroGP %X Self-reconfigurable modular robotics represents a new approach to robotic hardware, in which the robot is composed of many simple, identical interacting modules. We propose a novel application of modular robotics: the programmable smart membrane, a device capable of actively filtering objects based on numerous measurable attributes. Creating control software for modular robotic tasks like the smart membrane is one of the central challenges to realizing their potential advantages. We use genetic programming to evolve distributed control software for a 2-dimensional smart membrane capable of distinguishing objects based on color. The evolved controllers exhibit scalability to a large number of modules and robustness to the initial configurations of the robotic filter and the particles. %K genetic algorithms, genetic programming, modular robot, distributed control, smart membrane, self-reconfigurable, scalable, robust: Poster %R 10.1007/3-540-45355-5_18 %U http://dx.doi.org/10.1007/3-540-45355-5_18 %P 234-245 %0 Conference Proceedings %T Mathematical Modeling of COVID-19 Spread Using Genetic Programming Algorithm %A Benolic, Leo %A Car, Zlatan %A Filipovic, Nenad %Y Filipovic, Nenad %S 1st Serbian International Conference on Applied Artificial Intelligence %S Lecture Notes in Networks and Systems %D 2022 %8 may 19 20 %V 659 %I Springer %C Kragujevac, Serbia %F benolic:2022:SICAAI %X This paper analyses the possibilities of using Machine learning to develop a forecasting model for COVID-19 with a publicly available dataset from the Johns Hopkins University COVID-19 Data Repository and with the addition of a percentage of each variant from the GISAID Variant database. Genetic programming (GP), a symbolic regressor algorithm, is used for the estimation of new confirmed infected cases, hospitalized cases, cases in intensive care units (ICUs), and deceased cases. This metaheuristics method algorithm was used on a dataset for Austria and neighboring countries Czechia, Hungary, Slovenia, and Slovakia. Machine learning was done to create individual models for each country. Variance-based sensitivity analysis was initiated using the obtained mathematical models. This analysis showed us which input variables the output of the obtained models is sensitive to, like in the case of how much each covid variant affects the spread of the virus or the number of deceased cases. Individual short-term models have achieved very high R2 scores, while long-term predictions have achieved lower R2 scores. %K genetic algorithms, genetic programming, artificial intelligence, COVID-19, mathematical prediction models, variants %R 10.1007/978-3-031-29717-5_19 %U http://aai2022.kg.ac.rs/wp-content/uploads/upload/AAI_2022_Papers.zip %U http://dx.doi.org/10.1007/978-3-031-29717-5_19 %P 320-331 %0 Conference Proceedings %T Development of a Mathematical Model for Balloon Diameter Calculation in Percutaneous Transluminal Angioplasty Using Genetic Programming %A Benolic, Leo %Y Filipovic, Nenad %S Second Serbian International Conference on Applied Artificial Intelligence %S Lecture Notes in Networks and Systems %D 2023 %8 may 19 20 %V 999 %I Springer %C Kragujevac, Serbia %F benolic:2023:SICAAI %X This paper describes the development of a mathematical model using genetic programming to calculate the diameter of a percutaneous transluminal angioplasty (PTA) balloon dilatation catheter for a given pressure and balloon size. The dataset used for the study was provided by Boston Scientific, and the genetic programming algorithm was implemented in Python using parallel computing. The results showed high levels of accuracy, with R2 values of 0.99989 and 0.99954 for the best and parsimonious models, respectively. The developed model can be useful for in-silico simulations of angioplasty surgery and can contribute to improving the effectiveness of the PTA balloon dilatation catheter procedure. This study demonstrates the potential of machine learning techniques for optimizing medical device performance and design. Further work could investigate the use of other machine learning techniques and larger datasets to enhance the accuracy and generalizability of the models. %K genetic algorithms, genetic programming %R 10.1007/978-3-031-60840-7_2 %U https://link.springer.com/chapter/10.1007/978-3-031-60840-7_2 %U http://dx.doi.org/10.1007/978-3-031-60840-7_2 %P 7-20 %0 Journal Article %T A Multi-level Refinement Approach for Structural Synthesis of Optimal Probabilistic Models %A Benouhiba, Toufik %J Fundamenta Informaticae %D 2021 %V 179 %N 1 %I IOS press %F benouhiba:2021:FI %X Probabilistic models play an important role in many fields such as distributed systems and simulations. Like non-probabilistic systems, they can be synthesized using classical refinement-based techniques, but they also require identifying the probability distributions to be used and their parameters. Since a fully automated and blind refinement is generally undecidable, many works tried to synthesize them by looking for the parameters of the distributions. Syntax-guided synthesizing approaches are more powerful, they try to synthesize models structurally by using context-free grammars. However, many problems arise like huge search space, the complexity of generated models, and the limitation of context-free grammars to define constraints over the structure. In this paper, we propose a multi-step refinement approach, based on meta-models, offering several abstraction levels to reduce the size of the search space. More specifically, each refinement step is divided into two stages in which the desired shape of models is first described by context-sensitive constraints. In the second stage, model templates are instantiated by using global optimization techniques. We use our approach to a synthesize a set of optimal probabilistic models and show that context-sensitive constraints coupled with the multi-level abilities of the approach make the synthesis task more effective. %K genetic algorithms, genetic programming, model synthesis, refinement, Search-based software engineering, SBSE, constraint satisfaction, probabilistic model checking %9 journal article %R 10.3233/FI-2021-2011 %U https://journals.sagepub.com/doi/pdf/10.3233/FI-2021-2011 %U http://dx.doi.org/10.3233/FI-2021-2011 %P 1-33 %0 Conference Proceedings %T Evolving automatic target detection algorithms %A Benson, Karl %Y Ryan, Conor %Y O’Reilly, Una-May %Y Langdon, William B. %S Graduate Student Workshop %D 2000 %8 August %C Las Vegas, Nevada, USA %F benson:2000:E %K genetic algorithms, genetic programming %P 249-252 %0 Conference Proceedings %T Automatic Detection of Ships in Spaceborne SAR Imagery %A Benson, Karl A. %A Booth, David %A Cubillo, James %A Reeves, Colin %Y Whitley, Darrell %Y Goldberg, David %Y Cantu-Paz, Erick %Y Spector, Lee %Y Parmee, Ian %Y Beyer, Hans-Georg %S Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000) %D 2000 %8 October 12 jul %I Morgan Kaufmann %C Las Vegas, Nevada, USA %@ 1-55860-708-0 %F Benson:2000:GECCO %X This paper examines the evolution of automatic target detection algorithms and their application to the detection of shipping in spaceborne SAR imagery. ... . The FSM(GP) is clearly superior. %K genetic algorithms, genetic programming, ANN, Poster %U http://gpbib.cs.ucl.ac.uk/gecco2000/RW002.pdf %P 767 %0 Conference Proceedings %T Evolving Finite State Machines with Embedded Genetic Programming for Automatic Target Detection within SAR Imagery %A Benson, Karl A. %S Proceedings of the 2000 Congress on Evolutionary Computation CEC00 %D 2000 %8 June 9 jul %I IEEE Press %C La Jolla Marriott Hotel La Jolla, California, USA %@ 0-7803-6375-2 %F benson:2000:efsmegpatdsi %X This paper presents a model comprising Finite State Machines (FSMs) with embedded Genetic Programs (GPs) which co-evolve to perform the task of Automatic Target Detection (ATD). The fusion of a FSM and GPs allows for a control structure (main program), the FSM, and sub-programs, the GPs, to co-evolve in a symbiotic relationship. The GP outputs along with the FSM state transition levels are used to construct confidence intervals that enable each pixel within the image to be classified as either target or non-target, or to cause a state transition to take place and further analysis of the pixel to be performed. The algorithms produced using this method consist of nominally four GPs, with a typical node cardinality of less than ten, that are executed in an order dictated by the FSM. The results of the experimentation performed are compared to those obtained in two independent studies of the same problem using Kohonen Neural Networks and a two stage Genetic Programming strategy. %K genetic algorithms, genetic programming, image processing applications, Kohonen neural networks, automatic target detection, control structure, embedded genetic programming, evolving finite state machines, node cardinality, symbiotic relationship, embedded systems, finite state machines, object detection, self-organising feature maps %R 10.1109/CEC.2000.870838 %U http://dx.doi.org/10.1109/CEC.2000.870838 %P 1543-1549 %0 Conference Proceedings %T Performing Classification with an Environment Manipulating Mutable Automata (EMMA) %A Benson, Karl %S Proceedings of the 2000 Congress on Evolutionary Computation CEC00 %D 2000 %8 June 9 jul %I IEEE Press %C La Jolla Marriott Hotel La Jolla, California, USA %@ 0-7803-6375-2 %F benson:2000:PCEMMA %X In this paper a novel approach to performing classification is presented. Hypersurface Discriminant functions are evolved using Genetic Programming. These discriminant functions reside in the states of a Finite State Automata, which has the ability to reason 1 and logically combine the hypersurfaces to generate a complex decision space. An object may be classified by one or many of the discriminant functions, this is decided by the automata. During the evolution of this symbiotic architecture, feature selection for each of the discriminant functions is achieved implicitly, a task which is normally performed before a classification algorithm is trained. Since each dis-criminant function has different features, and objects may be classified with one or more discriminant functions, no two objects from the same class need be classified using the same features. Instead, the most appropriate features for a given object are used. %K genetic algorithms, genetic programming, system modelling and control, EMMA algorithm, classification, complex decision space, environment manipulating mutable automata, feature selection, finite state automata, hypersurface discriminant functions, object detection, symbiotic architecture, finite state machines, object detection, pattern classification %R 10.1109/CEC.2000.870305 %U http://dx.doi.org/10.1109/CEC.2000.870305 %P 264-271 %0 Conference Proceedings %T On the use of evolution to construct finite state machines and mathematical functions to perform automatic target detection %A Benson, Karl A. %A Booth, David %A Cubillo, James %A Reeves, Colin %Y Turner, Martin J. %Y Blacklege, Jonathan M. %S Proceedings of the 3rd IMA conference on image processing: mathematical methods, algorithms and applications %D 2000 %8 13 15 sep %I IEE, Horwood Publishing Ltd %C Leicester, UK %@ 1-898563-72-1 %F benson4 %K genetic algorithms, genetic programming %U http://www.amazon.co.uk/Image-Processing-III-Mathematical-Applications/dp/1898563721 %0 Conference Proceedings %T Evolving Automatic Target Detection Algorithms that logically Combine Decision Spaces %A Benson, Karl A. %Y Mirmehdi, Majid %Y Thomas, Barry %S Proceedings of the 11th British Machine Vision Conference %D 2000 %8 November 14 sep %I BMVA Press %C Bristol, UK %@ 1-901725-13-8 %F benson5 %X In this paper a novel approach to performing classification is presented. Discriminant functions are constructed by combining selected features from the feature set with simple mathematical functions such as + - times divide max min. These discriminant functions are capable of forming nonlinear discontinuous hypersurfaces. For multimodal data more than one discriminant function maybe combined with logical operators before classification is performed. An algorithm capable of making decisions as to whether a combination of discriminant functions is needed to classify a data sample, or whether a single discriminant function will suffice, is developed. The algorithms used to perform classification are not written by a human. The algorithms are learnt, or rather evolved, using Evolutionary Computing techniques. %K genetic algorithms, genetic programming, classification %U http://www.bmva.ac.uk/bmvc/2000/papers/p69.pdf %P 685-694 %0 Generic %T Accelerating Quantum Eigensolver Algorithms With Machine Learning %A Bensoussan, Avner %A Chachkarova, Elena %A Even-Mendoza, Karine %A Fortz, Sophie %A Lenihan, Connor %D 2024 %8 20 sep %F bensoussan2024acceleratingquantumeigensolveralgorithms %X we explore accelerating Hamiltonian ground state energy calculation on NISQ devices. We suggest using search-based methods together with machine learning to accelerate quantum algorithms, exemplified in the Quantum Eigensolver use case. We trained two small models on classically mined data from systems with up to 16 qubits, using XGBoost Python regressor. We evaluated our preliminary approach on 20-, 24- and 28-qubit systems by optimising the Eigensolver hyperparameters. These models predict hyperparameter values, leading to a 0.13-0.15 percent reduction in error when tested on 28-qubit systems. However, due to inconclusive results with 20- and 24-qubit systems, we suggest further examination of the training data based on Hamiltonian characteristics. In future work, we plan to train machine learning models to optimise other aspects or subroutines of quantum algorithm execution beyond its hyperparameters. %K genetic algorithms, genetic programming, genetic improvement, quant-ph %U https://arxiv.org/abs/2409.13587 %U http://arxiv.org/abs/2409.13587 v1 %0 Journal Article %T AccelerQ: Accelerating Quantum Eigensolvers with Machine Learning on Quantum Simulators %A Bensoussan, Avner %A Chachkarova, Elena %A Even-Mendoza, Karine %A Fortz, Sophie %A Lenihan, Connor %J Proc. ACM Program. Lang. %D 2025 %8 oct %V 9 %N OOPSLA2 %@ 2475-1421 %F Bensoussan:2025:OOPSLA2 %X We present AccelerQ, a framework for automatically tuning quantum eigensolver (QE) implementations–these are quantum programs implementing a specific QE algorithm–using machine learning and search-based optimisation. Rather than redesigning quantum algorithms or manually tweaking the code of an already existing implementation, AccelerQ treats QE implementations as black-box programs and learns to optimise their hyperparameters to improve accuracy and efficiency by incorporating search-based techniques and genetic algorithms (GA) alongside ML models to efficiently explore the hyperparameter space of QE implementations and avoid local minima. Our approach leverages two ideas: 1) train on data from smaller, classically simulable systems, and 2) use program-specific ML models, exploiting the fact that local physical interactions in molecular systems persist across scales, supporting generalisation to larger systems. We present an empirical evaluation of AccelerQ on two fundamentally different QE implementations: ADAPT-QSCI and QCELS. For each, we trained a QE predictor model, a lightweight XGBoost Python regressor, using data extracted classically from systems of up to 16 qubits. We deployed the model to optimise hyperparameters for executions on larger systems of 20-, 24-, and 28-qubit Hamiltonians, where direct classical simulation becomes impractical. We observed a reduction in error from 5.48 percent to 5.3 percent with only the ML model and further to 5.05 percent with GA for ADAPT-QSCI, and from 7.5 percent to 6.5 percent, with no additional gain with GA for QCELS. Given inconclusive results for some 20- and 24-qubit systems, we recommend further analysis of training data concerning Hamiltonian characteristics. Nonetheless, our results highlight the potential of ML and optimisation techniques for quantum programs and suggest promising directions for integrating software engineering methods into quantum software stacks. %K genetic algorithms, genetic programming, Machine Learning, Optimisation, Quantum Computing, Quantum Program Analysis, Search-based Software Engineering %9 journal article %R 10.1145/3763132 %U https://doi.org/10.1145/3763132 %U http://dx.doi.org/10.1145/3763132 %0 Thesis %T Automatic bias learning: an inquiry into the inductive basis of induction %A Bensusan, Hilan N. %D 1999 %8 feb %C UK %C University of Sussex %F Bensusan:thesis %X This thesis combines an epistemological concern about induction with a computational exploration of inductive mechanisms. It aims to investigate how inductive performance could be improved by using induction to select appropriate generalisation procedures. The thesis revolves around a meta-learning system, called The Entrencher, designed to investigate how inductive performances could be improved by using induction to select appropriate generalisation procedures. The performance of The Entrencher is discussed against the background of epistemological issues concerning induction, such as the role of theoretical vocabularies and the value of simplicity. After an introduction about machine learning and epistemological concerns with induction, Part I looks at learning mechanisms. It reviews some concepts and issues in machine learning and presents The Entrencher. The system is the first attempt to develop a learning system that induces over learning mechanisms through algorithmic representations of tasks. Part II deals with the need for theoretical terms in induction. Experiments where The Entrencher selects between different strategies for representation change are reported. The system is compared to other methods and some conclusions are drawn concerning how best to use the system. Part III considers the connection between simplicity and inductive success. Arguments for Occam’s razor are considered and experiments are reported where The Entrencher is used to select when, how and how much a decision tree needs to be pruned. Part IV looks at some philosophical consequences of the picture of induction that emerges from the experiments with The Entrencher and goes over the motivations for meta-learning. Based on the picture of induction that emerges in the thesis, a new position in the scientific realism debate, transcendental surrealism, is proposed and defended. The thesis closes with some considerations concerning induction, justification and epistemological naturalism. %K genetic algorithms, genetic programming, CIGA %9 D. Phil. %9 Ph.D. thesis %U http://www.cs.bris.ac.uk/Publications/Papers/1000410.pdf %0 Thesis %T Adaptive Behaviour through Morphological Plasticity in Natural and Artificial Systems %A Bentley, Katie Anne %D 2006 %C Gower Street, London, UK %C Computer Science, UCL, University of London %F Katie_Bentley:thesis %K genetic algorithms, alife %9 Ph.D. thesis %U http://discovery.ucl.ac.uk/1444539/1/U591845.pdf %0 Conference Proceedings %T Generic Evolutionary Design %A Bentley, P. J. %A Wakefield, J. P. %Y Chawdhry, Pravir K. %Y Roy, Rajkumar %Y Pant, Raj K. %S Soft Computing in Engineering Design and Manufacturing %D 1997 %8 23 27 jun %I Springer-Verlag %F Bentley:1997:WSC2 %X Generic evolutionary design means the creation of a range of different designs by evolution. This paper introduces generic evolutionary design by a computer, describing a system capable of the evolution of a wide range of solid object designs from scratch, using a genetic algorithm. The paper reviews relevant literature, and outlines a number of advances necessitated by the development of the system, including: a new generic representation of solid objects, a new multiobjective fitness ranking method, and variable-length chromosomes. A library of modular evaluation software is also described, which allows a user to define new design problems quickly and easily by picking combinations of modules to guide the evolution of designs. Finally, the feasibility of generic evolutionary design by a computer is demonstrated by presenting the successful evolution of both conventional and unconventional designs for a range of different solid-object design tasks, e.g. tables, heatsinks, prisms, boat hulls, aerodynamic cars. %K genetic algorithms, genetic programming %R 10.1007/978-1-4471-0427-8_31 %U http://eprints.hud.ac.uk/4053/ %U http://dx.doi.org/10.1007/978-1-4471-0427-8_31 %P 289-298 %0 Conference Proceedings %T Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms %A Bentley, P. J. %A Wakefield, J. P. %Y Chawdhry, P. K. %Y Roy, R. %Y Pant, R. K. %S Soft Computing in Engineering Design and Manufacturing %D 1997 %8 23 27 jun %I Springer-Verlag London %@ 3-540-76214-0 %F Bentley97 %X This paper investigates the problem of using a genetic algorithm to converge on a small, user-defined subset of acceptable solutions to multiobjective problems, in the Pareto-optimal (P-O) range. The paper initially explores exactly why separate objectives can cause problems in a genetic algorithm (GA). A technique to guide the GA to converge on the subset of acceptable solutions is then introduced. The paper then describes the application of six multiobjective techniques (three established methods and three new, or less commonly used methods) to four test functions. The previously unpublished distribution of solutions produced in the P-O range(s) by each method is described. The distribution of solutions and the ability of each method to guide the GA to converge on a small, user-defined subset of P-O solutions is then assessed, with the conclusion that two of the new multiobjective ranking methods are most useful. %K genetic algorithms, MOGA %U http://eprints.hud.ac.uk/4052/ %P 231-240 %0 Conference Proceedings %T The Future of Evolutionary Design Research %A Bentley, Peter J. %S AVOCAAD Second International Conference %D 1999 %8 August 10 apr %C Brussels, Belgium %F Bentley:1999:AVOCAAD %X The use of evolutionary algorithms to optimise designs is now well known, and well understood. The literature is overflowing with examples of designs that bear the hallmark of evolutionary optimisation: bridges, cranes, electricity pylons, electric motors, engine blocks, flywheels, satellite booms -the list is extensive and ever growing. But although the optimisation of engineering designs is perhaps the most practical and commercially beneficial form of evolutionary design for industry, such applications do not take advantage of the full potential of evolutionary design. Current research is now exploring how the related areas of evolutionary design such as evolutionary art, music and the evolution of artificial life can aid in the creation of new designs. By employing techniques from these fields, researchers are now moving away from straight optimisation, and are beginning to experiment with explorative approaches. Instead of using evolution as an optimiser, evolution is now beginning to be seen as an aid to creativity - providing new forms, new structures and even new concepts for designers. %K genetic algorithms, genetic programming, Computer, design, International %U http://cumincad.scix.net/cgi-bin/works/BrowseTree?field=series&separator=:&recurse=0&order=AZ&value=AVOCAAD %P 349-350 %0 Conference Proceedings %T Is evolution creative? %A Bentley, P. J. %Y Bentley, P. J. %Y Corne, D. %S Proceedings of the AISB’99 Symposium on Creative Evolutionary Systems %D 1999 %I The Society for the Study of Artificial Intelligence and Simulation of Behaviour %C Edinburgh %@ 1-902956-03-6 %F Bentley:1999:AISB %X Can evolution demonstrate some of the properties of creativity? This paper argues that it can, and provides examples which the author feels illustrate some of the awesome power and feats of design which resemble creativity. Is evolution, then, truly creative? This is clearly a much harder question, for it requires a definition of creativity that most subjective and controversial of words. This paper explores and discusses various aspects of creativity, attempting to determine to what extent evolution satisfies each definition. The paper ends by summarising the discussion, and presenting amalgamations of four different worldviews. %K genetic algorithms, genetic programming, gades, CE, sussex, System, systems %U http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC6.pdf %P 28-34 %0 Conference Proceedings %T Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem %A Bentley, Peter %A Kumar, Sanjeev %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 bentley:1999:TWGDACEEDP %X This paper explores the use of growth processes, or embryogenies, to map genotypes to phenotypes within evolutionary systems. Following a summary of the significant features of embryogenies, the three main types of embryogenies in Evolutionary Computation are then identified and explained: external, explicit and implicit. An experimental comparison between these three different embryogenies and an evolutionary algorithm with no embryogeny is performed. The problem set to the four evolutionary systems is to evolve tessellating tiles. In order to assess the scalability of the embryogenies, the problem is increased in difficulty by enlarging the size of tiles to be evolved. The results are surprising, with the implicit embryogeny outperforming all other techniques by showing no significant increase in the size of the genotypes or decrease in accuracy of evolution as the scale of the problem is increased. %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/p.bentley/BEKUC1.pdf %P 35-43 %0 Conference Proceedings %T Evolving fuzzy detectives: An investigation into the evolution of fuzzy rules %A Bentley, Peter J. %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 bentley:1999:EA %X This paper explores the use of genetic programming to evolve fuzzy rules for the purpose of fraud detection. The fuzzy rule evolver designed during this research is described in detail. Four key system evaluation criteria are identified: intelligibility, speed, handling noisy data, and accuracy. Three sets of experiments are then performed in order to assess the performance of different components of the system, in terms of these criteria. The paper concludes: 1. that many factors affect accuracy of classification, 2. intelligibility and processing speed mainly seem to be affected by the fuzzy membership functions and 3. noise can cause loss of accuracy proportionate to the square of noise. %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC7.pdf %P 38-47 %0 Book %T Evolutionary Design by Computers %E Bentley, Peter J. %D 1999 %I Morgan Kaufmann %@ 1-55860-605-X %F Bentley:evdes %X By bringing together the highest achievers in these fields for the first time, including a foreword by Richard Dawkins, this book provides the definitive ... %K genetic algorithms, genetic programming, Computers %U http://www.cs.ucl.ac.uk/staff/p.bentley/evdes.html %0 Book Section %T An introduction to evolutionary design by computers %A Bentley, Peter %E Bentley, Peter J. %B Evolutionary Design by Computers %D 1999 %I Morgan Kaufman %C San Francisco, USA %F Bentley:1999:intro %K genetic algorithms, genetic programming, Computer, Computers, design %P 1-73 %0 Conference Proceedings %T Evolving fuzzy detectives: an investigation into the evolution of fuzzy rules %A Bentley, Peter J. %Y Suzuki, Yukinori %Y Ovaska, Seppo J. %Y Furuhashi, Takeshi %Y Roy, Rajkumar %Y Dote, Yasuhiko %S Soft Computing in Industrial Applications %D 1999 %8 sep %I Springer-Verlag London %@ 1-85233-293-X %F Bentley:1999:WSC %X This paper explores the use of genetic programming to evolve fuzzy rules for the purpose of fraud detection. The fuzzy rule evolver designed during this research is described in detail. Four key system evaluation criteria are identified: intelligibility, speed, handling noisy data, and accuracy. Three sets of experiments are then performed in order to assess the performance of different components of the system, in terms of these criteria. The paper concludes: 1. that many factors affect accuracy of classification, 2. intelligibility and processing speed mainly seem to be affected by the fuzzy membership functions and 3. noise can cause loss of accuracy proportionate to the square of noise. %K genetic algorithms, genetic programming, evolution, fuzzy, industrial, industrial application, Rules %R 10.1007/978-1-4471-0509-1_8 %U http://www.cs.ucl.ac.uk/staff/P.Bentley/BECH4.pdf %U http://dx.doi.org/10.1007/978-1-4471-0509-1_8 %P 89-106 %0 Conference Proceedings %T Exploring component-based representations - the secret of creativity by evolution? %A Bentley, P. J. %Y Parmee, I. C. %S Evolutionary Design and Manufacture: Selected Papers from ACDM’00 %D 2000 %8 apr %I Springer-Verlag %C University of Plymouth, Devon, UK %F Bentley:2000:ACDM %X This paper investigates one of the newest and most exciting methods in computer science to date: employing computers as creative problem solvers by using evolution to explore for new solutions. The paper introduces and discusses the new understanding that explorative evolution relies upon a representation based on components rather than a parameterisation of a known solution. Evolution explores how the components can be arranged, how many are needed, and the type or function of each. The extra freedom provided by this simple idea is remarkable. By using evolutionary computation for exploration instead of optimisation, this technique enables us to expand the capabilities of computers. The paper describes how the approach has already shown impressive results in the creation of novel designs and architecture, fraud detection, composition of music, and creation of art. A framework for explorative evolution is provided, with discussion of the significance and difficulties posed by each element. The paper ends with an example of creative problem solving for a simple application- showing how evolution can shape pieces of paper to make them fall slowly through the air, by spiraling down like sycamore seeds. %K genetic algorithms, genetic programming, Adaptive, design %U http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC9.pdf %P 161-172 %0 Conference Proceedings %T “Evolutionary, my dear Watson” Investigating Committee-based Evolution of Fuzzy Rules for the Detection of Suspicious Insurance Claims %A Bentley, Peter J. %Y Whitley, Darrell %Y Goldberg, David %Y Cantu-Paz, Erick %Y Spector, Lee %Y Parmee, Ian %Y Beyer, Hans-Georg %S Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000) %D 2000 %8 October 12 jul %I Morgan Kaufmann %C Las Vegas, Nevada, USA %@ 1-55860-708-0 %F Bentley:2000:EA %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2000/RW074.pdf %P 702-709 %0 Conference Proceedings %T New Trends in Evolutionary Computation %A Bentley, Peter J. %A Gordon, Timothy %A Kim, Jungwon %A Kumar, Sanjeev %S Proceedings of the 2001 Congress on Evolutionary Computation CEC2001 %D 2001 %8 27 30 may %V 1 %I IEEE Press %C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea %@ 0-7803-6658-1 %F bentley:2001:NTEC %X In the last five years, the field of evolutionary computation (EC) has seen a resurgence of new ideas, many stemming from new biological inspirations. The paper outlines four of these new branches of research: creative evolutionary systems, computational embryology, evolvable hardware and artificial immune systems, showing how they aim to extend the capabilities of EC. Recent, unpublished results by researchers in each area at the Department of Computer Science, UCL are provided %K genetic algorithms, genetic programming, new trends, creative evolution, computation embryology, evolvable hardware, artificial immune systems %R 10.1109/CEC.2001.934385 %U http://dx.doi.org/10.1109/CEC.2001.934385 %P 162-169 %0 Conference Proceedings %T Ten steps to make a perfect creative evolutionary design system %A Bentley, Peter J. %A O’Reilly, Una-May %Y Bentley, Peter %Y Maher, Mary Lou %Y Poon, Josiah %S Non-Routine Design with Evolutionary Systems, GECCO-2001 Workshop %D 2001 %8 July %F Bentley:2001:geccowks %X A perfect creative evolutionary design system is impossible to achieve, but in this position paper we discuss 10 steps that might bring us a little closer to this dream. These important problems and requirements have been identified as a result of both authors’ experiences on a number of projects in this area. While our solutions may not solve all of the problems, they illustrate what we regard as the current state of the art in creative evolutionary design. %K genetic algorithms, genetic programming, Agency GP, design, evolutionary, SYSTEM, SYSTEMS, WORKSHOP %U http://sydney.edu.au/engineering/it/~josiah/gecco_workshop_bentley.pdf %0 Book Section %T An Introduction to Creative Evolutionary Systems %A Bentley, Peter J. %A Corne, David W. %E Bentley, Peter J. %E Corne, David W. %B Creative Evolutionary Systems %D 2001 %8 jul %I Morgan Kaufmann %@ 1-55860-673-4 %F bentley:2001:CES %X This chapter presents an overview of evolutionary algorithms explaining the main algorithms, and showing how all evolutionary algorithms are fundamentally the same. Evolutionary computation is all about search. In computer science and in artificial intelligence, when we use a search algorithm, we define a computational problem in terms of a search space, which can be viewed as a massive collection of potential solutions to the problem. Any position, or point, in the search space defines a particular solution, and the process of search can be viewed as the task of navigating that space. The chapter then defines and describes creative evolutionary systems, explaining why they were developed and how user interaction and changes of representation can expand the capabilities of evolution. The chapter also explores whether a creative evolutionary system can be said to actually work creatively. This chapter provides a mere introduction to the diversity of techniques that fall under the heading of creative evolutionary systems. %K genetic algorithms, genetic programming %R 10.1016/B978-155860673-9/50035-5 %U http://www.sciencedirect.com/science/book/9781558606739 %U http://dx.doi.org/10.1016/B978-155860673-9/50035-5 %P 1-75 %0 Book %T Creative evolutionary systems %E Bentley, Peter %E Corne, David %D 2002 %I Morgan Kaufmann %C USA %@ 1-55860-673-4 %F Bentley:2002:bookCES %X This book concentrates on applying important ideas in evolutionary computation to creative areas, such as art, music, architecture, and design. %K genetic algorithms, genetic programming, Computers %U http://www.amazon.com/Creative-Evolutionary-Kaufmann-Artificial-Intelligence/dp/1558606734 %0 Book %T Digital Biology. How Nature is Transforming Our Technology and Our Lives %A Bentley, Peter J. %D 2002 %I Simon and Schuster %C USA %@ 0-7432-0447-6 %F Bentley:2002:DB %K genetic algorithms, genetic programming, biology, digital, nature, technology %U http://www.amazon.com/Digital-Biology-Peter-J-Bentley/dp/0743204476 %0 Journal Article %T Guest Editorial Special Issue on Artificial Immune Systems %A Bentley, Peter J. %A Timmis, Jon %J Genetic Programming and Evolvable Machines %D 2003 %8 dec %V 4 %N 4 %@ 1389-2576 %F bentley:2003:GPEM %K artificial immune systems %9 journal article %R 10.1023/A:1026182810701 %U http://dx.doi.org/10.1023/A:1026182810701 %P 307-309 %0 Journal Article %T Fractal Proteins %A Bentley, Peter J. %J Genetic Programming and Evolvable Machines %D 2004 %8 mar %V 5 %N 1 %@ 1389-2576 %F bentley:2004:GPEM %X The fractal protein is a new concept intended to improve evolvability, scalability, exploitability and provide a rich medium for evolutionary computation. Here the idea of fractal proteins and fractal proteins with concentration levels are introduced, and a series of experiments showing how evolution can design and exploit them within gene regulatory networks is described. %K genetic algorithms, fractal proteins, development, evolvability, scalability, complexity %9 journal article %R 10.1023/B:GENP.0000017011.51324.d2 %U http://dx.doi.org/10.1023/B:GENP.0000017011.51324.d2 %P 71-101 %0 Conference Proceedings %T 7th International Conference on Artificial Immune Systems, ICARIS 2008 %E Bentley, Peter J. %E Lee, Doheon %E Jung, Sungwon %S Lecture Notes in Computer Science %D 2008 %8 aug 10 13 %V 5132 %I Springer %C Phuket, Thailand %@ 3-540-85071-6 %F DBLP:conf/icaris/2008 %X This book constitutes the refereed proceedings of the 7th International Conference on Artificial Immune Systems, ICARIS 2008, held in Phuket, Thailand, in ... %K Computers %0 Conference Proceedings %T Fault tolerant fusion of office sensor data using cartesian genetic programming %A Bentley, P. J. %A Lim, S. L. %S 2017 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2017 %8 nov %F Bentley:2017:ieeeSSCI %X The Smart Grid of the future will enable a cleaner, more efficient and fault tolerant system of power distribution. Sensing power use and predicting demand is an important component in the Smart Grid. In this work, we describe a Cartesian Genetic Programming (CGP) system applied to a smart office. In the building, power usage is directly proportional to the number of people present. CGP is used to perform data fusion on the data collected from smart sensors embedded in the building in order to predict the number of people over a two-month period. This is a challenging task, as the sensors are unreliable, resulting in incomplete data. It is also challenging because in addition to normal staff, the building underwent renovation during the test period, resulting the presence of additional personnel who would not normally be present. Despite these difficult real-world issues, CGP was able to learn human-readable rules that when used in combination, provide a method for data fusion that is tolerant to the observed faults in the sensors. %K genetic algorithms, genetic programming, Cartesian genetic programming %R 10.1109/SSCI.2017.8280827 %U http://dx.doi.org/10.1109/SSCI.2017.8280827 %0 Book %T Artificial Intelligence in Byte-sized Chunks %A Bentley, Peter J. %D 2024 %8 20 jun %I Michael O’Mara %F bentley:2024:AIchunks %X Artificial intelligence is headline news with the launch of the latest ChatGPT and Google Gemini. But when did we start making computers mimic the human mind? And what is the reality of the capabilities of AI now, and in the future? %K genetic algorithms, genetic programming %U https://www.amazon.co.uk/Artificial-Intelligence-Byte-sized-Chunks-Bite-Sized/dp/1789296560 %0 Conference Proceedings %T Genetic Programming for Vehicle Dispatch %A Benyahia, Ilham %A Potvin, J. Yves %S Proceedings of the 1997 IEEE International Conference on Evolutionary Computation %D 1997 %8 13 16 apr %I IEEE Press %C Indianapolis, USA %F benhahia:1997:GPvd %X Vehicle dispatching is aimed at allocating real time service requests to a fleet of vehicles in movement. This task is modeled as a multiattribute choice problem. Namely, different attribute values are associated with each vehicle to describe its situation with respect to the current service request. Based on this attribute description, a utility function that approximates the decision process of a professional dispatcher is computed. This utility function evolves through genetic programming. Computational results are reported on requests collected from a courier service company %K genetic algorithms, genetic programming %R 10.1109/ICEC.1997.592371 %U http://dx.doi.org/10.1109/ICEC.1997.592371 %P 547-552 %0 Journal Article %T Decision Support for Vehicle Dispatching Using Genetic Programming %A Benyahia, Ilham %A Potvin, Jean-Yves %J IEEE Transactions on Systems, Man, and Cybernetics part A: systems and humans %D 1998 %8 may %V 28 %N 3 %F Benyahia:1998:SMC %X Vehicle dispatching consists of allocating real-time service requests to a fleet of moving vehicles. In this paper, each vehicle is associated with a vector of attribute values that describes its current situation with respect to new incoming service requests. Using this attribute description, a utility function aimed at approximating the decision process of a professional dispatcher is constructed through genetic programming. Computational results are reported on requests collected from a courier service company and a comparison is provided with a neural network model and a simple dispatching policy. %K genetic algorithms, genetic programming %9 journal article %U http://ieeexplore.ieee.org/iel4/3468/14669/00668962.pdf %P 306-314 %0 Conference Proceedings %T Optimizing the Architecture of Adaptive Complex Applications Using Genetic Programming %A Benyahia, Ilham %A Talbot, Vincent %S The 14th International Conference on Distributed Multimedia Systems, DMS’2008 %D 2008 %8 April 6 sep %I Knowledge Systems Institute %C Hyatt Harborside at Logan Int’l Airport, Boston, USA %F conf/dms/BenyahiaT08 %K genetic algorithms, genetic programming %P 27-31 %0 Thesis %T Acquisition d’images steteoscopiques et calibration de cameras par algorithmes genetiques : application dans le domaine biomedical %A Benzeroual, Karim %D 2010 %8 20 jul %C France %C Ecole doctorale Sante, sciences, technologies, Tours %F Benzeroual:thesis %X This thesis Acquisition of stereoscopic images and camera calibration with genetic algorithms: application in the biomedical domain‖ consists to define a complete tool for acquiring the topography and texture of a 3D surface in order to apply it in the dermatological and more generally in the biomedical field. First, the objective is to study and to specify or design hardware devices such as professional cameras, assembly systems for stereo equipments, lighting system and trigger system for devices. Then, the thesis focuses on algorithmic and software aspects which relate to all mathematic and computational treatments needed to obtain a 3D surface. One of major issues addressed is the geometric calibration of stereo cameras. The developed approach pushes the limits of conventional methods in this field by proposing the use of more efficient and easier to implement optimization methods. We have shown that the algorithms using the principles of genetic algorithms can obtain more reliable results than their competitors and they can deal more easily the variable conditions of experiments. The real applications of our genetic algorithms for camera calibration cover many acquisition devices (industrial cameras, SLR cameras, stereo microscopes, beam splitters, pinhole and telecentric objectives), each acquisition device is adapted to a specific use following the requested study (microscopic areas, face or body parts). This thesis acquisition/calibration is a part of a global system called VirtualSkinLAB. %K genetic algorithms %9 Ph.D. thesis %U http://www.applis.univ-tours.fr/theses/2010/karim.benzeroual_3432.pdf %0 Journal Article %T Development of pipe deterioration models for water distribution systems using EPR %A Berardi, L. %A Kapelan, Z. %A Giustolisi, O. %A Savic, D. A. %J Journal of Hydroinformatics %D 2008 %V 10 %N 2 %@ 1464-7141 %F Berardi:2008:JH %X The economic and social costs of pipe failures in water and wastewater systems are increasing, putting pressure on utility managers to develop annual replacement plans for critical pipes that balance investment with expected benefits in a risk-based management context. In addition to the need for a strategy for solving such a multi-objective problem, analysts and water system managers need reliable and robust failure models for assessing network performance. In particular, they are interested in assessing a conduit’s propensity to fail and how to assign criticality to an individual pipe segment. pipe deterioration is modelled using Evolutionary Polynomial Regression. This data-driven technique yields symbolic formulae that are intuitive and easily understandable by practitioners. The case study involves a water quality zone within a distribution system and entails the collection of historical data to develop network performance indicators. Finally, an approach for incorporating such indicators into a decision support system for pipe rehabilitation/replacement planning is introduced and articulated. %K genetic algorithms, genetic programming, data-driven modelling, evolutionary polynomial regression, failure analysis, performance indicators, water systems %9 journal article %R 10.2166/hydro.2008.012 %U http://www.iwaponline.com/jh/010/0113/0100113.pdf %U http://dx.doi.org/10.2166/hydro.2008.012 %P 113-126 %0 Journal Article %T An effective multi-objective approach to prioritisation of sewer pipe inspection %A Berardi, L. %A Giustolisi, O. %A Savic, D. A. %A Kapelan, Z. %J Water Science & Technology %D 2009 %V 60 %N 4 %F Berardi:2009:WST %X The first step in the decision making process for proactive sewer rehabilitation is to assess the condition of conduits. In a risk-based decision context the set of sewers to be inspected first should be identified based on the trade-off between the risk of failures and the cost of inspections. In this paper the most effective inspection works are obtained by solving a multi-objective optimisation problem where the total cost of the survey programme and the expected cost of emergency repairs subsequent to blockages and collapses are considered simultaneously. A multi-objective genetic algorithm (MOGA) is used to identify a set of Pareto-optimal inspection programmes. Regardless of the proven effectiveness of the genetic-algorithm approach, the scrutiny of MOGA-based inspection strategies shows that they can differ significantly from each other, even when having comparable costs. A post-processing of MOGA solutions is proposed herein, which allows priority to be assigned to each survey intervention. Results are of practical relevance for decision makers, as they represent the most effective sequence of inspection works to be carried out based on the available funds. The proposed approach is demonstrated on a real large sewer system in the UK. %K genetic algorithms, genetic programming, EPR, decision support, multi-objective optimisation, pipe inspection, prioritisation, sewer, CCTV %9 journal article %R 10.2166/wst.2009.432 %U http://dx.doi.org/10.2166/wst.2009.432 %P 841-850 %0 Conference Proceedings %T GEVOSH: Using Grammatical Evolution to Generate Hashing Functions %A Berarducci, Patrick %A Jordan, Demetrius %A Martin, David %A Seitzer, Jennifer %Y Berkowitz, Eric G. %S Proceedings of the Fifteenth Midwest Artificial Intelligence and Cognitive Sciences Conference, MAICS 2004 %D 2004 %8 apr 16 18 %I Omnipress %C Chicago, USA %F DBLP:conf/maics/BerarducciJMS04 %X In this paper, we present system GEVOSH, Grammatically Evolved Hashing. GEVOSH evolves hashing functions using grammatical evolution techniques. Hashing functions are used to expedite search in a wide number of domains. In our work, GEVOSH created hashing functions that, on average, perform better than many standard (human-generated) hash functions extracted from the literature. In this paper, we present the architecture of system GEVOSH, its main components and algorithms, and resultant generated hash functions along with comparisons to standard, human-generated functions. %K genetic algorithms, genetic programming, Grammatical Evolution, genetic improvement %U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.4612.pdf %P 31-39 %0 Conference Proceedings %T GEVOSH: Using Grammatical Evolution to Generate Hashing Functions %A Berarducci, Patrick %A Jordan, Demetrius %A Martin, David %A Seitzer, Jennifer %Y Poli, R. %Y Cagnoni, S. %Y Keijzer, M. %Y Costa, E. %Y Pereira, F. %Y Raidl, G. %Y Upton, S. C. %Y Goldberg, D. %Y Lipson, H. %Y de Jong, E. %Y Koza, J. %Y Suzuki, H. %Y Sawai, H. %Y Parmee, I. %Y Pelikan, M. %Y Sastry, K. %Y Thierens, D. %Y Stolzmann, W. %Y Lanzi, P. L. %Y Wilson, S. W. %Y O’Neill, M. %Y Ryan, C. %Y Yu, T. %Y Miller, J. F. %Y Garibay, I. %Y Holifield, G. %Y Wu, A. S. %Y Riopka, T. %Y Meysenburg, M. M. %Y Wright, A. W. %Y Richter, N. %Y Moore, J. H. %Y Ritchie, M. D. %Y Davis, L. %Y Roy, R. %Y Jakiela, M. %S GECCO 2004 Workshop Proceedings %D 2004 %8 26 30 jun %C Seattle, Washington, USA %F berarducci:2004:ugw:pber %X In this paper, we present system GEVOSH, Grammatically Evolved Hashing. GEVOSH evolves hashing functions using grammatical evolution techniques. Hashing functions are used to expedite search in a wide number of domains. In our work, GEVOSH created hashing functions that, on average, perform better than many standard (human-generated) hash functions extracted from the literature. In this paper, we present the architecture of system GEVOSH, its main components and algorithms, and resultant generated hash functions along with comparisons to standard, human-generated functions. %K genetic algorithms, genetic programming, grammatical evolution, genetic improvement %U http://gpbib.cs.ucl.ac.uk/gecco2004/WUGW001.pdf %0 Journal Article %T Developing an Alternative Calculation Method for the Smart Readiness Indicator Based on Genetic Programming and Linear Regression %A Beras, Mitja %A Brezocnik, Miran %A Zuperl, Uros %A Kovacic, Miha %J Buildings %D 2025 %8 15 may %V 15 %N 10 %@ 2075-5309 %F Beras:2025:Buildings %X The European Union is planning to introduce a new tool for evaluating smart solutions in buildings—the Smart Readiness Indicator (SRI). As 54 energy efficiency categories must be evaluated, the triage process can be long and time-intensive. Altogether, 228 data points (or inputs) about the smartness of the buildings are required to complete the evaluation. The present paper proposes an alternative calculation method based on genetic programming (GP) for the calculation of Domains and linear regression (LR) for the calculation of Impact Factors and the total SRI score of the building. This novel calculation requires 20percent (Domain ventilation and dynamic building envelope) to 75percent (Domain cooling) fewer inputs than the original methodology. The present study evaluated 223 case study buildings, and 7 genetic programming models and 8 linear regression models were generated based on the results. The generated results are precise; the relative deviation from the experimental data for Domain scores (modeled with GP) ranged from 0.9percent to 2.9percent. The R2 for the LR models was 0.75 for most models (with two exceptions, with one with a value of 0.57 and the other with a value of 0.98). The developed method is scalable and could be used for preliminary and portfolio-level screening at early-stage assessments. %K genetic algorithms, genetic programming, SRI, modeling, linear regression, energy efficient buildings, smart buildings, optimisation %9 journal article %R 10.3390/buildings15101675 %U https://www.mdpi.com/2075-5309/15/10/1675 %U http://dx.doi.org/10.3390/buildings15101675 %P Articleno:1675 %0 Journal Article %T Achieving Maximum Smart Readiness Indicator Scores: A Financial Analysis with an In-Depth Feasibility Study in Non-Ideal Market Conditions %A Beras, Mitja %A Stepien, Krzysztof %A Kovacic, Miha %A Zuperl, Uros %J Buildings %D 2025 %V 15 %N 11 %@ 2075-5309 %F Beras:2025:buildings2 %X A new instrument, the smart readiness indicator (SRI), is being prepared to accelerate the implementation of smart solutions in buildings and establish a market that would require and accelerate the implementation of such solutions. we examine how the SRI score of a shopping center (with an already relatively advanced automation system) changes if we perform an energy optimization worth approximately 6.6 million EUR. As all the upgrades suggested by the SRI methodology cannot be implemented, this paper is the first of its kind to define the maximum feasible SRI score. The necessary measures are elaborated comprehensively, analyzed, and evaluated both technically and financially (IRR, ROI, and payback time). This type of approach is suitable for less developed EU markets without smart grids, DSM, and predictive functions. %K genetic algorithms, genetic programming, smart systems readiness indicator, SRI, methodology, smart systems, real estate energy renovations, energy efficiency, financial analysis, smartness %9 journal article %R 10.3390/buildings15111839 %U https://www.mdpi.com/2075-5309/15/11/1839 %U http://dx.doi.org/10.3390/buildings15111839 %P articleno:1839 %0 Conference Proceedings %T A Machine Learning Approach for the Integration of miRNA-Target Predictions %A Beretta, S. %A Castelli, M. %A Martinez, Yuliana %A Munoz, Luis %A Silva, Sara %A Trujillo, Leonardo %A Milanesi, Luciano %A Merelli, Ivan %S 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) %D 2016 %8 feb %F Beretta:2016:PDP %X Although several computational methods have been developed for predicting interactions between miRNA and target genes, there are substantial differences in the achieved results. For this reason, machine learning approaches are widely used for integrating the predictions obtained from different tools. In this work we adopt a method, called M3GP, which relies on a genetic programming approach, to classify results from three tools: miRanda, TargetScan, and RNAhybrid. Such algorithm is highly parallelisable and its adoption provides great advantages while handling problems involving big datasets, since it is independent from the implementation and from the architecture on which it is executed. More precisely, we apply this technique for the classification of the achieved miRNA target predictions and we compare its results with those obtained with other classifiers. %K genetic algorithms, genetic programming %R 10.1109/PDP.2016.125 %U http://dx.doi.org/10.1109/PDP.2016.125 %P 528-534 %0 Journal Article %T A Scalable Genetic Programming Approach to Integrate miRNA-Target Predictions: Comparing Different Parallel Implementations of M3GP %A Beretta, Stefano %A Castelli, Mauro %A Munoz, Luis %A Trujillo, Leonardo %A Martinez, Yuliana %A Popovic, Ales %A Milanesi, Luciano %A Merelli, Ivan %J Complexity %D 2018 %V 2018 %F Beretta:2018:complexity %X There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are complex and expensive. For this reason, in silico computational approaches able to model these molecular interactions are highly desirable. Although several computational methods have been developed for predicting the interactions between miRNA and target genes, there are substantial differences in the results achieved since most algorithms provide a large number of false positives. Accordingly, machine learning approaches are widely used to integrate predictions obtained from different tools. In this work, we adopt a method called multidimensional multiclass GP with multidimensional populations (M3GP), which relies on a genetic programming approach, to integrate and classify results from different miRNA-target prediction tools. The results are compared with those obtained with other classifiers, showing competitive accuracy. Since we aim to provide genome-wide predictions with M3GP and, considering the high number of miRNA-target interactions to test (also in different species), a parallel implementation of this algorithm is recommended. In this paper, we discuss the theoretical aspects of this algorithm and propose three different parallel implementations. We show that M3GP is highly parallelisable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets. %K genetic algorithms, genetic programming %9 journal article %R 10.1155/2018/4963139 %U http://downloads.hindawi.com/journals/complexity/2018/4963139.pdf %U http://dx.doi.org/10.1155/2018/4963139 %P ArticleID4963139 %0 Book Section %T Evolution of Algorithms for Multi-Species Emergent Assembly Behavior using Genetic Programming %A Beretz, John 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 beretz:2002:EAMEABGP %K genetic algorithms, genetic programming %P 21-30 %0 Conference Proceedings %T Beacon: Automated Test Generation for Stack-Trace Reproduction using Genetic Algorithms %A Bergel, Alexandre %A Slater Munoz, Ignacio %Y Zhang, Jie M. %Y Fredericks, Erik %S 2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST) %D 2021 %8 31 may %I IEEE %C internet %F Berge:2021:SBST %X Software crashes are a problem all developers face eventually. Manually reproducing crashes can be very expensive and require a lot of effort. Recent studies have proposed techniques to automatically generate tests to detect and reproduce errors. But even if this topic has been widely studied, there has been little to no progress done for dynamically typed languages. This becomes important because current approaches take advantage of the type information inherent to statically typed languages to generate the sequence of instructions needed to reproduce a crash, thus making it unclear to judge if type information is necessary to reproduce errors. The lack of explicit type declarations in dynamic languages difficult the task of generating the instructions to replicate an error because the type checking can only be done during runtime, making algorithms less knowledgeable about the program and, therefore, making it more difficult to use search-based approaches because the algorithms have less information to work with.This paper presents a Genetic Algorithm approach to produce crash reproductions on Python based only on the information contained in the error stack-trace. An empirical study analysing three different experiments were evaluated giving mostly positive results, achieving a high precision while reproducing the desired crashes (over 70 percent). The study shows that the presented approach is independent of the kind of typing of the language, and provides a solid base to further develop the topic. %K genetic algorithms, genetic programming, SBSE, Search-Based Software Testing, Automated Crash Reproduction, Dynamically Typed Languages, Python %R 10.1109/SBST52555.2021.00007 %U https://drive.google.com/file/d/1fcL-M3GmBus2fnixe8zGNyS00crxV4a-/view %U http://dx.doi.org/10.1109/SBST52555.2021.00007 %P 1-7 %0 Book Section %T Evolutionary Art Using Summed Multi-Objective Ranks %A Bergen, Steven %A Ross, Brian J. %E Riolo, Rick %E McConaghy, Trent %E Vladislavleva, Ekaterina %B Genetic Programming Theory and Practice VIII %S Genetic and Evolutionary Computation %D 2010 %8 20 22 may %V 8 %I Springer %C Ann Arbor, USA %F Bergen:2010:GPTP %X This paper shows how a sum of ranks approach to multi-objective evaluation is effective for some low-order search problems, as it discourages the generation of outlier solutions. Outliers, which often arise with the traditional Pareto ranking strategy, tend to exhibit good scores on a minority of feature tests, while having mediocre or poor scores on the rest. They arise from the definition of Pareto dominance, in which an individual can be superlative in as little as a single objective in order to be considered undominated. The application considered in this research is evolutionary art, inwhich images are synthesized that adhere to an aesthetic model based on color gradient distribution. The genetic programming system uses 4 different fitness measurements, that perform aesthetic and color palette analyses. Outliers are usually undesirable in this application, because the color gradient distribution measurements requires 3 features to be satisfactory simultaneously. Sum of ranks scoring typically results in images that score better on the majority of features, and are therefore arguably more visually pleasing. Although the ranked sum strategy was originally inspired by highly dimensional problems having perhaps 20 objectives or more, this research shows that it is likewise practical for low-dimensional problems. %K genetic algorithms, genetic programming, evolutionary art, multi-objective optimization %R 10.1007/978-1-4419-7747-2_14 %U http://www.springer.com/computer/ai/book/978-1-4419-7746-5 %U http://dx.doi.org/10.1007/978-1-4419-7747-2_14 %P 227-244 %0 Thesis %T Automatic Structure Generation using Genetic Programming and Fractal Geometry %A Bergen, Steve %D 2011 %C Brock University %F Bergen:mastersthesis %X Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages offered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and effectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the field of computational aesthetics and automated structure design. %K genetic algorithms, genetic programming %9 Masters thesis %U https://dr.library.brocku.ca/bitstream/handle/10464/3916/Brock_Bergen_Raphael_2011.pdf %0 Conference Proceedings %T Aesthetic 3D Model Evolution %A Bergen, Steve %A Ross, Brian %Y Machado, Penousal %Y Romero, Juan %Y Carballal, Adrian %S Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012 %S LNCS %D 2012 %8 November 13 apr %V 7247 %I Springer Verlag %C Malaga, Spain %F Bergen:2012:EvoMUSART %X Recently, evolutionary art has been exploring the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming. Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey was conducted, and survey takers showed a clear preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic evolution of 3D structures is a promising new research area for evolutionary art. %K genetic algorithms, genetic programming, Aesthetics, L-systems, 3D models, multi-objective evaluation %R 10.1007/978-3-642-29142-5_2 %U http://dx.doi.org/10.1007/978-3-642-29142-5_2 %P 11-22 %0 Journal Article %T Aesthetic 3D model evolution %A Bergen, Steve %A Ross, Brian J. %J Genetic Programming and Evolvable Machines %D 2013 %8 sep %V 14 %N 3 %@ 1389-2576 %F Bergen:2013:GPEM %O Special issue on biologically inspired music, sound, art and design %X A new research frontier for evolutionary 2D image generation is the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming. Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey was conducted, and survey takers showed a statistically significant preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic evolution of 3D structures is a promising new research area for evolutionary design. %K genetic algorithms, genetic programming, Aesthetics, L-systems, 3D models, Multi-objective evaluation %9 journal article %R 10.1007/s10710-013-9187-8 %U http://dx.doi.org/10.1007/s10710-013-9187-8 %P 339-367 %0 Conference Proceedings %T A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows and Itinerary Constraints %A Berger, Jean %A Sassi, Mourad %A Salois, Martin %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 berger:1999:AHGAVRPTWIC %K genetic algorithms and classifier systems %P 44-51 %0 Book Section %T Development of a Minimal Information Line Following Algorithm using Genetic Programming %A Berger, Eric %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 berger:2002:DMILFAGP %K genetic algorithms, genetic programming %U http://www.genetic-programming.org/sp2002/Berger.pdf %P 31-35 %0 Conference Proceedings %T Using Monte Carlo Tree Search to Enhance Search Space Exploration in Cartesian Genetic Programming %A Berghegger, Christina %A De La Torre, Camilo %A Cussat-Blanc, Sylvain %A Lavinas, Yuri %A Simoncini, David %Y Manzoni, Luca %Y Cussat-Blanc, Sylvain %Y Chen, Qi %S European Conference on Genetic Programming, EuroGP 2026 %D 2026 %8 August 10 apr %I Springer Nature %C Toulouse %F berghegger:2026:EuroGP %K genetic algorithms, genetic programming, cartesian genetic programming %0 Conference Proceedings %T Enhancing Information Retrieval by Automatic Acquisition of Textual Relations using Genetic Programming %A Bergstrom, Agneta %A Jaksetic, Patricija %A Nordin, Peter %S IUI 2000 %D 2000 %I ACM Press %F bergstrom:2000:eiraatrGP %X We have explored a novel method to find textual relations in electronic documents using genetic programming and semantic networks. This can be used for enhancing information retrieval and simplifying user interfaces. The automatic extraction of relations from text enables easier updating of electronic dictionaries and may reduce interface area both for search input and hit output on small screens such as cell phones and PDAs (Personal Digital Assistants). %K genetic algorithms, genetic programming, machine learning, natural language processing, semantic networks, information retrieval %U http://web.media.mit.edu/~lieber/IUI/Bergstrom/Bergstrom.pdf %0 Conference Proceedings %T Acquiring Textual Relations Automatically on the Web using Genetic Programming %A Bergstrom, Agneta %A Jaksetic, Patricija %A Nordin, Peter %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 bergstrom:2000:atrawGP %X The flood of electronic information is pouring over us, while the technology maintaining the information and making it available to us has not yet been able to catch up. One of the paradigms within information retrieval focuses on the use of thesauruses to analyse contextual/structural information. We have explored a method that automatically finds textual relations in electronic documents using genetic programming and semantic networks. Such textual relations can be used to extend and update thesauruses as well as semantic networks. The program is written in PROLOG and communicates with software for natural language parsing. The system is also an example of computationally expensive fitness function using a large database. The results from the experiment show feasibility for this type of automatic relation extraction. %K genetic algorithms, genetic programming: Poster %R 10.1007/978-3-540-46239-2_17 %U http://dx.doi.org/10.1007/978-3-540-46239-2_17 %P 237-246 %0 Journal Article %T Letter to the Editor on “Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models” by Ozgur Kisi & Jalal Shiri [Water Resources Management 25 (2011) 3135-3152] %A Beriro, Darren J. %A Abrahart, Robert J. %A Mount, Nick J. %A Nathanail, C. Paul %J Water Resources Management %D 2012 %V 26 %N 12 %F beriro:2012:WRM %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s11269-012-0049-6 %U http://link.springer.com/article/10.1007/s11269-012-0049-6 %U http://dx.doi.org/10.1007/s11269-012-0049-6 %0 Journal Article %T ’Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations’ by Jalal Shir and Ozgur Kisi [Computers and Geosciences (2011) 1692-1701] %A Beriro, Darren J. %A Abrahart, Robert J. %A Nathanail, C. Paul %J Computer & Geosciences %D 2013 %V 56 %@ 0098-3004 %F Beriro2012 %O Letter to the Editor %X This letter is not a request for the original authors to fill gaps or clarify discrepancies noted in their research although such additional material would of course be welcomed. Rather, it highlights that GEP and perhaps data-driven modelling more generally would benefit greatly from the development of a sound application protocol incorporating checks and balances, informed by consensus, for training, testing and reporting of complex procedures and solutions. The original authors’ thoughts on our general as well as specific issues would be warmly welcomed. %K genetic algorithms, genetic programming, Gene Expression Programming, Groundwater depth fluctuation, Neuro-fuzzy, Data-driven modelling, Time series analyse %9 journal article %R 10.1016/j.cageo.2012.04.014 %U http://www.sciencedirect.com/science/article/pii/S0098300412001379?v=s5 %U http://dx.doi.org/10.1016/j.cageo.2012.04.014 %P 216-220 %0 Journal Article %T ’Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations’ by Jalal Shir & Ozgur Kisi [Computers and Geosciences (2011) 1692-1701] %A Beriro, Darren J. %A Abrahart, Robert J. %A Nathanail, C. Paul %J Computer & Geosciences %D 2013 %V 56 %@ 0098-3004 %F Beriro:2013:CG %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.cageo.2012.04.014 %U http://www.sciencedirect.com/science/article/pii/S0098300412001379 %U http://dx.doi.org/10.1016/j.cageo.2012.04.014 %P 216-220 %0 Conference Proceedings %T Learning fuzzy rules using Genetic Programming: Context-free grammar definition for high-dimensionality problems %A Berlanga, Francisco Jose %A Del Jesus, Maria Jose %A Herrera, Francisco %Y Cordon, Oscar %Y Herrera, Francisco %S International workshop on Genetic Fuzzy System, GFS 2005 %D 2005 %G en %F Berlanga:2005:GFS %X The inductive learning of a fuzzy rule-based classification system (FRBCS) with high interpretability is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficult comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered. In this work we tackle this problem, the FRBCS learning with high interpretability for high-dimensionality problems. We propose a genetic-programming-based method, where the evolved disjunctive normal form fuzzy rules compete in order to obtain an FRBCS with high interpretability (few rules and few antecedent conditions per rule) while maintaining a good performance. %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.415.2932 %0 Conference Proceedings %T A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems %A Berlanga, F. J. %A del Jesus, M. J. %A Gacto, M. J. %A Herrera, F. %Y Rutkowski, Leszek %Y Tadeusiewicz, Ryszard %Y Zadeh, Lotfi A. %Y Zurada, Jacek %S Proceedings 8th International Conference on Artificial Intelligence and Soft Computing ICAISC %S Lecture Notes on Artificial Intelligence (LNAI) %D 2006 %8 jun 25 29 %V 4029 %I Springer-Verlag %C Zakopane, Poland %@ 3-540-35748-3 %F Berlanga:2006:ICAISC %X In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. Moreover it leads to an FRBCS with a rule base with a high cardinality. In this paper, we propose a genetic-programming-based method for the learning of an FRBCS, where disjunctive normal form (DNF) rules compete and cooperate among themselves in order to obtain an understandable and compact set of fuzzy rules, which presents a good classification performance with high dimensionality problems. This proposal uses a token competition mechanism to maintain the diversity of the population. The good results obtained with several classification problems support our proposal. %K genetic algorithms, genetic programming %R 10.1007/11785231_20 %U http://dx.doi.org/10.1007/11785231_20 %P 182-191 %0 Conference Proceedings %T A novel genetic cooperative-competitive fuzzy rule based learning method using genetic programming for high dimensional problems %A Berlanga, Francisco Jose %A del Jesus, Maria Jose %A Herrera, Francisco %S 3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008 %D 2008 %8 April 7 mar %C Witten-Boommerholz, Germany %F Berlanga:2008:GEFS %X In this contribution, we present GP-COACH, a novel GFS based on the cooperative-competitive learning approach, that uses genetic programming to code fuzzy rules with a different number of variables, for getting compact and accurate rule bases for high dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) and uses a token competition mechanism to maintain the diversity of the population. It makes the rules compete and cooperate among themselves, giving out a compact set of fuzzy rules that presents a good performance. The good results obtained in an experimental study involving several high dimensional classification problems support our proposal. %K genetic algorithms, genetic programming, genetic cooperative-competitive fuzzy rule based learning method, high dimensional classification problems, high dimensional problems, token competition mechanism, fuzzy set theory, knowledge based systems, learning (artificial intelligence) %R 10.1109/GEFS.2008.4484575 %U http://dx.doi.org/10.1109/GEFS.2008.4484575 %P 101-106 %0 Journal Article %T GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems %A Berlanga, F. J. %A Rivera, A. J. %A del Jesus, M. J. %A Herrera, F. %J Information Sciences %D 2010 %V 180 %N 8 %@ 0020-0255 %F Berlanga20101183 %X we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability. %K genetic algorithms, genetic programming, Classification, Fuzzy rule-based systems, Genetic fuzzy systems, High-dimensional problems, Interpretability-accuracy trade-off %9 journal article %R 10.1016/j.ins.2009.12.020 %U http://www.sciencedirect.com/science/article/B6V0C-4Y34R0J-1/2/82039ab1549f5a0d0fc4d73b2a30bfa6 %U http://dx.doi.org/10.1016/j.ins.2009.12.020 %P 1183-1200 %0 Conference Proceedings %T Age-at-Death Estimation based on Symbolic Regression Ensemble Learning from Multiple Annotations %A Bermejo, Enrique %A Cordon, Oscar %A Irurita, Javier %A Aleman, Inmaculada %A Salvador, Angel Rubio %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F bermejo:2024:CEC %X The present study addresses the problem of semiautomatic age-at-death estimation from pubic symphysis, a crucial yet complex task in forensic anthropology. Its accuracy directly depends on the quality of the pubic bone trait labeling developed by the forensic practitioners, affected by an inherent uncertainty in their definition. As interpretability is a mandatory requirement, we propose an approach where the model design is based on evolutionary learning, considering genetic programming to frame the problem as a symbolic regression task. Additionally, ensemble learning is considered to address the challenges posed by noise, uncertainty, and conflicting annotations inherent in data collected from multiple subjects. Ensemble learning provides an effective approach to navigate these challenges by facilitating consensus-building through decision making and information fusion. Hence, observer committees are formed, comprising multiple forensic specialists with different skills and expertise which provide alternative annotations. Several ensemble configurations combining different weak learners and aggregation operators are tested to assess their effectiveness in improving accuracy and reliability in age-at-death predictions. Their performance is compared against models trained on single annotations, revealing an improvement in predictive accuracy. The obtained results also highlight the benefits of incorporating diverse perspectives to address the complexities associated with human variability and anatomical assessments. %K genetic algorithms, genetic programming, Uncertainty, Accuracy, Annotations, Forensics, Decision making, Predictive models, Mathematical models, Age-at-death estimation, Ensemble learning, Symbolic regression %R 10.1109/CEC60901.2024.10611921 %U http://dx.doi.org/10.1109/CEC60901.2024.10611921 %0 Journal Article %T Interpretable Machine Learning for Age-at-Death Estimation From the Pubic Symphysis %A Bermejo, Enrique %A Villegas, Antonio David %A Irurita, Javier %A Damas, Sergio %A Cordon, Oscar %J Expert Systems %D 2025 %8 mar %V 42 %N 3 %F Bermejo:2025:exsy %X Age-at-death estimation is an arduous task in human identification based on characteristics such as appearance, morphology or ossification patterns in skeletal remains. This process is performed manually, although in recent years there have been several studies that attempt to automate it. One of the most recent approaches involves considering interpretable machine learning methods, obtaining simple and easily understandable models. The ultimate goal is not to fully automate the task but to obtain an accurate model supporting the forensic anthropologists in the age-at-death estimation process. We propose a semi-automatic method for age-at-death estimation based on nine pubic symphysis traits identified from Todd’s pioneering method. Genetic programming is used to learn simple mathematical expressions following a symbolic regression process, also developing feature selection. Our method follows a component-scoring approach where the values of the different traits are evaluated by the expert and aggregated by the corresponding mathematical expression to directly estimate the numeric age-at-death value. Oversampling methods are considered to deal with the strongly imbalanced nature of the problem. State-of-the-art performance is achieved thanks to an interpretable model structure that allows us to both validate existing knowledge and extract some new insights in the discipline. %K genetic algorithms, genetic programming, decision support system, interpretable machine learning, symbolic regression age estimation %9 journal article %R 10.1111/exsy.70021 %U https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.70021 %U http://dx.doi.org/10.1111/exsy.70021 %P e70021 %0 Conference Proceedings %T Generation of New Scalarizing Functions Using Genetic Programming %A Bernabe Rodriguez, Amin V. %A Coello Coello, Carlos A. %Y Baeck, Thomas %Y Preuss, Mike %Y Deutz, Andre %Y Wang2, Hao %Y Doerr, Carola %Y Emmerich, Michael %Y Trautmann, Heike %S 16th International Conference on Parallel Problem Solving from Nature, Part II %S LNCS %D 2020 %8 July 9 sep %V 12270 %I Springer %C Leiden, Holland %F Bernabe-Rodriguez:2020:PPSN %X In recent years, there has been a growing interest in multiobjective evolutionary algorithms (MOEAs) with a selection mechanism different from Pareto dominance. This interest has been mainly motivated by the poor performance of Pareto-based selection mechanisms when dealing with problems having more than three objectives (the so-called many-objective optimization problems). Two viable alternatives for solving many-objective optimization problems are decomposition-based and indicator-based MOEAs. However, it is well-known that the performance of decomposition-based MOEAs (and also of indicator-based MOEAs designed around R2) heavily relies on the scalarising function adopted. In this paper, we propose an approach for generating novel scalarizing functions using genetic programming. Using our proposed approach, we were able to generate two new scalarizing functions (called AGSF1 and AGSF2), which were validated using an indicator-based MOEA designed around R2 (MOMBI-II). This validation was conducted using a set of standard test problems and two performance indicators (hypervolume and s-energy). Our results indicate that AGSF1 has a similar performance to that obtained when using the well-known Achievement Scalarizing Function (ASF). However, AGSF2 provided a better performance than ASF in most of the test problems adopted. Nevertheless, our most remarkable finding is that genetic programming can indeed generate novel (and possible more competitive) scalarizing functions. %K genetic algorithms, genetic programming, Multi-objective optimization, Scalarizing functions %R 10.1007/978-3-030-58115-2_1 %U http://dx.doi.org/10.1007/978-3-030-58115-2_1 %P 3-17 %0 Conference Proceedings %T Designing Scalarizing Functions Using Grammatical Evolution %A Bernabe Rodriguez, Amin V. %A Coello Coello, Carlos A. %Y Ryan, Conor %Y Mahdinejad, Mahsa %Y Murphy, Aidan %S Grammatical Evolution Workshop - 25 years of GE %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F bernabe-rodriguez:2023:GEWS2023 %X In this paper, we present a grammatical evolution-based framework to produce new scalarizing functions, which are known to have a significant impact on the performance of both decomposition-based multi-objective evolutionary algorithms (MOEAs) and indicator-based MOEAs which use R2. We perform two series of experiments using this framework. First, we produce many scalarizing functions using different benchmark problems to explore the behavior of the resulting functions according to the geometry of the problem adopted to generate them. Then, we perform a second round of experiments adopting two combinations of problems which yield better results in some test instances. We present the experimental validation of these new functions compared against the Achievement Scalarizing Function (ASF), which is known to provide a very good performance. For this comparative study, we adopt several benchmark problems and we are able to show that our proposal is able to generate new scalarizing functions that can outperform ASF in different problems. %K genetic algorithms, genetic programming, grammatical evolution, multi-objective optimization, scalarizing functions %R 10.1145/3583133.3596354 %U http://dx.doi.org/10.1145/3583133.3596354 %P 2004-2012 %0 Journal Article %T Improving multi-objective evolutionary algorithms using Grammatical Evolution %A Bernabe Rodriguez, Amin V. %A Alejo-Cerezo, Braulio I. %A Coello Coello, Carlos A. %J Swarm and Evolutionary Computation %D 2024 %V 84 %@ 2210-6502 %F BERNABERODRIGUEZ:2024:swevo %X Multi-objective evolutionary algorithms (MOEAs) have become an effective choice to solve multi-objective optimization problems (MOPs). However, it is well known that Pareto dominance-based MOEAs struggle in MOPs with four or more objective functions due to a lack of selection pressure in high dimensional spaces. The main choices for dealing with such problems are decomposition-based and indicator-based MOEAs. In this work, we propose the use of Grammatical Evolution (an evolutionary computation search technique) to generate functions that can improve decomposition-based and indicator-based MOEAs. Namely, we propose a methodology to generate new scalarizing functions, which are known to have a great impact in the performance of decomposition-based MOEAs and in some indicator-based MOEAs. Additionally, we propose another methodology to generate hypervolume approximations, since the hypervolume is a popular performance indicator used not only in indicator-based MOEAs but also to assess performance of MOEAs. Using our first methodology, we generate two new scalarizing functions and provide their corresponding experimental validation to show that they exhibit a competitive behavior when compared against some well-known scalarizing functions such as ASF, PBI and the Tchebycheff scalarizing function. Using our second methodology, we produce 4 different hypervolume approximations and compare their performance against the Monte Carlo method and against two other state-of-the-art hypervolume approximations. The experimental results show that our functions exhibit a good compromise in terms of quality and execution time %K genetic algorithms, genetic programming, Grammatical evolution, Evolutionary algorithms, Multi-objective optimization %9 journal article %R 10.1016/j.swevo.2023.101434 %U http://delta.cs.cinvestav.mx/~ccoello/journals/amin-swevo-final.pdf.gz %U http://dx.doi.org/10.1016/j.swevo.2023.101434 %P ArticleNumber:101434 %0 Conference Proceedings %T Time Series Forecasting through Polynomial Artificial Neural Networks and Genetic Programming %A Bernal-Urbina, M. %A Flores-Mendez, A. %Y Wang, Jun %S 2008 IEEE World Congress on Computational Intelligence %D 2008 %8 January 6 jun %I IEEE Press %C Hong Kong %F Bernal-Urbina:2008:ijcnn %X The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analysed grow exponentially depending on the degree selected for the polynomial approximation. In this paper, a novel optimisation algorithm that determines the architecture of the PANN through Genetic Programming is presented. Some examples of non linear time series are included and the results are compared with those obtained by PANN with Genetic Algorithm. %K genetic algorithms, genetic programming %R 10.1109/IJCNN.2008.4634270 %U NN0903.pdf %U http://dx.doi.org/10.1109/IJCNN.2008.4634270 %P 3325-3330 %0 Conference Proceedings %T Inferring Temporal Parametric L-systems Using Cartesian Genetic Programming %A Bernard, Jason %A McQuillan, Ian %S 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI) %D 2020 %8 nov %F Bernard:2020:ICTAI %X Lindenmayer Systems (L-systems) are formal grammars that use rewriting rules to replace, in parallel, every symbol in a string with a replacement string. By iterating, a sequence of strings is produced whose symbols can model temporal processes by interpreting them as simulation instructions. Among the types of L-systems, parametric L-systems are considered useful for simulating mechanisms that change based on different influences as the parameters change. Typically, L-systems are found by taking precise measurements and using existing knowledge, which can be addressed by automatic inference. This paper presents the Plant Model Inference Tool for Parametric L-systems (PMIT-PARAM) that can automatically learn parametric L-systems from a sequence of strings generated, where at least one parameter represents time. PMIT-PARAM is evaluated on a test suite of 20 known parametric L-systems, and is found to be able to infer the correct rewriting rules for the 18 L-systems containing only non-erasing productions; however, it can find appropriate parametric equations for all 20 of the L-systems. Inferring L-systems algorithmically not only can automatically learn models and simulations of a process with potentially less effort than doing so by hand, but it may also help reveal the scientific principles governing how the process’ mechanisms change over time. %K genetic algorithms, genetic programming, cartesian genetic programming %R 10.1109/ICTAI50040.2020.00095 %U http://dx.doi.org/10.1109/ICTAI50040.2020.00095 %P 580-588 %0 Conference Proceedings %T Learning Stochastic Tree Edit Distance %A Bernard, Marc %A Habrard, Amaury %A Sebban, Marc %Y Furnkranz, Johannes %Y Scheffer, Tobias %Y Spiliopoulou, Myra %S Machine Learning: ECML 2006 %S Lecture Notes in Computer Science %D 2006 %V 4212 %I Springer %F Bernard:2006:ECML %X Trees provide a suited structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, or conversion of tree structured documents. In this context, many applications require the calculation of similarities between tree pairs. The most studied distance is likely the tree edit distance (ED) for which improvements in terms of complexity have been achieved during the last decade. However, this classic ED usually uses a priori fixed edit costs which are often difficult to tune, that leaves little room for tackling complex problems. In this paper, we focus on the learning of a stochastic tree ED. We use an adaptation of the Expectation-Maximization algorithm for learning the primitive edit costs. We carried out series of experiments that confirm the interest to learn a tree ED rather than a priori imposing edit costs. %R 10.1007/11871842_9 %U http://dx.doi.org/10.1007/11871842_9 %P 42-53 %0 Conference Proceedings %T An Evolutionary Methodology to Enhance Processor Software-Based Diagnosis %A Bernardi, P. %A Sanchez, E. %A Schillaci, M. %A Squillero, G. %A Sonza Reorda, 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 16 21 jul %I IEEE Press %C Vancouver %@ 0-7803-9487-9 %F Bernardi:2006:CEC %X The widespread use of cheap processor cores requires the ability to quickly point out the manufacturing process criticalities in an effort to enhance the production yield. Fault diagnosis is an integral part of the industrial effort towards these goals. This paper describes an innovative application of evolutionary algorithms: iterative refinement of a diagnostic test set. Several enhancements in the used evolutionary core are additionally outlined, highlighting their relevance for the specific problem. Experimental results are reported in the paper showing the effectiveness of the approach for a widely-known microcontroller core. %K genetic algorithms, genetic programming, microGP %R 10.1109/CEC.2006.1688401 %U http://dx.doi.org/10.1109/CEC.2006.1688401 %P 3201-3206 %0 Conference Proceedings %T Grammar-Based Immune Programming for Symbolic Regression %A Bernardino, Heder S. %A Barbosa, Helio J. C. %Y Andrews, Paul S. %Y Timmis, Jon %Y Owens, Nick D. L. %Y Aickelin, Uwe %Y Hart, Emma %Y Hone, Andrew %Y Tyrrell, Andy M. %S Proceedings of the 8th International Conference on Artificial Immune Systems (ICARIS) %S Lecture Notes in Computer Science %D 2009 %8 aug 9 12 %V 5666 %I Springer %C York, UK %G English %F Bernardino:2009:ICARIS %X This paper presents a Grammar-based Immune Programming (GIP) that can evolve programs in an arbitrary language using a clonal selection algorithm. A context-free grammar that defines this language is used to decode candidate programs (antibodies) to a valid representation. The programs are represented by tree data structures as the majority of the program evolution algorithms do. The GIP is applied to symbolic regression problems and the results found show that it is competitive when compared with other algorithms from the literature. %K genetic algorithms, genetic programming, grammatical evolution, Artificial immune system, immune programming, symbolic regression %R 10.1007/978-3-642-03246-2_26 %U http://dx.doi.org/10.1007/978-3-642-03246-2_26 %P 274-287 %0 Conference Proceedings %T Comparing two ways of inferring a differential equation model via Grammar-based Immune Programming %A Bernardino, Heder Soares %A Barbosa, Helio Jose Correa %Y Dvorkin, Eduardo %Y Goldschmit, Marcela %Y Storti, Mario %S Proceedings of the Iberian Latin American Congress on Computational Methods in Engineering (CILAMCE) %D 2010 %8 nov 15 18 %I Asociacion Argentina de Mecanica Computacional http://www.amcaonline.org.ar %C Buenos Aires %F Bernardino:2010:CILAMCE %X An ordinary differential equation (ODE) is a mathematical form to describe physical or biological systems composed by time-derivatives of physical positions or chemical concentrations as a function of its current state. Given observed pairs, a relevant modelling problem is to find the symbolic expression of a differential equation which mathematically describes the concerned phenomenon. The Grammar-based Immune Programming (GIP) is a method for evolving programs in an arbitrary language by immunological inspiration. A program can be a computer program, a numerical function in symbolic form, or a candidate design, such as an analogue circuit. GIP can be used to solve symbolic regression problems in which the objective is to find an analytical expression of a function that better fits a given data set. At least two ways are available to solve model inference problems in the case of ordinary differential equations by means of symbolic regression techniques. The first one consists in taking numerical derivatives from the given data obtaining a set of approximations. Then a symbolic regression technique can be applied to these approximations. Another way is to numerically integrate the ODE corresponding to the candidate solution and compare the results with the observed data. Here, by means of numerical experiments, we compare the relative performance of these two ways to infer models using the GIP method. %K genetic algorithms, genetic programming, grammatical evolution, Artificial immune systems, grammar-based immune programming, symbolic regression, model inference %U http://www.cimec.org.ar/ojs/index.php/mc/article/view/3656/3569 %P 9107-9124 %0 Journal Article %T Grammar-based immune programming %A Bernardino, Heder S. %A Barbosa, Helio J. C. %J Natural Computing %D 2011 %8 mar %V 10 %N 1 %@ 1567-7818 %G English %F Bernardino:2011:NC %X This paper describes Grammar-based Immune Programming (GIP) for evolving programs in an arbitrary language by immunological inspiration. GIP is based on Grammatical Evolution (GE) in which a grammar is used to define a language and decode candidate solutions to a valid representation (program). However, by default, GE uses a Genetic Algorithm in the search process while GIP uses an artificial immune system. Some modifications are needed of an immune algorithm to use a grammar in order to efficiently decode antibodies into programs. Experiments are performed to analyse algorithm behaviour over different aspects and compare it with GEVA, a well known GE implementation. The methods are applied to identify a causal model (an ordinary differential equation) from an observed data set, to symbolically regress an iterated function f(f(x)) = g(x), and to find a symbolic representation of a discontinuous function %K genetic algorithms, genetic programming, Grammatical evolution, Artificial immune system, AIS, Immune programming, Symbolic regression, Model inference %9 journal article %R 10.1007/s11047-010-9217-x %U http://dx.doi.org/10.1007/s11047-010-9217-x %P 209-241 %0 Conference Proceedings %T Inferring Systems of Ordinary Differential Equations via Grammar-Based Immune Programming %A Bernardino, Heder S. %A Barbosa, Helio J. C. %Y Lio, Pietro %Y Nicosia, Giuseppe %Y Stibor, Thomas %S Proceedings of the International Conference on Artificial Immune Systems (ICARIS) %S Lecture Notes in Computer Science %D 2011 %8 jul 18 21 %V 6825 %I Springer %C Cambridge, UK %G English %F Bernardino:2011:ICARIS %X Grammar-based Immune Programming (GIP) is a method for evolving programs in an arbitrary language using an immunological inspiration. GIP is applied here to solve the relevant modelling problem of finding a system of differential equations, in analytical form, which better explains a given set of data obtained from a certain phenomenon. Computational experiments are performed to evaluate the approach, showing that GIP is an efficient technique for symbolic modelling. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-22371-6_19 %U http://dx.doi.org/10.1007/978-3-642-22371-6_19 %P 198-211 %0 Conference Proceedings %T Inferring strains on a locally deformed pipe via grammar-based immune programming %A Bernardino, Heder S. %A Castro, Eduardo S. %A Guerreiro, Joao N. C. %A Barbosa, Helio J. C. %Y Silva, Andrea R. D. %S Proceedings of the Iberian Latin American Congress on Computational Methods in Engineering (CILAMCE) %D 2011 %8 nov 13 16 %C Ouro Preto, MG, Brazil %F Bernardino:2011:CILAMCE %K genetic algorithms, genetic programming %0 Conference Proceedings %T Simultaneous topology, shape, and sizing optimization of Truss structures via grammatical evolution %A Bernardino, Heder S. %A Barbosa, Helio J. C. %Y Pimenta, Paulo M. %S Proceedings of the 10th World Congress on Computational Mechanics (WCCM) %D 2012 %8 August 13 jul %C Sao Paulo, Brazil %F Bernardino:2012:WCCM %K genetic algorithms, genetic programming %0 Book Section %T Inferência de Modelos Utilizando a Programação Imunológica Gramatical %A Bernardino, Heder S. %A Barbosa, Helio J. C. %E Lobato, Fran Sérgio %E Steffen Jr., Valder %E da Silva Neto, Antonio José %B Técnicas de Inteligência Computacional com Aplicações em Problemas Inversos de Engenharia %D 2014 %7 1 %I Omnipax %C Curitiba, PR %F bernardinobarbosa2014 %X In this chapter we present the grammar-based immune programming, a technique for evolving programs by combining a search mechanism, inspired by the clonal selection theory, with the grammatical evolution representation which makes a clear distinction between the search and the solution spaces, thus offering more flexibility. The technique is applied to the inverse problem of model identification - in symbolic form - from data. Examples of the inference of systems of ordinary differential equations are presented. %K genetic algorithms, genetic programming, grammatical evolution, Inverse problems, Model identification, Grammar-based immune programming %R 10.7436/2014.tica.04 %U http://omnipax.com.br/site/?page_id=549 %U http://dx.doi.org/10.7436/2014.tica.04 %P 37-50 %0 Conference Proceedings %T Grammar-based Immune Programming to Assist in the Solution of Functional Equations %A Bernardino, Heder %A Barbosa, Helio %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 Bernardino:2015:CEC %X Grammar-based immune programming is proposed here as a tool to assist the search for a general solution to a functional equation. A external archive is incorporated to the algorithm in order to store good solutions found during the search. By inspecting such particular solutions the user is able to generalize and construct a general solution to the functional equation considered. The main objective here is to provide the user with a large diverse set of particular solutions to the problem at hand. Preliminary computational experiments are performed where some functional equations from the literature are tackled. %K genetic algorithms, genetic programming %R 10.1109/CEC.2015.7257021 %U http://dx.doi.org/10.1109/CEC.2015.7257021 %P 1167-1174 %0 Conference Proceedings %T An interval type-2 Fuzzy Logic based system for model generation and summarization of arbitrage opportunities in stock markets %A Bernardo, Dario %A Hagras, Hani %A Tsang, Edward %S 12th UK Workshop on Computational Intelligence (UKCI 2012) %D 2012 %F Bernardo:2012:UKCI %X Today stock market exchange and finance are centres of attention all over the world. In finance, arbitrage is the practice of taking advantage of a price misalignment between two or more stock markets where profit can be earned by striking a combination of matching deals that capitalise upon the misalignment. If one strikes when misalignment has been observed, such deals are practically risk-free. However, when risk-free profit is around, everyone would compete to take advantage of it. Therefore, the question is whether arbitrage opportunities can be predicted; after all, misalignment does not happen instantaneously. Furthermore, financial operators do not like black boxes in forecasting. In this paper, we will present a type-2 Fuzzy Logic System (FLS) for the modelling and prediction of financial applications. The proposed system is capable of generating summarised models from pre-specified number of linguistic rules, which enables the user to understand the generated models for arbitrage opportunities prediction. The system is able to use this summarised model for the prediction of arbitrage opportunities in stock markets. We have performed several experiments based on the arbitrage data which is used in stock markets to spot ahead of time arbitrage opportunities. The proposed type-2 FLS has outperformed the Evolving Decision Rule (EDR) procedure (which is based on Genetic Programming (GP) and decision trees). Like GP, the type-2 FLS is capable of providing a white box model which could be easily understood and analysed by the lay user. %K genetic algorithms, genetic programming, fuzzy logic, pricing, profitability, risk management, stock markets, financial application modelling, financial application prediction, financial operators, interval type-2 fuzzy logic based system, linguistic rules, model generation, model summarisation, price misalignment, risk-free profit, stock market exchange, type-2 FLS, Finance, Firing, Fuzzy logic, Fuzzy sets, Predictive models, Stock markets, Uncertainty, Financial Applications, Type-2 Fuzzy logic Systems, arbitrage, prediction %R 10.1109/UKCI.2012.6335765 %U http://dx.doi.org/10.1109/UKCI.2012.6335765 %0 Conference Proceedings %T A Genetic Type-2 fuzzy logic based system for financial applications modelling and prediction %A Bernardo, Dario %A Hagras, Hani %A Tsang, Edward %S IEEE International Conference on Fuzzy Systems (FUZZ 2013) %D 2013 %8 jul %F Bernardo:2013:ieeeFUZZ %X Following the global economic crisis, many financial organisations around the World are seeking efficient frameworks for predicting and assessing financial risks. However, in the current economic situation, transparency became an important factor where there is a need to fully understand and analyse a given financial model. In this paper, we will present a Genetic Type-2 Fuzzy Logic System (FLS) for the modelling and prediction of financial applications. The proposed system is capable of generating summarised optimised type-2 FLSs based financial models which are easy to read and analyse by the lay user. The system is able to use the summarised model for prediction within financial applications. We have performed several evaluations in two distinctive financial domains one for the prediction of good/bad customers in a credit card approval application and the other domain was in the prediction of arbitrage opportunities in the stock markets. The proposed Genetic type-2 FLS has outperformed white box financial models like the Evolving Decision Rule (EDR) procedure (which is based on Genetic Programming (GP) and decision trees) and gave a comparable performance to black box models like neural networks while the proposed system provided a white box model which is easy to understand and analyse by the lay user. %K genetic algorithms, genetic programming, type-2 fuzzy logic %R 10.1109/FUZZ-IEEE.2013.6622310 %U http://dx.doi.org/10.1109/FUZZ-IEEE.2013.6622310 %0 Conference Proceedings %T Accuracy and Size Trade-off of a Cartesian Genetic Programming Flow for Logic Optimization %A Berndt, Augusto %A Campos, Isac S. %A Lima, Bryan %A Grellert, Mateus %A Carvalho, Jonata T. %A Meinhardt, Cristina %A De Abreu, Brunno A. %S 2021 34th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design (SBCCI) %D 2021 %8 aug %F Berndt:2021:SBCCI %X Logic synthesis tools face tough challenges when providing algorithms for synthesizing circuits with increased inputs and complexity. Traditional approaches for logic synthesis have been in the spotlight so far. However, due to advances in machine learning and their high performance in solving specific problems, such algorithms appear as an attractive option to improve electronic design tools. In our work, we explore Cartesian Genetic Programming for logic optimization of exact or approximate combinational circuits. The proposed CGP flow receives input from the circuit description in the format of AND-Inverter Graphs and its expected behavior as a truth-table. The CGP may improve solutions found by other techniques used for bootstrapping the evolutionary process or initialize the search from random (unbiased) individuals seeking optimal circuits. We propose two different evaluation methods for the CGP: to minimize the number of AIG nodes or optimize the circuit accuracy. We obtain at least 22.percent superior results when considering the ratio between accuracy and size for the benchmarks used, compared with the teams from the IWLS 2020 contest that obtained the best accuracy and size results. It is noteworthy that any logic synthesis approach based on AIGs can easily incorporate the proposed flow. The results obtained show that their usage may achieve improved logic circuits. %K genetic algorithms, genetic programming, cartesian genetic programming %R 10.1109/SBCCI53441.2021.9529968 %U http://dx.doi.org/10.1109/SBCCI53441.2021.9529968 %0 Journal Article %T A CGP-based Logic Flow: Optimizing Accuracy and Size of Approximate Circuits %A Souza Berndt, Augusto Andre %A Abreu, Brunno %A Campos, Isac S. %A Lima, Bryan %A Grellert, Mateus %A Carvalho, Jonata T. %A Meinhardt, Cristina %J Journal of Integrated Circuits and Systems %D 2022 %V 17 %N 1 %F Berndt:2022:JICS %O Selected Papers from Symposium on Integrated Circuits and Systems Design, 2021 %X Logic synthesis tools face tough challenges when providing algorithms for synthesizing circuits with increased inputs and complexity. Machine learning techniques show high performance in solving specific problems, being an attractive option to improve electronic design tools. We explore Cartesian Genetic Programming (CGP) for logic optimization of exact or approximate Boolean functions in our work. The proposed CGP-based flow receives the expected circuit behavior as a truth-table and either performs the synthesis starting from random circuits or optimizes a circuit description provided in the format of an AND-Inverter Graph. The optimization flow improves solutions found by other techniques, using them for bootstrapping the evolutionary process. We use two metrics to evaluate our CGP-based flow: (i) the number of AIG nodes or (ii) the circuit accuracy. The results obtained showed that the CGP-based flow provided at least 22.6percent superior results when considering the trade-off between accuracy and size compared with two other methods that brought the best accuracy and size outcomes, respectively. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, Logic Minimization, Machine Learning, Multi-variable Optimization, Evolutionary Computing %9 journal article %R 10.29292/jics.v17i1.546 %U https://jics.org.br/ojs/index.php/JICS/article/view/546/380 %U http://dx.doi.org/10.29292/jics.v17i1.546 %0 Journal Article %T Finding Alternatives and Reduced Formulations for Process-Based Models %A Bernhardt, Knut %J Evolutionary Computation %D 2008 %8 Spring %V 16 %N 1 %@ 1063-6560 %F Bernhardt:2008:EC %X This paper addresses the problem of model complexity commonly arising in constructing and using process-based models with intricate interactions. Apart from complex process details the dynamic behaviour of such systems is often limited to a discrete number of typical states. Thus, models reproducing the system’s processes in all details are often too complex and over-parameterised. In order to reduce simulation times and to get a better impression of the important mechanisms, simplified formulations are desirable. In this work a data adaptive model reduction scheme that automatically builds simple models from complex ones is proposed. The method can be applied to the transformation and reduction of systems of ordinary differential equations. It consists of a multistep approach using a low dimensional projection of the model data followed by a Genetic Programming/Genetic Algorithm hybrid to evolve new model systems. As the resulting models again consist of differential equations, their process-based interpretation in terms of new state variables becomes possible. Transformations of two simple models with oscillatory dynamics, simulating a mathematical pendulum and predator-prey interactions respectively, serve as introductory examples of the method’s application. The resulting equations of force indicate the predator-prey system’s equivalence to a nonlinear oscillator. In contrast to the simple pendulum it contains driving and damping forces that produce a stable limit cycle. %K genetic algorithms, genetic programming, Model reduction, complexity, dimension reduction %9 journal article %R 10.1162/evco.2008.16.1.63 %U http://dx.doi.org/10.1162/evco.2008.16.1.63 %P 63-88 %0 Conference Proceedings %T A Platform-Independent Collaborative Interface for Genetic Programming Applications: Image Analysis for Scientific Inquiry %A Bersano-Begey, Tommaso F. %A Daida, Jason M. %A Vesecky, John F. %A Ludwig, Frank L. %Y Koza, John R. %S Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996 %D 1996 %8 28–31 jul %I Stanford Bookstore %C Stanford University, CA, USA %@ 0-18-201031-7 %F bersano-begey:1996:pici %K genetic algorithms, genetic programming %P 1-8 %0 Conference Proceedings %T A Java Collaborative Interface for Genetic Programming Applications: Image Analysis and Scientific Inquiry %A Bersano-Begey, Tommaso F. %A Daida, Jason M. %A Vesecky, John F. %A Ludwig, Frank L. %S Proceedings of the 1997 IEEE International Conference on Evolutionary Computation %D 1997 %8 13 16 apr %I IEEE Press %C Indianapolis %F bersano-begey:1997:jcifGPa %K genetic algorithms, genetic programming %U ftp://ftp.eecs.umich.edu/people/daida/papers/ICEC97image.pdf %0 Conference Proceedings %T Controlling Exploration, Diversity and Escaping Local Optima in GP: Adapting Weights of Training Sets to Model Resource Consumption %A Bersano-Begey, Tommaso F. %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 Bersano-Begey:1997:cedslo %K genetic algorithms, genetic programming %P 7-10 %0 Conference Proceedings %T A Discussion on Generality and Robustness and a Framework for Fitness Set Construction in Genetic Programming to Promote Robustness %A Bersano-Begey, Tommaso F. %A Daida, Jason M. %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 Bersano-Begey:1997:grffc %K genetic algorithms, genetic programming %P 11-18 %0 Conference Proceedings %T Multi-Agent Teamwork, Adaptive Learning and Adversarial Planning in Robocup Using a PRS Architecture %A Bersano-Begey, Tommaso F. %A Kenny, Patrick G. %A Durfee, Edmund H. %S IJCAI97 %D 1997 %F bersano-begey:1997: %O accepted %X Our approach for the Robocup97 competition is to emphasise teamwork among agents by augmenting reactions (based on awareness of the current situation) with predictions (based on predefined multiagent manoeuvres). These predictions are accomplished by allowing agents to cooperatively accomplish predefined plans, which are elaborated reactively and hierarchically to ensure responsiveness to changing circumstances. By supporting the runtime construction of plans, our approach simplifies the introduction of new plans, strategies, and actions, and produces a framework for dynamic adaptation and plan recognition through automatically generating belief networks. Our implementation is built on top of UM-PRS, a procedural reasoning system architecture for real-time environments, which allows specifying, executing, and integrating plans based on subgoaling and preconditions %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.1962 %0 Conference Proceedings %T Chemical Crossover %A Bersini, Hugues %Y Whitley, Darrell %Y Goldberg, David %Y Cantu-Paz, Erick %Y Spector, Lee %Y Parmee, Ian %Y Beyer, Hans-Georg %S Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000) %D 2000 %8 October 12 jul %I Morgan Kaufmann %C Las Vegas, Nevada, USA %@ 1-55860-708-0 %F Bersini:2000:GECCO %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2000/AA140.pdf %P 825-832 %0 Conference Proceedings %T Multiobjective Parsimony Enforcement for Superior Generalisation Performance %A Bernstein, Yaniv %A Li, Xiaodong %A Ciesielski, Vic %A Song, Andy %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 berstein:2004:mpefsgp %X Program Bloat - the phenomenon of ever-increasing program size during a GP run - is a recognised and widespread problem. Traditional techniques to combat program bloat are program size limitations or parsimony pressure (penalty functions). These techniques suffer from a number of problems, in particular their reliance on parameters whose optimal values it is difficult to a priori determine. In this paper we introduce POPE-GP, a system that makes use of the NSGA-II multiobjective evolutionary algorithm as an alternative, parameter-free technique for eliminating program bloat. We test it on a classification problem and find that while vastly reducing program size, it does improve generalisation performance. %K genetic algorithms, genetic programming, Multiobjective evolutionary algorithms, Combinatorial & numerical optimization %R 10.1109/CEC.2004.1330841 %U http://goanna.cs.rmit.edu.au/~ybernste/papers/Bernstein_CEC_2004.pdf %U http://dx.doi.org/10.1109/CEC.2004.1330841 %P 83-89 %0 Conference Proceedings %T Consistency Modifications for Automatically Tuned Monte-Carlo Tree Search %A Berthier, Vincent %A Doghmen, Hassen %A Teytaud, Olivier %Y Battiti, Roberto %S Learning and Intelligent OptimizatioN, LION 4 %D 2010 %8 jan 18 22 %C Venice %G EN %F Berthier:2010:LION %X Monte-Carlo Tree Search algorithms (MCTS [4, 6]), including upper confidence trees (UCT [9]), are known for their impressive ability in high dimensional control problems. Whilst the main test bed is the game of Go, there are increasingly many applications [13, 12, 7]; these algorithms are now widely accepted as strong candidates for high-dimensional control applications. Unfortunately, it is known that for optimal performance on a given problem, MCTS requires some tuning; this tuning is often handcrafted or automated, with in some cases a loss of consistency, i.e. a bad behavior asymptotically in the computational power. This highly undesirable property led to a stupid behavior of our main MCTS program MoGo in a real-world situation described in section 3. This is a big trouble for our several works on automatic parameter tuning [3] and the genetic programming of new features in MoGo. We will see in this paper: – A theoretical analysis of MCTS consistency; – Detailed examples of consistent and inconsistent known algorithms; – How to modify a MCTS implementation in order to ensure consistency, independently of the modifications to the scoring module (the module which is automatically tuned and genetically programmed in MoGo); – As a by product of this work, we’ll see the interesting property that some heavily tuned MCTS implementations are better than UCT in the sense that they do not visit the complete tree (whereas UCT asymptotically does), whilst preserving the consistency at least if consistency modifications above have been made. %K genetic algorithms, genetic programming, Game Go, Mathematics/Optimization and Control, Monte-Carlo Tree Search Consistency Ko-fights %U http://hal.archives-ouvertes.fr/docs/00/43/71/46/PDF/consistency.pdf %0 Journal Article %T Novel Methods Generated by Genetic Programming for the Guillotine-Cutting Problem %A Bertolini, Vittorio %A Barra, Carlos Rey %A Sepulveda, Mauricio %A Parada, Victor %J Scientific Programming %D 2018 %V 2018 %I Hindawi %F Bertolini:2018:SP %X New constructive algorithms for the two-dimensional guillotine-cutting problem are presented. The algorithms were produced from elemental algorithmic components using evolutionary computation. A subset of the components was selected from a previously existing constructive algorithm. The algorithms’ evolution and testing process used a set of 46 instances from the literature. The structure of three new algorithms is described, and the results are compared with those of an existing constructive algorithm for the problem. Several of the new algorithms are competitive with respect to a state-of-the-art constructive algorithm. A subset of novel instructions, which are responsible for the majority of the new algorithms’ good performances, has also been found. %K genetic algorithms, genetic programming %9 journal article %R 10.1155/2018/6971827 %U http://downloads.hindawi.com/journals/sp/2018/6971827.pdf %U http://dx.doi.org/10.1155/2018/6971827 %P 6971827:1-6971827:13 %0 Conference Proceedings %T Reconstructing Incomplete Signals Using Nonlinear Interpolation and Genetic Algorithms %A Bertram, Robert R. %A Daida, Jason M. %A Vesecky, John F. %A Meadows, Guy A. %A Wolf, Christian %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 Bertram:1997:ris %K genetic algorithms, genetic programming %P 19-27 %0 Conference Proceedings %T Reconstructing Incomplete Signals Using Nonlinear Interpolation and Genetic Algorithms %A Bertram, Robert R. %A Daida, Jason M. %A Vesecky, John F. %A Meadows, Guy A. %A Wolf, Christian %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 %F bertram:1998:risiGA %X This paper describes a general, nonanalytical method for deriving Fourier series coefficients using a genetic algorithm. Non-analytical methods are often needed in problems where lost portions of a complex signal require restoration. We discuss some of the difficulties involved in working with the associated trigonometric polynomials and propose an alternative solution for adapting genetic algorithms for this class of problems. We demonstrate the efficacy of our approach with a case study. Our particular case study features the processing of data that has been collected by a novel optical waveslope instrument, which measures the topography of water surfaces. %K genetic algorithms %U http://citeseer.ist.psu.edu/244792.html %P 447-454 %0 Conference Proceedings %T Evolving Form and Function: Dual-Objective Optimization in Neural Symbolic Regression Networks %A Bertschinger, Amanda %A Bagrow, James %A Bongard, Joshua %Y Mouret, Jean-Baptiste %Y Qin, Kai %Y Handl, Julia %Y Li, Xiaodong %Y Wagner, Markus %Y Garza-Fabre, Mario %Y Smith-Miles, Kate %Y Allmendinger, Richard %Y Bi, Ying %Y Dick, Grant %Y Gandomi, Amir H. %Y Martins, Marcella Scoczynski Ribeiro %Y Assimi, Hirad %Y Veerapen, Nadarajen %Y Sun, Yuan %Y Munyoz, Mario Andres %Y Kheiri, Ahmed %Y Su, Nguyen %Y Thiruvady, Dhananjay %Y Song, Andy %Y Neumann, Frank %Y Silva, Carla %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F bertschinger:2024:GECCO %X Data increasingly abounds, but distilling their underlying relationships down to something interpretable remains challenging. One approach is genetic programming, which ’symbolically regresses’ a data set down into an equation. However, symbolic regression (SR) faces the issue of requiring training from scratch for each new dataset. To generalize across all datasets, deep learning techniques have been applied to SR. These networks, however, are only able to be trained using a symbolic objective: NN-generated and target equations are symbolically compared. But this does not consider the predictive power of these equations, which could be measured by a behavioral objective that compares the generated equation’s predictions to actual data. Here we introduce a method that combines gradient descent and evolutionary computation to yield neural networks that minimize the symbolic and behavioral errors of the equations they generate from data. As a result, these evolved networks are shown to generate more symbolically and behaviorally accurate equations than those generated by networks trained by state-of-the-art gradient based neural symbolic regression methods. We hope this method suggests that evolutionary algorithms, combined with gradient descent, can improve SR results by yielding equations with more accurate form and function. %K genetic algorithms, genetic programming, symbolic regression, neuroevolution, multi-objective optimization, Evolutionary Machine Learning %R 10.1145/3638529.3654030 %U http://dx.doi.org/10.1145/3638529.3654030 %P 277-285 %0 Journal Article %T Robust technical trading strategies using GP for algorithmic portfolio selection %A Berutich, Jose Manuel %A Lopez, Francisco %A Luna, Francisco %A Quintana, David %J Expert Systems with Applications %D 2016 %V 46 %@ 0957-4174 %F Berutich:2016:ESA %X This paper presents a Robust Genetic Programming approach for discovering profitable trading rules which are used to manage a portfolio of stocks from the Spanish market. The investigated method is used to determine potential buy and sell conditions for stocks, aiming to yield robust solutions able to withstand extreme market conditions, while producing high returns at a minimal risk. One of the biggest challenges GP evolved solutions face is over-fitting. GP trading rules need to have similar performance when tested with new data in order to be deployed in a real situation. We explore a random sampling method (RSFGP) which instead of calculating the fitness over the whole dataset, calculates it on randomly selected segments. This method shows improved robustness and out-of-sample results compared to standard genetic programming (SGP) and a volatility adjusted fitness (VAFGP). Trading strategies (TS) are evolved using financial metrics like the volatility, CAPM alpha and beta, and the Sharpe ratio alongside other Technical Indicators (TI) to find the best investment strategy. These strategies are evaluated using 21 of the most liquid stocks of the Spanish market. The achieved results clearly outperform Buy and Hold, SGP and VAFGP. Additionally, the solutions obtained with the training data during the experiments clearly show during testing robustness to step market declines as seen during the European sovereign debt crisis experienced recently in Spain. In this paper the solutions learned were able to operate for prolonged periods, which demonstrated the validity and robustness of the rules learned, which are able to operate continuously and with minimal human intervention. To sum up, the developed method is able to evolve TSs suitable for all market conditions with promising results, which suggests great potential in the method generalization capabilities. The use of financial metrics alongside popular TI enables the system to increase the stock return while proving resilient through time. The RSFGP system is able to cope with different types of markets achieving a portfolio return of 31.81percent for the testing period 2009-2013 in the Spanish market, having the IBEX35 index returned 2.67percent. %K genetic algorithms, genetic programming, Algorithmic trading, Portfolio management, Trading rule, Finance, buy and hold %9 journal article %R 10.1016/j.eswa.2015.10.040 %U https://e-archivo.uc3m.es/rest/api/core/bitstreams/169bcfb0-d6cc-4f0a-870b-dd9915924000/content %U http://dx.doi.org/10.1016/j.eswa.2015.10.040 %P 307-315 %0 Thesis %T Robust Optimization of Algorithmic Trading Systems %A Berutich Lindquist, Jose Manuel %D 2017 %8 22 may %C Malaga, Andalucia, Spain %C Lenguajes y Ciencias de la Computacion, Universidad de Malaga %F Berutich:thesis %X GAs (Genetic Algorithms) and GP (Genetic Programming) are investigated for finding robust Technical Trading Strategies (TTSs). TTSs evolved with standard GA/GP techniques tend to suffer from over-fitting as the solutions evolved are very fragile to small disturbances in the data. The main objective of this thesis is to explore optimization techniques for GA/GP which produce robust TTSs that have a similar performance during both optimization and evaluation, and are also able to operate in all market conditions and withstand severe market shocks. In this thesis, two novel techniques that increase the robustness of TTSs and reduce over-fitting are described and compared to standard GA/GP optimization techniques and the traditional investment strategy Buy & Hold. The first technique employed is a robust multi-market optimization methodology using a GA. Robustness is incorporated via the environmental variables of the problem, i.e. variablity in the dataset is introduced by conducting the search for the optimum parameters over several market indices, in the hope of exposing the GA to differing market conditions. This technique shows an increase in the robustness of the solutions produced, with results also showing an improvement in terms of performance when compared to those offered by conducting the optimization over a single market. The second technique is a random sampling method we use to discover robust TTSs using GP. Variability is introduced in the dataset by randomly sampling segments and evaluating each individual on different random samples. This technique has shown promising results, substantially beating Buy & Hold. Overall, this thesis concludes that Evolutionary Computation techniques such as GA and GP combined with robust optimization methods are very suitable for developing trading systems, and that the systems developed using these techniques can be used to provide significant economic profits in all market conditions. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U https://hdl.handle.net/10630/15353 %0 Conference Proceedings %T Function choice, resiliency and growth in genetic programming %A Besetti, Sireesha %A Soule, Terence %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 1068303 %K genetic algorithms, genetic programming, Poster, function choice, growth, resilience %R 10.1145/1068009.1068303 %U http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1771.pdf %U http://dx.doi.org/10.1145/1068009.1068303 %P 1771-1772 %0 Conference Proceedings %T Learning the Classification of Traffic Accident Types %A Beshah, Tibebe %A Ejigu, Dejene %A Kromer, Pavel %A Snasel, Vaclav %A Platos, Jan %A Abraham, Ajith %S 4th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2012 %D 2012 %F Beshah:2012:INCoS %X This paper presents an application of evolutionary fuzzy classifier design to a road accident data analysis. A fuzzy classifier evolved by the genetic programming was used to learn the labelling of data in a real world road accident data set. The symbolic classifier was inspected in order to select important features and the relations among them. Selected features provide a feedback for traffic management authorities that can exploit the knowledge to improve road safety and mitigate the severity of traffic accidents. %K genetic algorithms, genetic programming, data analysis, fuzzy set theory, learning (artificial intelligence), pattern classification, road accidents, road traffic, traffic engineering computing, evolutionary fuzzy classifier design, feature selection, machine learning, real world road accident data set, road accident data analysis, road safety improvement, symbolic classifier, traffic accident severity mitigation, traffic accident type classification, traffic management authorities, Accidents, Biological cells, Indexes, Injuries, Labeling, Vehicles, fuzzy rules, machine learning, traffic accidents %R 10.1109/iNCoS.2012.75 %U http://dx.doi.org/10.1109/iNCoS.2012.75 %P 463-468 %0 Conference Proceedings %T Coevolving Mutualists Guide Simulated Evolution %A Best, Michael L. %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 best:1999:CMGSE %K evolution strategies and evolutionary programming, poster papers %P 941 %0 Conference Proceedings %T BioX++ – New results and conceptions concerning the intelligent control of biotechnological processes %A Bettenhausen, K. D. %A Gehlen, S. %A Marenbach, P. %A Tolle, H. %Y Munack, A. %Y Schügerl, K. %S 6th International Conference on Computer Applications in Biotechnology %D 1995 %I Elsevier Science %F bettenhausen:1995:biox %X BioX++ facilities the transparent generation of process control stratgies and sequences based on automatically self-organized structured process models. Experimental results showing the increased product yeild and the discussion of approach-specific problems are part of this paper as well as the new approaches actually examined. %K genetic algorithms, genetic programming, Expert systems, neural networks, fuzzy systems, learning control, fermentation, biotechnology %U http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_03.pdf %P 324-327 %0 Conference Proceedings %T Self-organizing modeling of biotechnological batch and fed-batch fermentations %A Bettenhausen, Kurt Dirk %A Marenbach, Peter %Y Breitenecker, F. %Y Husinsky, I. %S EUROSIM’95 %D 1995 %I Elsevier %C Vienna, Austria %F bettenhausen:1995:sombbff %X An approach for the automatic generation of dynamic nonlinear process models obtained from experimantal process data and theoretical biological and chemical reflections using genetic programming for the supervision and coordination of the symbolic model structure during automatic development BioX++ includes (amongs fuzzy rule learning, expert system, NN also refered to) GP to produce process models, constants adapted using standard algorithmic techniques. %K genetic algorithms, genetic programming, fermentation, biotechnology %U http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_23.ps.gz %0 Conference Proceedings %T Self-organizing Structured modeling of a Biotechnological Fed-batch fermentation by Means of Genetic Programming %A Bettenhausen, K. D. %A Marenbach, P. %A Freyer, Stephan %A Rettenmaier, Hans %A Nieken, Ulrich %Y Zalzala, A. M. S. %S First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA %D 1995 %8 December 14 sep %V 414 %I IEE %C Sheffield, UK %@ 0-85296-650-4 %F bettenhausen:1995:sombbffGP %X 12–14 September 1995, Halifax Hall, University of Sheffield, UK see also http://www.iee.org.uk/LSboard/Conf/program/galprog.htm The article describes an approach for the self-organizing generation of models of complex and unknown processes by means of GP and its application on a biotechnological fed-batch production. First experiments of the symbolic generation of structured models within an industrial cooperation with BASF are presented. %K genetic algorithms, genetic programming, symbolic modelling, system identification, biotechnology, predictive control %R 10.1049/cp:19951095 %U http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_24.pdf %U http://dx.doi.org/10.1049/cp:19951095 %P 481-486 %0 Journal Article %T Operational Analysis of Signalized Street Segments Using Multi-gene Genetic Programming and Functional Network Techniques %A Beura, Sambit Kumar %A Bhuyan, Prasanta Kumar %J Arabian Journal for Science and Engineering %D 2018 %V 43 %N 10 %F beura:2018:AJSE %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s13369-018-3176-4 %U http://link.springer.com/article/10.1007/s13369-018-3176-4 %U http://dx.doi.org/10.1007/s13369-018-3176-4 %0 Journal Article %T Service quality analysis of signalized intersections from the perspective of bicycling %A Beura, Sambit Kumar %A Kumar, Kondamudi Vinod %A Suman, Shakti %A Bhuyan, Prasanta Kumar %J Journal of Transport & Health %D 2020 %V 16 %@ 2214-1405 %F BEURA:2020:JTH %X Bicycling reduces the risk of various health problems associated with sedentary lifestyles. Hence, it is important to encourage bicycle commuting by enhancing the bikeability of transportation facilities. To support this process, this article proposes efficient bicycle level of service (BLOS) models for the assessment of signalized intersections under heterogeneous traffic conditions. Here, BLOS denotes bicyclists’ perceived level of satisfaction. Method Extensive data sets (geometrical, traffic operational and built-environmental) are collected from 70 well-diversified intersection approaches of India. All approaches are also rated by 200 on-site bicyclists based on their perceived satisfaction levels on a Likert scale of 1-6 (excellent-worst). The attributes having significant influences on these ratings are then identified through Spearman’s correlation analysis. Subsequently, three highly efficient techniques namely, associativity functional network (FN), genetic programming (GP) and step-wise regression are used to develop reliable BLOS models. Results As observed, the intersection BLOS is significantly (p < 0.001) influenced by total eight attributes of which crossing pedestrian volume, parking turn-over and average bicycle delay are the most dominating ones. Using these variables, the FN tool has produced the most efficient BLOS model with a coefficient of determination (R2) value of 0.92 with averaged observations. Further, the classification of BLOS ratings into six symmetrical levels A-F (excellent-worst) has reported that around 8percent intersection approaches in India are offering BLOS C-F. Conclusion The important measures of BLOS improvement at signalized intersections include the efficient management of crossing pedestrians, restrictions on nearby parking activities, and minimization of bicycle delay. The deficiencies in these aspects have perhaps made the intersection approaches in India to offer BLOS C-F. The BLOS models and transportation engineering solutions proposed in this study for the improvement of public health through bicycling are highly efficient for developing countries. %K genetic algorithms, genetic programming, Developing country, Signalized intersection, Bicycle level of service, Prediction modelling, Functional network %9 journal article %R 10.1016/j.jth.2020.100827 %U http://www.sciencedirect.com/science/article/pii/S2214140519300866 %U http://dx.doi.org/10.1016/j.jth.2020.100827 %P 100827 %0 Journal Article %T Bicycle Comfort Level Rating (BCLR) model for urban street segments in mid-sized cities of India %A Beura, Sambit Kumar %A Chellapilla, Haritha %A Panda, Mahabir %A Bhuyan, Prasanta Kumar %J Journal of Transpor & Health %D 2021 %V 20 %@ 2214-1405 %F BEURA:2021:JTH %X Introduction The perceived comfort levels of on-street bicyclists are affected by both road characteristics and environmental healthiness. A thorough knowledge of these factors helps to encourage bicycle use and improve human health. This study thus aims to incorporate the parameters describing environmental healthiness in the evaluation of urban street performance. Methods For analysis purpose, extensive data are collected from sixty street segments of three Indian mid-sized cities. Variables having significant influences on bicycling comfort are identified using Spearman’s correlation technique and a ’Bicycle Comfort Level Rating’ (BCLR) model is developed using the step-wise regression technique. A service scale is also defined using the Genetic Programming (GP) cluster technique to convert model outputs to letter-graded bicycling comfort levels A-F (excellent-worst). Results As observed, the bicycling comfort is influenced by total eight attributes. Of all, air quality index (AQI) is the most significant one (Spearman’s correlation coefficient = 0.645). The BCLR model developed using all identified parameters has produced a high coefficient of determination (R2) value of 0.87 with overall observations. Results have also shown that around 97percent segments are offering average-worst levels of bicycling comfort (C-F) at their present scenario. Conclusion An unhealthy environment largely discourages the use of bicycles as a choice mode of transport (as the users are more likely to be exposed to environmental hazards). Hence, the improvement in factors like air quality is essential to encourage the bicycling activity. The roadway parameters like traffic volume, road width and roadside commercial activities, etc. should also be prioritized in the planning process to provide better bicycling comfort. The developed BCLR model is highly reliable for its applications in mid-sized cities of India and other developing countries. This model along with other outcomes of this study would be helpful to enhance the quality of bicycling and public health. %K genetic algorithms, genetic programming, Bicycling comfort, Road segment, Environmental healthiness, Heterogeneous traffic, Genetic programming clustering %9 journal article %R 10.1016/j.jth.2020.100971 %U https://www.sciencedirect.com/science/article/pii/S2214140520301754 %U http://dx.doi.org/10.1016/j.jth.2020.100971 %P 100971 %0 Journal Article %T Estimating Vehicle Delay at Unsignalized Intersections with Gene-Expression Programming %A Beura, Sambit Kumar %A Rao, K. Ramachandra %J Transportation Research Procedia %D 2025 %V 86 %@ 2352-1465 %F Beura:2025:trpro %O VSI: TRPRO EWGT 2024 %X The performance of an unsignalized intersection is typically evaluated based on the delay experienced by vehicles in low-priority movements. Several studies in the literature have examined delays at unsignalized intersections under homogeneous and lane-disciplined traffic conditions. However, it is challenging to find a standard delay model for mixed traffic conditions. To address this gap, the present study proposes mathematical models to estimate average traffic delays for the two least-priority movements: through and right-turning movements (for left-hand drive conditions) from minor streets. The required datasets for this investigation were obtained from thirty unsignalized intersections, encompassing both three-legged and four-legged configurations, with divided and undivided road intersection typologies collected from different parts of India. From data analysis, it was observed that the delay incurred by vehicles in through movements on the minor approach is significantly influenced by the degree of saturation (volume-to-capacity ratio), conflicting traffic volume, crossing pedestrian volume, and intersection crossing distance. Similarly, the delay incurred by vehicles in right-turning movements is significantly influenced by the same list of variables except for the intersection crossing distance. Subsequently, Gene-expression programming (GEP), a novel variant of conventional genetic programming, was successfully used to develop delay models for the respective movements. The resultant models demonstrated high prediction accuracies, with coefficient of determination (R2) values between the predicted and observed delays surpassing 0.93. A comparative analysis corroborated the superior predictive capabilities of the developed models when compared to existing ones %K genetic algorithms, genetic programming, Delay, Unsignalized intersection, Heterogeneous traffic, Gene-Expression Programming, gene expression programming %9 journal article %R 10.1016/j.trpro.2025.04.023 %U https://www.sciencedirect.com/science/article/pii/S2352146525002704 %U http://dx.doi.org/10.1016/j.trpro.2025.04.023 %P 175-182 %0 Conference Proceedings %T Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling %A Bevilacqua, Vitoantonio %A Nuzzolese, Nicola %A Mininno, Ernesto %A Iacca, Giovanni %Y Huang, De-Shuang %Y Han, Kyungsook %Y Hussain, Abir %S Intelligent Computing Methodologies - 12th International Conference, ICIC 2016, Lanzhou, China, August 2-5, 2016, Proceedings, Part III %S Lecture Notes in Computer Science %D 2016 %V 9773 %I Springer %F conf/icic/BevilacquaNMI16 %X We propose in this paper a modification of one of the modern state-of-the-art genetic programming algorithms used for data-driven modelling, namely the Bi-objective Genetic Programming (BioGP). The original method is based on a concurrent minimization of both the training error and complexity of multiple candidate models encoded as Genetic Programming trees. Also, BioGP is empowered by a predator-prey co-evolutionary model where virtual predators are used to suppress solutions (preys) characterised by a poor trade-off error vs complexity. In this work, we incorporate in the original BioGP an adaptive mechanism that automatically tunes the mutation rate, based on a characterisation of the current population (in terms of entropy) and on the information that can be extracted from it. We show through numerical experiments on two different datasets from the energy domain that the proposed method, named BioAGP (where A stands for Adaptive), performs better than the original BioGP, allowing the search to maintain a good diversity level in the population, without affecting the convergence rate. %K genetic algorithms, genetic programming, multi-objective evolutionary algorithms, adaptive genetic programming, machine learning, home automation, energy efficiency %R 10.1007/978-3-319-42297-8_24 %U https://link.springer.com/chapter/10.1007%2F978-3-319-42297-8_24 %U http://dx.doi.org/10.1007/978-3-319-42297-8_24 %P 248-259 %0 Conference Proceedings %T Fitness Noise and Localization Errors of the Optimum in General Quadratic Fitness Models %A Beyer, Hans-Georg %A Arnold, Dirk V. %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 beyer:1999:FNLEOGQFM %K evolution strategies and evolutionary programming %U http://gpbib.cs.ucl.ac.uk/gecco1999/beyer_GECCO99.ps.gz %P 817-824 %0 Conference Proceedings %T 04081 Abstracts Collection – Theory of Evolutionary Algorithms %A Beyer, Hans-Georg %A Jansen, Thomas %A Reeves, Colin %A Vose, Michael D. %Y Beyer, Hans-Georg %Y Jansen, Thomas %Y Reeves, Colin %Y Vose, Michael D. %S Theory of Evolutionary Algorithms %S Dagstuhl Seminar Proceedings %D 2004 %N 04081 %I Internationales Begegnungs- und Forschungszentrum (IBFI), Schloss Dagstuhl, Germany %C Dagstuhl, Germany %F beyer_et_al:DSP:2006:498 %O $<$http://drops.dagstuhl.de/opus/volltexte/2006/498$>$ [date of citation: 2006-01-01] %X From 15.02.04 to 20.02.04, the Dagstuhl Seminar 04081 “Theory of Evolutionary Algorithms” was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available. %K genetic algorithms, genetic programming, Evolutionary algorithms, co-evolution, run time analysis, landscape analysis, Markov chains %U http://drops.dagstuhl.de/opus/volltexte/2006/498 %0 Journal Article %T On the Impact of Systematic Noise on the Evolutionary Optimization Performance – A Sphere Model Analysis %A Beyer, Hans-Georg %A Olhofer, Markus %A Sendhoff, Bernhard %J Genetic Programming and Evolvable Machines %D 2004 %8 dec %V 5 %N 4 %@ 1389-2576 %F beyer:2004:GPEM %X Quality evaluations in optimisation processes are frequently noisy. In particular evolutionary algorithms have been shown to cope with such stochastic variations better than other optimization algorithms. So far mostly additive noise models have been assumed for the analysis. However, we will argue in this paper that this restriction must be relaxed for a large class of applied optimization problems. We suggest systematic noise as an alternative scenario, where the noise term is added to the objective parameters or to environmental parameters inside the fitness function. We thoroughly analyse the sphere function with systematic noise for the evolution strategy with global intermediate recombination. The progress rate formula and a measure for the efficiency of the evolutionary progress lead to a recommended ratio between [mu] and [lambda]. Furthermore, analysis of the dynamics identifies limited regions of convergence dependent on the normalized noise strength and the normalised mutation strength. A residual localisation error R[infin] can be quantified and a second [mu] to [lambda] ratio is derived by minimising R[infin]. %K ES, evolution strategies, noisy optimisation, performance analysis, robust optimization %9 journal article %R 10.1023/B:GENP.0000036020.79188.a0 %U http://dx.doi.org/10.1023/B:GENP.0000036020.79188.a0 %P 327-360 %0 Journal Article %T A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise %A Beyer, Hans-Georg %A Arnold, Dirk V. %A Meyer-Nieberg, Silja %J Genetic Programming and Evolvable Machines %D 2005 %8 mar %V 6 %N 1 %@ 1389-2576 %F beyer:2005:GPEM %X Differential-geometric methods are applied to derive steady state conditions for the (mgr/mgrI,lambda)-ES on the general quadratic test function disturbed by fitness noise of constant strength. A new approach for estimating the expected final fitness deviation observed under such conditions is presented. The theoretical results obtained are compared with real ES runs, showing a surprisingly excellent agreement. %K ES, evolution strategies, final fitness error, noisy optimization, optimization quality, robust optimization %9 journal article %R 10.1007/s10710-005-7617-y %U http://dx.doi.org/10.1007/s10710-005-7617-y %P 7-24 %0 Conference Proceedings %T GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation %E Beyer, Hans-Georg %E O’Reilly, Una-May %E Arnold, Dirk V. %E Banzhaf, Wolfgang %E Blum, Christian %E Bonabeau, Eric W. %E Cantu-Paz, Erick %E Dasgupta, Dipankar %E Deb, Kalyanmoy %E Foster, James A. %E de Jong, Edwin D. %E Lipson, Hod %E Llora, Xavier %E Mancoridis, Spiros %E Pelikan, Martin %E Raidl, Guenther R. %E Soule, Terence %E Tyrrell, Andy M. %E Watson, Jean-Paul %E Zitzler, Eckart %D 2005 %8 25 29 jun %I ACM Press %C Washington DC, USA %@ 1-59593-010-8 %F GECCO2005 %X The papers in this two volume proceedings are presented at the 7th Annual Genetic and Evolutionary Computation COnference (GECCO-2005), held in Washington, D.C., June 25-29, 2005.This year is an exceptional one for the GECCO conference series. First, the International Society for Genetic and Evolutionary Computation (ISGEC) which has always been GECCO’s sponsor has changed to become a Special Interest Group of the ACM named SIGEVO. Being part of ACM reflects the evolution and integration of our very successful discipline into the main stream of computer science. As a consequence, the GECCO-2005 proceedings are an ACM publication and they are incorporated into the ACM Digital Library. This guarantees an even broader dissemination of Darwinian and other nature-inspired computation methods.Second, we had 549 regular paper submissions representing the absolute record of all conferences emphasising the field of evolutionary computation. Paper reviewing has been done by double blind assignment. On average each paper was evaluated by five independent reviewers. Finally, 253 paper (46.1%) have been accepted as full (max. 8 pages) papers. Additionally, 120 submissions were accepted as posters.A goal of GECCO is to encourage new areas and paradigms of evolutionary computation to gather momentum and flourish. This is accomplished by the establishment of new independent tracks each year. This year, as a result of a recombinative and creative process, GECCO-2005 comprises 16 tracks consisting of core tracks (’C’), tracks previously in GECCOs (’P’), not yet belonging to the core track family), ’recombined’ tracks from GECCO 2004 (’R’), and newly created tracks (’N’):. %K genetic algorithms, genetic programming, A-Life, Evolutionary Robotics and Adaptive Behaviour, Ant Colony Optimisation and Swarm Intelligence, Artificial Immune Systems, Biological Applications, Coevolution, Estimation of Distribution Algorithms, Evolutionary Combinatorial Optimisation, Evolutionary Multi-objective Optimization, Evolutionary Strategies, Evolutionary Programming, Evolvable Hardware, Meta-heuristics and Local Search, Real World Applications, Search-based Software Engineering %U http://portal.acm.org/citation.cfm?id=1068009&jmp=cit&coll=GUIDE&dl=GUIDE&CFID=48779769&CFTOKEN=55479664#supp %0 Journal Article %T Special Issue: Best of GECCO 2005 %A Beyer, Hans-Georg %J Genetic Programming and Evolvable Machines %D 2006 %8 aug %V 7 %N 2 %@ 1389-2576 %F Beyer:2006:GPEM %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-006-9002-x %U http://dx.doi.org/10.1007/s10710-006-9002-x %P 129-130 %0 Journal Article %T Self-adaptation of evolution strategies under noisy fitness evaluations %A Beyer, Hans-Georg %A Meyer-Nieberg, Silja %J Genetic Programming and Evolvable Machines %D 2006 %8 dec %V 7 %N 4 %@ 1389-2576 %F Beyer:2007:GPEM %X This paper investigates the self-adaptation behaviour of (1,L)-evolution strategies (ES) on the noisy sphere model. To this end, the stochastic system dynamics is approximated on the level of the mean value dynamics. Being based on this microscopic analysis, the steady state behavior of the ES for the scaled noise scenario and the constant noise strength scenario will be theoretically analysed and compared with real ES runs. An explanation will be given for the random walk like behaviour of the mutation strength in the vicinity of the steady state. It will be shown that this is a peculiarity of the (1,L)-ES and that intermediate recombination strategies do not suffer from such behaviour. %K Evolution strategies, Self-adaptation, Noisy optimisation, Noisy sphere model %9 journal article %R 10.1007/s10710-006-9017-3 %U http://dx.doi.org/10.1007/s10710-006-9017-3 %P 295-328 %0 Book Section %T Evolution and Analysis of DNA Classifiers %A Bezdek, Trevor %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 bezdek:1999:EADC %K genetic algorithms, genetic programming %P 21-30 %0 Journal Article %T Natalio Krasnogor, Steve Gustafson, David A. Pelta, and Jose L. Verdegay (eds): Systems self-assembly: multidisciplinary snapshots Elsevier, 2008, 310 pp, 41 colour plates, hard cover, $160 USD list price, ISBN 978-0-444-52865-0 %A Bhalla, Navneet %J Genetic Programming and Evolvable Machines %D 2009 %8 dec %V 10 %N 4 %@ 1389-2576 %F Bhalla:2009:GPEM %O Book Review %9 journal article %R 10.1007/s10710-009-9088-z %U http://dx.doi.org/10.1007/s10710-009-9088-z %P 473-475 %0 Conference Proceedings %T Coevolutionary Construction of Features for Transformation of Representation in Machine Learning %A Bhanu, Bir %A Krawiec, Krzysztof %Y Barry, Alwyn M. %S GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference %D 2002 %8 August %I AAAI %C New York %F bhanu:2002:GECCO:workshop %X The main objective of this paper is to study the usefulness of cooperative coevolutionary algorithms (CCA) for improving the performance of classification of machine learning (ML) classifiers, in particular those following the symbolic paradigm. For this purpose, we present a genetic programming (GP) -based coevolutionary feature construction procedure. In the experimental part, we confront the coevolutionary methodology with difficult real-world ML task with unknown internal structure and complex interrelationships between solution subcomponents (features), as opposed to artificial problems considered usually in the literature. %K genetic algorithms, genetic programming %U https://www.researchgate.net/publication/2496301 %P 249-254 %0 Conference Proceedings %T Learning Composite Operators For Object Detection %A Bhanu, Bir %A Lin, Yingqiang %Y Langdon, W. B. %Y Cantú-Paz, E. %Y Mathias, K. %Y Roy, R. %Y Davis, D. %Y Poli, R. %Y Balakrishnan, K. %Y Honavar, V. %Y Rudolph, G. %Y Wegener, J. %Y Bull, L. %Y Potter, M. A. %Y Schultz, A. C. %Y Miller, J. F. %Y Burke, E. %Y Jonoska, N. %S GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %D 2002 %8 September 13 jul %I Morgan Kaufmann Publishers %C New York %@ 1-55860-878-8 %F bhanu:2002:gecco %K genetic algorithms, genetic programming, real world applications, composite operators, genetic image segmentation, object detection %U http://gpbib.cs.ucl.ac.uk/gecco2002/RWA165_v2.pdf %P 1003-1010 %0 Journal Article %T Object detection in multi-modal images using genetic programming %A Bhanu, Bir %A Lin, Yingqiang %J Applied Soft Computing %D 2004 %8 may %V 4 %N 2 %F bhanu:2004:ASC %X In this paper, we learn to discover composite operators and features that are synthesized from combinations of primitive image processing operations for object detection. Our approach is based on genetic programming (GP). The motivation for using GP-based learning is that we hope to automate the design of object detection system by automatically synthesizing object detection procedures from primitive operations and primitive features. There are many basic operations that can operate on images and the ways of combining these primitive operations to perform meaningful processing for object detection are almost infinite. The human expert, limited by experience, knowledge and time, can only try a very small number of conventional combinations. Genetic programming, on the other hand, attempts many unconventional combinations that may never be imagined by human experts. In some cases, these unconventional combinations yield exceptionally good results. To improve the efficiency of GP, we propose soft composite operator size limit to control the code-bloat problem while at the same time avoid severe restriction on the GP search. Our experiments, which are performed on selected regions of images to improve training efficiency, show that GP can synthesize effective composite operators consisting of pre-designed primitive operators and primitive features to effectively detect objects in images and the learned composite operators can be applied to the whole training image and other similar testing images. %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.asoc.2004.01.004 %U http://dx.doi.org/10.1016/j.asoc.2004.01.004 %P 175-201 %0 Conference Proceedings %T Coevolution and Linear Genetic Programming for Visual Learning %A Krawiec, Krzysztof %A Bhanu, Bir %Y Cantú-Paz, E. %Y Foster, J. A. %Y Deb, K. %Y Davis, D. %Y Roy, R. %Y O’Reilly, U.-M. %Y Beyer, H.-G. %Y Standish, R. %Y Kendall, G. %Y Wilson, S. %Y Harman, M. %Y Wegener, J. %Y Dasgupta, D. %Y Potter, M. A. %Y Schultz, A. C. %Y Dowsland, K. %Y Jonoska, N. %Y Miller, J. %S Genetic and Evolutionary Computation – GECCO-2003 %S LNCS %D 2003 %8 December 16 jul %V 2723 %I Springer-Verlag %C Chicago %@ 3-540-40602-6 %F bhanu:2003:gecco %X a novel genetically-inspired visual learning method is proposed. Given the training images, this general approach induces a sophisticated feature-based recognition system, by using cooperative coevolution and linear genetic programming for the procedural representation of feature extraction agents. The paper describes the learning algorithm and provides a firm rationale for its design. An extensive experimental evaluation, on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery, shows the competitiveness of the proposed approach with human-designed recognition systems. %K genetic algorithms, genetic programming, Coevolution %R 10.1007/3-540-45105-6_39 %U http://dx.doi.org/10.1007/3-540-45105-6_39 %P 332-343 %0 Conference Proceedings %T Feature Synthesis Using Genetic Programming for Face Expression Recognition %A Bhanu, Bir %A Yu, Jiangang %A Tan, Xuejun %A Lin, Yingqiang %Y Deb, Kalyanmoy %Y Poli, Riccardo %Y Banzhaf, Wolfgang %Y Beyer, Hans-Georg %Y Burke, Edmund %Y Darwen, Paul %Y Dasgupta, Dipankar %Y Floreano, Dario %Y Foster, James %Y Harman, Mark %Y Holland, Owen %Y Lanzi, Pier Luca %Y Spector, Lee %Y Tettamanzi, Andrea %Y Thierens, Dirk %Y Tyrrell, Andy %S Genetic and Evolutionary Computation – GECCO-2004, Part II %S Lecture Notes in Computer Science %D 2004 %8 26 30 jun %V 3103 %I Springer-Verlag %C Seattle, WA, USA %@ 3-540-22343-6 %F bhanu:fsu:gecco2004 %K genetic algorithms, genetic programming %R 10.1007/b98645 %U http://dx.doi.org/10.1007/b98645 %P 896-907 %0 Journal Article %T Synthesizing feature agents using evolutionary computation %A Bhanu, Bir %A Lin, Yingqiang %J Pattern Recognition Letters %D 2004 %8 January %V 25 %N 13 %F Bhanu:2004:PRL %O Pattern Recognition for Remote Sensing (PRRS 2002) %X genetic programming (GP) with smart crossover and smart mutation is proposed to discover integrated feature agents that are evolved from combinations of primitive image processing operations to extract regions-of-interest (ROIs) in remotely sensed images. The motivation for using genetic programming is to overcome the limitations of human experts, since GP attempts many unconventional ways of combination, in some cases, these unconventional combinations yield exceptionally good results. Smart crossover and smart mutation identify and keep the effective components of integrated operators called ’agents’ and significantly improve the efficiency of GP. Our experimental results show that compared to normal GP, our GP algorithm with smart crossover and smart mutation can find good agents more quickly during training to effectively extract the regions-of-interest and the learned agents can be applied to extract ROIs in other similar images. %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.patrec.2004.06.005 %U http://www.sciencedirect.com/science/article/B6V15-4CRY8J6-2/2/d245bfcfeee2d509066321e19d84a0fd %U http://dx.doi.org/10.1016/j.patrec.2004.06.005 %P 1519-1531 %0 Book %T Evolutionary Synthesis of Pattern Recognition Systems %A Bhanu, Bir %A Lin, Yingqiang %A Krawiec, Krzysztof %S Monographs in Computer Science %D 2005 %I Springer-Verlag %C New York %@ 0-387-21295-7 %F Bhanu:book %K genetic algorithms, genetic programming, visual learning, feature synthesis, Computer vision, Image processing, Object detection, Pattern recognition %U http://www.springer.com/west/home/computer/imaging?SGWID=4-149-22-39144807-detailsPage=ppmmedia|aboutThisBook %0 Journal Article %T Evolutionary Approach for Automated Discovery of Censored Production Rules %A Bharadwaj, Kamal K. %A Al-Maqaleh, Basheer M. %J International Journal of Computer, Information Science and Engineering %D 2007 %V 1 %N 10 %I World Academy of Science, Engineering and Technology %@ 1307-6892 %G en %F Bharadwaj:2007:waset %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. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski & Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations, in which the conditional statement ’If P Then D’ holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the ’If P Then D’ part of the CPR expresses important information, while the Unless C part acts only as a switch and changes the polarity of D to D. This paper presents a classification algorithm based on evolutionary approach that discovers comprehensible rules with exceptions in the form of CPRs. The proposed approach has flexible chromosome encoding, where each chromosome corresponds to a CPR. Appropriate genetic operators are suggested and a fitness function is proposed that incorporates the basic constraints on CPRs. Experimental results are presented to demonstrate the performance of the proposed algorithm. %K genetic algorithms, genetic programming, data mining, machine learning, evolutionary algorithms %9 journal article %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.7101 %P 11-16 %0 Journal Article %T A Detection of Duplicate Records from Multiple Web Databases using pattern matching in UDD %A Bharambe, Dewendra %A Jain, Susheel %A Jain, Anurag %J International Journal of Emerging Technology and Advanced Engineering %D 2013 %8 may %V 3 %N 5 %@ 2250–2459 %G en %F Bharambe:2013:ijetae %X Record matching refers to the task of finding entries that refer to the same entity in two or more files, is a vital process in data integration. Most of the supervised record matching methods require training data provided by users. Such methods can not apply for web database scenario, where query results dynamically generated. In existing system, an unsupervised record matching method effectively identifies the duplicates from query result records of multiple web databases by identifying the duplicate and non duplicate set in the source and from that non duplicate set again searches for the existence of duplication. Then use two co-operative classifiers from the non duplicate set, they are Weighted Component Similarity Summing (WCSS) Classifier and Support Vector Machine (SVM) classifier. These two classifiers can be used to identify the query results iteratively from multiple web databases. In this paper we modify record matching algorithm with genetic algorithm. The genetic programming is time consuming so we proposed UDD with genetic programming. A performance evaluation for accuracy is done for the dataset with duplicates using UDD and UDD with Genetic algorithm. %K genetic algorithms, genetic programming, data deduplication, UDD, SVM, WCSS, genetic algorithm, pattern matching %9 journal article %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.413.7928 %P 412-417 %0 Journal Article %T Controlling The Problem Of Bloating Using Stepwise Crossover And Double Mutation Technique %A Bhardwaj, Arpit %A Sakalle, Aditi %A Chouhan, Harshita %A Bhardwaj, Harshit %J Advanced Computing : an International Journal %D 2011 %8 nov %V 2 %N 6 %I Academy & Industry Research Collaboration Center (AIRCC) %@ 2229726X %F Bhardwaj:2011:ACIJ %X During the evolution of solutions using genetic programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness—a phenomenon commonly referred to as bloat. The conception of bloat in Genetic Programming is a well naturalised phenomenon characterised by variable-length genomes gradually maturating in size during evolution. ’In a very real sense, bloating makes genetic programming a race against time, to find the best solution possible before bloat puts an effective stop to the search.’ In this paper we are proposing a Stepwise crossover and double mutation operation in order to reduce the bloat. In this especial crossover operation we are using local elitism replacement in combination with depth limit and size of the trees to reduce the problem of bloat substantially without compromising the performance. The use of local elitism in crossover and mutation increases the accuracy of the operation and also reduces the problem of bloat and further improves the performance. To shew our approach we have designed a Multiclass Classifier using GP by taking few benchmark datasets. %K genetic algorithms, genetic programming, bloat, stepwise crossover, double mutation, elitism, fitness, Java, Oracle 10g %9 journal article %R 10.5121/acij.2011.2606 %U http://airccse.org/journal/acij/papers/1111acij06.pdf %U http://dx.doi.org/10.5121/acij.2011.2606 %P 59-68 %0 Conference Proceedings %T Performance improvement in genetic programming using modified crossover and node mutation %A Bhardwaj, Arpit %A Tiwari, Aruna %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 Bhardwaj:2013:GECCOcomp %X During the evolution of solutions using Genetic Programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness’a phenomenon commonly referred to as bloat. Bloating increases time to find the best solution. Sometimes, best solution can never be obtained. In this paper we are proposing a modified crossover and point mutation operation in GP algorithm in order to reduce the problem of bloat. To demonstrate our approach, we have designed a Multiclass Classifier using GP by taking few benchmark datasets. The results obtained show that by applying modified crossover together with modified node mutation reduces the problem of bloat substantially without compromising the performance. %K genetic algorithms, genetic programming %R 10.1145/2464576.2480787 %U http://dx.doi.org/10.1145/2464576.2480787 %P 1721-1722 %0 Conference Proceedings %T A Novel Genetic Programming Based Classifier Design Using a New Constructive Crossover Operator with a Local Search Technique %A Bhardwaj, Arpit %A Tiwari, Aruna %Y Huang, De-Shuang %Y Bevilacqua, Vitoantonio %Y Figueroa, Juan Carlos %Y Premaratne, Prashan %S International Conference on Intelligent Computing (ICIC 2013) %S Lecture Notes in Computer Science %D 2013 %8 jul 28 31 %V 7995 %I Springer %C Nanning, China %F Bhardwaj:2013:ICIC %X A common problem in genetic programming search algorithms is the destructive nature of the crossover operator in which the offspring of good parents generally has worse performance than the parents. Designing constructive crossover operators and integrating some local search techniques into the breeding process have been suggested as solutions. In this paper, we proposed the integration of variants of local search techniques in the breeding process, done by allowing parents to produce many off springs and applying a selection procedure to choose high performing off springs. Our approach has removed the randomness of crossover operator. To demonstrate our approach, we designed a Multiclass classifier and tested it on various benchmark datasets. Our method has shown the tremendous improvement over the other state of the art methods. %K genetic algorithms, genetic programming, Crossover, Local Search Technique %R 10.1007/978-3-642-39479-9_11 %U http://dx.doi.org/10.1007/978-3-642-39479-9_11 %P 86-95 %0 Conference Proceedings %T Classification of EEG signals using a novel genetic programming approach %A Bhardwaj, Arpit %A Tiwari, Aruna %A Varma, M. Vishaal %A Krishna, M. Ramesh %Y Smith, Stephen L. %Y Cagnoni, Stefano %Y Patton, Robert M. %S GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC) %D 2014 %8 December 16 jul %I ACM %C Vancouver, BC, Canada %F Bhardwaj:2014:GECCOcomp %X In this paper, we present a new method for classification of electroencephalogram (EEG) signals using Genetic Programming (GP). The Empirical Mode Decomposition (EMD) is used to extract the features of EEG signals which served as an input for the GP. In this paper, new constructive crossover and mutation operations are also produced to improve GP. In these constructive crossover and mutation operators hill climbing search is integrated to remove the destructive nature of these operators. To improve GP, we apply constructive crossover on all the individuals which remain after reproduction. A new concept of selecting the global prime off-springs of the generation is also proposed. The constructive mutation approach is applied to poor individuals who are left after selecting globally prime off-springs. Improvement of the method is measured against classification accuracy, training time and the number of generations for EEG signal classification. As we show in the results section, the classification accuracy can be estimated to be 98.69percent on the test cases, which is better than classification accuracy of Liang and coworkers method which was published in 2010. %K genetic algorithms, genetic programming %R 10.1145/2598394.2609851 %U http://doi.acm.org/10.1145/2598394.2609851 %U http://dx.doi.org/10.1145/2598394.2609851 %P 1297-1304 %0 Conference Proceedings %T A Genetically Optimized Neural Network for Classification of Breast Cancer Disease %A Bhardwaj, Arpit %A Tiwari, Aruna %A Chandarana, Dharmil %A Babel, Darshil %S 7th International Conference on Biomedical Engineering and Informatics (BMEI 2014) %D 2014 %8 oct %F Bhardwaj:2014:BMEI %X In this paper, we propose a new, Genetically Optimised Neural Network (GONN) algorithm, for solving classification problems. We evolve a neural network genetically to optimise its structure for classification. We introduce new crossover and mutation operations which differ from a normal Genetic programming life-cycle to reduce the destructive nature of these operations. We use the GONN algorithm to classify breast cancer tumours as benign or malignant. Accurate classification of a breast cancer tumour is an important task in medical diagnosis. Our algorithm gives better classification accuracy of almost 4percent and 2percent more than a Back Propagation neural network and a Support Vector Machine respectively. %K genetic algorithms, genetic programming, ANN, SVN %R 10.1109/BMEI.2014.7002862 %U http://dx.doi.org/10.1109/BMEI.2014.7002862 %P 693-698 %0 Journal Article %T Breast cancer diagnosis using Genetically Optimized Neural Network model %A Bhardwaj, Arpit %A Tiwari, Aruna %J Expert Systems with Applications %D 2015 %V 42 %N 10 %@ 0957-4174 %F Bhardwaj:2015:ESA %X One in every eight women is susceptible to breast cancer, at some point of time in her life. Early detection and effective treatment is the only rescue to reduce breast cancer mortality. Accurate classification of a breast cancer tumour is an important task in medical diagnosis. Machine learning techniques are gaining importance in medical diagnosis because of their classification capability. In this paper, we propose a new, Genetically Optimised Neural Network (GONN) algorithm, for solving classification problems. We evolve a neural network genetically to optimize its architecture (structure and weight) for classification. We introduce new crossover and mutation operators which differ from standard crossover and mutation operators to reduce the destructive nature of these operators. We use the GONN algorithm to classify breast cancer tumors as benign or malignant. To demonstrate our results, we had taken the WBCD database from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, ROC curves and AUC under ROC curves of GONN with classical model and classical back propagation model. Our algorithm gives classification accuracy of 98.24percent, 99.63percent and 100percent for 50-50, 60-40, 70-30 training-testing partition respectively and 100percent for 10 fold cross validation. The results show that our approach works well with the breast cancer database and can be a good alternative to the well-known machine learning methods. %K genetic algorithms, genetic programming, Genetically Optimised Neural Network, Artificial Neural Network, Modified Crossover Operator %9 journal article %R 10.1016/j.eswa.2015.01.065 %U http://www.sciencedirect.com/science/article/pii/S0957417415000883 %U http://dx.doi.org/10.1016/j.eswa.2015.01.065 %P 4611-4620 %0 Conference Proceedings %T An Analysis of Integration of Hill Climbing in Crossover and Mutation operation for EEG Signal Classification %A Bhardwaj, Arpit %A Tiwari, Aruna %A Varma, M. Vishaal %A Krishna, M. Ramesh %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 Bhardwaj:2015:GECCO %X A common problem in the diagnosis of epilepsy is the volatile and unpredictable nature of the epileptic seizures. Hence, it is essential to develop Automatic seizure detection methods. Genetic programming (GP) has a potential for accurately predicting a seizure in an EEG signal. However, the destructive nature of crossover operator in GP decreases the accuracy of predicting the onset of a seizure. Designing constructive crossover and mutation operators (CCM) and integrating local hill climbing search technique with the GP have been put forward as solutions. In this paper, we proposed a hybrid crossover and mutation operator, which uses both the standard GP and CCM-GP, to choose high performing individuals in the least possible time. To demonstrate our approach, we tested it on a benchmark EEG signal dataset. We also compared and analysed the proposed hybrid crossover and mutation operation with the other state of art GP methods in terms of accuracy and training time. Our method has shown remarkable classification results. These results affirm the potential use of our method for accurately predicting epileptic seizures in an EEG signal and hint on the possibility of building a real time automatic seizure detection system. %K genetic algorithms, genetic programming, Biological and Biomedical Applications %R 10.1145/2739480.2754710 %U http://doi.acm.org/10.1145/2739480.2754710 %U http://dx.doi.org/10.1145/2739480.2754710 %P 209-216 %0 Journal Article %T A novel genetic programming approach for epileptic seizure detection %A Bhardwaj, Arpit %A Tiwari, Aruna %A Krishna, Ramesh %A Varma, Vishaal %J Computer Methods and Programs in Biomedicine %D 2016 %V 124 %@ 0169-2607 %F Bhardwaj:2016:CMPB %X The human brain is a delicate mix of neurons (brain cells), electrical impulses and chemicals, known as neurotransmitters. Any damage has the potential to disrupt the workings of the brain and cause seizures. These epileptic seizures are the manifestations of epilepsy. The electroencephalograph (EEG) signals register average neuronal activity from the cerebral cortex and label changes in activity over large areas. A detailed analysis of these electroencephalograph (EEG) signals provides valuable insights into the mechanisms instigating epileptic disorders. Moreover, the detection of interictal spikes and epileptic seizures in an EEG signal plays an important role in the diagnosis of epilepsy. Automatic seizure detection methods are required, as these epileptic seizures are volatile and unpredictable. This paper deals with an automated detection of epileptic seizures in EEG signals using empirical mode decomposition (EMD) for feature extraction and proposes a novel genetic programming (GP) approach for classifying the EEG signals. Improvements in the standard GP approach are made using a Constructive Genetic Programming (CGP) in which constructive crossover and constructive subtree mutation operators are introduced. A hill climbing search is integrated in crossover and mutation operators to remove the destructive nature of these operators. A new concept of selecting the Globally Prime offspring is also presented to select the best fitness offspring generated during crossover. To decrease the time complexity of GP, a new dynamic fitness value computation (DFVC) is employed to increase the computational speed. We conducted five different sets of experiments to evaluate the performance of the proposed model in the classification of different mixtures of normal, interictal and ictal signals, and the accuracies achieved are outstandingly high. The experimental results are compared with the existing methods on same datasets, and these results affirm the potential use of our method for accurately detecting epileptic seizures in an EEG signal. %K genetic algorithms, genetic programming, Constructive crossover, Dynamic fitness value computation, Epilepsy %9 journal article %R 10.1016/j.cmpb.2015.10.001 %U http://www.sciencedirect.com/science/article/pii/S016926071500262X %U http://dx.doi.org/10.1016/j.cmpb.2015.10.001 %P 2-18 %0 Conference Proceedings %T A novel genetic programming approach to control bloat using crossover and mutation with intelligence technique %A Bhardwaj, Harshit %A Dashore, Pankaj %S 2015 International Conference on Computer, Communication and Control (IC4) %D 2015 %8 sep %F Bhardwaj:2015:IC4 %X Bloat is a problem that occurs when there is no advancement in fitness measure, but the size of the tree grows exponentially. Bloat eventually increases the time required to reach the optimal solution. To overcome this defect, Crossover and Mutation with Intelligence technique is proposed. We also used double tournament, in which we apply two tournaments on the basis of size and fitness respectively to select the individuals to perform Crossover with Intelligence. Our approach of overcoming bloat is tested experimentally on some benchmark datasets picked up from UCI repository and by some observations. The results verified that our Crossover and Mutation with Intelligence degrades the bloat phenomena. %K genetic algorithms, genetic programming %R 10.1109/IC4.2015.7375619 %U http://dx.doi.org/10.1109/IC4.2015.7375619 %0 Conference Proceedings %T Breast Cancer Diagnosis using Simultaneous Feature Selection and Classification: A Genetic Programming Approach %A Bhardwaj, Harshit %A Sakalle, Aditi %A Bhardwaj, Arpit %A Tiwari, Aruna %A Verma, Madhushi %S 2018 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2018 %8 nov %F Bhardwaj:2018:ieeeCompIntl %X Breast cancer is the most prevalent type of cancer found in women worldwide. It is becoming a leading cause of death among women in the whole world. Early detection and effective treatment of this disease is the only rescue to reduce breast cancer mortality. Because of the effective classification and high diagnostic capability expert systems are gaining popularity in this field. But the problem with machine learning algorithms is that if redundant and irrelevant features are available in the dataset then they are not being able to achieve desired performance. Therefore, in this paper, a simultaneous feature selection and classification technique using Genetic Programming (GPsfsc) is proposed for breast cancer diagnosis. To demonstrate our results, we had taken the Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) databases from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, and Mann Whitney test results of GONN with classical multi-tree GP algorithm for feature selection (GPmtfs). The experimental results on WBC and WDBC datasets show that the proposed method produces better classification accuracy with reduced features. Therefore, our proposed method is of great significance and can serve as first-rate clinical tool for the detection of breast cancer. %K genetic algorithms, genetic programming, Computational intelligence, Feature Selection, Breast Cancer Diagnosis, Classification %R 10.1109/SSCI.2018.8628935 %U http://dx.doi.org/10.1109/SSCI.2018.8628935 %P 2186-2192 %0 Journal Article %T Classification of electroencephalogram signal for the detection of epilepsy using Innovative Genetic Programming %A Bhardwaj, Harshit %A Sakalle, Aditi %A Bhardwaj, Arpit %A Tiwari, Aruna %J Expert Systems %D 2019 %8 feb %V 36 %N 1 %F Bhardwaj:2019:ES %X Epilepsy, sometimes called seizure disorder, is a neurological condition that justifies itself as a susceptibility to seizures. A seizure is a sudden burst of rhythmic discharges of electrical activity in the brain that causes an alteration in behaviour, sensation, or consciousness. It is essential to have a method for automatic detection of seizures, as these seizures are arbitrary and unpredictable. A profound study of the electroencephalogram (EEG) recordings is required for the accurate detection of these epileptic seizures. In this study, an Innovative Genetic Programming framework is proposed for classification of EEG signals into seizure and non-seizure. An empirical mode decomposition technique is used for the feature extraction followed by genetic programming for the classification. Moreover, a method for intron deletion, hybrid crossover, and mutation operation is proposed, which are responsible for the increase in classification accuracy and a decrease in time complexity. This suggests that the Innovative Genetic Programming classifier has a potential for accurately predicting the seizures in an EEG signal and hints on the possibility of building a real-time seizure detection system. %K genetic algorithms, genetic programming %9 journal article %R 10.1111/exsy.12338 %U http://dx.doi.org/10.1111/exsy.12338 %P e12338 %0 Book %T Advances and Trends in Genetic Programming: Volume 1: Classification Techniques and Life Cycles Paperback %A Bhardwaj, Arpit %A Tiwari, Aruna %A Suri, Jasjit S. %D 2022 %I Academic Press %F bhardwaj:atgp1 %K genetic algorithms, genetic programming %U https://www.amazon.co.uk/s?k=advances+and+trends+in+genetic+programming+%3A+volume+1%3A+classification+techniques+and+life+cycles %0 Conference Proceedings %T Network Optimization Using Genetic Programming %A A, Krishna Bhargava %A Sinha, Deepak Kumar %A Sinha, Garima %S 2023 International Conference on Computer Science and Emerging Technologies (CSET) %D 2023 %8 oct %F Bhargava:2023:CSET %X Evolutionary Algorithms form the base for creating Artificial Intelligence applications and systems. Evolutionary Computation forms the basis of those algorithms that are used in day to day and future applications. Computer networks as we know, form a major contributor to those data sources. Big Data and all types of computer related data are found on the internet and the internet forms sources of data all over the world. Therefore, data modulation techniques form a basis of data transfer over the internet all over the world. Therefore, Evolutionary Networks are the type of computer networks that evolve over time due to the evolutionary nature of the algorithms involved. Our intention in the paper is to create a fault free data modulation technique used in computer networking. In the network, the timing is based on the evolutionary data of the customer. The internet history of the customer is taken into consideration and the data modulation is based on the customer history. How this is done is based on the pheromone concentration of ant colony optimisation and the probable path of the bee algorithm. These evolutionary algorithms are fed to the firewall and routing tables of the network to form the evolutionary network generation and user behaviour. When the algorithms working behind firewall and routing tables are replaced by Evolutionary Algorithms, then they start to showcase evolutionary behaviour. This is the foundation principle on which this paper is based. Once the firewall and the routing table are evolved, the computer network to which they are connected becomes Evolutionary Networks. %K genetic algorithms, genetic programming, Firewalls (computing), Soft sensors, Modulation, Evolutionary computation, Routing, Behavioural sciences, Evolutionary Computation, Evolutionary Algorithms, Evolutionary Networking, Network Fitness, Data Fitness, Evolutionary based Data Modulation %R 10.1109/CSET58993.2023.10346779 %U http://dx.doi.org/10.1109/CSET58993.2023.10346779 %0 Journal Article %T Soil Classification Using GATREE %A Bhargavi, P. %A Jyothi, S. %J International Journal of Computer Science & Information Technology %D 2010 %8 oct %V 2 %N 5 %I Academy & Industry Research Collaboration Centre (AIRCC) %@ 09754660 %F Bhargavi:2010:IJCSIT %X This paper details the application of a genetic programming framework for classification of decision tree of Soil data to classify soil texture. The database contains measurements of soil profile data. We have applied GATree for generating classification decision tree. GATree is a decision tree builder that is based on Genetic Algorithms(GAs). The idea behind it is rather simple but powerful. Instead of using statistic metrics that are biased towards specific trees we use a more flexible, global metric of tree quality that try to optimise accuracy and size. GATree offers some unique features not to be found in any other tree inducers while at the same time it can produce better results for many difficult problems. Experimental results are presented which illustrate the performance of generating best decision tree for classifying soil texture for soil data set. %K genetic algorithms, genetic programming, data mining, soil profile, soil database, classification %9 journal article %R 10.5121/ijcsit.2010.2514 %U http://airccse.org/journal/jcsit/1010ijcsit14.pdf %U http://dx.doi.org/10.5121/ijcsit.2010.2514 %P 184-191 %0 Conference Proceedings %T Genetic programming evolved spatial descriptor for Indian monuments classification %A Bhatt, M. S. %A Patalia, T. P. %S 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS) %D 2015 %8 nov %F Bhatt:2015:ieeeCGVIS %X Travel and tourism are the largest service industries in India. Every year people visit tourist places. and upload pictures of their visit on social networking sites or share via mobile device with friends and relatives. Millions of such photographs are uploaded and it is almost impossible to manually classify these pictures as per the monuments they have visited. Classification is helpful to hoteliers for development of new hotel with state of the art amenities, to travel service providers, to restaurant owners, to government agencies for security etc. The proposed system extracts Genetic programming evolved spatial descriptor and classifies the Indian monuments visited by tourists based on linear Support Vector Machine(SVM). The proposed system is divided into 3 main phases: preprocessing, genetic programming evolution and classification. The Preprocessing phase converts images into a form suitable for processing by genetic programming system using Generalized Co-Occurrence Matrix. The second phase generates best so far spatial descriptor in the form of program based on the fitness. The Fitness is calculated using SVM. Once program is obtained as output it can be used for classification. The proposed system is implemented in MATLAB and achieves high accuracy. %K genetic algorithms, genetic programming %R 10.1109/CGVIS.2015.7449908 %U http://dx.doi.org/10.1109/CGVIS.2015.7449908 %P 131-136 %0 Conference Proceedings %T A Hybrid LLM-Coevolution Framework to Generate Abusive Tax Strategies %A Bhattacharaya, Joy %A Hemberg, Erik %A O’Reilly, Una-May %Y Manzoni, Luca %Y Cussat-Blanc, Sylvain %Y Chen, Qi %S European Conference on Genetic Programming, EuroGP 2026 %D 2026 %8 August 10 apr %I Springer Nature %C Toulouse %F bhattacharaya:2026:EuroGP %K genetic algorithms, genetic programming %0 Conference Proceedings %T Genetic Programming: A Review of Some Concerns %A Bhattacharya, Maumita %A Nath, Baikunth %Y Alexandrov, V. N. %Y Dongarra, J. J. %Y Juliano, B. A. %Y Renner, R. S. %Y Tan, C. J. Kenneth %S Proceedings of International Conference Computational Science Part II - ICCS 2001 %S Lecture Notes in Computer Science %D 2001 %8 may 28 30 %V 2074 %I Springer %C San Francisco, CA, USA %F Bhattacharya:2001:GPR %O Late Submissions %X Genetic Programming (GP) is gradually being accepted as a promising variant of Genetic Algorithm (GA) that evolves dynamic hierarchical structures, often described as programs. In other words GP seemingly holds the key to attain the goal of ’automated program generation’. However one of the serious problems of GP lies in the ’code growth’ or ’size problem’ that occurs as the structures evolve, leading to excessive pressure on system resources and unsatisfying convergence. Several researchers have addressed the problem. However, absence of a general framework and physical constraints, viz, infinitely large resource requirements have made it difficult to find any generic explanation and hence solution to the problem. This paper surveys the major research works in this direction from a critical angle. Overview of a few other major GP concerns is covered in brief. We conclude with a general discussion on code growth and other critical aspects of GP techniques, while attempting to highlight on future research directions to tackle such problems. %K genetic algorithms, genetic programming, bloat %R 10.1007/3-540-45718-6_109 %U http://dx.doi.org/10.1007/3-540-45718-6_109 %P 1031-1040 %0 Conference Proceedings %T A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria %A Bhattacharya, Maumita %A Abraham, Ajith %A Nath, Baikunth %Y Abraham, Ajith %Y Koppen, Mario %S 2001 International Workshop on Hybrid Intelligent Systems %S LNCS %D 2001 %8 November 12 dec %I Springer-Verlag %C Adelaide, Australia %@ 3-7908-1480-6 %F bhattacharya:2001:HIS %X Genetic programming (GP), a relatively young and growing branch of evolutionary computation is gradually proving to be a promising method of modelling complex prediction and classification problems. This paper evaluates the suitability of a linear genetic programming (LGP) technique to predict electricity demand in the State of Victoria, Australia, while comparing its performance with two other popular soft computing techniques. The forecast accuracy is compared with the actual energy demand. To evaluate, we considered load demand patterns for ten consecutive months taken every 30 minutes for training the different prediction models. Test results show that while the linear genetic programming method delivered satisfactory results, the neuro fuzzy system performed best for this particular application problem, in terms of accuracy and computation time, as compared to LGP and neural networks. %K genetic algorithms, genetic programming, Linear genetic programming, neuro-fuzzy, neural networks, forecasting, electricity demand %U http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6 %P 379-394 %0 Conference Proceedings %T Representational Semantics for Genetic Programming Based Learning in High-Frequency Financial Data %A Bhattacharyya, Siddhartha %A Pictet, Olivier %A Zumbach, Gilles %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 bhattacharyya:1998:rsGPlhf %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/bhattacharyya_1998_rsGPlhf.pdf %P 11-16 %0 Journal Article %T Inductive, Evolutionary, and Neural Computing Techniques for Discrimination: A Comparative Study %A Bhattacharyya, Siddhartha %A Pendharkar, Parag C. %J Decision Sciences %D 1998 %8 Fall %V 29 %N 4 %@ 00117315 %F bhattacharyya:1998:DS %X This paper provides a comparative study of machine learning techniques for two-group discrimination. Simulated data is used to examine how the different learning techniques perform with respect to certain data distribution characteristics. Both linear and nonlinear discrimination methods are considered. The data has been previously used in the comparative evaluation of a number of techniques and helps relate our findings across a range of discrimination techniques. %K genetic algorithms, genetic programming, Discriminant Analysis, Inductive Learning, Machine Learning, and Neural Networks %9 journal article %U http://tigger.uic.edu/~sidb/papers/DiscCompPaper_DecSci.pdf %P 871-899 %0 Conference Proceedings %T Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing %A Bhattacharyya, Siddhartha %S KDD ’00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining %D 2000 %I ACM Press %C Boston, Massachusetts, United States %@ 1-58113-233-6 %F 347186 %X Predictive models in direct marketing seek to identify individuals most likely to respond to promotional solicitations or other intervention programs. While standard modelling approaches embody single objectives, real-world decision problems often seek multiple performance measures. Decision-makers here desire solutions that simultaneously optimise on multiple objectives, or obtain an acceptable tradeoff amongst objectives. Multi-criteria problems often characterise a range of solutions, none of which dominate the others with respect to the multiple objectives, these specify the Pareto-frontier of nondominated solutions, each offering a different level of tradeoff. This paper proposes the use of evolutionary computation based procedures for obtaining a set of nondominated models with respect to multiple stated objectives. The targeting depth-of-file presents a crucial real-world criterion in direct marketing, and models here are tailored for specified file-depths. Decision-makers are thus able to obtain a set of models along the Pareto-frontier, for a specific file-depth. The choice of a model to implement can be thus based on observed tradeoffs in the different objectives, based on possibly subjective and problem specific judgements. Given distinct models tailored for different file-depths, the implementation decision can also consider performance tradeoffs at the different depths-offile. Empirical results from a real-world problem illustrate the benefits of the proposed approach. Both linear and nonlinear models obtained by genetic search are examined. %K genetic algorithms, genetic programming, Algorithms, Design, Experimentation, Human Factors, Management, Measurement, Performance, Theory, Pareto-optimal models, data mining, database marketing, evolutionary computation, multiple objectives %R 10.1145/347090.347186 %U http://tigger.uic.edu/~sidb/papers/MultiObj_KDD2000.pdf %U http://dx.doi.org/10.1145/347090.347186 %P 465-473 %0 Book Section %T Evolutionary Induction of Trading Models %A Bhattacharyya, Siddhartha %A Mehta, Kumar %E Chen, Shu-Heng %B Evolutionary Computation in Economics and Finance %S Studies in Fuzziness and Soft Computing %D 2002 %8 2002 %V 100 %I Physica Verlag %@ 3-7908-1476-8 %F bhattacharyya:2002:ECEF %X Financial markets data present a challenging opportunity for the learning of complex patterns not readily discernable. This paper investigates the use of genetic algorithms for the mining of financial time-series for patterns aimed at the provision of trading decision models. A simple yet flexible representation for trading rules is proposed, and issues pertaining to fitness evaluation examined. Two key issues in fitness evaluation, the design of a suitable fitness function reflecting desired trading characteristics and choice of appropriate training duration, are discussed and empirically examined. Two basic measures are also proposed for characterising rules obtained with alternate fitness criteria. %K genetic algorithms, genetic programming %R 10.1007/978-3-7908-1784-3_17 %U http://tigger.uic.edu/~sidb/papers/EvolInductionOfTradingModels.pdf %U http://dx.doi.org/10.1007/978-3-7908-1784-3_17 %P 311-332 %0 Journal Article %T Knowledge-intensive genetic discovery in foreign exchange markets %A Bhattacharyya, Siddhartha %A Pictet, Olivier V. %A Zumbach, Gilles %J IEEE Transactions on Evolutionary Computation %D 2002 %8 apr %V 6 %N 2 %@ 1089-778X %F bhattacharyya:2002:trEC %X This paper considers the discovery of trading decision models from high-frequency foreign exchange (FX) markets data using genetic programming (GP). It presents a domain-related structuring of the representation and incorporation of semantic restrictions for GP-based searching of trading decision models. A defined symmetry property provides a basis for the semantics of FX trading models. The symmetry properties of basic indicator types useful in formulating trading models are defined, together with semantic restrictions governing their use in trading model specification. The semantics for trading model specification have been defined with respect to regular arithmetic, comparison and logical operators. This study also explores the use of two fitness criteria for optimization, showing more robust performance with a risk-adjusted measure of returns %K genetic algorithms, genetic programming, Data mining, financial markets, foreign exchange markets, machine learning, semantic restrictions, trading models %9 journal article %R 10.1109/4235.996016 %U http://tigger.uic.edu/~sidb/papers/KnowIntenGPForex__IEEE_EC.pdf %U http://dx.doi.org/10.1109/4235.996016 %P 169-181 %0 Conference Proceedings %T Regime-Wise Genetic Programming Model for Improved Streamflow Forecasting %A Bhavita, K. %A Swathi, D. %A Manideep, J. %A Sandeep, D. Sree %A Rathinasamy, Maheswaran %S Water Resources and Environmental Engineering I %D 2019 %I Springer %F bhavita:2019:WREE %K genetic algorithms, genetic programming %R 10.1007/978-981-13-2044-6_17 %U http://link.springer.com/chapter/10.1007/978-981-13-2044-6_17 %U http://dx.doi.org/10.1007/978-981-13-2044-6_17 %0 Conference Proceedings %T Differentiating Between Individual Class Performance in Genetic Programming Fitness for Classification with Unbalanced Data %A Bhowan, Urvesh %A Johnston, Mark %A Zhang, Mengjie %Y Tyrrell, Andy %S 2009 IEEE Congress on Evolutionary Computation %D 2009 %8 18 21 may %I IEEE Press %C Trondheim, Norway %F Bhowan:2009:cec %X This paper investigates improvements to the fitness function in Genetic Programming to better solve binary classification problems with unbalanced data. Data sets are unbalanced when there is a majority of examples for one particular class over the other class(es). We show that using overall classification accuracy as the fitness function evolves classifiers with a performance bias toward the majority class at the expense of minority class performance. We develop four new fitness functions which consider the accuracy of majority and minority class separately to address this learning bias. Results using these fitness functions show that good accuracy for both the minority and majority classes can be achieved from evolved classifiers while keeping overall performance high and balanced across the two classes. %K genetic algorithms, genetic programming %R 10.1109/CEC.2009.4983294 %U P289.pdf %U http://dx.doi.org/10.1109/CEC.2009.4983294 %P 2802-2809 %0 Conference Proceedings %T Genetic Programming for Image Classification with Unbalanced Data %A Bhowan, Urvesh %A Zhang, Mengjie %A Johnston, Mark %S Proceeding of the 24th International Conference Image and Vision Computing New Zealand, IVCNZ ’09 %D 2009 %8 23 25 nov %I IEEE %C Wellington %F Bhowan:2009:IVCNZ %X Image classification methods using unbalanced data can produce results with a performance bias. If the class representing important objects-of-interest is in the minority class, learning methods can produce the deceptive appearance of good looking results while recognition ability on the important minority class can be poor. This paper develops and compares two Genetic Programming (GP) methods for image classification problems with class imbalance. The first focuses on adapting the fitness function in GP to evolve classifiers with good individual class accuracy. The second uses a multi-objective approach to simultaneously evolve a set of classifiers along the trade-off surface representing minority and majority class accuracies. Evaluating our GP methods on two benchmark binary image classification problems with class imbalance, our results show that good solutions were evolved using both GP methods. %K genetic algorithms, genetic programming %R 10.1109/IVCNZ.2009.5378388 %U http://dx.doi.org/10.1109/IVCNZ.2009.5378388 %P 316-321 %0 Conference Proceedings %T Multi-Objective Genetic Programming for Classification with Unbalanced Data %A Bhowan, Urvesh %A Zhang, Mengjie %A Johnston, Mark %Y Nicholson, Ann E. %Y Li, Xiaodong %S Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI’09) %S Lecture Notes in Computer Science %D 2009 %8 dec 1 4 %V 5866 %I Springer %C Melbourne, Australia %F DBLP:conf/ausai/BhowanZJ09 %X Existing learning and search algorithms can suffer a learning bias when dealing with unbalanced data sets. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach to evolve a Pareto front of classifiers along the optimal trade-off surface representing minority and majority class accuracy for binary class imbalance problems. A major advantage of the MOGP approach is that by explicitly incorporating the learning bias into the search algorithm, a good set of well-performing classifiers can be evolved in a single experiment while canonical (single-solution) Genetic Programming (GP) requires some objective preference be a priori built into a fitness function. Our results show that a diverse set of solutions was found along the Pareto front which performed as well or better than canonical GP on four class imbalance problems. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-10439-8_38 %U http://dx.doi.org/10.1007/978-3-642-10439-8_38 %P 370-380 %0 Conference Proceedings %T Genetic Programming for Classification with Unbalanced Data %A Bhowan, Urvesh %A Zhang, Mengjie %A Johnston, Mark %Y Esparcia-Alcazar, Anna Isabel %Y Ekart, Aniko %Y Silva, Sara %Y Dignum, Stephen %Y Uyar, A. Sima %S Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010 %S LNCS %D 2010 %8 July 9 apr %V 6021 %I Springer %C Istanbul %F Bhowan:2010:EuroGP %X Learning algorithms can suffer a performance bias when data sets only have a small number of training examples for one or more classes. In this scenario learning methods can produce the deceptive appearance of good looking results even when classification performance on the important minority class can be poor. This paper compares two Genetic Programming (GP) approaches for classification with unbalanced data. The first focuses on adapting the fitness function to evolve classifiers with good classification ability across both minority and majority classes. The second uses a multi-objective approach to simultaneously evolve a Pareto front (or set) of classifiers along the minority and majority class trade-off surface. Our results show that solutions with good classification ability were evolved across a range of binary classification tasks with unbalanced data. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-12148-7_1 %U http://dx.doi.org/10.1007/978-3-642-12148-7_1 %P 1-13 %0 Conference Proceedings %T AUC analysis of the pareto-front using multi-objective GP for classification with unbalanced data %A Bhowan, Urvesh %A Zhang, Mengjie %A Johnston, Mark %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 Bhowan:2010:gecco %X Learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach using the accuracy of the minority and majority class as learning objectives. We focus our analysis on the classification ability of evolved Pareto-front solutions using the Area Under the ROC Curve (AUC) and investigate which regions of the objective trade-off surface favour high-scoring AUC solutions. We show that a diverse set of well-performing classifiers is simultaneously evolved along the Pareto-front using the MOGP approach compared to canonical GP where only one solution is found along the objective trade-off surface, and that in some problems the MOGP solutions had better AUC than solutions evolved with canonical GP using hand-crafted fitness functions. %K genetic algorithms, genetic programming %R 10.1145/1830483.1830639 %U http://dx.doi.org/10.1145/1830483.1830639 %P 845-852 %0 Conference Proceedings %T A Comparison of Classification Strategies in Genetic Programming with Unbalanced Data %A Bhowan, Urvesh %A Zhang, Mengjie %A Johnston, Mark %Y Li, Jiuyong %S Australasian Conference on Artificial Intelligence %S Lecture Notes in Computer Science %D 2010 %8 dec %V 6464 %I Springer %C Adelaide %F conf/ausai/BhowanZJ10 %X Machine learning algorithms like Genetic Programming (GP) can evolve biased classifiers when data sets are unbalanced. In this paper we compare the effectiveness of two GP classification strategies. The first uses the standard (zero) class-threshold, while the second uses the best class-threshold determined dynamically on a solution-by-solution basis during evolution. These two strategies are evaluated using five different GP fitness across across a range of binary class imbalance problems, and the GP approaches are compared to other popular learning algorithms, namely, Naive Bayes and Support Vector Machines. Our results suggest that there is no overall difference between the two strategies, and that both strategies can evolve good solutions in binary classification when used in combination with an effective fitness function. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-17432-2_25 %U http://dx.doi.org/10.1007/978-3-642-17432-2_25 %P 243-252 %0 Conference Proceedings %T Evolving ensembles in multi-objective genetic programming for classification with unbalanced data %A Bhowan, Urvesh %A Johnston, Mark %A Zhang, Mengjie %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 Bhowan:2011:GECCO %X Machine learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-objective Genetic Programming approach using negative correlation learning to evolve accurate and diverse ensembles of non-dominated solutions where members vote on class membership. We also compare two popular Pareto-based fitness schemes on the classification tasks. We show that the evolved ensembles achieve high accuracy on both classes using six unbalanced binary data sets, and that this performance is usually better than many of its individual members. %K genetic algorithms, genetic programming %R 10.1145/2001576.2001756 %U http://dx.doi.org/10.1145/2001576.2001756 %P 1331-1338 %0 Conference Proceedings %T Ensemble Learning and Pruning in Multi-Objective Genetic Programming for Classification with Unbalanced Data %A Bhowan, Urvesh %A Johnston, Mark %A Zhang, Mengjie %Y Wang, Dianhui %Y Reynolds, Mark %S Proceedings of the 24th Australasian Joint Conference Advances in Artificial Intelligence (AI 2011) %S Lecture Notes in Computer Science %D 2011 %8 dec 5 8 %V 7106 %I Springer %C Perth, Australia %F conf/ausai/BhowanJZ11 %X Machine learning algorithms can suffer a performance bias when data sets are unbalanced. This paper develops a multi-objective genetic programming approach to evolving accurate and diverse ensembles of non-dominated solutions where members vote on class membership. We explore why the ensembles can also be vulnerable to the learning bias using a range of unbalanced data sets. Based on the notion that smaller ensembles can be better than larger ensembles, we develop a new evolutionary-based pruning method to find groups of highly-cooperative individuals that can improve accuracy on the important minority class. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-25832-9_20 %U http://dx.doi.org/10.1007/978-3-642-25832-9_20 %P 192-202 %0 Journal Article %T Evolving Diverse Ensembles using Genetic Programming for Classification with Unbalanced Data %A Bhowan, Urvesh %A Johnston, Mark %A Zhang, Mengjie %A Yao, Xin %J IEEE Transactions on Evolutionary Computation %D 2013 %8 jun %V 17 %N 3 %@ 1089-778X %F Bhowan:2012:ieeeTEC %X In classification, machine learning algorithms can suffer a performance bias when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while the other class(es) make up the majority. In this scenario, classifiers can have good accuracy on the majority class but very poor accuracy on the minority class(es). This paper proposes a Multi-objective Genetic Programming (MOGP) approach to evolving accurate and diverse ensembles of genetic program classifiers with good performance on both the minority and majority classes. The evolved ensembles comprise of nondominated solutions in the population where individual members vote on class membership. This paper evaluates the effectiveness of two popular Pareto-based fitness strategies in the MOGP algorithm (SPEA2 and NSGAII), and investigates techniques to encourage diversity between solutions in the evolved ensembles. Experimental results on six (binary) class imbalance problems show that the evolved ensembles outperform their individual members, as well as single-predictor methods such as canonical GP, Naive Bayes and Support Vector Machines, on highly unbalanced tasks. This highlights the importance of developing an effective fitness evaluation strategy in the underlying MOGP algorithm to evolve good ensemble members. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TEVC.2012.2199119 %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6198882 %U http://dx.doi.org/10.1109/TEVC.2012.2199119 %P 368-386 %0 Journal Article %T Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced Data %A Bhowan, Urvesh %A Johnston, Mark %A Zhang, Mengjie %J IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics %D 2012 %8 apr %V 42 %N 2 %@ 1083-4419 %F Bhowan:2012:SMC %X Machine learning algorithms such as genetic programming (GP) can evolve biased classifiers when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. In this scenario, classifiers can have good accuracy on the majority class but very poor accuracy on the minority class(es) due to the influence that the larger majority class has on traditional training criteria in the fitness function. This paper aims to both highlight the limitations of the current GP approaches in this area and develop several new fitness functions for binary classification with unbalanced data. Using a range of real-world classification problems with class imbalance, we empirically show that these new fitness functions evolve classifiers with good performance on both the minority and majority classes. Our approaches use the original unbalanced training data in the GP learning process, without the need to artificially balance the training examples from the two classes (e.g., via sampling). %K genetic algorithms, genetic programming, GP learning process, biased classifiers, binary classification, class imbalance, data sets, fitness functions, machine learning algorithms, majority class, minority class, training criteria, unbalanced data, unbalanced training data, data handling, learning (artificial intelligence), pattern classification %9 journal article %R 10.1109/TSMCB.2011.2167144 %U http://dx.doi.org/10.1109/TSMCB.2011.2167144 %P 406-421 %0 Conference Proceedings %T Comparing ensemble learning approaches in genetic programming for classification with unbalanced data %A Bhowan, Urvesh %A Johnston, Mark %A Zhang, Mengjie %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 Bhowan:2013:GECCOcomp %X This paper compares three approaches to evolving ensembles in Genetic Programming (GP) for binary classification with unbalanced data. The first uses bagging with sampling, while the other two use Pareto-based multi-objective GP (MOGP) for the trade-off between the two (unequal) classes. In MOGP, two ways are compared to build the ensembles: using the evolved Pareto front alone, and using the whole evolved population of dominated and non-dominated individuals alike. Experiments on several benchmark (binary) unbalanced tasks find that smaller, more diverse ensembles chosen during ensemble selection perform best due to better generalisation, particularly when the combined knowledge of the whole evolved MOGP population forms the ensemble. %K genetic algorithms, genetic programming %R 10.1145/2464576.2464643 %U http://dx.doi.org/10.1145/2464576.2464643 %P 135-136 %0 Journal Article %T Reusing Genetic Programming for Ensemble Selection in Classification of Unbalanced Data %A Bhowan, Urvesh %A Johnston, Mark %A Zhang, Mengjie %A Yao, Xin %J IEEE Transactions on Evolutionary Computation %D 2014 %8 dec %V 18 %N 6 %@ 1089-778X %F Bhowan:2014:ieeeTEC %X Classification algorithms can suffer from performance degradation when the class distribution is unbalanced. This paper develops a two-step approach to evolving ensembles using genetic programming (GP) for unbalanced data. The first step uses multi-objective (MO) GP to evolve a Pareto approximated front of GP classifiers to form the ensemble by trading-off the minority and the majority class against each other during learning. The MO component alleviates the reliance on sampling to artificially re-balance the data. The second step, which is the focus this paper, proposes a novel ensemble selection approach using GP to automatically find/choose the best individuals for the ensemble. This new GP approach combines multiple Pareto-approximated front members into a single composite genetic program solution to represent the (optimised) ensemble. This ensemble representation has two main advantages/novelties over traditional genetic algorithm (GA) approaches. Firstly, by limiting the depth of the composite solution trees, we use selection pressure during evolution to find small highly-cooperative groups of individuals for the ensemble. This means that ensemble sizes are not fixed a priori (as in GA), but vary depending on the strength of the base learners. Secondly, we compare different function set operators in the composite solution trees to explore new ways to aggregate the member outputs and thus, control how the ensemble computes its output. We show that the proposed GP approach evolves smaller, more diverse ensembles compared to an established ensemble selection algorithm, while still performing as well as, or better than the established approach. The evolved GP ensembles also perform well compared to other bagging and boosting approaches, particularly on tasks with high levels of class imbalance. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TEVC.2013.2293393 %U http://dx.doi.org/10.1109/TEVC.2013.2293393 %P 893-908 %0 Conference Proceedings %T Genetic Programming for Feature Selection and Question-Answer Ranking in IBM Watson %A Bhowan, Urvesh %A McCloskey, D. J. %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 Bhowan:2015:EuroGP %X IBM Watson is an intelligent open-domain question answering system capable of finding correct answers to natural language questions in real-time. Watson uses machine learning over a large heterogeneous feature set derived from many distinct natural language processing algorithms to identify correct answers. This paper develops a Genetic Programming (GP) approach for feature selection in Watson by evolving ranking functions to order candidate answers generated in Watson. We leverage GP automatic feature selection mechanisms to identify Watson key features through the learning process. Our experiments show that GP can evolve relatively simple ranking functions that use much fewer features from the original Watson feature set to achieve comparable performances to Watson. This methodology can aid Watson implementers to better identify key components in an otherwise large and complex system for development, troubleshooting, and/or customer or domain-specific enhancements. %K genetic algorithms, genetic programming, IBM Watson, Question answer ranking, Feature selection: Poster %R 10.1007/978-3-319-16501-1_13 %U http://dx.doi.org/10.1007/978-3-319-16501-1_13 %P 153-166 %0 Conference Proceedings %T An automatic region detection and processing approach in genetic programming for binary image classification %A Bi, Ying %A Zhang, Mengjie %A Xue, Bing %S 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ) %D 2017 %8 dec %F Bi:2017:IVCNZ %X In image classification, region detection is an effective approach to reducing the dimensionality of the image data but requires human intervention. Genetic Programming (GP) as an evolutionary computation technique can automatically identify important regions, and conduct feature extraction, feature construction and classification simultaneously. In this paper, an automatic region detection and processing approach in GP (GP-RDP) method is proposed for image classification. This approach is able to evolve important image operators to deal with detected regions for facilitating feature extraction and construction. To evaluate the performance of the proposed method, five recent GP methods and seven non-GP methods based on three types of image features are used for comparison on four image data sets. The results reveal that the proposed method can achieve comparable performance on easy data sets and significantly better performance on difficult data sets than the other comparable methods. To further demonstrate the interpretability and understandability of the proposed method, two evolved programs are analysed. The analysis shows the good interpretability of the GP-RDP method and proves that the GP-RDP method is able to identify prominent regions, evolve effective image operators to process these regions, extract and construct good features for efficient image classification. %K genetic algorithms, genetic programming %R 10.1109/IVCNZ.2017.8402469 %U http://dx.doi.org/10.1109/IVCNZ.2017.8402469 %0 Conference Proceedings %T An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %Y Cagnoni, Stefano %Y Zhang, Mengjie %S 21st International Conference on the Applications of Evolutionary Computation, EvoIASP 2018 %S LNCS %D 2018 %8 April 6 apr %V 10784 %I Springer %C Parma, Italy %F Bi:2018:evoApplications %X Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification. %K genetic algorithms, genetic programming, Image classification, Feature extraction, Image analysis %R 10.1007/978-3-319-77538-8_29 %U http://dx.doi.org/10.1007/978-3-319-77538-8_29 %P 421-438 %0 Conference Proceedings %T Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification %A Bi, Ying %A Zhang, Mengjie %A Xue, Bing %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 Bi:2018:CEC %X Feature extraction is an essential process to image classification. Existing feature extraction methods can extract important and discriminative image features but often require domain expert and human intervention. Genetic Programming (GP) can automatically extract features which are more adaptive to different image classification tasks. However, the majority GP-based methods only extract relatively simple features of one type i.e. local or global, which are not effective and efficient for complex image classification. In this paper, a new GP method (GP-GLF) is proposed to achieve automatically and simultaneously global and local feature extraction to image classification. To extract discriminative image features, several effective and well-known feature extraction methods, such as HOG, SIFT and LBP, are employed as GP functions in global and local scenarios. A novel program structure is developed to allow GP-GLF to evolve descriptors that can synthesise feature vectors from the input image and the automatically detected regions using these functions. The performance of the proposed method is evaluated on four different image classification data sets of varying difficulty and compared with seven GP based methods and a set of non-GP methods. Experimental results show that the proposed method achieves significantly better or similar performance than almost all the peer methods. Further analysis on the evolved programs shows the good interpretability of the GP-GLF method. %K genetic algorithms, genetic programming %R 10.1109/CEC.2018.8477911 %U https://openaccess.wgtn.ac.nz/articles/conference_contribution/Genetic_Programming_for_Automatic_Global_and_Local_Feature_Extraction_to_Image_Classification/13884998 %U http://dx.doi.org/10.1109/CEC.2018.8477911 %0 Conference Proceedings %T A Gaussian Filter-Based Feature Learning Approach Using Genetic Programming to Image Classification %A Bi, Ying %A Xue, Bing %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 bi:2018:AJCAI %X To learn image features automatically from the problems being tackled is more effective for classification. However, it is very difficult due to image variations and the high dimensionality of image data. This paper proposes a new feature learning approach based on Gaussian filters and genetic programming (GauGP) for image classification. Genetic programming (GP) is a well-known evolutionary learning technique and has been applied to many visual tasks, showing good learning ability and interpretability. In the proposed GauGP method, a new program structure, a new function set and a new terminal set are developed, which allow it to detect small regions from the input image and to learn discriminative features using Gaussian filters for image classification. The performance of GauGP is examined on six different data sets of varying difficulty and compared with four GP methods, eight traditional approaches and convolutional neural networks. The experimental results show GauGP achieves significantly better or similar performance in most cases. %K genetic algorithms, genetic programming, ANN, Feature learning, Image classification, Gaussian filter, Evolutionary computation, Feature extraction %R 10.1007/978-3-030-03991-2_25 %U http://link.springer.com/chapter/10.1007/978-3-030-03991-2_25 %U http://dx.doi.org/10.1007/978-3-030-03991-2_25 %P 251-257 %0 Journal Article %T A Survey on Genetic Programming to Image Analysis %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J Journal of Zhengzhou University (Engineering Science) %D 2018 %V 39 %N 6 %F Bi:2018:JZU %O In Chinese %X As an evolutionary computation (EC) technique, Genetic programming (GP) has been widely applied to image analysis in recent decades. However, there was no comprehensive and systematic literature review in this area. To provide guidelines for the state-of-the-art research, this paper presented a survey of the literature in recent years on GP for image analysis, including feature extraction, image classification, edge detection, and image segmentation. In addition, this paper summarised the current issues and challenges, such as computationally expensive, generalisation ability and transfer learning, on GP for image analysis, and pointd out promising research directions for future work %K genetic algorithms, genetic programming, image analysis, evolutionary computation, feature extraction, image classification %9 journal article %U https://yingbi92.github.io/homepage/2020/%E9%81%97%E4%BC%A0%E8%A7%84%E5%88%92%E5%9C%A8%E5%9B%BE%E5%83%8F%E5%88%86%E6%9E%90%E4%B8%8A%E7%9A%84%E5%BA%94%E7%94%A8%E7%BB%BC%E8%BF%B0%E2%80%94v4.pdf %P 3-13 %0 Conference Proceedings %T An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification %A Bi, Ying %A Xue, Bing %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 Bi:2019:CEC %X Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (ANN). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP ac %K genetic algorithms, genetic programming, ANN %R 10.1109/CEC.2019.8790151 %U http://dx.doi.org/10.1109/CEC.2019.8790151 %P 3197-3204 %0 Conference Proceedings %T An automated ensemble learning framework using genetic programming for image classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %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 Bi:2019:GECCO %X An ensemble consists of multiple learners and can achieve a better generalisation performance than a single learner. Genetic programming (GP) has been applied to construct ensembles using different strategies such as bagging and boosting. However, no GP-based ensemble methods focus on dealing with image classification, which is a challenging task in computer vision and machine learning. This paper proposes an automated ensemble learning framework using GP (EGP) for image classification. The new method integrates feature learning, classification function selection, classifier training, and combination into a single program tree. To achieve this, a novel program structure, a new function set and a new terminal set are developed in EGP. The performance of EGP is examined on nine different image classification data sets of varying difficulty and compared with a large number of commonly used methods including recently published methods. The results demonstrate that EGP achieves better performance than most competitive methods. Further analysis reveals that EGP evolves good ensembles simultaneously balancing diversity and accuracy. To the best of our knowledge, this study is the first work using GP to automatically generate ensembles for image classification. %K genetic algorithms, genetic programming, Ensemble Learning, Image Classification, Feature Learning, Machine Learning, Computer Vision %R 10.1145/3321707.3321750 %U http://dx.doi.org/10.1145/3321707.3321750 %P 365-373 %0 Journal Article %T An Effective Feature Learning Approach Using Genetic Programming With Image Descriptors for Image Classification [Research Frontier] %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J IEEE Computational Intelligence Magazine %D 2020 %8 may %V 15 %N 2 %@ 1556-6048 %F Bi:2020:CIM %X Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation and good global search ability, genetic programming can achieve this. In this paper, a new genetic programming-based feature learning approach is developed to automatically select and combine five existing well-developed descriptors to extract high-level features for image classification. The new approach can automatically learn various numbers of global and/or local features from different types of images. The results show that the new approach achieves significantly better classification performance in almost all the comparisons on eight data sets of varying difficulty. Further analysis reveals the effectiveness of the new approach to finding the most effective feature descriptors or combinations of them to extract discriminative features for different classification tasks. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/MCI.2020.2976186 %U http://dx.doi.org/10.1109/MCI.2020.2976186 %P 65-77 %0 Conference Proceedings %T Automatically Extracting Features for Face Classification Using Multi-Objective Genetic Programming %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %Y Allmendinger, Richard %Y Marin, Hugo Terashima %Y Montes, Efren Mezura %Y Bartz-Beielstein, Thomas %Y Filipic, Bogdan %Y Tang, Ke %Y Howard, David %Y Hart, Emma %Y Eiben, Gusz %Y Eftimov, Tome %Y La Cava, William %Y Naujoks, Boris %Y Oliveto, Pietro %Y Volz, Vanessa %Y Weise, Thomas %Y Derbel, Bilel %Y Li, Ke %Y Li, Xiaodong %Y Zapotecas, Saul %Y Zhang, Qingfu %Y Wang, Rui %Y Cheng, Ran %Y Wu, Guohua %Y Li, Miqing %Y Ishibuchi, Hisao %Y Fieldsend, Jonathan %Y Akman, Ozgur %Y Alyahya, Khulood %Y Branke, Juergen %Y Woodward, John R. %Y Tauritz, Daniel R. %Y Baioletti, Marco %Y Uribe, Josu Ceberio %Y McCall, John %Y Milani, Alfredo %Y Wagner, Stefan %Y Affenzeller, Michael %Y Alexander, Bradley %Y Brownlee, Alexander (Sandy) %Y Haraldsson, Saemundur O. %Y Wagner, Markus %Y Sanchez-Pi, Nayat %Y Marti, Luis %Y Fernandez Alzueta, Silvino %Y Valledor Pellicer, Pablo %Y Stuetzle, Thomas %Y Johns, Matthew %Y Ross, Nick %Y Keedwell, Ed %Y Mahmoud, Herman %Y Walker, David %Y Stein, Anthony %Y Nakata, Masaya %Y Paetzel, David %Y Vaughan, Neil %Y Smith, Stephen %Y Cagnoni, Stefano %Y Patton, Robert M. %Y De Falco, Ivanoe %Y Della Cioppa, Antonio %Y Scafuri, Umberto %Y Tarantino, Ernesto %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 Hemberg, Erik %Y Alshammari, Riyad %Y Makanju, Tokunbo %Y Zelinka, Ivan %Y Das, Swagatam %Y Nagaratnam, Ponnuthurai %Y Senkerik, Roman %E Fuijimino-shi %S Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion %S GECCO ’20 %D 2020 %8 jul 8 12 %I Association for Computing Machinery %C internet %F Bi:2020:GECCOcomp %X This paper proposes a new multi-objective feature extraction algorithm using genetic programming (GP) for face classification. The new multi-objective GP-based feature extraction algorithm with the idea of non-dominated sorting, which aims to maximise the objective of the classification accuracy and minimise the objective of the number of extracted features. The results show that the proposed algorithm achieves significantly better performance than the baseline methods on two different face classification datasets. %K genetic algorithms, genetic programming, feature extraction, evolutionary multi-objective, face classification %R 10.1145/3377929.3389989 %U https://doi.org/10.1145/3377929.3389989 %U http://dx.doi.org/10.1145/3377929.3389989 %P 117-118 %0 Conference Proceedings %T Genetic Programming-Based Feature Learning for Facial Expression Classification %A Bi, Ying %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 Bi:2020:CEC %X Facia1 expression classification is an important but challenging task in artificial intelligence and computer vision. To effectively solve facial expression classification, it is necessary to detect/locate the face and extract features from the face. However, these two tasks are often conducted separately and manually in a traditional facial expression classification system. Genetic programming (GP) can automatically evolve solutions for a task without rich human intervention. However, very few GP-based methods have been specifically developed for facial expression classification. Therefore, this paper proposes a GP-based feature learning approach to facial expression classification. The proposed approach can automatically select small regions of a face and extract appearance features from the small regions. The experimental results on four different facial expression classification data sets show that the proposed approach achieves significantly better results in almost all the comparisons. To further show the effectiveness of the proposed approach, different numbers of training images are used in the experiments. The results indicate that the proposed approach achieves significantly better performance than any of the baseline methods using a small number of training images. Further analysis shows that the proposed approach not only selects informative regions of the face but also finds a good combination of various features to obtain a high classification accuracy. %K genetic algorithms, genetic programming: Poster %R 10.1109/CEC48606.2020.9185491 %U http://dx.doi.org/10.1109/CEC48606.2020.9185491 %P paperid24102 %0 Conference Proceedings %T Evolving Deep Forest with Automatic Feature Extraction for Image Classification Using Genetic Programming %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %Y Baeck, Thomas %Y Preuss, Mike %Y Deutz, Andre %Y Wang2, Hao %Y Doerr, Carola %Y Emmerich, Michael %Y Trautmann, Heike %S 16th International Conference on Parallel Problem Solving from Nature, Part I %S LNCS %D 2020 %8 July 9 sep %V 12269 %I Springer %C Leiden, Holland %F Bi:2020:PPSN %X Deep forest is an alternative to deep neural networks to use multiple layers of random forests without back-propagation for solving various problems. In this study, we propose a genetic programming-based approach to automatically and simultaneously evolving effective structures of deep forest connections and extracting informative features for image classification. First, in the new approach we define two types of modules: forest modules and feature extraction modules. Second, an encoding strategy is developed to integrate forest modules and feature extraction modules into a tree and the search strategy is introduced to search for the best solution. With these designs, the proposed approach can automatically extract image features and find forests with effective structures simultaneously for image classification. The parameters in the forest can be dynamically determined during the learning process of the new approach. The results show that the new approach can achieve better performance on the datasets having a small number of training instances and competitive performance on the datasets having a large number of training instances. The analysis of evolved solutions shows that the proposed approach uses a smaller number of random forests over the deep forest method. %K genetic algorithms, genetic programming, EvoDF, Evolutionary deep learning, Deep forest, Image classification, Feature extraction %R 10.1007/978-3-030-58112-1_1 %U https://openaccess.wgtn.ac.nz/articles/chapter/Evolving_deep_forest_with_automatic_feature_extraction_for_image_classification_using_genetic_programming/13158329 %U http://dx.doi.org/10.1007/978-3-030-58112-1_1 %P 3-18 %0 Thesis %T Genetic Programming for Feature Learning in Image Classification %A Bi, Ying %D 2020 %C New Zealand %C Computer Science, Victoria University of Wellington %F YingBi_thesis %X Image classification is an important and fundamental task in computer vision and machine learning. The task is to classify images into one of some predefined groups based on the content in the images. However, image classification is a challenging task due to high variations across images, such as illumination, viewpoint, scale variations, deformation, and occlusion. To effectively solve image classification, it is necessary to extract or learn a set of meaningful features from raw pixels or images. The effectiveness of these features significantly affects classification performance. Feature learning aims to automatically learn effective features from images for classification. However, feature learning is difficult due to the high variations of images and the large search space. Genetic Programming (GP) as an Evolutionary Computation (EC) technique is known for its powerful global search ability and high interpretability of the evolved solutions. Compared with other EC methods, GP has a flexible representation of variable length and can search the solution space without any assumptions on the solution structure. The potential of GP in feature learning for image classification has not been comprehensively investigated due to the use of simple representations, e.g., functions and program structures. The overall goal of this thesis is to further investigate and explore the potential of GP for image classification by developing a new GP-based approach with a new representation to automatically learning effective features for different types of image classification tasks. Firstly, this thesis proposes a new GP based approach with image descriptors to learning global and/or local features for image classification by developing a new program structure, a new function set, a new terminal set, and a new fitness function. These new designs allow GP to detect small regions from the relatively large input image, extract features using image descriptors from the detected regions or the input image, and combine the extracted features for classification. The results show that the new approach significantly out performs five GP-based methods, eight traditional methods, and three convolutional neural network methods in almost all the comparisons on eight different datasets. Secondly, this thesis proposes a new GP-based approach with a flexible program structure and image-related operators for feature learning in image classification. The new approach learns effective features transformed by multiple layers, i.e., a filtering layer, a pooling layer, a feature extraction layer, and a feature concatenation layer, in a flexible way. The results show that the new approach achieves better performance than a large number of effective methods on 12 benchmark datasets. The solutions and features learned by the new approach provide high interpretability. Thirdly, this thesis proposes the first GP-based approach to automatically and simultaneously learning features and evolving ensembles for image classification. The new approach can learn high-level features through multiple transformations, select effective classification algorithms and optimise the parameters for these classification algorithms to build effective ensembles. The new approach outperforms a large number of benchmark methods on 12 different image classification datasets. Finally, this thesis proposes a multi-population GP-based approach with knowledge transfer and ensembles to improving both the generalisation performance and computational efficiency of GP-based feature learning algorithms for image classification. The new approach can achieve better generalisation performance and computational efficiency than baseline GP-based feature learning method. The new approach can achieve better performance on 11 datasets than a large number of benchmark methods, including many neural network-based methods. %K genetic algorithms, genetic programming, ANN, CNN, FLGP, IEGP, ensembles, STGP %9 Ph.D. thesis %R 10.26686/wgtn.19529515 %U https://openaccess.wgtn.ac.nz/ndownloader/files/34714144 %U http://dx.doi.org/10.26686/wgtn.19529515 %0 Journal Article %T Genetic Programming with Image-Related Operators and A Flexible Program Structure for Feature Learning in Image Classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2021 %8 feb %V 25 %N 1 %@ 1941-0026 %F Bi:2020:TEVC %X Feature extraction is essential for solving image classification by transforming low-level pixel values into high-level features. However, extracting effective features from images is challenging due to high variations across images in scale, rotation, illumination, and background. Existing methods often have a fixed model complexity and require domain expertise. Genetic programming with a flexible representation can find the best solution without the use of domain knowledge. This paper proposes a new genetic programming-based approach to automatically learning informative features for different image classification tasks. In the new approach, a number of image-related operators, including filters, pooling operators and feature extraction methods, are employed as functions. A flexible program structure is developed to integrate different functions and terminals into a single tree/solution. The new approach can evolve solutions of variable depths to extract various numbers and types of features from the images. The new approach is examined on 12 different image classification tasks of varying difficulty and compared with a large number of effective algorithms. The results show that the new approach achieves better classification performance than most benchmark methods. The analysis of the evolved programs/solutions and the visualisation of the learned features provide deep insights on the proposed approach. %K genetic algorithms, genetic programming, Feature Learning, Image Classification, Representation, Evolutionary Computation %9 journal article %R 10.1109/TEVC.2020.3002229 %U http://dx.doi.org/10.1109/TEVC.2020.3002229 %P 87-101 %0 Journal Article %T Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Cybernetics %D 2021 %8 apr %V 51 %N 4 %@ 2168-2275 %F Bi:2020:CYB %X Image classification is a popular task in machine learning and computer vision, but it is very challenging due to high variation crossing images. Using ensemble methods for solving image classification can achieve higher classification performance than using a single classification algorithm. However, to obtain a good ensemble, the component (base) classifiers in an ensemble should be accurate and diverse. To solve image classification effectively, feature extraction is necessary to transform raw pixels into high-level informative features. However, this process often requires domain knowledge. This article proposes an evolutionary approach based on genetic programming to automatically and simultaneously learn informative features and evolve effective ensembles for image classification. The new approach takes raw images as inputs and returns predictions of class labels based on the evolved classifiers. To achieve this, a new individual representation, a new function set, and a new terminal set are developed to allow the new approach to effectively find the best solution. More important, the solutions of the new approach can extract informative features from raw images and can automatically address the diversity issue of the ensembles. In addition, the new approach can automatically select and optimize the parameters for the classification algorithms in the ensemble. The performance of the new approach is examined on 13 different image classification datasets of varying difficulty and compared with a large number of effective methods. The results show that the new approach achieves better classification accuracy on most datasets than the competitive methods. Further analysis demonstrates that the new approach can evolve solutions with high accuracy and diversity. %K genetic algorithms, genetic programming, Ensemble learning, feature learning, image classification, representation %9 journal article %R 10.1109/TCYB.2020.2964566 %U http://dx.doi.org/10.1109/TCYB.2020.2964566 %P 1769-1783 %0 Book %T Genetic Programming for Image Classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %S Adaptation, Learning, and Optimization book series %D 2021 %8 feb 9 %V 24 %I Springer Nature %F bi2021gpimage %O An Automated Approach to Feature Learning %X This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation. Front Matter Pages i-xxviii. Introduction Pages 1-10. Computer Vision and Machine Learning Pages 11-48. Evolutionary Computation and Genetic Programming Pages 49-74. Multi-layer Representation for Binary Image Classification Pages 75-95. Evolutionary Deep Learning Using GP with Convolution Operators Pages 97-115. GP with Image Descriptors for Learning Global and Local Features Pages 117-143. GP with Image-Related Operators for Feature Learning Pages 145-177. GP for Simultaneous Feature Learning and Ensemble Learning Pages 179-205. Random Forest-Assisted GP for Feature Learning Pages 207-226. Conclusions and Future Directions Pages 227-237. Back Matter Pages 239-258. Evolutionary Computation Research Group, School of Engineering and Computer Science Victoria University of Wellington, Wellington, New Zealand %K genetic algorithms, genetic programming, Evolutionary Computation, Feature Learning, Image Classification, Computer Vision, Machine Learning, Feature Extraction, Feature Selection, Feature Construction, Model Interpretability %R 10.1007/978-3-030-65927-1 %U https://link.springer.com/book/10.1007/978-3-030-65927-1 %U http://dx.doi.org/10.1007/978-3-030-65927-1 %0 Journal Article %T Multi-objective genetic programming for feature learning in face recognition %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J Applied Soft Computing %D 2021 %8 may %V 103 %@ 1568-4946 %F Bi:2021:ASC %X Face recognition is a challenging task due to high variations of pose, expression, ageing, and illumination. As an effective approach to face recognition, feature learning can be formulated as a multi-objective optimisation task of maximising classification accuracy and minimising the number of learned features. However, most of the existing algorithms focus on improving classification accuracy without considering the number of learned features. In this paper, we propose new multi-objective genetic programming (GP) algorithms for feature learning in face recognition. To achieve effective face feature learning, a new individual representation is developed to allow GP to select informative regions from the input image, extract features using various descriptors, and combine the extracted features for classification. Then two new multi-objective genetic programming (GP) algorithms, one with the idea of non-dominated sorting (NSGPFL) and the other with the idea of Strength Pareto (SPGPFL), are proposed to simultaneously optimise these two objectives. NSGPFL and SPGPFL are compared with a single-objective GP for feature learning (GPFL), a single-objective GP for weighting two objectives (GPFLW), and a large number of baseline methods. The experimental results show the effectiveness of the NSGPFL and SPGPFL algorithms by achieving better or comparable classification performance and learning a small number of features. %K genetic algorithms, genetic programming, Multi-objective optimisation, Evolutionary computation, Feature learning, Face recognition %9 journal article %R 10.1016/j.asoc.2021.107152 %U https://yingbi92.github.io/homepage/2021/MOGP.pdf %U http://dx.doi.org/10.1016/j.asoc.2021.107152 %P 107152 %0 Journal Article %T A Divide-and-Conquer Genetic Programming Algorithm with Ensembles for Image Classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2021 %8 dec %V 25 %N 6 %@ 1089-778X %F Bi:TEVC2 %X Genetic programming (GP) has been applied to feature learning in image classification and achieved promising results. However, one major limitation of existing GP-based methods is the high computational cost, which may limit their applications on large-scale image classification tasks. To address this, this paper develops a divide-and-conquer GP algorithm with knowledge transfer and ensembles to achieve fast feature learning in image classification. In the new algorithm framework, a divideand-conquer strategy is employed to split the training data and the population into small subsets or groups to reduce computational time. A new knowledge transfer method is proposed to improve GP learning performance. A new fitness function based on log-loss and a new ensemble formulation strategy are developed to build an effective ensemble for image classification. The performance of the proposed approach has been examined on 12 image classification datasets of varying difficulty. The results show that the new approach achieves better classification performance in significantly less computation time than the baseline GP-based algorithm. The comparisons with state-of-theart algorithms show that the new approach achieves better or comparable performance in almost all the comparisons. Further analysis demonstrates the effectiveness of ensemble formulation and knowledge transfer in the proposed approach. %K genetic algorithms, genetic programming, Feature Learning, Knowledge Transfer, Ensemble Learning, Divide-and-Conquer, Image Classification %9 journal article %R 10.1109/TEVC.2021.3082112 %U http://dx.doi.org/10.1109/TEVC.2021.3082112 %P 1148-1162 %0 Journal Article %T Using a Small Number of Training Instances in Genetic Programming for Face Image Classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J Information Sciences %D 2022 %8 may %V 593 %@ 0020-0255 %F Bi:2022:InformationSciences %X Classifying faces is a difficult task due to image variations in illumination, occlusion, pose, expression, etc. Typically, it is challenging to build a generalised classifier when the training data is small, which can result in poor generalisation. This paper proposes a new approach for the classification of face images based on multi-objective genetic programming (MOGP). In MOGP, image descriptors that extract effective features are automatically evolved by optimising two different objectives at the same time: the accuracy and the distance measure. The distance measure is a new measure intended to enhance generalisation of learned features and/or classifiers. The performance of MOGP is evaluated on eight face datasets. The results show that MOGP significantly outperforms 17 competitive methods. %K genetic algorithms, genetic programming, MOGP, Image classification, Fitness measure, Small data, Evolutionary computation %9 journal article %R 10.1016/j.ins.2022.01.055 %U https://www.sciencedirect.com/science/article/abs/pii/S0020025522000871 %U http://dx.doi.org/10.1016/j.ins.2022.01.055 %P 488-504 %0 Journal Article %T Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2022 %8 apr %V 26 %N 2 %@ 1089-778X %F Learning_and_Sharing_A_Multitask_Genetic_Programming_Approach_to_Image_Feature_Learning %O Special Issue on Multitask Evolutionary Computation %X Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask learning problem because different tasks may have a similar feature space. Genetic programming (GP) has been successfully applied to image feature learning for classification. However, most of the existing GP methods solve one task, independently, using sufficient training data. No multitask GP method has been developed for image feature learning. Therefore, this paper develops a multitask GP approach to image feature learning for classification with limited training data. Owing to the flexible representation of GP, a new knowledge sharing mechanism based on a new individual representation is developed to allow GP to automatically learn what to share across two tasks and to improve its learning performance. The shared knowledge is encoded as a common tree, which can represent the common/general features of two tasks. With the new individual representation, each task is solved using the features extracted from a common tree and a task-specific tree representing task-specific features. To find the best common and task-specific trees, a new evolutionary search process and fitness functions are developed. The performance of the new approach is examined on six multitask learning problems of 12 image classification datasets with limited training data and compared with 17 competitive methods. Experimental results show that the new approach outperforms these comparison methods in almost all the comparisons. Further analysis reveals that the new approach learns simple yet effective common trees with high effectiveness and transferability. %K genetic algorithms, genetic programming, Multitask Learning, Knowledge Sharing, Feature Learning, Image Classification %9 journal article %R 10.1109/TEVC.2021.3097043 %U http://dx.doi.org/10.1109/TEVC.2021.3097043 %P 218-232 %0 Journal Article %T Dual-Tree Genetic Programming for Few-Shot Image Classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2022 %8 jun %V 26 %N 3 %@ 1089-778X %F Dual-Tree_Genetic_Programming_for_Few-Shot_Image_Classification %X Few-shot image classification is an important but challenging task due to high variations across images and a small number of training instances. A learning system often has poor generalisation performance due to the lack of sufficient training data. Genetic programming (GP) has been successfully applied to image classification and achieved promising performance. This paper proposes a GP-based approach with a dual-tree representation and a new fitness function to automatically learn image features for few-shot image classification. The dual-tree representation allows the proposed approach to have better search ability and learn richer features than a single-tree representation when the number of training instances is very small. The fitness function based on the classification accuracy and the distances of the training instances to the class centroids aims to improve the generalisation performance. The proposed approach can deal with different types of few-shot image classification tasks with various numbers of classes and different %K genetic algorithms, genetic programming, Representation, Fitness Evaluation, Few-Shot Learning, Image Classification %9 journal article %R 10.1109/TEVC.2021.3100576 %U https://yingbi92.github.io/homepage/2021/DTGPN.pdf %U http://dx.doi.org/10.1109/TEVC.2021.3100576 %P 555-569 %0 Journal Article %T Instance Selection-Based Surrogate-Assisted Genetic Programming for Feature Learning in Image Classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Cybernetics %D 2023 %8 feb %V 53 %N 2 %@ 2168-2275 %F Ying_Bi:cybernetics1 %X Genetic programming (GP) has been applied to feature learning for image classification and achieved promising results. However, many GP-based feature learning algorithms are computationally expensive due to a large number of expensive fitness evaluations, especially when using a large number of training instances/images. Instance selection aims to select a small subset of training instances, which can reduce the computational cost. Surrogate-assisted evolutionary algorithms often replace expensive fitness evaluations by building surrogate models. This article proposes an instance selection-based surrogate-assisted GP for fast feature learning in image classification. The instance selection method selects multiple small subsets of images from the original training set to form surrogate training sets of different sizes. The proposed approach gradually uses these surrogate training sets to reduce the overall computational cost using a static or dynamic strategy. At each generation, the proposed approach evaluates the entire population on the small surrogate training sets and only evaluates ten current best individuals on the entire training set. The features learned by the proposed approach are fed into linear support vector machines for classification. Extensive experiments show that the proposed approach can not only significantly reduce the computational cost but also improve the generalisation performance over the baseline method, which uses the entire training set for fitness evaluations, on 11 different image datasets. The comparisons with other state-of-the-art GP and non-GP methods further demonstrate the effectiveness of the proposed approach. Further analysis shows that using multiple surrogate training sets in the proposed approach achieves better performance than using a single surrogate training set and using a random instance selection method. %K genetic algorithms, genetic programming, evolutionary computation, EC, feature learning, instance selection, surrogate %9 journal article %R 10.1109/TCYB.2021.3105696 %U http://dx.doi.org/10.1109/TCYB.2021.3105696 %P 1118-1132 %0 Journal Article %T Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Cybernetics %D 2022 %V 52 %N 8 %@ 2168-2275 %F Ying_Bi:Cybernetics2 %X Being able to learn discriminative features from low-quality images has raised much attention recently due to their wide applications ranging from autonomous driving to safety surveillance. However, this task is difficult due to high variations across images, such as scale, rotation, illumination, and viewpoint, and distortions in images, such as blur, low contrast, and noise. Image preprocessing could improve the quality of the images, but it often requires human intervention and domain knowledge. Genetic programming (GP) with a flexible representation can automatically perform image preprocessing and feature extraction without human intervention. Therefore, this study proposes a new evolutionary learning approach using GP (EFLGP) to learn discriminative features from images with blur, low contrast, and noise for classification. In the proposed approach, we develop a new program structure (individual representation), a new function set, and a new terminal set. With these new designs, EFLGP can detect small regions from a large input low-quality image, select image operators to process the regions or detect features from the small regions, and output a flexible number of discriminative features. A set of commonly used image preprocessing operators is employed as functions in EFLGP to allow it to search for solutions that can effectively handle low-quality image data. The performance of EFLGP is comprehensively investigated on eight datasets of varying difficulty under the original (clean), blur, low contrast, and noise scenarios, and compared with a large number of benchmark methods using handcrafted features and deep features. The experimental results show that EFLGP achieves significantly better or similar results in most comparisons. The results also reveal that EFLGP is more invariant than the benchmark methods to blur, low contrast, and noise. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TCYB.2021.3049778 %U http://dx.doi.org/10.1109/TCYB.2021.3049778 %P 8272-8285 %0 Journal Article %T Multitask Feature Learning as Multiobjective Optimization: A New Genetic Programming Approach to Image Classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Cybernetics %D 2023 %8 may %V 53 %N 5 %@ 2168-2267 %F Ying_Bi:Cybernetics3 %X Feature learning is a promising approach to image classification. However, it is difficult due to high image variations. When the training data are small, it becomes even more challenging, due to the risk of overfitting. Multitask feature learning has shown the potential for improving generalization. However, existing methods are not effective for handling the case that multiple tasks are partially conflicting. Therefore, for the first time, this article proposes to solve a multitask feature learning problem as a multiobjective optimization problem by developing a genetic programming approach with a new representation to image classification. In the new approach, all the tasks share the same solution space and each solution is evaluated on multiple tasks so that the objectives of all the tasks can be optimized simultaneously using a single population. To learn effective features, a new and compact program representation is developed to allow the new approach to evolving solutions shared across tasks. The new approach can automatically find a diverse set of nondominated solutions that achieve good tradeoffs between different tasks. To further reduce the risk of overfitting, an ensemble is created by selecting non-dominated solutions to solve each image classification task. The results show that the new approach significantly outperforms a large number of benchmark methods on six problems consisting of 15 image classification datasets of varying difficulty. Further analysis shows that these new designs are effective for improving the performance. The detailed analysis clearly reveals the benefits of solving multitask feature learning as multi-objective optimisation in improving the generalisation. %K genetic algorithms, genetic programming, Evolutionary computation (EC), feature learning, GP, image classification, multiobjective optimisation, multitask learning %9 journal article %R 10.1109/TCYB.2022.3174519 %U http://dx.doi.org/10.1109/TCYB.2022.3174519 %P 3007-3020 %0 Journal Article %T A new artificial intelligent approach to buoy detection for mussel farming %A Bi, Ying %A Xue, Bing %A Briscoe, Dana %A Vennell, Ross %A Zhang, Mengjie %J Journal of the Royal Society of New Zealand %D 2023 %V 53 %N 1 %I Taylor & Francis %@ 0303-6758 %F Bi:JRSNZ %X Aquaculture is an important industry in New Zealand (NZ). Mussel farmers often manually check the state of the buoys that are required to support the crop, which is labour-intensive. Artificial intelligence (AI) can provide automatic and intelligent solutions to many problems but has seldom been applied to mussel farming. In this paper, a new AI-based approach is developed to automatically detect buoys from mussel farm images taken from a farm in the South Island of NZ. The overall approach consists of four steps, i.e. data collection and preprocessing, image segmentation, keypoint detection and feature extraction, and classification. A convolutional neural network (CNN) method is applied to perform image segmentation. A new genetic programming (GP) method with a new representation, a new function set and a new terminal set is developed to automatically evolve descriptors for extracting features from key points. The new approach is applied to seven subsets and one full dataset containing images of buoys over different backgrounds and compared to three baseline methods. The new approach achieves better performance than the compared methods. Further analysis of the parameters and the evolved solutions provides more insights into the performance of the new approach to buoy detection. %K genetic algorithms, genetic programming, Artificial intelligence, computer vision, aquaculture, evolutionary learning, object detection, deep learning, ANN %9 journal article %R 10.1080/03036758.2022.2090966 %U http://dx.doi.org/10.1080/03036758.2022.2090966 %P 27-51 %0 Journal Article %T Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2024 %8 apr %V 28 %N 2 %@ 1089-778X %F Ying_Bi:ieeeTEC %X Data-efficient image classification is a challenging task that aims to solve image classification using small training data. Neural network-based deep learning methods are effective for image classification, but they typically require large-scale training data and have major limitations, such as requiring expertise to design network architectures and having poor interpretability. Evolutionary deep learning (EDL) is a recent hot topic that combines evolutionary computation with deep learning. However, most EDL methods focus on evolving architectures of neural networks, which still suffers from limitations such as poor interpretability. To address this, this article proposes a new genetic programming-based EDL approach to data-efficient image classification. The new approach can automatically evolve variable-length models using many important operators from both image and classification domains. It can learn different types of image features from colour or grayscale images, and construct effective and diverse ensembles for image classification. A flexible multilayer representation enables the new approach to automatically construct shallow or deep models/trees for different tasks and perform effective transformations on the input data via multiple internal nodes. The new approach is applied to solve five image classification tasks with different training set sizes. The results show that it achieves a better performance in most cases than deep learning methods for data-efficient image classification. A deep analysis shows that the new approach has good convergence and evolves models with high interpretability, different lengths/sizes/shapes, and good transferability. %K genetic algorithms, genetic programming, ANN, Evolutionary Deep Learning, Image Classification, Small Data, Evolutionary Computation, Deep Learning %9 journal article %R 10.1109/TEVC.2022.3214503 %U https://arxiv.org/abs/2209.13233 %U http://dx.doi.org/10.1109/TEVC.2022.3214503 %P 307-322 %0 Journal Article %T A survey on evolutionary computation for computer vision and image analysis: Past, present, and future trends %A Bi, Ying %A Xue, Bing %A Mesejo, Pablo %A Cagnoni, Stefano %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2023 %8 feb %V 27 %N 1 %@ 1089-778X %F Ying_Bi:ieeeTEC2 %X Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, image-related tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research. %K genetic algorithms, genetic programming, evolutionary Computation, Image Analysis, Computer Vision, Pattern Recognition, Image Processing, Artificial Intelligence %9 journal article %R 10.1109/TEVC.2022.3220747 %U https://arxiv.org/abs/2209.06399v1 %U http://dx.doi.org/10.1109/TEVC.2022.3220747 %P 5-25 %0 Conference Proceedings %T Evolutionary Deep-Learning for Image Classification: A Genetic Programming Approach %A Bi, Ying %A Xue, Bing %A Zhang, Mengjie %Y DeSouza, Gui %Y Yen, Gary %S 2023 IEEE Congress on Evolutionary Computation (CEC) %D 2023 %8 January 5 jul %C Chicago, USA %F Bi:2023:CEC %O Tutorial %K genetic algorithms, genetic programming %U https://2023.ieee-cec.org/program-html/ %0 Journal Article %T A Genetic Programming Approach with Building Block Evolving and Reusing to Image Classification %A Bi, Ying %A Liang, Jing %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2024 %8 oct %V 28 %N 5 %@ 1089-778X %F BiYing:ieeeTEC %K genetic algorithms, genetic programming, Image classification, Feature extraction, Task analysis, Training data, Training, Sociology, Random forests, Ensemble learning, evolutionary computation (EC), feature extraction %9 journal article %R 10.1109/TEVC.2023.3284712 %U http://dx.doi.org/10.1109/TEVC.2023.3284712 %P 1366-1380 %0 Journal Article %T Change detection in remote sensing images based on multi-tree genetic programming %A Bi, Ying %A Zhang, Tuo %A Lian, Jintao %A Chang, Yaxin %A Liang, Jing %J Applied Soft Computing %D 2025 %8 nov %V 183 %@ 1568-4946 %F Bi:2025:ASOC %X Change detection in remote sensing images plays a crucial role in applications such as environmental monitoring, urban planning, and disaster management. Accurately identifying and distinguishing changed areas within complex image data poses significant challenges. Existing methods often struggle with high false-positive rates and limited adaptability. using multi-tree genetic programming (GP) to automate the construction of ensembles for change detection in remote sensing images. The method employs a unique multi-tree GP representation comprising three distinct trees that difference, spectral, and texture features to identify changes. These trees are combined into an ensemble using a majority voting strategy to make predictions. The approach integrates multi-tree crossover and mutation strategies to generate new individuals, which are evaluated based on a fitness function derived from classification accuracy. To validate its effectiveness, the proposed multi-tree GP approach is evaluated on four benchmark datasets (SZTAKI, EGY_BCD, LEVIR_CD+, and S2Looking) and compared with eight methods. In most cases, the proposed approach achieves higher maximum change detection accuracy. Notably, on the SZTAKI dataset (Img_10), it achieves an accuracy of 96.11 percent, representing a 5.55 percent improvement over the worst baseline (KNN) and a 0.55 percent gain over the best baseline (SpectralFormer). Experimental results demonstrate that the proposed approach outperforms standard GP, as well as several classic classifiers and neural network based methods, establishing it as an effective tool for remote sensing change detection. The method capability of to leverage diverse features and integrate them through ensemble learning underscores its potential in enhancing change detection accuracy using remote sensing imagery. %K genetic algorithms, genetic programming, MTEGP, Change detection, Remote sensing, Ensemble learning, Classification %9 journal article %R 10.1016/j.asoc.2025.113609 %U https://www.sciencedirect.com/science/article/pii/S1568494625009202 %U http://dx.doi.org/10.1016/j.asoc.2025.113609 %P 113609 %0 Journal Article %T A Two-Stage Genetic Programming Approach to Feature Construction and Ensemble Evolving for Road Extraction From Remote Sensing Images %A Bi, Ying %A Chang, Yaxin %A Liang, Jing %A Yue, Caitong %A Qu, Boyang %A Liu, Mengnan %J IEEE Transactions on Geoscience and Remote Sensing %D 2025 %V 63 %@ 0196-2892 %F Bi:2025:TGRS %K genetic algorithms, genetic programming, Feature extraction, Roads, Remote sensing, Accuracy, Data mining, Deep learning, Training data, Image classification, Faces, Ensemble evolving, feature construction, road extraction %9 journal article %R 10.1109/TGRS.2025.3624487 %U http://dx.doi.org/10.1109/TGRS.2025.3624487 %0 Conference Proceedings %T Runtime NBTI Mitigation for Processor Lifespan Extension via Selective Node Control %A Bian, Song %A Shintani, Michihiro %A Wang, Zheng %A Hiromoto, Masayuki %A Chattopadhyay, Anupam %A Sato, Takashi %S 2016 IEEE 25th Asian Test Symposium (ATS) %D 2016 %8 nov %F Bian:2016:ATS %X Negative bias temperature instability (NBTI) has become one of the major reliability concerns for nanoscale CMOS technology. The NBTI effect degrades pMOS transistors by stressing them with negatively biased voltage, while the transistors heal themselves as the negative bias is removed. In this paper, we propose a cross-layer mitigation technique for NBTI-induced timing degradation in processors. The NOP (No Operation) instruction is replaced by a custom NOP instruction for healing purpose. Cells that are likely to be stressed under negative bias are classified and their upstream cell will be replaced by the internal node control (INC) logics. Upon encountering a custom NOP instruction, the INC logics will force the NBTI-stressed cell to be in its healing mode. The optimal INC logic insertion through genetic programming approach achieves much greater delay mitigation of 44.3percent than prior works in a 10-year span with less than 4percent of power and negligible area overhead. %K genetic algorithms, genetic programming %R 10.1109/ATS.2016.31 %U http://dx.doi.org/10.1109/ATS.2016.31 %P 234-239 %0 Conference Proceedings %T Refining Fitness Functions for Search-Based Program Repair %A Bian, Zhiqiang %A Petke, Justyna %A Blot, Aymeric %Y Mechtaev, Sergey %Y Tan, Shin Hwei %Y Monperrus, Martin %Y Zhang, Lingming %S APR @ ICSE 2021 %D 2021 %8 January %I IEEE %C Internet %F bian:2021:apr_icse %X Debugging is a time-consuming task for software engineers. Automated Program Repair (APR) has proved successful in automatically fixing bugs for many real-world applications. Search-based APR generates program variants that are then evaluated on the test suite of the original program, using a fitness function. In the vast majority of search based APR work only the Boolean test case result is taken into account when evaluating the fitness of a program variant. We pose that more fine-grained fitness functions could lead to a more diverse fitness landscape,and thus provide better guidance for the APR search algorithms. We thus present 2Phase, a fitness function that also incorporates the output of test case failures, and compare it with ARJAe,that shares the same principles, and the standard fitness, that only takes the Boolean test case result into consideration. We conduct the comparison on 16 buggy programs from the QuixBugs benchmark using the Gin genetic improvement framework. The results show no significant difference in the performance of all three fitness functions considered. However, Gin was able to find 8 correct fixes, more than any of the APR tools in the recent QuixBugs study. %K genetic algorithms, genetic programming, genetic improvement, APR, Software Engineering, SBSE, Program Repair, Fitness Function, GenProg, ARJAe, 2Phase, Gin, EvoSuite, QuixBugs %R 10.1109/APR52552.2021.00008 %U http://www.cs.ucl.ac.uk/staff/a.blot/files/bian_apr-icse_2021.pdf %U http://dx.doi.org/10.1109/APR52552.2021.00008 %P 1-8 %0 Generic %T How Far Can You Get By Combining Change Detection Algorithms? %A Bianco, Simone %A Ciocca, Gianluigi %A Schettini, Raimondo %D 2015 %8 may 12 %F oai:arXiv.org:1505.02921 %O Comment: Submitted to IEEE Transactions on Image Processing %X In this paper we investigate if simple change detection algorithms can be combined and used to create a more robust change detection algorithm by leveraging their individual peculiarities. We use Genetic Programming to combine the outputs (i.e. binary masks) of the detection algorithms with unary, binary and n-ary functions performing both masks’ combination and post-processing. Genetic Programming allows us to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations. Using different experimental settings, we created two algorithms that we named IUTIS-1 and IUTIS-2 (In Unity There Is Strength). These algorithms are compared against state-of-the-art change detection algorithms on the video sequences and ground truth annotations of the ChandeDetection.net (CDNET 2014) challenge. Results demonstrate that starting from simple algorithms we can achieve comparable results of more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications. Moreover, when our framework is applied to more complex algorithms, the resulting IUTIS-3 outperforms all the 33 state-of-the-art algorithms considered. %K genetic algorithms, genetic programming, computer science - computer vision and pattern recognition %U http://arxiv.org/abs/1505.02921 %0 Journal Article %T Combination of Video Change Detection Algorithms by Genetic Programming %A Bianco, Simone %A Ciocca, Gianluigi %A Schettini, Raimondo %J IEEE Transactions on Evolutionary Computation %D 2017 %8 dec %V 21 %N 6 %@ 1089-778X %F Bianco:ieeeTEC %X Within the field of Computer Vision, change detection algorithms aim at automatically detecting significant changes occurring in a scene by analysing the sequence of frames in a video stream. In this paper we investigate how state-of-the-art change detection algorithms can be combined and used to create a more robust algorithm leveraging their individual peculiarities. We exploited Genetic Programming (GP) to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations on the outputs of the algorithms. In particular, algorithms combination and post-processing operations are achieved with unary, binary and n-ary functions embedded into the GP framework. Using different experimental settings for combining existing algorithms we obtained different GP solutions that we termed IUTIS (In Unity There Is Strength). These solutions are then compared against state-of-the-art change detection algorithms on the video sequences and ground truth annotations of the Change Detection. net (CDNET 2014) challenge. Results demonstrate that using GP, our solutions are able to outperform all the considered single state-of-the-art change detection algorithms, as well as other combination strategies. The performance of our algorithm are significantly different from those of the other state-of-the-art algorithms. This fact is supported by the statistical significance analysis conducted with the Friedman Test and Wilcoxon Rank Sum post-hoc tests. %K genetic algorithms, genetic programming, Change detection, algorithm combining and selection, CDNET %9 journal article %R 10.1109/TEVC.2017.2694160 %U http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7898824 %U http://dx.doi.org/10.1109/TEVC.2017.2694160 %P 914-928 %0 Journal Article %T Neural architecture search for image saliency fusion %A Bianco, Simone %A Buzzelli, Marco %A Ciocca, Gianluigi %A Schettini, Raimondo %J Information Fusion %D 2020 %V 57 %@ 1566-2535 %F BIANCO:2020:IF %X Saliency detection methods proposed in the literature exploit different rationales, visual clues, and assumptions, but there is no single best saliency detection algorithm that is able to achieve good results on all the different benchmark datasets. In this paper we show that fusing different saliency detection algorithms together by exploiting neural network architectures makes it possible to obtain better results. Designing the best architecture for a given task is still an open problem since the existing techniques have some limits with respect to the problem formulation, to the search space, and require very high computational resources. To overcome these problems, in this paper we propose a three-step fusion approach. In the first step, genetic programming techniques are exploited to combine the outputs of existing saliency algorithms using a set of provided operations. Having a discrete search space allows us a fast generation of the candidate solutions. In the second step, the obtained solutions are converted into backbone Convolutional Neural Networks (CNNs) where operations are all implemented with differentiable functions, allowing an efficient optimization of the corresponding parameters (in a continuous space) by backpropagation. In the last step, to enrich the expressiveness of the initial architectures, the networks are further extended with additional operations on intermediate levels of the processing that are once again efficiently optimized through backpropagation. Extensive experimental evaluations show that the proposed saliency fusion approach outperforms the state-of-the-art on the MSRAB dataset and it is able to generalize to unseen data of different benchmark datasets %K genetic algorithms, genetic programming, Saliency fusion, Evolutionary algorithms, Neural architecture search %9 journal article %R 10.1016/j.inffus.2019.12.007 %U http://www.sciencedirect.com/science/article/pii/S1566253519302374 %U http://dx.doi.org/10.1016/j.inffus.2019.12.007 %P 89-101 %0 Conference Proceedings %T Improving Image Filters with Cartesian Genetic Programming %A Biau, Julien %A Wilson, Dennis %A Cussat-Blanc, Sylvain %A Luga, Herve %Y Bäck, Thomas %Y Wagner, Christian %Y Garibaldi, Jonathan M. %Y Lam, H. K. %Y Cottrell, Marie %Y Merelo, Juan Julián %Y Warwick, Kevin %S Proceedings of the 13th International Joint Conference on Computational Intelligence, IJCCI 2021, Online Streaming, October 25-27, 2021 %D 2021 %I SCITEPRESS %F DBLP:conf/ijcci/BiauWCL21 %K genetic algorithms, genetic programming, Cartesian Genetic Programming %R 10.5220/0010640000003063 %U https://doi.org/10.5220/0010640000003063 %U http://dx.doi.org/10.5220/0010640000003063 %P 17-27 %0 Conference Proceedings %T Improving Image Filter Efficiency: A Multi-objective Genetic Algorithm Approach to Optimize Computing Efficiency %A Biau, Julien %A Cussat-Blanc, Sylvain %A Luga, Herve %Y Smith, Stephen %Y Correia, Joao %Y Cintrano, Christian %S 27th International Conference, EvoApplications 2024 %S LNCS %D 2024 %8 March 5 apr %V 14634 %I Springer %C Aberystwyth %F Biau:2024:evoapplications %X For real-time applications in embedded systems, an efficient image filter is not defined solely by its accuracy but by the delicate balance it strikes between precision and computational cost. While one approach to manage an algorithm computing demands involves evaluating its complexity, an alternative strategy employs a multi-objective algorithm to optimize both precision and computational cost. we introduce a multi-objective adaptation of Cartesian Genetic Programming aimed at enhancing image filter performance. We refine the existing Cartesian Genetic Programming framework for image processing by integrating the elite Non-dominated Sorting Genetic Algorithm into the evolutionary process, thus enabling the generation of a set of Pareto front solutions that cater to multiple objectives. To assess the effectiveness of our framework, we conduct a study using a Urban Traffic dataset and compare our results with those obtained using the standard framework employing a mono-objective evolutionary strategy. Our findings reveal two key advantages of this adaptation. Firstly, it generates individuals with nearly identical precision in one objective while achieving a substantial enhancement in the other objective. Secondly, the use of the Pareto front during the evolution process expands the research space, yielding individuals with improved fitness. %K genetic algorithms, genetic programming, Genetic Improvement, Cartesian Genetic Programming, CGP-IP-GI, MOGA, NSGA-II, Island model, Python, Multi-Objective, Image processing, OpenCV, Real Time Applications, Embedded Systems %R 10.1007/978-3-031-56852-7_2 %U https://rdcu.be/dDZHh %U http://dx.doi.org/10.1007/978-3-031-56852-7_2 %P 19-34 %0 Conference Proceedings %T Tree Structured Rules in Genetic Algorithms %A Bickel, Authur S. %A Bickel, Riva Wenig %Y Grefenstette, John J. %S Genetic Algorithms and their Applications: Proceedings of the second International Conference on Genetic Algorithms %D 1987 %8 28 31 jul %I Lawrence Erlbaum Associates %C MIT, Cambridge, MA, USA %F Bickel:1989:tsrGA %X We apply genetic algorithm techniques to the creation and use of lists of tree- structured production rules varying in length and complexity. Actions, conditions and operators are randomly chosen from tables of possibilities. Use of the techniques of facilitated by the notions of time delay and dependency in performing tests or observations and by accounting for indeterminate test results. GENES, a general program, can adapt to a variety of systems by changing the contents of the various tables to accomodate the domain of a particular problem and by substituting a library of appropriate cases. %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icga1987/Bickel_1989_tsrGA.pdf %P 77-81 %0 Conference Proceedings %T Evolutionary optimization of mechanical and control design. Application to active endoscopes %A Bidaud, Philippe %A Chapelle, Frederic %A Dumont, G. %Y Bianchi, Giovanni %Y Guinot, Jean-Claude %Y Rzymkowski, Cezary %S Theory and Practice of Robots and Manipulators: Proceedings of the Fourteenth Cism-IFToMM Symposium %S RoManSy %D 2002 %8 jul %N 14 %I Springer Verlag %C Udine, Italy %F Bidaud:2002:romansy %K genetic algorithms, genetic programming %U http://www.springer.com/physics/classical+continuum+physics/book/978-3-211-83691-0 %P 317-330 %0 Conference Proceedings %T Evolution of Cellular Automata with Conditionally Matching Rules %A Bidlo, Michal %A Vasicek, Zdenek %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 Bidlo:2013:CEC %K genetic algorithms, genetic programming, Automata, two-dimensional cellular automata %R 10.1109/CEC.2013.6557699 %U http://dx.doi.org/10.1109/CEC.2013.6557699 %P 1178-1185 %0 Conference Proceedings %T Evolution of Approximate Functions for Image Thresholding %A Bidlo, Michal %S 2021 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2021 %8 dec %F Bidlo:2021:SSCI %X This paper investigates the use of approximate addition and multiplication for designing image thresholding functions. Cartesian Genetic Programming is applied for the evolutionary design of circuits using various implementations of the approximate operations. The results are presented for various experimental setups and compared with the case when only exact addition and multiplication is considered. It will be shown that for some range of error metrics of the approximate operations the evolution provides solutions that are better than those provided by the exact operations. Moreover, approximate components allows reducing the implementation area of the resulting functions. %K genetic algorithms, genetic programming, Cartesian genetic programming %R 10.1109/SSCI50451.2021.9659876 %U http://dx.doi.org/10.1109/SSCI50451.2021.9659876 %0 Thesis %T Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm %A Bieleveld, Michel Jan Marinus %D 2016 %8 September %C Brazil %C Computer Engineering, Escola Politecnica %G en %F MichelJanMarinusBieleveldCorr16 %X Biodiversity, the variety of life on the planet, is declining due to climate change, population and species interactions and as the result f demographic and landscape dynamics. Integrated model-based assessments play a key role in understanding and exploring these complex dynamics and have proven use in conservation planning. Model-based assessments using Species Distribution Models constitute an efficient means of translating limited point data to distribution probability maps for current and future scenarios in support of conservation decision making. The aims of this doctoral study were to investigate; (1) the use of a hybrid genetic programming to build high quality models that handle noisy real-world presence and absence data, (2) the extension of this solution to exploit the parallelism inherent to genetic programming for fast scenario based decision making tasks, and (3) a conceptual framework to share models in the hope of enabling research synthesis. Subsequent to this, the quality of the method, evaluated with the true skill statistic, was examined with two case studies. The first with a dataset obtained by defining a virtual species, and the second with data extracted from the North American Breeding Bird Survey relating to mourning dove (Zenaida macroura). In these studies, the produced models effectively predicted the species distribution up to 30percent of error rate both presence and absence samples. The parallel implementation based on a twenty-node c3.xlarge Amazon EC2 StarCluster showed a linear speedup due to the multiple-deme coarse-grained design. The hybrid fuzzy genetic programming algorithm generated under certain consitions during the case studies significantly better transferable models. %K genetic algorithms, genetic programming, applied and specific algorithms, bioclimatologia, ecological niche models, fuzzy logic, species distribution modelling %9 Tese de Doutorado %9 Ph.D. thesis %U http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/ %0 Conference Proceedings %T PHOG: Probabilistic Model for Code %A Bielik, Pavol %A Raychev, Veselin %A Vechev, Martin T. %Y Balcan, Maria-Florina %Y Weinberger, Kilian Q. %S Proceedings of the 33nd International Conference on Machine Learning, ICML 2016 %S JMLR Workshop and Conference Proceedings %D 2016 %8 jun 19 24 %V 48 %I PMLR %C New York City, NY, USA %F DBLP:conf/icml/BielikRV16 %X We introduce a new generative model for code called probabilistic higher order grammar (PHOG). PHOG generalises probabilistic context free grammars (PCFGs) by allowing conditioning of a production rule beyond the parent non-terminal, thus capturing rich contexts relevant to programs. Even though PHOG is more powerful than a PCFG, it can be learned from data just as efficiently. We trained a PHOG model on a large JavaScript code corpus and show that it is more precise than existing models, while similarly fast. As a result, PHOG can immediately benefit existing programming tools based on probabilistic models of code. %K genetic algorithms, genetic programming, seeding %U http://proceedings.mlr.press/v48/bielik16.html %P 2933-2942 %0 Journal Article %T Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism %A Biesheuvel, Cornelis J. %A Siccama, Ivar %A Grobbee, Diederick E. %A Moons, Karel G. M. %J Journal of Clinical Epidemiology %D 2004 %8 jun %V 57 %N 6 %F Biesheuvel:2004:JCE %X Objective Genetic programming is a search method that can be used to solve complex associations between large numbers of variables. It has been used, for example, for myoelectrical signal recognition, but its value for medical prediction as in diagnostic and prognostic settings, has not been documented. Study design and setting We compared genetic programming and the commonly used logistic regression technique in the development of a prediction model using empirical data from a study on diagnosis of pulmonary embolism. Using part (67%) of the data, we developed and internally validated (using bootstrapping techniques) a diagnostic prediction model by genetic programming and by logistic regression, and compared both on their predictive ability in the remaining data (validation set). Results In the validation set, the area under the ROC curve of the genetic programming model was significantly larger (0.73; 95%CI: 0.64-0.82) than that of the logistic regression model (0.68; 0.59-0.77). The calibration of both models was similar, indicating a similar amount of overoptimism. Conclusion Although the interpretation of a genetic programming model is less intuitive and this is the first empirical study quantifying its value for medical prediction, genetic programming seems a promising technique to develop prediction rules for diagnostic and prognostic purposes. %K genetic algorithms, genetic programming, Logistic regression, Prediction, Diagnostic research, Discrimination, Reliability %9 journal article %R 10.1016/j.jclinepi.2003.10.011 %U http://igitur-archive.library.uu.nl/med/2006-0906-200235/grobbee_04_geneticprogrammingoutperformed.pdf %U http://dx.doi.org/10.1016/j.jclinepi.2003.10.011 %P 551-560 %0 Thesis %T Diagnostic Research : improvements in design and analysis %A Biesheuvel, Cornelis Jan %D 2005 %C Holland %C Universiteit Utrecht %F biesheuvel:thesis %X In the era of evidence-based medicine, diagnostic procedures also need to undergo critical evaluations. In contrast to guidelines for randomised trials and observational etiologic studies, principles and methods for diagnostic evaluations are still incomplete. The research described in this thesis was conducted to further improve the methods for design and analysis of diagnostic studies. In the past, most diagnostic accuracy studies followed a univariable or single test approach with the aim to quantify the sensitivity, specificity or likelihood ratio. However, single test studies and measures do not reflect a test’s added value. It is not the singular association between a particular test result or predictor and the diagnostic outcome that is informative, but the test’s value independent of diagnostic information. Multivariable modelling is necessary to estimate the value of a particular test conditional on other test results. However, diagnostic prediction rules are not the solution to everything. They have certain drawbacks, such as overoptimistic accuracy when applied to new patients. Recently, methods have been described to overcome some of these drawbacks. Typically, in diagnostic research one selects a cohort of patients with an indication for the diagnostic procedure at interest as defined by the patients’ suspicion of having the disease of interest. The data are analysed cross-sectionally. When appropriate analyses are applied, results from nested case-control studies should be virtually identical to results based on a full cohort analysis. We showed that the nested case-control design offers investigators a valid and efficient alternative for a full cohort approach in diagnostic research. This may be particularly important when the results of the test under study are costly or difficult to collect. It is suggested that randomised controlled trials deliver the highest level of evidence to answer research questions. The paradigm of a randomised study design has also been applied to diagnostic research. We described that a randomised study design is not always necessary to evaluate the value of a diagnostic test to change patient outcome. A test’s effect on patient outcome can be inferred and indeed considered as quantified -using decision analysis- 1) if the test is meant to include or exclude a disease for which an established reference is available, 2) if a cross-sectional accuracy study has shown the test’s ability to adequately detect the presence or absence of that disease based on the reference, and finally 3) if proper, randomised therapeutic studies have provided evidence on efficacy of the optimal management of this disease. In such instances diagnostic research does not require an additional randomised comparison between two (or more) ’test-treatment strategies’ (one with and one without the test under study) to establish the test’s effect on patient outcome. Accordingly, diagnostic research -including the quantification of the effects of diagnostic testing on patient outcome- may be executed more efficiently. Diagnostic research aims to quantify a test’s added contribution given other diagnostic information available to the physician in determining the presence or absence of a particular disease. Commonly, diagnostic prediction rules use dichotomous logistic regression analysis to predict the presence or absence of a disease. We showed that genetic programming and polytomous modelling are promising alternatives for the conventional dichotomous logistic regression analysis to develop diagnostic prediction rules. The main advantage of genetic programming is the possibility to create more flexible models with better discrimination. This is especially important in large data sets in which complex interactions between predictors and outcomes may be present. %K genetic algorithms, genetic programming, diagnosis, methodology, prediction research %9 Ph.D. thesis %U http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/ %0 Journal Article %T A comparative analysis of boosting and genetic programming techniques for predicting mechanical properties of soilcrete materials %A Bin Inqiad, Waleed %A Javed, Muhammad Faisal %A Siddique, Muhammad Shahid %A Alarifi, Saad S. %A Alabduljabbar, Hisham %J Materials Today Communications %D 2024 %V 40 %@ 2352-4928 %F Bin-Inqiad:2024:mtcomma %X This study aims to predict compressive strength (CS) and modulus of elasticity (E) of soilcrete mixes to foster their widespread use in the industry. Soilcfigrete has the potential to promote sustainable construction practices by making use of locally available raw materials. However, the accurate determination of mechanical properties of soilcrete mixes is inevitable to foster their widespread use. Thus, this study employs different machine learning algorithms including Extreme Gradient Boosting (XGB), Gene Expression Programming (GEP), AdaBoost, and Multi Expression Programming (MEP) for this purpose. The XGB and AdaBoost algorithms were implemented using python programming language while MEP and GEP were implemented using specialized softwares. The data used for model development was obtained from previously published literature containing five input parameters and two output parameters. This data was split into two sets named training and testing sets for training and testing of the algorithms respectively. The developed models for CS and E prediction were validated using several error metrices and residual comparison. The objective function value which should be closer to zero for an accurate model is the least for XGB model for prediction of both variables (0.0036 for CS and 0.00315 for E). Moreover, shapley analysis was carried out using XGB model to get insights into the underlying model framework. The results highlighted that water-to-binder ratio (W/B), metakaolin (MK), and ultrasonic pulse velocity (UV) are the most significant variables for predicting E and CS of soilcrete materials. These insights can be used practically to optimise the mixture composition of soilcrete mixes according to different site requirements %K genetic algorithms, genetic programming, Machine learning, Soilcrete materials, Metakaolin, Compressive strength, Modulus of elasticity, Shapley analysis, gene expression programming %9 journal article %R 10.1016/j.mtcomm.2024.109920 %U https://www.sciencedirect.com/science/article/pii/S2352492824019019 %U http://dx.doi.org/10.1016/j.mtcomm.2024.109920 %P 109920 %0 Journal Article %T Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites (ECC) %A Bin Inqiad, Waleed %A Javed, Muhammad Faisal %A Siddique, Muhammad Shahid %A Khan, Naseer Muhammad %A Alkhattabi, Loai %A Abuhussain, Maher %A Alabduljabbar, Hisham %J Materials Today Communications %D 2024 %V 39 %@ 2352-4928 %F Bin-Inqiad:2024:mtcomm %X Plain concrete is weak against tension and has low Tensile Strain Capacity (TSC) which significantly affects its long-term performance. To overcome this issue, Engineered Cementitious Composites (ECC) were developed by incorporating polymer fibres in the cement matrix which increases ductility and provides higher TSC than plain concrete and they have emerged as a viable alternative to brittle plain concrete. This study is conducted in an attempt to develop empirical prediction models for TSC prediction of ECC without requiring extensive experimental procedures. For this purpose, two evolutionary programming techniques known as Multi Expression Programming (MEP), Gene Expression Programming (GEP) along with two boosting-based techniques: AdaBoost and Extreme Gradient Boosting (XGB) were developed using data collected from published literature. The gathered dataset had seven input parameters including water-to-binder ratio, sand, fibre content, cement, fly ash, superplasticizer, and age etc. and only one output parameter i.e., TSC. The error assessment of developed models was done using correlation coefficient, Mean Absolute Error (MAE), and Objective Function (OF) etc. and the error comparison showed that XGB has the highest accuracy having the least OF value of 0.081 as compared to 0.11 of AdaBoost, 0.13 of GEP, and 0.16 of MEP. Shapley additive analysis was conducted on the XGB model since it proved to be the most accurate, and the results highlighted that fibre content, age, and water-to-binder ratio are the most important features to predict TSC of ECC %K genetic algorithms, genetic programming, Machine learning, Engineered cementitious composites, Tensile strain capacity, Fibres, Shapley additive analysis, gene expression programming %9 journal article %R 10.1016/j.mtcomm.2024.109222 %U https://www.sciencedirect.com/science/article/pii/S2352492824012030 %U http://dx.doi.org/10.1016/j.mtcomm.2024.109222 %P 109222 %0 Conference Proceedings %T LLM Fault Localisation within Evolutionary Computation Based Automated Program Repair %A Bin Murtaza, Sardar %A Mccoy, Aidan %A Ren, Zhiyuan %A Murphy, Aidan %A Banzhaf, Wolfgang %Y Hemberg, Erik %Y Senkerik, Roman %Y O’Reilly, Una-May %Y Pluhacek, Michal %Y Eftimov, Tome %S Large Language Models for and with Evolutionary Computation Workshop %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F bin-murtaza:2024:GECCOcomp %X Repairing bugs can be a daunting task for even a human experienced in debugging, so naturally, attempting to automatically repair programs with a computer system is quite challenging. The existing methods of automated program repair leave a lot of room for improvement. Fault localization, which aims to find lines of code that are potentially buggy, minimises the search space of an automated program repair system. Recent work has shown improvement in these fault localization methods, with the use of Large Language Models. Here, we propose a system where a LLM-based fault localization tool, which we call SemiAutoFL, is used within a fully automatic program repair program, ARJA-e. We show that using LLM-based fault localization with ARJA-e can significantly improve its performance on real world bugs. ARJA-e with SemiAutoFL can repair 10 bugs that ARJA-e was previously unable to so do. This finding adds to our understanding of how to improve fault localization and automated program repair, highlighting the potential for more efficient and accurate fault localisation methods being applied to automated program repair. %K genetic improvement, fault localisation, large language models, ANN %R 10.1145/3638530.3664174 %U http://dx.doi.org/10.1145/3638530.3664174 %P 1824-1829 %0 Conference Proceedings %T An abstraction-based genetic programming system %A Binard, Franck %A Felty, Amy %Y Bosman, Peter A. N. %S Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO’2007) %D 2007 %8 July 11 jul %I ACM Press %C London, United Kingdom %F 1274004 %X We extend tree-based typed Genetic Programming (GP) representation schemes by introducing System F, an expressive l-calculus, for representing programs and types. At the level of programs, System F provides higher-order programming capabilities with functions and types as first-class objects, e.g., functions can take other functions and types as parameters. At the level of types, System F provides parametric polymorphism. The expressiveness of the system provides the potential for a genetic programming system to evolve looping, recursion, lists, trees and many other typical programming structures and behaviour. This is done without introducing additional external symbols in the set of predefined functions and terminals of the system. In fact, we actually remove programming structures such as if/then/else, which we replace by two abstraction operators. We also change the composition of parse trees so that they may directly include types. %K genetic algorithms, genetic programming, lambda calculus, languages, polymorphism, types %R 10.1145/1274000.1274004 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2415.pdf %U http://dx.doi.org/10.1145/1274000.1274004 %P 2415-2422 %0 Conference Proceedings %T Genetic Programming with Polymorphic Types and Higher-Order Functions %A Binard, Franck %A Felty, Amy %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 Binard:2008:gecco %X we introduce our new approach to program representation for genetic programming (GP). We replace the usual s-expression representation scheme by a strongly-typed abstraction-based representation scheme. This allows us to represent many typical computational structures by abstractions rather than by functions defined in the GP system terminal set. The result is a generic GP system that is able to express programming structures such as recursion and data types without explicit definitions. We demonstrate the expressive power of this approach by evolving simple Boolean programs without defining a set of terminals. We also evolve programs that exhibit recursive behaviour without explicitly defining recursion specific syntax in the terminal set. we present our approach and experimental results. %K genetic algorithms, genetic programming, lambda calculus, polymorphism, types %R 10.1145/1389095.1389330 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1187.pdf %U http://dx.doi.org/10.1145/1389095.1389330 %P 1187-1194 %0 Thesis %T Abstraction-Based Genetic Programming %A Binard, Franck J. L. %D 2009 %C Ottawa, Canada %C Ottawa-Carleton Institute for Computer Science, School of Information Technology and Engineering, Faculty of Engineering, University of Ottawa %F Binard:thesis %X This thesis describes a novel method for representing and automatically generating computer programs in an evolutionary computation context. Abstraction-Based Genetic Programming (ABGP) is a typed Genetic Programming representation system that uses System F, an expressive lambda-calculus, to represent the computational components from which the evolved programs are assembled. ABGP is based on the manipulation of closed, independent modules expressing computations with effects that have the ability to affect the whole genotype . These modules are plugged into other modules according to precisely defined rules to form complete computer programs. The use of System F allows the straightforward representation and use of many typical computational structures and behaviours (such as iteration, recursion, lists and trees) in modular form. This is done without introducing additional external symbols in the set of predefined functions and terminals of the system. In fact, programming structures typically included in GP terminal sets, such as if then else, may be removed and represented as abstractions in ABGP for the same problems. ABGP also provides a search space partitioning system based on the structure of the genotypes, similar to the species partitioning system of living organisms and derived from the Curry-Howard isomorphism. This thesis also presents the results obtained by applying this method to a set of problems. %K genetic algorithms, genetic programming %9 Ph.D. thesis %R 10.20381/ruor-13147 %U http://www.site.uottawa.ca/~fbinard/Articles/FranckBinardPhDThesisLastVersion.pdf %U http://dx.doi.org/10.20381/ruor-13147 %0 Book %T Abstraction-Based Genetic Programming: An Application of the polymorphically-typed lambda calculus to genetic programming %A Binard, Franck %D 2009 %8 June %I Verlag Dr. Mueller %@ 3-639-19173-0 %F Binard:book %X Abstraction-Based Genetic Programming (ABGP) is a novel Genetic Programming (GP) system in which the set of all possible genotypes is partitioned by the proofs to which each program is linked via the Curry-Howard isomorphism. In the context of ABGP, proofs are related to computer programs in the same way as species are related to organisms in the biological world. They can be seen as patterns into which alleles of genes may be plugged in. In this analogy, genes are types and an allele of a gene is a closed typed computational block that may be combined with other blocks to form an organism. The type of an allele is the gene to which it corresponds. %K genetic algorithms, genetic programming %U https://www.amazon.co.uk/Abstraction-Based-Genetic-Programming-Application-polymorphically-typed/dp/3639191730 %0 Conference Proceedings %T A Genetic Multiple Kernel Relevance Vector Regression Approach %A Bing, Wu %A Wen-qiong, Zhang %A Jia-hong, Liang %S Second International Workshop on Education Technology and Computer Science (ETCS), 2010 %D 2010 %8 mar %V 3 %F Bing:2010:ETCS %X Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The kernel function and parameter selection is a key problem in the research of RVM. The real-world application and recent researches have emphasised the requirement to multiple kernel learning. This paper proposes a novel regression technique, called Genetic Multiple Kernel Relevance Vector Regression (GMK RVR), which combines genetic programming and relevance vector regression to evolve a multiple kernel function. The proposed technique are compared with those of a standard RVR using the Polynomial, Gaussian RBF and Sigmoid kernel with various parameter settings, based on several benchmark problems. Numerical experiments show that the GMK performs better than such widely used kernels and prove the validation of the GMK. %K genetic algorithms, GMK validation, Gaussian RBF, Sigmoid kernel, benchmark problems, genetic multiple kernel relevance vector regression, kernel function, multiple kernel function, multiple kernel learning, parameter selection, relevance vector machine, sparse Bayesian extension, state-of-the-art technique, support vector machine, Bayes methods, learning (artificial intelligence), pattern classification, regression analysis, support vector machines %R 10.1109/ETCS.2010.154 %U http://dx.doi.org/10.1109/ETCS.2010.154 %P 52-55 %0 Journal Article %T Application of gene expression programming in hot metal forming for intelligent manufacturing %A Bingol, Sedat %A Kilicgedik, Hidir Yanki %J Neural Computing and Applications %D 2018 %V 30 %N 3 %F bingol:2018:NCaA %K genetic algorithms, genetic programming, gene expression programming %9 journal article %R 10.1007/s00521-016-2718-5 %U http://link.springer.com/article/10.1007/s00521-016-2718-5 %U http://dx.doi.org/10.1007/s00521-016-2718-5 %0 Journal Article %T A comparison of tree- and line-oriented observational slicing %A Binkley, David %A Gold, Nicolas %A Islam, Syed %A Krinke, Jens %A Yoo, Shin %J Empirical Software Engineering %D 2019 %8 oct %V 24 %@ 1382-3256 %F emse19 %O Special Section on Source Code Analysis and Manipulation %X Observation-based slicing and its generalization observational slicing are recently-introduced, language-independent dynamic slicing techniques. They both construct slices based on the dependencies observed during program execution, rather than static or dynamic dependence analysis. The original implementation of the observation-based slicing algorithm used lines of source code as its program representation. A recent variation, developed to slice modeling languages (such as Simulink), used an XML representation of an executable model. We ported the XML slicer to source code by constructing a tree representation of traditional source code through the use of srcML. This work compares the tree- and line-based slicers using four experiments involving twenty different programs, ranging from classic benchmarks to million-line production systems. The resulting slices are essentially the same size for the majority of the programs and are often identical. However, structural constraints imposed by the tree representation sometimes force the slicer to retain enclosing control structures. It can also bog down trying to delete single-token subtrees. This occasionally makes the tree-based slices larger and the tree-based slicer slower than a parallelised version of the line-based slicer. In addition, a Java versus C comparison finds that the two languages lead to similar slices, but Java code takes noticeably longer to slice. The initial experiments suggest two improvements to the tree-based slicer: the addition of a size threshold, for ignoring small subtrees, and subtree replacement. The former enables the slicer to run 3.4 times faster while producing slices that are only about 9percent larger. At the same time the subtree replacement reduces size by about 8 to 12 percent and allows the tree-based slicer to produce more natural slices. %K genetic algorithms, genetic programming, ORBS, source code deletion, srcML, XML %9 journal article %R 10.1007/s10664-018-9675-9 %U http://www.cs.ucl.ac.uk/staff/j.krinke/publications/emse19.pdf %U http://dx.doi.org/10.1007/s10664-018-9675-9 %P 3077-3113 %0 Journal Article %T Genetic Algorithms, Classifier Systems and Genetic Programming and their Use in the Models of Adaptive Behaviour and Learning %A Birchenhall, C. R. %J The Economic Journal %D 1995 %V 105 %N 430 %@ 00130133 %F Birchenhall:1995:EJ %K genetic algorithms, genetic programming %9 journal article %U http://links.jstor.org/sici?sici=0013-0133%28199505%29105%3A430%3C788%3AGACSAG%3E2.0.CO%3B2-%23 %P 788-795 %0 Conference Proceedings %T EEG Wavelet Classification for Fall Detection with Genetic Programming %A Bird, Jordan J. %S PETRA ’22: The 15th International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece, 29 June 2022 - 1 July 2022 %D 2022 %I ACM %F DBLP:conf/petra/Bird22 %K genetic algorithms, genetic programming %R 10.1145/3529190.3535339 %U https://doi.org/10.1145/3529190.3535339 %U http://dx.doi.org/10.1145/3529190.3535339 %P 376-382 %0 Journal Article %T Fall compensation detection from EEG using neuroevolution and genetic hyperparameter optimisation %A Bird, Jordan J. %A Lotfi, Ahmad %J Genetic Programming and Evolvable Machines %D 2023 %8 jun %V 24 %N 2 %@ 1389-2576 %F Bird:2023:GPEM %O Online first %X Detecting fall compensatory behaviour from large EEG datasets poses a difficult problem in big data which can be alleviated by evolutionary computation-based machine learning strategies. hyperheuristic optimisation solutions via evolutionary optimisation of deep neural network topologies and genetic programming of machine learning pipelines will be investigated. Wavelet extractions from signals recorded during physical activities present a binary problem for detecting fall compensation. The earlier results show that a Gaussian process model achieves an accuracy of 86.48percent. Following this, artificial neural networks are evolved through evolutionary algorithms and score similarly to most standard models; the hyperparameters chosen are well outside the bounds of batch or manual searches. Five iterations of genetic programming scored higher than all other approaches, at a mean 90.52percent accuracy. The best pipeline extracted polynomial features and performed Principal Components Analysis, before machine learning through a randomised set of decision trees, and passing the class prediction probabilities to a 72-nearest-neighbour algorithm. The best genetic solution could infer data in 0.02 seconds, whereas the second best genetic programming solution (89.79percent) could infer data in only 0.3 milliseconds. %K genetic algorithms, genetic programming, ANN, Evolutionary optimisation, Fall detection, EEG, Hyperheuristics, Signal classification %9 journal article %R 10.1007/s10710-023-09453-3 %U https://rdcu.be/dcJdp %U http://dx.doi.org/10.1007/s10710-023-09453-3 %P Articlenumber:6 %0 Thesis %T Optimizing Resource Allocations for Dynamic Interactive Applications %A Bird, Sarah Lynn %D 2014 %8 may %C USA %C University of California, Berkeley %F Bird:thesis %X Modern computing systems are under intense pressure to provide guaranteed responsiveness to their workloads. Ideally, applications with strict performance requirements should be given just enough resources to meet these requirements consistently, without unnecessarily siphoning resources from other applications. However, executing multiple parallel, real-time applications while satisfying response time requirements is a complex optimization problem and traditionally operating systems have provided little support to provide QoS to applications. As a result, client, cloud, and embedded systems have all resorted to over-provisioning and isolating applications to guarantee responsiveness. Instead, we present PACORA, a resource allocation framework designed to provide responsiveness guarantees to a simultaneous mix of high-throughput parallel, interactive, and real-time applications in an efficient, scalable manner. By measuring application behavior directly and using convex optimization techniques, PACORA is able to understand the resource requirements of applications and perform near-optimal resource allocation two percent from the best allocation in 1.4 milliseconds while only requiring a few hundred bytes of storage per application. %K genetic algorithms, genetic programming, PACORA, Matlab %9 Ph.D. thesis %U https://aspire.eecs.berkeley.edu/publication/optimizing-resource-allocations-for-dynamic-interactive-applications/ %0 Book Section %T Schemas and Genetic Programming %A Birk, Andreas %A Paul, Wolfgang J. %E Cruse, Holk %E Dean, Jeffrey %E Ritter, Helge %B Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic II %S Studies in Cognitive Systems %D 2001 %V 26 %I Kluwer %@ 0-7923-6666-2 %G en %F oai:CiteSeerPSU:397549 %X To investigate the mechanisms which enable systems to learn is among the most challenging of research activities. In computer science alone it is pursued by at least three communities (Carbonel 1990; Natarajan 1991; Ritter et al. 1991). The overwhelming majority of all studies treats situations with strong inductive bias, i.e. there is a fairly narrow class H of algorithms and the concept or algorithm to be learned is known a priori to lie in that class H. With the help of schemas and genetic programming we describe systems which: interact with the real world make theories about the consequences of their actions and dynamically adjust inductive bias. We present experimental data related to learning geometric concepts and moving a block in a microworld. %K genetic algorithms, genetic programming %R 10.1007/978-94-010-0870-9_50 %U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-135-22-33673255-0,00.html %U http://dx.doi.org/10.1007/978-94-010-0870-9_50 %P 345-357 %0 Journal Article %T Learning implicit yield surface models with uncertainty quantification for noisy datasets %A Birky, Donovan %A Emery, John %A Hamel, Craig %A Hochhalter, Jacob %J Computer Methods in Applied Mechanics and Engineering %D 2025 %V 436 %@ 0045-7825 %F Birky:2025:cma %X Materials often exhibit stochastic mechanical behaviours due to their inherent intrinsic variability. Data acquisition also introduces extrinsic noise into data. To learn yield surface models under uncertainty, we present a method that uses genetic programming based symbolic regression (GPSR) and a multi-objective fitness function (MOSR). Previous works have demonstrated using an implicit fitness metric in GPSR that compares the partial derivatives of proposed models with those of the data, allowing the generation of mechanics-guided, implicit yield surface models. MOSR adds to that a Bayesian fitness metric to simultaneously quantify parameter uncertainty. We test this method on benchmark implicit and physical test problems to demonstrate MOSR’s efficacy in finding implicit model forms on noisy data compared to the conventional implicit fitness metric. The results show that the MOSR algorithm prevents overfitting to noisy data, improves parameter estimates on data even with no noise present, and reduces model complexity, improving overall model interpretability. The MOSR method affords the ability to learn new and improved yield surface models while simultaneously quantifying the uncertainty in model parameters, leading to enhanced model interpretability %K genetic algorithms, genetic programming, Symbolic regression, Machine learning, Uncertainty quantification %9 journal article %R 10.1016/j.cma.2025.117738 %U https://www.sciencedirect.com/science/article/pii/S0045782525000106 %U http://dx.doi.org/10.1016/j.cma.2025.117738 %P 117738 %0 Conference Proceedings %T Supporting Menu Layout Design by Genetic Programming %A Birtolo, Cosimo %A Armenise, Roberto %A Troiano, Luigi %Y Filipe, Joaquim %Y Cordeiro, José %S Proceedings of the 12th International Conference on Enterprise Information Systems (ICEIS 2010) %D 2010 %8 August 12 jun %C Funchal, Madeira, Portugal %F Birtolo:2010:ICEIS %X Graphical User Interfaces heavily rely on menus to access application functionalities. Therefore designing properly menus poses relevant usability issues to face. Indeed, trading off between semantic preferences and usability makes this task not so easy to be performed. Meta-heuristics represent a promising approach in assisting designers to specify menu layouts. In this paper, we propose a preliminary experience in adopting Genetic Programming as a natural means for evolving a menu hierarchy towards optimal structure. %K genetic algorithms, genetic programming: poster, SBSE %U https://www.poste.it/azienda/research_development/pubblicazioni/ICEIS10%20-%20SUPPORTING%20MENU%20LAYOUT%20DESIGN%20BY%20GP.pdf %0 Conference Proceedings %T Using Adaptive Agents to Study Bilateral Contracts and Trade Networks %A Bisat, Mona T. %A Richter, Charles W. %A Sheble, Gerald B. %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 bisat:1998:ussbctn %X This research is an extension of research done by Charles Richter, Gerald Sheble’ and Dan Ashlock (1997, 1998) on double auction bidding strategies for electric utilities which trade competitively. This research considers the network topology and whether a successful bid transaction can occur given the flow constraints on the network. The ATC (Available Transmission Capacity) of the network is a flow constraint indicator that is used to provide feedback to agents attempting to engage in bilateral contracts. The aim is to develop adaptive agents that are able to recognize with whom they can enter a profitable bilateral contract. In other words, the agents develop preferential partner selection lists and bidding strategies in a simulated electric market. The idea of evolving preferred trading partner lists comes from the Trade Network Game (TNG) developed by Tesfatsion, Ashlock and Stanley (1995). The strategies being developed by the method described here are adaptive. The strategies are encoded in GP-Automata, a technique which combines genetic programming and finite state automata. %K genetic algorithms, genetic programming, electricity transmission capacity, trade network game, double auction bid, power utility %U http://dakotarichter.com/papers/gp98PosterPaperBisatRichterSheble.pdf %P 23-27 %0 Thesis %T Model and Algorithm Selection in Statistical Learning and Optimization %A Bischl, Bernd %D 2014 %8 July %C Germany %C Fakultaet Statistik, Technische Universitaet Dortmund %F Bischl:thesis %X Modern data-driven statistical techniques, e.g., non-linear classification and regression machine learning methods, play an increasingly important role in applied data analysis and quantitative research. For real-world we do not know a priori which methods will work best. Furthermore, most of the available models depend on so called hyper- or control parameters, which can drastically influence their performance. This leads to a vast space of potential models, which cannot be explored exhaustively. Modern optimization techniques, often either evolutionary or model-based, are employed to speed up this process. A very similar problem occurs in continuous and discrete optimization and, in general, in many other areas where problem instances are solved by algorithmic approaches: Many competing techniques exist, some of them heavily parametrized. Again, not much knowledge exists, how, given a certain application, one makes the correct choice here. These general problems are called algorithm selection and algorithm configuration. Instead of relying on tedious, manual trial-and-error, one should rather employ available computational power in a methodical fashion to obtain an appropriate algorithmic choice, while supporting this process with machine-learning techniques to discover and exploit as much of the search space structure as possible. In this cumulative dissertation I summarize nine papers that deal with the problem of model and algorithm selection in the areas of machine learning and optimization. Issues in benchmarking, resampling, efficient model tuning, feature selection and automatic algorithm selection are addressed and solved using modern techniques. I apply these methods to tasks from engineering, music data analysis and black-box optimization. The dissertation concludes by summarizing my published R packages for such tasks and specifically discusses two packages for parallelization on high performance computing clusters and parallel statistical experiments. %K genetic algorithms, genetic programming, SVM, model selection, algorithm selection, algorithm configuration, tuning, benchmarking, machine learning %9 Ph.D. thesis %R 10.17877/DE290R-7142 %U https://eldorado.tu-dortmund.de/bitstream/2003/32861/1/phd.pdf %U http://dx.doi.org/10.17877/DE290R-7142 %0 Journal Article %T Modeling, optimization and comparative study on abatement of fluoride from synthetic solution using activated laterite soil and fly ash %A Bishayee, Bhaskar %A Kumar, Abhay %A Lahiri, Sandip Kumar %A Dutta, Susmita %A Ruj, Biswajit %J Groundwater for Sustainable Development %D 2023 %V 23 %@ 2352-801X %F BISHAYEE:2023:gsd %X The supply of potable water by proper treatment of fluoride laden ground water is a challenging task. Batch experiments were executed with activated laterite soil and fly ash individually for removal of fluoride. The process parameters such as particle size (100-620 ?m), dose of adsorbent (10-60 g/L), initial concentration of fluoride (2-12 mg/L), agitation speed (20-120 rpm), contact time (0.5-10 h) and pH (4.5-9.5) were investigated and it was observed that activated laterite soil had better fluoride removal efficiency than fly ash. At pH 4.5, contact time 10 h, particle size 100 ?m, adsorbent dose 60 g/L, initial concentration of fluoride 12 mg/L, and agitation speed 120 rpm, maximum removal efficiencies of fluoride using activated laterite soil and fly ash were found as 85.91 pm 0.62percent and 77.1 pm 0.39percent, respectively. Kinetic study was performed and Pseudo 2nd order kinetic model was found to fit the kinetic data best. The Freundlich isotherm model fit the equilibrium data fairly well. The values of adsorption equilibrium constant as used in Freundlich isotherm model vis-a-vis adsorption capacity (KF) for activated laterite soil and fly ash were obtained as 0.1331 and 0.0772 ((mgg-1)(Lmg-1)1/n). A thermodynamic analysis was conducted to examine the process behaviour. In order to determine whether the adsorbent may be used for further cases, regeneration of the adsorbents was also done. Generally, two types of adsorption mechanisms happen like electrostatic attraction and ion exchange. The electrostatic force of attraction favours the adsorption of negatively charged fluoride ions on positively charged adsorbent surfaces. In ion exchange, fluoride ions are exchanged by hydroxyl and hydronium ion. Based on experimental data, Multi-Gene Genetic Programming (MGGP) model could accurately predict the removal efficiency of fluoride under various operating situations. Finally, using Genetic Algorithm (GA) optimization the maximum output values for both adsorbents were estimated as 99.14percent and 99.02percent %K genetic algorithms, genetic programming, Fluoride, Adsorption, Natural and waste material, Kinetic and equilibrium study %9 journal article %R 10.1016/j.gsd.2023.101016 %U https://www.sciencedirect.com/science/article/pii/S2352801X23001169 %U http://dx.doi.org/10.1016/j.gsd.2023.101016 %P 101016 %0 Conference Proceedings %T Feature Construction Using Genetic Programming for Classification of Images by Aesthetic Value %A Bishop, Andrew %A Ciesielski, Victor %A Trist, Karen %Y Romero, Juan %Y McDermott, James %Y Correia, Joao %S Evolutionary and Biologically Inspired Music, Sound, Art and Design - Third European Conference, EvoMUSART 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers %S Lecture Notes in Computer Science %D 2014 %V 8601 %I Springer %F conf/evoW/BishopCT14 %K genetic algorithms, genetic programming %U http://dx.doi.org/10.1007/978-3-662-44335-4 %P 62-73 %0 Conference Proceedings %T Evolutionary Exploration of Triply Periodic Minimal Surfaces via Quality Diversity %A Bishop, Jordan T. %A Jooste, Jason %A Howard, David %Y Sarker, Ruhul %Y Siarry, Patrick %Y Handl, Julia %Y Li, Xiaodong %Y Wagner, Markus %Y Garza-Fabre, Mario %Y Smith-Miles, Kate %Y Allmendinger, Richard %Y Bi, Ying %Y Dick, Grant %Y Gandomi, Amir H. %Y Martins, Marcella Scoczynski Ribeiro %Y Assimi, Hirad %Y Veerapen, Nadarajen %Y Sun, Yuan %Y Munyoz, Mario Andres %Y Kheiri, Ahmed %Y Su, Nguyen %Y Thiruvady, Dhananjay %Y Song, Andy %Y Neumann, Frank %Y Silva, Carla %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F bishop:2024:GECCO %X Triply Periodic Minimal Surfaces (TPMSs) are a family of mathematical structures that exhibit constant zero mean curvature and 3-dimensional periodicity. They are often used to produce cellular solids with advantageous structural, thermal, and optical properties. Existing applications represent TPMSs as trigonometric approximations of a Fourier series. Due to the mathematical difficulty of determining new exact forms and their approximations, previous work has mostly evaluated metrics based on geometry, manufacturability, and mechanical performance across parameterisations of a small set of known TPMS equations. In this work, we define TPMS-like structures as having low estimated mean curvature, and apply a coupling of Grammatical Evolution and Quality Diversity to generate a diverse set of novel structures of this kind. We additionally explore the effect of being TPMS-like on the manufacturability of evolved structures. Results show that many TPMS-like designs can be found for different combinations of total surface area and Gaussian curvature, and that there is not a strong relationship between how TPMS-like a design is and its manufacturability. Our method serves as a basis for future application of novel TPMS-like structures and exploration of the pairing of evolutionary design with generative approaches from broader machine learning. %K genetic algorithms, genetic programming, grammatical evolution, triply periodic minimal surface, quality diversity, evolutionary design, constrained optimisation, Real World Applications %R 10.1145/3638529.3654039 %U http://dx.doi.org/10.1145/3638529.3654039 %P 1165-1173 %0 Journal Article %T Conservative theory for long-term reliability-growth prediction [of software] %A Bishop, P. %A Bloomfield, R. %J IEEE Transactions on Reliability %D 1996 %8 dec %V 45 %N 4 %C Adelard, London, UK %@ 0018-9529 %F Bishop96 %X This paper describes a different approach to software reliability growth modeling which enables long-term predictions. Using relatively common assumptions, it is shown that the average value of the failure rate of the program, after a particular use-time, t, is bounded by N/(e/spl middot/t), where N is the initial number of faults. This is conservative since it places a worst-case bound on the reliability rather than making a best estimate. The predictions might be relatively insensitive to assumption violations over the longer term. The theory offers the potential for making long-term software reliability growth predictions based solely on prior estimates of the number of residual faults. The predicted bound appears to agree with a wide range of industrial and experimental reliability data. Less pessimistic results can be obtained if additional assumptions are made about the failure rate distribution of faults. %K software reliability, reliability theory, failure analysis, long-term reliability-growth prediction, software reliability growth modeling, program failure rate, use-time, initial fault number, worst-case bound, residual fault number, failure rate distribution %9 journal article %U http://ieeexplore.ieee.org/iel1/24/12134/00556578.pdf?isNumber=12134&prod=JNL&arnumber=556578&arSt=550&ared=560&arAuthor=Bishop%2C+P.%3B+Bloomfield%2C+R. %P 550-560 %0 Thesis %T Optimization of Hydrometallurgical Processing of Lean Manganese Bearing Resources %A Biswas, Arijit %D 2010 %C Kharagpur, India %C Metallurgical and Materials Engineering, Indian Institute of Technology %F Biswas:thesis %X An evolutionary multi-objective optimization framework is evolved to model the extraction process of manganese from lean manganese bearing resources. The primary objective of this thesis is to develop a generic flowsheet and to come up with a data driven modelling approach for this purpose. Flowsheets developed for processing low grade manganese ores, such as Polymetallic Sea nodules, via various processing routes are optimized using an Evolutionary Multi-objective strategy. The work also aims to provide a considerable insight towards understanding of the leaching processes pertinent to manganese extraction. To analyse and optimize the process flow sheets for treatment of low grade manganese ores, two hydrometallurgical routes based upon ammoniacal and acid leaching in presence of reducing agents are taken up. The analyses suggested that of particular significance is the grade of the ore being treated, the energy consumed and the associated costs, options for by-product recovery, and the relative price of the products. A process scheme has been optimized here for simultaneously maximizing the metal throughput and minimizing the direct operating costs incurred within constraints set for the operating variables. This leads to a multi-objective optimization problem, which has been conducted during this study for the leaching of polymetallic nodules. To analyse the non-linear kinetics of the leaching reaction of lean manganese bearing ores, an analytical model is developed along with a number of data driven models. Terrestrial lean manganese ores need to be processed in acidic medium in presence of reducing agents like glucose, lactose and sucrose, in order to extract manganese values from them. In this study data driven models based on Neural Network and Genetic Programming are compared for two different categories of manganese ores leached in sulphuric acid medium. A Predator-prey Genetic Algorithm approach developed for this purpose is pitted against a number of other established evolutionary techniques, in addition to a commercial software. A leaching model is evolved using the fitted leaching parameters from different data driven models and is thoroughly tested for the goodness of fit against the experimental data. The strategy adopted, once again, is generic in nature and the framework can be extended for any kind of hydrometallurgical process flowsheeting. %K genetic algorithms, genetic programming, Applied science, Chemical Engineering, Evolutionary Algorithm, Evolutionary neural network, Manganese ore, Materials science, Polymetallic sea nodules, Process flowsheeting, Sequential modular approach, Split fraction %9 Ph.D. thesis %U http://www.idr.iitkgp.ac.in/xmlui/handle/123456789/951 %0 Journal Article %T Data-Driven Multiobjective Analysis of Manganese Leaching from Low Grade Sources Using Genetic Algorithms, Genetic Programming, and Other Allied Strategies %A Biswas, Arijit %A Maitre, Ogier %A Mondal, Debanga Nandan %A Das, Syamal Kanti %A Sen, Prodip Kumar %A Collet, Pierre %A Chakraborti, Nirupam %J Materials and Manufacturing Processes %D 2011 %V 26 %N 3 %@ 1042-6914 %F Biswas:2011:MMP %X Data-driven models are constructed for leaching processes of various low grade manganese resources using various nature inspired strategies based upon genetic algorithms, neural networks, and genetic programming and subjected to a bi-objective Pareto optimization, once again using several evolutionary approaches. Both commercially available software and in-house codes were used for this purpose and were pitted against each other. The results led to an optimum trade-off between maximising the recovery, which is a profit oriented requirement, along with a minimisation of the acid consumption, which addresses the environmental concerns. The results led to a very complex scenario, often with different trends shown by the different methods, which were systematically analysed. %K genetic algorithms, genetic programming, Evolutionary algorithm, Leaching, Manganese, Multiobjective optimisation, Ocean nodules, Optimisation, Pareto frontier %9 journal article %R 10.1080/10426914.2010.544809 %U http://www.tandfonline.com/doi/abs/10.1080/10426914.2010.544809 %U http://dx.doi.org/10.1080/10426914.2010.544809 %P 415-430 %0 Conference Proceedings %T The Gene Expression Programming Applied to Demand Forecast %A Bittencourt, Evandro %A Schossland, Sidney %A Landmann, Raul %A Murilo de Aguiar, Denio %A De Oliveira, Adilson Gomes %Y Corchado, Emilio %Y Novais, Paulo %Y Analide, Cesar %Y Sedano, Javier %S Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010) %S Advances in Intelligent and Soft Computing %D 2010 %8 jun %V 73 %I Springer %C Guimaraes, Portugal %F conf/softcomp/BittencourtSLAO10 %X This paper examines the use of artificial intelligence (in particular the application of Gene Expression Programming, GEP) to demand forecasting. In the world of production management, many data that are produced in function of the of economic activity characteristics in which they belong, may suffer, for example, significant impacts of seasonal behaviours, making the prediction of future conditions difficult by means of methods commonly used. The GEP is an evolution of Genetic Programming,which is part of the Genetic Algorithms. GEP seeks for mathematical functions, adjusting to a given set of solutions using a type of genetic heuristics from a population of random functions. In order to compare the GEP, we have used the others quantitatives method. Thus, from a data set of about demand of consumption of twelve products line metal fittings, we have compared the forecast data. %K genetic algorithms, genetic programming, gene expression programming %R 10.1007/978-3-642-13161-5_25 %U http://dx.doi.org/10.1007/978-3-642-13161-5_25 %P 197-200 %0 Conference Proceedings %T Simultaneous Synthesis of Multiple Functions using Genetic Programming with Scaffolding %A Bladek, Iwo %A Krawiec, Krzysztof %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 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation %D 2016 %8 20 24 jul %I ACM %C Denver, USA %F Bladek:2016:GECCOcomp %X We consider simultaneous evolutionary synthesis of multiple functions, and verify whether such approach leads to computational savings compared to conventional synthesis of functions one-by-one. We also extend the proposed synthesis model with scaffolding, a technique originally intended to facilitate evolution of recursive programs \citeMoraglio:2012:CEC, and consisting in fetching the desired output from a test case, rather than calling a function. Experiment concerning synthesis of list manipulation programs in Scala allows us to conclude that parallel synthesis indeed pays off, and that engagement of scaffolding leads to further improvements. %K genetic algorithms, genetic programming, scaffolding, multisynthesis, problem decomposition, Scala: Poster %R 10.1145/2908961.2908992 %U http://dx.doi.org/10.1145/2908961.2908992 %P 97-98 %0 Conference Proceedings %T Evolutionary Program Sketching %A Bladek, Iwo %A Krawiec, Krzysztof %Y Castelli, Mauro %Y McDermott, James %Y Sekanina, Lukas %S EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming %S LNCS %D 2017 %8 19 21 apr %V 10196 %I Springer Verlag %C Amsterdam %F Bladek:2017:EuroGP %X Program synthesis can be posed as a satisfiability problem and approached with generic SAT solvers. Only short programs can be however synthesized in this way. Program sketching by Solar-Lezama assumes that a human provides a partial program (sketch), and that synthesis takes place only within the uncompleted parts of that program. This allows synthesizing programs that are overall longer, while maintaining manageable computational effort. In this paper, we propose Evolutionary Program Sketching (EPS), in which the role of sketch provider is handed over to genetic programming (GP). A GP algorithm evolves a population of partial programs, which are being completed by a solver while evaluated. We consider several variants of EPS, which vary in program terminals used for completion (constants, variables, or both) and in the way the completion outcomes are propagated to future generations. When applied to a range of benchmarks, EPS outperforms the conventional GP, also when the latter is given similar time budget. %K genetic algorithms, genetic programming, program synthesis, satisfiability modulo theory, program sketching %R 10.1007/978-3-319-55696-3_1 %U http://repozytorium.put.poznan.pl/publication/495662 %U http://dx.doi.org/10.1007/978-3-319-55696-3_1 %P 3-18 %0 Journal Article %T Counterexample-Driven Genetic Programming: Heuristic Program Synthesis from Formal Specifications %A Bladek, Iwo %A Krawiec, Krzysztof %A Swan, Jerry %J Evolutionary Computation %D 2018 %8 Fall %V 26 %N 3 %@ 1063-6560 %F Bladek:EC %X Conventional genetic programming (GP) can only guarantee that synthesized programs pass tests given by the provided input-output examples. The alternative to such test-based approach is synthesizing programs by formal specification, typically realized with exact, non-heuristic algorithms. In this paper, we build on our earlier study on Counterexample-Based Genetic Programming (CDGP), an evolutionary heuristic that synthesizes programs from formal specifications. The candidate programs in CDGP undergo formal verification with a Satisfiability Modulo Theory (SMT) solver, which results in counterexamples that are subsequently turned into tests and used to calculate fitness. The original CDGP is extended here with a fitness threshold parameter that decides which programs should be verified, a more rigorous mechanism for turning counterexamples into tests, and other conceptual and technical improvements. We apply it to 24 benchmarks representing two domains: the linear integer arithmetic (LIA) and the string manipulation (SLIA) problems, showing that CDGP can reliably synthesize provably correct programs in both domains. We also confront it with two state-of-the art exact program synthesis methods and demonstrate that CDGP effectively trades longer synthesis time for smaller program size. %K genetic algorithms, genetic programming, formal verification, counterexamples, SMT %9 journal article %R 10.1162/evco_a_00228 %U http://dx.doi.org/10.1162/evco_a_00228 %P 441-469 %0 Conference Proceedings %T Solving symbolic regression problems with formal constraints %A Bladek, Iwo %A Krawiec, Krzysztof %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 Bladek:2019:GECCO %X In many applications of symbolic regression, domain knowledge constrains the space of admissible models by requiring them to have certain properties, like monotonicity, convexity, or symmetry. As only a handful of variants of genetic programming methods proposed to date can take such properties into account, we introduce a principled approach capable of synthesizing models that simultaneously match the provided training data (tests) and meet user-specified formal properties. To this end, we formalize the task of symbolic regression with formal constraints and present a range of formal properties that are common in practice. We also conduct a comparative experiment that confirms the feasibility of the proposed approach on a suite of realistic symbolic regression benchmarks extended with various formal properties. The study is summarized with discussion of results, properties of the method, and implications for symbolic regression. %K genetic algorithms, genetic programming, symbolic regression, constraints, formal verification, generalization %R 10.1145/3321707.3321743 %U https://www.cs.put.poznan.pl/ibladek/publications/conferences/gecco19_srfc_paper.pdf %U http://dx.doi.org/10.1145/3321707.3321743 %P 977-984 %0 Thesis %T Machine Learning and Formal Verification for Acquisition of Knowledge in Heuristic Program Synthesis %A Bladek, Iwo %D 2022 %C Poznan, Poland %C Politechnika Poznanska %G en %F bladek:thesis %X we present and evaluate our techniques for efficient heuristic program synthesis based on genetic programming (GP). The common theme among our approaches is that various kinds of information (knowledge) are collected during runtime or a separate training phase, and then are used to guide GP search. Three of the described techniques, i.e., Evolutionary Program Sketching, Counterexample-Driven GP, and Counterexample-Driven Symbolic Regression, use formal verification/synthesis to either find locally optimal code fragments, or discover counterexamples exposing incorrect behavior of candidate programs. The fourth approach, Neuro-Guided GP, uses machine learning to learn the probability distribution of program instructions given input-output examples, and then uses it to bias variation operators of GP. The computational experiments show that all presented methods outperform or provide some advantages over existing state of the art methods. %K genetic algorithms, genetic programming, Counterexample-Driven Genetic Programming, CDGP, Counterexample Driven Symbolic Regression, CDSR, Neuro-Guided Genetic Programming, ANN, Evolutionary Program Sketching, EPS, SMT, formal verification of software, machine learning, program synthesis, programowanie genetyczne, formalna weryfikacja oprogramowania, uczenie maszynowe, synteza programow %9 Ph.D. thesis %U https://sin.put.poznan.pl/dissertations/details/d2987 %0 Journal Article %T Counterexample-Driven Genetic Programming for Symbolic Regression with Formal Constraints %A Bladek, Iwo %A Krawiec, Krzysztof %J IEEE Transactions on Evolutionary Computation %D 2023 %8 oct %V 27 %N 5 %@ 1089-778X %F Bladek:ieeeTEC %X In symbolic regression with formal constraints, the conventional formulation of regression problem is extended with desired properties of the target model, like symmetry, monotonicity, or convexity. We present a genetic programming algorithm that solves such problems using a Satisfiability Modulo Theories solver to formally verify the candidate solutions. The essence of the method consists in collecting the counter examples resulting from model verification and using them to improve search guidance. The method is exact: upon successful termination, the produced model is guaranteed to meet the specified constraints. We compare the effectiveness of the proposed method with standard constraint-agnostic machine learning regression algorithms on a range of benchmarks, and demonstrate that it outperforms them on several performance indicators. %K genetic algorithms, genetic programming, Satisfiability Modulo Theories, SMT, Symbolic regression, SR %9 journal article %R 10.1109/TEVC.2022.3205286 %U http://dx.doi.org/10.1109/TEVC.2022.3205286 %P 1327-1339 %0 Conference Proceedings %T Learning the Caesar and Vigenere Cipher by Hierarchical Evolutionary Re-Combination %A Blair, Alan %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 Blair:2013:CEC %X We describe a new programming language called HERCL, designed for evolutionary computation with the specific aim of allowing new programs to be created by combining patches of code from different parts of other programs, at multiple scales. Large-scale patches are followed up by smaller-scale patches or mutations, recursively, to produce a global random search strategy known as hierarchical evolutionary re-combination. We demonstrate the proposed system on the task of learning to encode with the Caesar or Vigenere Cipher, and show how the evolution of one task may fruitfully be cross-pollinated with evolved solutions from other related tasks. %K genetic algorithms, genetic programming, HERCL, Evolutionary computation %R 10.1109/CEC.2013.6557624 %U http://www.cse.unsw.edu.au/~blair/pubs/2013BlairCEC.pdf %U http://dx.doi.org/10.1109/CEC.2013.6557624 %P 605-612 %0 Conference Proceedings %T Incremental evolution of HERCL programs for robust control %A Blair, Alan %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 Blair:2014:GECCOcomp %X We explore the evolution of programs for control tasks using the recently introduced Hierarchical Evolutionary Re-Combination Language (HERCL) which has been designed as an austere and general-purpose language, with a view toward modular evolutionary computation, combining elements from Linear GP with stack-based operations from FORTH. We show that HERCL programs can be evolved to robustly perform a benchmark double pole balancing task from a specified range of initial conditions, with the poles remaining balanced for up to an hour of simulated time. %K genetic algorithms, genetic programming, artificial life, robotics, and evolvable hardware: Poster %R 10.1145/2598394.2598424 %U http://doi.acm.org/10.1145/2598394.2598424 %U http://dx.doi.org/10.1145/2598394.2598424 %P 27-28 %0 Conference Proceedings %T Adversarial Evolution and Deep Learning - How Does an Artist Play with Our Visual System? %A Blair, Alan %Y Ekart, Aniko %Y Liapis, Antonios %Y Castro, Luz %S 8th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMusArt 2019 %S LNCS %D 2019 %8 24 26 apr %V 11453 %I Springer %C Leipzig, Germany %F Blair:2019:evomusart %X We create artworks using adversarial coevolution between a genetic program (hercl) generator and a deep convolutional neural network (LeNet) critic. The resulting artificially intelligent artist, whimsically named Hercule LeNet, aims to produce images of low algorithmic complexity which nevertheless resemble a set of real photographs well enough to fool an adversarially trained deep learning critic modelled on the human visual system. Although it is not exposed to any pre-existing art, or asked to mimic the style of any human artist, nevertheless it discovers for itself many of the stylistic features associated with influential art movements of the 19th and 20th Century. A detailed analysis of its work can help us to better understand the way an artist plays with the human visual system to produce aesthetically appealing images. %K genetic algorithms, genetic programming, ANN, Evolutionary art, ai-generated art, Artist-critic coevolution, Adversarial training, Computational creativity %R 10.1007/978-3-030-16667-0_2 %U http://dx.doi.org/10.1007/978-3-030-16667-0_2 %P 18-34 %0 Journal Article %T An evolutionary approach for generating software models: The case of Kromaia in Game Software Engineering %A Blasco, Daniel %A Font, Jaime %A Zamorano, Mar %A Cetina, Carlos %J Journal of Systems and Software %D 2021 %8 jan %V 171 %@ 0164-1212 %F Blasco:2021:JSS %O Winner Bronze HUMIES %X In the context of Model-Driven Engineering applied to video games, software models are high-level abstractions that represent source code implementations of varied content such as the stages of the game, vehicles, or enemy entities (e.g., final bosses). we present our Evolutionary Model Generation (EMoGen) approach to generate software models that are comparable in quality to the models created by human developers. Our approach is based on an evolution (mutation and crossover) and assessment cycle to generate the software models. We evaluated the software models generated by EMoGen in the Kromaia video game, which is a commercial video game released on Steam and PlayStation 4. Each model generated by EMoGen has more than 1000 model elements. The results, which compare the software models generated by our approach and those generated by the developers, show that our approach achieves results that are comparable to the ones created manually by the developers in the retail and digital versions of the video game case study. However, our approach only takes five hours of unattended time in comparison to ten months of work by the developers. We perform a statistical analysis, and we make an implementation of EMoGen readily available. %K genetic algorithms, genetic programming, SBSE, MDE, Model-Driven Engineering, Search-based software engineering, Game Software Engineering %9 journal article %R 10.1016/j.jss.2020.110804 %U http://www.human-competitive.org/sites/default/files/blasco-font-zamorano-cetina-text.txt %U http://dx.doi.org/10.1016/j.jss.2020.110804 %P 110804 %0 Conference Proceedings %T Improving Network Intrusion Detection by Means of Domain-Aware Genetic Programming %A Blasco, Jorge %A Orfila, Agustin %A Ribagorda, Arturo %S International Conference on Availability, Reliability, and Security, ARES ’10 %D 2010 %8 feb %F Blasco:2010:ARES %X One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper we explore the use of Genetic Programming (GP) for such a purpose. Although GP has already been studied for this task, the inner features of network intrusion detection have been systematically ignored. To avoid the blind use of GP shown in previous research, we guide the search by means of a fitness function based on recent advances on IDS evaluation. For the experimental work we use a well-known dataset (i.e. KDD-99) that has become a standard to compare research although its drawbacks. Results clearly show that an intelligent use of GP achieves systems that are comparable (and even better in realistic conditions) to top state-of-the-art proposals in terms of effectiveness, improving them in efficiency and simplicity. %K genetic algorithms, genetic programming, domain-aware genetic programming, fitness function, intrusive traffic, network intrusion detection, normal traffic, security of data %R 10.1109/ARES.2010.53 %U http://dx.doi.org/10.1109/ARES.2010.53 %P 327-332 %0 Conference Proceedings %T Multiobjective Genetic Programming: Reducing Bloat Using SPEA2 %A Bleuler, Stefan %A Brack, Martin %A Thiele, Lothar %A Zitzler, Eckart %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 bleuler:2001:mgprbus %X This study investigates the use of multiobjective techniques in Genetic Programming (GP) in order to evolve compact programs and to reduce the effects caused by bloating. The proposed approach considers the program size as a second, independent objective besides the program functionality. In combination with a recent multiobjective evolutionary technique, SPEA2, this method outperforms four other strategies to reduce bloat with regard to both convergence speed and size of the produced programs on a even-parity problem. %K genetic algorithms, genetic programming, SPEA, SPEA2, Pareto, external set, bloating, compact programs, convergence speed, even-parity problem, multiobjective evolutionary technique, multiobjective genetic programming, program functionality, convergence, programming theory %R 10.1109/CEC.2001.934438 %U ftp://ftp.tik.ee.ethz.ch/pub/people/zitzler/BBTZ2001b.ps.gz %U http://dx.doi.org/10.1109/CEC.2001.934438 %P 536-543 %0 Book Section %T Reducing Bloat in GP with Multiple Objectives %A Bleuler, Stefan %A Bader, Johannes %A Zitzler, Eckart %E Knowles, Joshua %E Corne, David %E Deb, Kalyanmoy %B Multiobjective Problem Solving from Nature: from concepts to applications %S Natural Computing %D 2008 %I Springer %F Bleuler:2008:MPSN %X This chapter investigates the use of multiobjective techniques in genetic programming (GP) in order to evolve compact programs and to reduce the effects caused by bloating. The underlying approach considers the program size as a second, independent objective besides program functionality, and several studies have found this concept to be successful in reducing bloat. Based on one specific algorithm, we demonstrate the principle of multiobjective GP and show how to apply Pareto-based strategies to GP. This approach outperforms four classical strategies to reduce bloat with regard to both convergence speed and size of the produced programs on an even-parity problem. Additionally, we investigate the question of why the Pareto-based strategies can be more effective in reducing bloat than alternative strategies on several test problems. The analysis falsifies the hypothesis that the small but less functional individuals that are kept in the population act as building blocks building blocks for larger correct solutions. This leads to the conclusion that the advantages are probably due to the increased diversity in the population. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-72964-8_9 %U http://dx.doi.org/10.1007/978-3-540-72964-8_9 %P 177-200 %0 Conference Proceedings %T Genetic Programming and Redundancy %A Blickle, Tobias %A Thiele, Lothar %Y Hopf, J. %S Genetic Algorithms within the Framework of Evolutionary Computation (Workshop at KI-94, Saarbrücken) %D 1994 %I Max-Planck-Institut für Informatik (MPI-I-94-241) %C Im Stadtwald, Building 44, D-66123 Saarbrücken, Germany %F BT94 %X The Genetic Programming optimization method (GP) elaborated by John Koza [Koza, 1992] is a variant of Genetic Algorithms. The search space of the problem domain consists of computer programs represented as parse trees, and the crossover operator is realized by an exchange of subtrees. Empirical analyses show that large parts of those trees are never used or evaluated which means that these parts of the trees are irrelevant for the solution or redundant. This paper is concerned with the identification of the redundancy occurring in GP. It starts with a mathematical description of the behaviour of GP and the conclusions drawn from that description among others explain the size problem which denotes the phenomenon that the average size of trees in the population grows with time. %K genetic algorithms, genetic programming %U http://www.tik.ee.ethz.ch/~tec/publications/bt94/GPandRedundancy.ps.gz %P 33-38 %0 Report %T A Comparison of Selection Schemes Used in Genetic Algorithms %A Blickle, Tobias %A Thiele, Lothar %D 1995 %8 dec %N 11 %I TIK Institut fur Technische Informatik und Kommunikationsnetze, Computer Engineering and Networks Laboratory, ETH, Swiss Federal Institute of Technology %C Gloriastrasse 35, 8092 Zurich, Switzerland %F blickle:1995:css %X Genetic Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme for which a new description model is introduced in this paper. With this a mathematical analysis of tournament selection, truncation selection, linear and exponential ranking selection and proportional selection is carried out that allows an exact prediction of the fitness values after selection. The further analysis derives the selection intensity, selection variance, and the loss of diversity for all selection schemes. For completion a pseudo- code formulation of each method is included. The selection schemes are compared and evaluated according to their properties leading to an unified view of these different selection schemes. Furthermore the correspondence of binary tournament selection and ranking selection in the expected fitness distribution is proven. %K genetic algorithms, genetic programming %9 TIK-Report %U https://tik-old.ee.ethz.ch/file/6c0e384dceb283cd4301339a895b72b8/TIK-Report11.pdf %0 Journal Article %T Optimieren nach dem Vorbild der Natur, Evolutionare Algorithmen %A Blickle, Tobias %J Bulletin SEV/VSE %D 1995 %V 86 %N 25 %F blickle:1995:ea %K genetic algorithms, genetic programming %9 journal article %U http://www.handshake.de/user/blickle/publications/EA.ps %P 21-26 %0 Report %T YAGPLIC User Manual %A Blickle, Tobias %D 1995 %I Computer Engineering and Communication Network Lab (TIK), Swiss Federal Institute of Technology (ETH) %C Gloriastrasse 35, CH-8092, Zurich %F blickle:1995:YAGPLIC %K genetic algorithms, genetic programming %0 Report %T Evolving Compact Solutions in Genetic Programming: A Case Study %A Blickle, Tobias %D 1996 %I TIK Institut fur Technische Informatik und Kommunikationsnetze, Computer Engineering and Networks Laboratory, ETH, Swiss Federal Institute of Technology %C Gloriastrasse 35, 8092 Zurich, Switzerland %F blickle:1996:ecs %X Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence $y=g(\bf x)$ that approximates a set of data points ($\bf x_i,y_i$). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data. %K genetic algorithms, genetic programming %9 TIK-Report %U http://www.handshake.de/user/blickle/publications/ppsn1.ps %0 Conference Proceedings %T Evolving Compact Solutions in Genetic Programming: A Case Study %A Blickle, Tobias %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 blickle96 %X Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence y=g ( x ) that approximates a set of data points ( x i , y i ). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data. %K genetic algorithms, genetic programming, bloat, deleting crossover %R 10.1007/3-540-61723-X_1020 %U http://www.handshake.de/user/blickle/publications/ppsn1.ps %U http://dx.doi.org/10.1007/3-540-61723-X_1020 %P 564-573 %0 Thesis %T Theory of Evolutionary Algorithms and Application to System Synthesis %A Blickle, Tobias %D 1996 %8 nov %C Zurich, Switzerland %C Swiss Federal Institute of Technology %F blickle:thesis %X The subject of this thesis are Evolutionary Algorithms and their application to the automated synthesis and optimization of complex digital systems composed of hardware and software elements. In Part I the probabilistic optimization method of Evolutionary Algorithms (EAs) is presented. EAs apply the principles of natural evolution (selection and random variation) to a random set of points (population of individuals) in the search space. Evolutionary Algorithms are first embedded in the context of global optimization and the most important and widely used methods for constraint- handling are introduced, including a new method called IOS (individual objective switching). This is followed by a new formal description of selection schemes based on fitness distributions. This description enables an extensive and uniform examination of various selection schemes leading to new insights about the impact of the selection method parameters on the optimization process. Subsequently the variation (recombination) process of Evolutionary Algorithms is examined. As different analysis techniques are necessary depending on the representation of the problem (e.g. bit string, vector, tree, graph) only the recombination process for tree-representation (Genetic Programming) is considered. A major part of the explanation treats the problem of “bloating”, i.e., the tree-size increase during optimization. Furthermore, a new redundancy based explanation of bloating is given and several methods to avoid bloating are compared. Part II is dedicated to the application of Evolutionary Algorithms to the optimization of complex digital systems. These systems are composed of hardware and software components and characterized by a high complexity caused by their heterogeneity (hardware/ software, electrical/mechanical, analog/digital). Computer-aided synthesis at the abstract system level is advantageous for application specific systems or embedded systems as it enables time-to-market to be reduced with a decrease in design errors and costs. The main task of system-synthesis is the transformation of a behavioral specification (for example given by an algorithm) into a structural specification, such as a composition of processors, general or dedicated hardware modules, memories and busses, while regarding various restrictions, e.g. maximum costs, data throughput rate, reaction time. Problems related to system synthesis are for example performance estimation, architecture optimization and design-space exploration. This thesis introduces a formal description of system-synthesis based on a new graph model where the specification is translated into a specification graph. The main tasks of system-synthesis (allocation, binding and scheduling) are defined for this graph and the process of system synthesis is formulated as a constrained global optimization problem. This optimization problem is solved by Evolutionary Algorithms using the results of Part I of the thesis. Finally, an example of synthesizing implementations of a video codec chip H.261 is described demonstrating the effectiveness of the proposed methodology and the capability of the EA to obtain the Pareto points of the design space in a single optimization run. %K genetic algorithms, genetic programming %9 Ph.D. thesis %R 10.3929/ethz-a-001710359 %U http://www.handshake.de/user/blickle/publications/diss.pdf %U http://dx.doi.org/10.3929/ethz-a-001710359 %0 Journal Article %T A Comparison of Selection Schemes used in Evolutionary Algorithms %A Blickle, Tobias %A Thiele, Lothar %J Evolutionary Computation %D 1996 %8 Winter %V 4 %N 4 %@ 1063-6560 %F DBLP:journals/ec/BlickleT96 %X Evolutionary algorithms are a common probabilistic optimisation method based on the model of natural evolution. One important operator in these algorithms is the selection scheme, for which in this paper a new description model, based on fitness distributions, is introduced. With this, a mathematical analysis of tournament selection, truncation selection, ranking selection, and exponential ranking selection is carried out that allows an exact prediction of the fitness values after selection. The correspondence of binary tournament selection and ranking selection in the expected fitness distribution is proved. Furthermore, several properties of selection schemes are derived (selection intensity, selection variance, loss of diversity), and the three selection schemes are compared using these properties. %K genetic algorithms, genetic programming, Selection, evolutionary algorithms, diversity, selection intensity, tournament selection, truncation selection, linear ranking %9 journal article %R 10.1162/evco.1996.4.4.361 %U http://www.handshake.de/user/blickle/publications/ECfinal.ps %U http://dx.doi.org/10.1162/evco.1996.4.4.361 %P 361-394 %0 Conference Proceedings %T Fuzzy Edit Sequences in Genetic Improvement %A Blot, Aymeric %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 Blot:2019:GI %X Genetic improvement uses automated search to find improved versions of existing software. Edit sequences have been proposed as a very convenient way to represent code modifications, focusing on the changes themselves rather than duplicating the entire program. However, edits are usually defined in terms of practical operations rather than in terms of semantic changes; indeed, crossover and other edit sequence mutations usually never guarantee semantic preservation. We propose several changes to usual edit sequences, specifically augmenting edits with content data and using fuzzy matching, in an attempt to improve semantic preservation. %K genetic algorithms, genetic programming, genetic improvement, GI, SBSE, search-based software engineering, fuzzy matching %R 10.1109/GI.2019.00016 %U http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2019.pdf %U http://dx.doi.org/10.1109/GI.2019.00016 %P 30-31 %0 Conference Proceedings %T On Adaptive Specialisation in Genetic Improvement %A Blot, Aymeric %A Petke, Justyna %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 Blot:2019:GI7 %X Genetic improvement uses automated search to find improved versions of existing software. Software can either be evolved with general-purpose intentions or with a focus on a specific application (e.g., to improve its efficiency for a particular class of problems). Unfortunately, software specialisation to each problem application is generally performed independently, fragmenting and slowing down an already very time-consuming search process. We propose to incorporate specialisation as an online mechanism of the general search process, in an attempt to automatically devise application classes, by benefiting from past execution history. %K genetic algorithms, genetic programming, genetic improvement, SBSE, Search-Based Software Engineering, Software Specialisation, Algorithm Selection, Algorithm Configuration %R 10.1145/3319619.3326839 %U http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-gecco_2019.pdf %U http://dx.doi.org/10.1145/3319619.3326839 %P 1703-1704 %0 Conference Proceedings %T Comparing Genetic Programming Approaches for Non-Functional Genetic Improvement Case Study: Improvement of MiniSAT’s Running Time %A Blot, Aymeric %A Petke, Justyna %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 Blot:2020:EuroGP %X Genetic improvement (GI) uses automated search to find improved versions of existing software. While most GI work use genetic programming (GP) as the underlying search process, focus is usually given to the target software only. As a result, specifics of GP algorithms for GI are not well understood and rarely compared to one another. we propose a robust experimental protocol to compare different GI search processes and investigate several variants of GP and random-based approaches. Through repeated experiments, we report a comparative analysis of these approaches, using one of the previously used GI scenarios: improvement of runtime of the MiniSAT satisfiability solver. We conclude that the test suites used have the most significant impact on the GI results. Both random and GP-based approaches are able to find improved software, even though the percentage of viable software variants is significantly smaller in the random case (14.5percent vs. 80.1percent). We also report that GI produces MiniSAT variants up to twice as fast as the original on sets of previously unseen instances from the same application domain. %K genetic algorithms, genetic programming, Genetic improvement, GI, GP, Search based software engineering, SBSE, Boolean satisfiability, SAT, minisat-2.2.0, linux perf, AST, XML %R 10.1007/978-3-030-44094-7_5 %U http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_eurogp_2020.pdf %U http://dx.doi.org/10.1007/978-3-030-44094-7_5 %P 68-83 %0 Conference Proceedings %T Stack-Based Genetic Improvement %A Blot, Aymeric %A Petke, Justyna %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 Blot:2020:GIsp %X Genetic improvement (GI) uses automated search to find improved versions of existing software. If originally GI directly evolved populations of software, most GI work nowadays use a solution representation based on a list of mutations. This representation however has some limitations, notably in how genetic material can be re-combined. We introduce a novel stack-based representation and discuss its possible benefits %K genetic algorithms, genetic programming, genetic improvement, SBSE, GI, Automated Program Repair, APR, Search-based software engineering %R 10.1145/3387940.3392174 %U http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2020-1.pdf %U http://dx.doi.org/10.1145/3387940.3392174 %P 289-290 %0 Conference Proceedings %T Synthetic Benchmarks for Genetic Improvement %A Blot, Aymeric %A Petke, Justyna %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 Blot:2020:GI %X Genetic improvement (GI) uses automated search to find improved versions of existing software. If over the years the potential of many GI approaches have been demonstrated, the intrinsic cost of evaluating real-world software makes comparing these approaches in large-scale meta-analyses very expensive. We propose and describe a method to construct synthetic GI benchmarks, to circumvent this bottleneck and enable much faster quality assessment of GI approaches. %K genetic algorithms, genetic programming, genetic improvement, SBSE %R 10.1145/3387940.3392175 %U http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2020-2.pdf %U http://dx.doi.org/10.1145/3387940.3392175 %P 287-288 %0 Journal Article %T Empirical Comparison of Search Heuristics for Genetic Improvement of Software %A Blot, Aymeric %A Petke, Justyna %J IEEE Transactions on Evolutionary Computation %D 2021 %8 oct %V 25 %N 5 %@ 1089-778X %F blot:2021:tevc %X Genetic improvement uses automated search to improve existing software. It has been successfully used to optimise various program properties, such as runtime or energy consumption, as well as for the purpose of bug fixing. Genetic improvement typically navigates a space of thousands of patches in search for the program mutation that best improves the desired software property. While genetic programming has been dominantly used as the search strategy, more recently other search strategies, such as local search, have been tried. It is, however, still unclear which strategy is the most effective and efficient. In this paper, we conduct an in-depth empirical comparison of a total of 18 search processes using a set of 8 improvement scenarios. Additionally, we also provide new genetic improvement benchmarks and we report on new software patches found. Our results show that, overall, local search approaches achieve better effectiveness and efficiency than genetic programming approaches. Moreover, improvements were found in all scenarios (between 15percent and 68percent). A replication package can be found online: https://github.com/bloa/tevc_2020_artefact. %K genetic algorithms, genetic programming, genetic improvement, GI, Search-Based Software Engineering, SBSE, Stochastic Local Search, PyGGI, Uniform interleaved crossover %9 journal article %R 10.1109/TEVC.2021.3070271 %U http://www.cs.ucl.ac.uk/staff/a.blot/publis/#blot:2021:tevc %U http://dx.doi.org/10.1109/TEVC.2021.3070271 %P 1001-1011 %0 Conference Proceedings %T Using Genetic Improvement to Optimise Optimisation Algorithm Implementations %A Blot, Aymeric %A Petke, Justyna %Y Hadj-Hamou, Khaled %S 23ème congrès annuel de la Société FranÇaise de Recherche Opérationnelle et d’Aide à la Décision, ROADEF’2022 %D 2022 %8 23–25 feb %I INSA Lyon %C Villeurbanne - Lyon, France %F blot:hal-03595447 %X Genetic improvement (GI) \citePetke:gisurvey uses automated search to improve existing software. It has been successfully used to fix software bugs or improve non-functional properties of software such as running time, memory usage, or energy consumption. Recently, it has been shown that genetic programming, the eponymous GI typical search algorithm, was outperformed by local search strategies \citeblot:2021:tevc. One result of that work was that GI was able to find interesting algorithmic changes in the implementation [Hui Li and Qingfu Zhang., 2009] of two state-of-the-art evolutionary algorithms, MOEA-D and NSGA-II. Here, we reproduce and extend this result with a simple local search, obtaining 10percent faster software variants with little to no impact on solution quality in 6/18 GI runs. %K genetic algorithms, genetic programming, Genetic improvement, SBSE, Algorithm design, Software engineering, Optimisation, MOEA/D, NSGA-II, PyGGI , perf, IGD, XML, SrcML %U https://hal.archives-ouvertes.fr/hal-03595447 %U https://hal.archives-ouvertes.fr/hal-03595447/file/roadef_2022.pdf %0 Generic %T MAGPIE: Machine Automated General Performance Improvement via Evolution of Software %A Blot, Aymeric %A Petke, Justyna %D 2022 %8 April %I arXiv %F blot:2022:corr_1 %X Performance is one of the most important qualities of software. Several techniques have thus been proposed to improve it, such as program transformations, optimisation of software parameters, or compiler flags. Many automated software improvement approaches use similar search strategies to explore the space of possible improvements, yet available tooling only focuses on one approach at a time. This makes comparisons and exploration of interactions of the various types of improvement impractical. We propose MAGPIE, a unified software improvement framework. It provides a common edit sequence based representation that isolates the search process from the specific improvement technique, enabling a much simplified synergistic workflow. We provide a case study using a basic local search to compare compiler optimisation, algorithm configuration, and genetic improvement. We chose running time as our efficiency measure and evaluated our approach on four real-world software, written in C, C++, and Java. Our results show that, used independently, all techniques find significant running time improvements: up to 25percent for compiler optimisation, 97percent for algorithm configuration, and 61percent for evolving source code using genetic improvement. We also show that up to 10percent further increase in performance can be obtained with partial combinations of the variants found by the different techniques. Furthermore, the common representation also enables simultaneous exploration of all techniques, providing a competitive alternative to using each technique individually. %K genetic algorithms, genetic programming, genetic improvement, SBSE, parameter tuning, algorithm configuration, compiler optimisation, local search, multi-objective, running time, solution quality, metaheuristic-based search strategies, srcML, linear GP, GCC, LLVM, Clang, OpenJDK, GraalVM Java %R 10.48550/arxiv.2208.02811 %U https://arxiv.org/abs/2208.02811 %U http://dx.doi.org/10.48550/arxiv.2208.02811 %0 Generic %T A Comprehensive Survey of Benchmarks for Automated Improvement of Software’s Non-Functional Properties %A Blot, Aymeric %A Petke, Justyna %D 2022 %8 16 dec %I arXiv %F blot2022comprehensive %X Performance is a key quality of modern software. Although recent years have seen a spike in research on automated improvement of software’s execution time, energy, memory consumption, etc., there is a noticeable lack of standard benchmarks for such work. It is also unclear how such benchmarks are representative of current software. Furthermore, frequently non-functional properties of software are targeted for improvement one-at-a-time, neglecting potential negative impact on other properties. In order to facilitate more research on automated improvement of non-functional properties of software, we conducted a survey gathering benchmarks used in previous work. We considered 5 major online repositories of software engineering work: ACM Digital Library, IEEE Xplore, Scopus, Google Scholar, and ArXiV. We gathered 5000 publications (3749 unique), which were systematically reviewed to identify work that empirically improves non-functional properties of software. We identified 386 relevant papers. We find that execution time is the most frequently targeted property for improvement (in 62 percent of relevant papers), while multi-objective improvement is rarely considered (5 percent). Static approaches are prevalent (in 53 percent of papers), with exploratory approaches (evolutionary in 18 percent and non-evolutionary in 14 percent of papers) increasingly popular in the last 10 years. Only 40 percent of 386 papers describe work that uses benchmark suites, rather than single software, of those SPEC is most popular (covered in 33 papers). We also provide recommendations for choice of benchmarks in future work, noting, e.g., lack of work that covers Python or JavaScript. We provide all programs found in the 386 papers on our dedicated web page https://bloa.github.io/nfunc_survey/ We hope that this effort will facilitate more research on the topic of automated improvement of software’s non-functional properties. %K genetic algorithms, genetic programming, genetic improvement, software performance, non-functional properties, benchmark %U https://arxiv.org/abs/2212.08540 %0 Conference Proceedings %T Automated Software Performance Improvement with Magpie %A Blot, Aymeric %S "13th International Workshop on Genetic Improvement %F Blot:2024:GI %0 Journal Article %D 2024 %8 16 apr %I ACM %C Lisbon %F 2024"c %O Invited tutorial %X we present Magpie, a powerful tool for both Genetic Improvement researchers and practitioners. Magpie stands at the forefront of software evolution, providing a streamlined approach to model, evolve, and automatically improve software systems. Addressing both functional and non-functional concerns, Magpie offers a user-friendly no-code interface that seamlessly integrates various search processes, as well as enabling easy Python code injection for advanced users to further tailor and specialise the improvement process to meet their specific needs. We will provide a concise overview of Magpie’s internals before exploring diverse real-world scenarios. Aymeric Blot is a Senior Lecturer at the Universite of Rennes, France. %K genetic algorithms, genetic programming, Genetic Improvement %9 journal article %R 10.1145/3643692 %U http://gpbib.cs.ucl.ac.uk/gi2024/an_2024_GI.pdf %U http://dx.doi.org/10.1145/3643692 %P ix %0 Journal Article %T A Comprehensive Survey of Benchmarks for Improvement of Software’s Non-Functional Properties %A Blot, Aymeric %A Petke, Justyna %J ACM Computing Surveys %D 2025 %V 57 %N 7 %F blot:2025:ACMsurveys %X Despite recent increase in research on improvement of non-functional properties of software, such as energy usage or program size, there is a lack of standard benchmarks for such work. This absence hinders progress in the field, and raises questions about the representativeness of current benchmarks of real-world software. To address these issues and facilitate further research on improvement of non-functional properties of software, we conducted a comprehensive survey on the benchmarks used in the field thus far. We searched five major online repositories of research work, collecting 5499 publications (4066 unique), and systematically identified relevant papers to construct a rich and diverse corpus of 425 relevant studies. We find that execution time is the most frequently improved property in research work (63percent), while multi-objective improvement is rarely considered (7percent). Static approaches for improvement of non-functional software properties are prevalent (51percent), with exploratory approaches (18percent evolutionary and 15percent non-evolutionary) increasingly popular in the last 10 years. Only 39percent of the 425 papers describe work that uses benchmark suites, rather than single software, of those SPEC is most popular (63 papers). We also provide recommendations for future work, noting, for instance, lack of benchmarks for non-functional improvement that covers Python, JavaScript, or mobile devices. All the details regarding the 425 identified papers are available on our dedicated webpage: https://bloa.github.io/nfunc_survey %K genetic algorithms, genetic programming, genetic improvement, software performance, non-functional properties, benchmark %9 journal article %R 10.1145/3711119 %U https://discovery.ucl.ac.uk/id/eprint/10203326/1/main.pdf %U http://dx.doi.org/10.1145/3711119 %P Articleno.168 %0 Conference Proceedings %T "14th International Workshop on Genetic Improvement %F blot:2025:GI %0 Journal Article %D 2025 %8 27 apr %I IEEE %C Ottawa %F 2025"a %X The GI workshops continue to bring together researchers from across the world to exchange ideas about using optimisation techniques, particularly evolutionary computation, such as genetic programming, to improve existing software. Contents: \citeTan:2025:GI \citeBlot_magpie:2025:GI \citechan:2025:GI \citelangdon:2025:GI \citesongpetchmongkol:2025:GI, \citebose:2025:GI, \citewang:2025:GI, \citebouras:2025:GI, See also \citelangdon:2025:SEN %K genetic algorithms, genetic programming, Genetic Improvement %9 journal article %R 10.1109/GI66624.2025 %U http://geneticimprovementofsoftware.com/events/icse2025 %U http://dx.doi.org/10.1109/GI66624.2025 %0 Conference Proceedings %T Automated Software Performance Improvement with Magpie %A Blot, Aymeric %S "14th International Workshop on Genetic Improvement %F Blot_magpie:2025:GI %0 Journal Article %D 2025 %8 27 apr %I IEEE %C Ottawa %F 2025"b %O Invited tutorial %X In this tutorial, I will present Magpie (https://github.com/bloa/magpie), a powerful tool for both Genetic Improvement researchers and practitioners. Magpie stands at the forefront of software evolution, providing a streamlined approach to model, evolve, and automatically improve software systems. Addressing both functional and non-functional concerns, Magpie offers a user-friendly no-code interface that seamlessly integrates various search processes, as well as enabling easy Python code injection for advanced users to further tailor and specialise the improvement process to meet their specific needs. We will provide a concise overview of Magpie internals before exploring diverse real-world scenarios. %K genetic algorithms, genetic programming, Genetic Improvement %9 journal article %R 10.1109/GI66624.2025.00008 %U http://gpbib.cs.ucl.ac.uk/gi2025/blot_2025_GI.pdf %U http://dx.doi.org/10.1109/GI66624.2025.00008 %P ix %0 Conference Proceedings %T GECCO ’13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion %E Blum, Christian %E Alba, Enrique %E Bartz-Beielstein, Thomas %E Loiacono, Daniele %E Luna, Francisco %E Mehnen, Joern %E Ochoa, Gabriela %E Preuss, Mike %E Tantar, Emilia %E Vanneschi, Leonardo %E McClymont, Kent %E Keedwell, Ed %E Hart, Emma %E Sim, Kevin %E Gustafson, Steven %E Vladislavleva, Ekaterina %E Auger, Anne %E Bischl, Bernd %E Brockhoff, Dimo %E Hansen, Nikolaus %E Mersmann, Olaf %E Posik, Petr %E Trautmann, Heike %E Iqbal, Muhammad %E Shafi, Kamran %E Urbanowicz, Ryan %E Wagner, Stefan %E Affenzeller, Michael %E Walker, David %E Everson, Richard %E Fieldsend, Jonathan %E Stonedahl, Forrest %E Rand, William %E Smith, Stephen L. %E Cagnoni, Stefano %E Patton, Robert M. %E Pappa, Gisele L. %E Woodward, John %E Swan, Jerry %E Krawiec, Krzysztof %E Tantar, Alexandru-Adrian %E Bosman, Peter A. N. %E Vega-Rodriguez, Miguel %E Chaves-Gonzalez, Jose M. %E Gonzalez-Alvarez, David L. %E Santander-Jimenez, Sergio %E Spector, Lee %E Keijzer, Maarten %E Holladay, Kenneth %E Tusar, Tea %E Naujoks, Boris %D 2013 %8 June 10 jul %C Amsterdam, The Netherlands %F Blum:2013:GECCOcomp %K genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Biological and biomedical applications, Digital entertainment technologies and Arts, Estimation of distribution algorithms, Evolution strategies and evolutionary programming, Evolutionary combinatorial optimization and metaheuristics, Evolutionary multiobjective optimization, Generative and developmental systems, Genetics based machine learning, Integrative genetic and evolutionary computation, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory, Introductory tutorials, Advanced tutorials, Specialized techniques and applications tutorials, Workshop on problem understanding and real-world optimisation, Symbolic regression and modeling workshop, Black box optimization benchmarking 2013 (BBOB 2013), Sixteenth international workshop on learning classifier systems, Evolutionary computation software systems (EvoSoft’13), Visualisation methods in genetic and evolutionary computation (VizGEC 2013), Evolutionary computation and multi-agent systems and simulation (EcoMass) seventh annual workshop, Medical applications of genetic and evolutionary computation (MedGEC’13), 3rd workshop on evolutionary computation for the automated design of algorithms, Green and efficient energy applications of genetic and evolutionary computation workshop, International workshop on evolutionary computation in bioinformatics, Stack-based workshop, Student workshop, Late-breaking abstracts, Keynote talks %U http://dl.acm.org/citation.cfm?id=2464576 %0 Conference Proceedings %T GECCO ’13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference %E Blum, Christian %E Alba, Enrique %E Auger, Anne %E Bacardit, Jaume %E Bongard, Josh %E Branke, Juergen %E Bredeche, Nicolas %E Brockhoff, Dimo %E Chicano, Francisco %E Dorin, Alan %E Doursat, Rene %E Ekart, Aniko %E Friedrich, Tobias %E Giacobini, Mario %E Harman, Mark %E Iba, Hitoshi %E Igel, Christian %E Jansen, Thomas %E Kovacs, Tim %E Kowaliw, Taras %E Lopez-Ibanez, Manuel %E Lozano, Jose A. %E Luque, Gabriel %E McCall, John %E Moraglio, Alberto %E Motsinger-Reif, Alison %E Neumann, Frank %E Ochoa, Gabriela %E Olague, Gustavo %E Ong, Yew-Soon %E Palmer, Michael E. %E Pappa, Gisele Lobo %E Parsopoulos, Konstantinos E. %E Schmickl, Thomas %E Smith, Stephen L. %E Solnon, Christine %E Stuetzle, Thomas %E Talbi, El-Ghazali %E Tauritz, Daniel %E Vanneschi, Leonardo %D 2013 %8 June 10 jul %C Amsterdam, The Netherlands %F Blum:2013:GECCO %K genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Biological and biomedical applications, Digital entertainment technologies and arts, Estimation of distribution algorithms, Evolution strategies and evolutionary programming, Evolutionary combinatorial optimization and metaheuristics, Evolutionary multiobjective optimization, Generative and developmental systems, Genetics based machine learning, Integrative genetic and evolutionary computation, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory %U http://dl.acm.org/citation.cfm?id=2463372 %0 Conference Proceedings %T Optimized Collision Free Robot Move Statement Generation by the Evolutionary Software GLEAM %A Blume, Christian %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 blume:2000:ocfromsgesGLEAM %X The GLEAM algorithm and its implementation are a new evolutionary method application in the field of robotics. The GLEAM software generates control code for real industrial robots. Therefore GLEAM allows a time related description of the robot movement (not only a static description of robot arm configurations). This internal representation of primitive move commands is mapped to a representation of move statements of an industrial robot language, which can be loaded at the robot control and executed. %K genetic algorithms, genetic programming, Industrial Machining Robots %R 10.1007/3-540-45561-2_32 %U http://dx.doi.org/10.1007/3-540-45561-2_32 %P 327-338 %0 Book Section %T SoccerBots: Evolving Intelligent Soccer Players %A Bobrovnikoff, Dmitri %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 bobrovnikoff:2000:SEISP %K genetic algorithms, genetic programming %P 40-45 %0 Conference Proceedings %T Testing Software using Order-Based Genetic Algorithms %A Boden, Edward B. %A Martino, Gilford F. %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 boden:1996:tsobGA %K Genetic Algorithms %U http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap76.pdf %P 461-466 %0 Conference Proceedings %T Extremal Optimization: Methods derived from Co-Evolution %A Boettcher, Stefan %A Percus, Allon G. %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 boettcher:1999:EOMC %K evolution strategies and evolutionary programming %U http://gpbib.cs.ucl.ac.uk/gecco1999/9904056.pdf %P 825-832 %0 Conference Proceedings %T The Assessment and Application of Lineage Information in Genetic Programs for Producing Better Models %A Boetticher, G. D. %A Kaminsky, K. %S IEEE International Conference on Information Reuse and Integration %D 2006 %8 sep %I IEEE %C Waikoloa Village, HI, USA %@ 0-7803-9788-6 %F Boetticher:2006:IRI %X One of the challenges in data mining, and in particular genetic programs, is to provide sufficient coverage of the search space in order to produce an acceptable model. Traditionally, genetic programs generate equations (chromosomes) and consider all chromosomes within a population for breeding purposes. Considering the enormity of the search space for complex problems, it is imperative to examine genetic programs breeding efforts in order to produce better solutions with less training. This research examines chromosome lineage within genetic programs in order to identify breeding patterns. Fitness values for chromosomes are sorted, then partitioned into five classes. Initial experiments reveal a distinct difference between upper, middle, and lower classes. Based upon initial results, a novel genetic programming process is proposed which breeds a new generation exclusively from the top 20 percent of a population. A second set of experiments statistically validate this proposed approach %K genetic algorithms, genetic programming %R 10.1109/IRI.2006.252403 %U http://dx.doi.org/10.1109/IRI.2006.252403 %P 141-146 %0 Conference Proceedings %T Franken-swarm: grammatical evolution for the automatic generation of swarm-like meta-heuristics %A Bogdanova, Anna %A Junior, Jair Pereira %A Aranha, Claus %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 Bogdanova:2019:GECCOcomp %K genetic algorithms, genetic programming, grammatical evolution %R 10.1145/3319619.3321902 %U http://dx.doi.org/10.1145/3319619.3321902 %P 411-412 %0 Journal Article %T Selection of ?-Caputo derivatives’ functional parameters in generalized water transport equation by genetic programming technique %A Bohaienko, Vsevolod %J Results in Control and Optimization %D 2021 %V 5 %@ 2666-7207 %F BOHAIENKO:2021:RCO %X The paper considers the usage of genetic programming technique to select an analytic form of functional parameter of the ?-Caputo fractional derivative. We study one-dimensional space-time fractional water transport equation with such derivatives with respect to both time and space variables that generalizes the classical Richards equation. Having water head values measured by Watermark sensors as inputs, the statement of parameters identification problem is performed. The forms of functional parameters are represented as trees and found using a genetic programming algorithm. We compare the accuracy of field data description by the model with fixed and variable forms of derivatives’ functional parameters and obtained up to 30percent increase in accuracy for the training dataset and up to 15percent increase for the testing dataset when the considered method was used to select parameters’ forms %K genetic algorithms, genetic programming, Moisture transport, Fractional differential equation, Parameters identification, -Caputo derivative %9 journal article %R 10.1016/j.rico.2021.100068 %U https://www.sciencedirect.com/science/article/pii/S2666720721000394 %U http://dx.doi.org/10.1016/j.rico.2021.100068 %P 100068 %0 Conference Proceedings %T MABE 2.0: an introduction to MABE and a road map for the future of MABE development %A Bohm, Clifford %A Lalejini, Alexander %A Schossau, Jory %A Ofria, Charles %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 Bohm:2019:GECCOcomp %K genetic algorithms, genetic programming %R 10.1145/3319619.3326825 %U http://dx.doi.org/10.1145/3319619.3326825 %P 1349-1356 %0 Journal Article %T Using the Comparative Hybrid Approach to Disentangle the Role of Substrate Choice on the Evolution of Cognition %A Bohm, Clifford %A Albani, Sarah %A Ofria, Charles %A Ackles, Acacia %J Artificial Life %D 2022 %8 jan %V 28 %N 4 %@ 1064-5462 %F Bohm:2022:AlifeJ %X Understanding the structure and evolution of natural cognition is a topic of broad scientific interest, as is the development of an engineering toolkit to construct artificial cognitive systems. One open question is determining which components and techniques to use in such a toolkit. To investigate this question, we employ agent-based AI, using simple computational substrates (i.e., digital brains) undergoing rapid evolution. Such systems are an ideal choice as they are fast to process, easy to manipulate, and transparent for analysis. Even in this limited domain, however, hundreds of different computational substrates are used. While benchmarks exist to compare the quality of different substrates, little work has been done to build broader theory on how substrate features interact. We propose a technique called the Comparative Hybrid Approach and develop a proof-of-concept by systematically analysing components from three evolvable substrates: recurrent artificial neural networks, Markov brains, and Cartesian genetic programming. We study the role and interaction of individual elements of these substrates by recombining them in a piecewise manner to form new hybrid substrates that can be empirically tested. Here, we focus on network sparsity, memory discretization, and logic operators of each substrate. We test the original substrates and the hybrids across a suite of distinct environments with different logic and memory requirements. While we observe many trends, we see that discreteness of memory and the Markov brain logic gates correlate with high performance across our test conditions. Our results demonstrate that the Comparative Hybrid Approach can identify structural subcomponents that predict task performance across multiple computational substrates. %K genetic algorithms, genetic programming, Digital evolution, artificial intelligence, cognitive substrate, neuroscience, neuroevolution, Markov brain %9 journal article %R 10.1162/artl_a_00372 %U http://dx.doi.org/10.1162/artl_a_00372 %P 423-439 %0 Conference Proceedings %T The Evolution of Heterogeneous Logic: An Analysis of the Buffet Method %A Bohm, Cliff %A Hintze, Arend %Y Banzhaf, Wolfgang %Y Burlacu, Bogdan %Y Kelly, Stephen %Y Lalejini, Alexander %Y Olivetti de Franca, Fabricio %S Genetic Programming Theory and Practice XXII %D 2025 %8 jun 5 7 %C Michigan State University, USA %F Bohm:2025:GPTP %O lightning talk %K genetic algorithms, genetic programming %0 Conference Proceedings %T Exact Uniform Initialization for Genetic Programming %A Bohm, Walter %A Geyer-Schulz, Andreas %Y Belew, Richard K. %Y Vose, Michael %S Foundations of Genetic Algorithms IV %D 1996 %8 March %I Morgan Kaufmann %C University of San Diego, CA, USA %@ 1-55860-460-X %F bohm:1996:eui %X In this paper we solve the problem of exactly uniform generation of complete derivation trees from k-bounded context-free languages. The result is applied and is used for developing an exact uniform initialization routine for a genetic programming variant based on an explicit representation of the grammar of the context-free language (simple genetic algorithm over k-bounded context-free languages) [Geyer-Schulz1996b] \citeDBLP:books/daglib/0090318. In this genetic programming variant the grammar is used to generate complete derivation trees which constitute the genomes for the algorithm. For the case that no a priori information about the solution is available, we prove that this (simple random sampling) algorithm is optimal in the sense of a minimax strategy. An exact uniform initialization routine for Koza’s genetic programming variant [Koza1992] is derived as a special case. %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bohm_1996_eui.pdf %P 379-407 %0 Conference Proceedings %T Zoetrope Genetic Programming for regression %A Boisbunon, Aurelie %A Fanara, Carlo %A Grenet, Ingrid %A Daeden, Jonathan %A Vighi, Alexis %A Schoenauer, Marc %Y Chicano, Francisco %Y Tonda, Alberto %Y Krawiec, Krzysztof %Y Helbig, Marde %Y Cleghorn, Christopher W. %Y Wilson, Dennis G. %Y Yannakakis, Georgios %Y Paquete, Luis %Y Ochoa, Gabriela %Y Bacardit, Jaume %Y Gagne, Christian %Y Mostaghim, Sanaz %Y Jourdan, Laetitia %Y Schuetze, Oliver %Y Posik, Petr %Y Segura, Carlos %Y Tinos, Renato %Y Cotta, Carlos %Y Heywood, Malcolm %Y Zhang, Mengjie %Y Trujillo, Leonardo %Y Miikkulainen, Risto %Y Xue, Bing %Y Neumann, Aneta %Y Allmendinger, Richard %Y Ishikawa, Fuyuki %Y Medina-Bulo, Inmaculada %Y Neumann, Frank %Y Sutton, Andrew M. %S Proceedings of the 2021 Genetic and Evolutionary Computation Conference %S GECCO ’21 %D 2021 %8 jul 10 14 %I Association for Computing Machinery %C internet %F Boisbunon:2021:GECCO %X The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targetting evolutionary symbolic regression. The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-the-art performances with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches. %K genetic algorithms, genetic programming, ZGP, Symbolic regression, regression, Representation of mathematical functions, Supervised learning by regression, Learning linear models, Feature selection %R 10.1145/3449639.3459349 %U https://hal.archives-ouvertes.fr/hal-03155694/file/ZGP_regression_arxiv.pdf %U http://dx.doi.org/10.1145/3449639.3459349 %P 776-784 %0 Conference Proceedings %T Quality Diversity Genetic Programming for Learning Decision Tree Ensembles %A Boisvert, Stephen %A Sheppard, John %Y Hu, Ting %Y Lourenco, Nuno %Y Medvet, Eric %S EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming %S LNCS %D 2021 %8 July 9 apr %V 12691 %I Springer Verlag %C Virtual Event %F Boisvert:2021:EuroGP %X Quality Diversity (QD) algorithms are a class of population-based evolutionary algorithms designed to generate sets of solutions that are both fit and diverse. In this paper, we describe a strategy for applying QD concepts to the generation of decision tree ensembles by optimizing collections of trees for both individually accurate and collectively diverse predictive behavior. We compare three variants of this QD strategy with two existing ensemble generation strategies over several classification data sets. We then briefly highlight the effect of the evolutionary algorithm at the core of the strategy. The examined algorithms generate ensembles with distinct predictive behaviors as measured by classification accuracy and intrinsic diversity. The plotted behaviors hint at highly data-dependent relationships between these metrics. QD-based strategies are suggested as a means to optimize classifier ensembles along this performance curve along with other suggestions for future work. %K genetic algorithms, genetic programming, Decision tree ensemble, Quality diversity %R 10.1007/978-3-030-72812-0_1 %U http://dx.doi.org/10.1007/978-3-030-72812-0_1 %P 3-18 %0 Conference Proceedings %T Discovering comprehensible classification rules by using Genetic Programming: a case study in a medical domain %A Bojarczuk, Celia C. %A Lopes, Heitor S. %A Freitas, Alex 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 bojarczuk:1999:DGP %X This work it is intended to discover classification rules for diagnosing certain pathologies. These rules are capable of discriminating among 12 different pathologies, whose main symptom is chest pain. In order to discover these rules it was used genetic programming as well as some concepts of data mining, particularly the emphasis on the discovery of comprehensible knowledge. %K genetic algorithms, genetic programming, data mining, classification, medical applications %U http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/gecco99.ps %P 953-958 %0 Journal Article %T Genetic programming for knowledge discovery in chest-pain diagnosis %A Bojarczuk, Celia C. %A Lopes, Heitor S. %A Freitas, Alex A. %J IEEE Engineering in Medicine and Biology Magazine %D 2000 %8 jul aug %V 19 %N 4 %@ 0739-5175 %F bojarczuk:2000:kdcp %X Explores a promising data mining approach. Despite the small number of examples available in the authors’ application domain (taking into account the large number of attributes), the results of their experiments can be considered very promising. The discovered rules had good performance concerning predictive accuracy, considering both the rule set as a whole and each individual rule. Furthermore, what is more important from a data mining viewpoint, the system discovered some comprehensible rules. It is interesting to note that the system achieved very consistent results by working from ’tabula rasa,’ without any background knowledge, and with a small number of examples. The authors emphasize that their system is still in an experiment in the research stage of development. Therefore, the results presented here should not be used alone for real-world diagnoses without consulting a physician. Future research includes a careful selection of attributes in a preprocessing step, so as to reduce the number of attributes (and the corresponding search space) given to the GP. Attribute selection is a very active research area in data mining. Given the results obtained so far, GP has been demonstrated to be a really useful data mining tool, but future work should also include the application of the GP system proposed here to other data sets, to further validate the results reported in this article. %K genetic algorithms, genetic programming, data mining, knowledge discovery, chest-pain diagnosis, predictive accuracy, rule set, comprehensible rules, background knowledge, preprocessing step, data sets, medical applications %9 journal article %U http://ieeexplore.ieee.org/iel5/51/18543/00853480.pdf %P 38-44 %0 Conference Proceedings %T Data mining with constrained-syntax genetic programming: applications to medical data sets %A Bojarczuk, Celia C. %A Lopes, Heitor S. %A Freitas, Alex A. %S Proceedings Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2001) %D 2001 %F bojarczuk:2001:idamap %O a workshop at MedInfo-2001 %X This work is intended to discover classification rules for diagnosing certain pathologies. In order to discover these rules we have developed a new constrained-syntax genetic programming algorithm based on some concepts of data mining, particularly with emphasis on the discovery of comprehensible knowledge. We compare the performance of the proposed GP algorithm with a genetic algorithm and with the very well-known decision-tree algorithm C4.5. %K genetic algorithms, genetic programming, data mining, classification, medical applications, Constrained-Syntax Genetic Programming %U http://www.ailab.si/idamap/idamap2001/papers/bojarczuk.pdf %0 Conference Proceedings %T An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients %A Bojarczuk, Celia C. %A Lopes, Heitor S. %A Freitas, Alex A. %Y Ryan, Conor %Y Soule, Terence %Y Keijzer, Maarten %Y Tsang, Edward %Y Poli, Riccardo %Y Costa, Ernesto %S Genetic Programming, Proceedings of EuroGP’2003 %S LNCS %D 2003 %8 14 16 apr %V 2610 %I Springer-Verlag %C Essex %@ 3-540-00971-X %F bojarczuk03 %X This paper proposes a constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is compared with C4.5 in a real-world medical data set. This data set represents a difficult classification problem, and a new preprocessing method was devised for mining the data %K genetic algorithms, genetic programming, data mining, classification, medical applications %R 10.1007/3-540-36599-0_2 %U https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html %U http://dx.doi.org/10.1007/3-540-36599-0_2 %P 11-21 %0 Journal Article %T A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets %A Bojarczuk, Celia C. %A Lopes, Heitor S. %A Freitas, Alex A. %A Michalkiewicz, Edson L. %J Artificial Intelligence in Medicine %D 2004 %8 jan %V 30 %N 1 %@ 0933-3657 %F bojarczuk:2004:EMBM %X We propose a constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is compared with C4.5, a well-known decision-tree-building algorithm, and with another GP that uses Boolean inputs (BGP), in five medical data sets: chest pain, Ljubljana breast cancer, dermatology, Wisconsin breast cancer, and pediatric adrenocortical tumour. For this last data set a new preprocessing step was devised for survival prediction. Computational experiments show that, overall, the GP algorithm obtained good results with respect to predictive accuracy and rule comprehensibility, by comparison with C4.5 and BGP. %K genetic algorithms, genetic programming, data mining, classification, medical applications %9 journal article %R 10.1016/j.artmed.2003.06.001 %U https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html %U http://dx.doi.org/10.1016/j.artmed.2003.06.001 %P 27-48 %0 Conference Proceedings %T Queen Bee Based Genetic Programming Method for a Hive Like Behavior %A Bojtar, Veronika %A Botzheim, Janos %S 2020 IEEE 20th International Symposium on Computational Intelligence and Informatics (CINTI) %D 2020 %8 nov %F Bojtar:2020:CINTI %X Designing the behavioural attributes of a robot is challenging, and the complexity of this task is even more increased in the case of swarm robotics. For effectively solving such problems special types of evolutionary algorithms can be used such as Genetic Programming and Queen Bee Based Genetic Programming method. The revolutionary idea behind these algorithms is that they use tree based representation for the individuals in a population, thus being able to solve structure optimization problems. The goal of this paper is to introduce the idea of the Queen Bee Based Genetic Programming method and compare its effectiveness with Genetic Programming through the evolution of a successful hive based behavioral program. %K genetic algorithms, genetic programming, Statistics, Sociology, Task analysis, Informatics, Evolution (biology), queen bee based genetic programming, hive like behavior %R 10.1109/CINTI51262.2020.9305824 %U http://dx.doi.org/10.1109/CINTI51262.2020.9305824 %P 000127-000132 %0 Conference Proceedings %T Optimising Energy Consumption Heuristically on Android Mobile Phones %A Bokhari, Mahmoud %A Wagner, Markus %Y Petke, Justyna %Y White, David R. %Y Weimer, Westley %S Genetic Improvement 2016 Workshop %D 2016 %8 jul 20 24 %I ACM %C Denver %F Bokhari:2016:GI %X In this paper we outline our proposed framework for optimising energy consumption on Android mobile phones. To model the power usage, we use an event-based modelling technique. The internal battery fuel gauge chip is used to measure the amount of energy being consumed and accordingly the model is built on. We use the model to estimate components’ energy usages. In addition, we propose the use of evolutionary computations to prolong the battery life. This can be achieved by using the power consumption model as a fitness function, re-configuring the smartphone’s default settings and modifying existing code of applications. %K genetic algorithms, genetic programming, Genetic Improvement, SBSE, Embedded systems, Computing methodologies, Heuristic function construction, Randomized search, Power Consumption Modelling, Energy Optimisation %R 10.1145/2908961.2931691 %U http://cs.adelaide.edu.au/~markus/pub/2016-gecco-gi-energy.pdf %U http://dx.doi.org/10.1145/2908961.2931691 %P 1139-1140 %0 Generic %T Validation of Internal Meters of Mobile Android Devices %A Bokhari, Mahmoud A. %A Xia, Yuanzhong %A Zhou, Bo %A Alexander, Brad %A Wagner, Markus %D 2017 %8 24 jan %F Bokhari:2017:arXiv %X In this paper we outline our results for validating the precision of the internal power meters of smart-phones under different workloads. We compare its results with an external power meter. This is the first step towards creating customized energy models on the fly and towards optimizing battery efficiency using genetic program improvements. Our experimental results indicate that the internal meters are sufficiently precise when large enough time windows are considered. This is part of our work on the dreaming smart-phone. For a technical demonstration please watch our videos https://www.youtube.com/watch?v=xeeFz2GLFdU and https://www.youtube.com/watch?v=C7WHoLW1KYw. %K Software engineering, Adaptive systems, System improvement, Computational intelligence %U https://arxiv.org/abs/1701.07095 %U http://arxiv.org/abs/1701.07095 %0 Conference Proceedings %T Deep Parameter Optimisation on Android Smartphones for Energy Minimisation - A Tale of Woe and a Proof-of-Concept %A Bokhari, Mahmoud A. %A Bruce, Bobby R. %A Alexander, Brad %A Wagner, Markus %Y Petke, Justyna %Y White, David R. %Y Langdon, W. B. %Y Weimer, Westley %S GI-2017 %D 2017 %8 15 19 jul %I ACM %C Berlin %F Bokhari:2017:GI %X With power demands of mobile devices rising, it is becoming increasingly important to make mobile software applications more energy efficient. Unfortunately, mobile platforms are diverse and very complex which makes energy behaviours difficult to model. This complexity presents challenges to the effectiveness of off-line optimisation of mobile applications. we demonstrate that it is possible to automatically optimise an application for energy on a mobile device by evaluating energy consumption in-vivo. In contrast to previous work, we use only the device’s own internal meter. Our approach involves many technical challenges but represents a realistic path toward learning hardware specific energy models for program code features. %K genetic algorithms, genetic programming, genetic improvement, non-functional properties, mobile devices, multi-objective optimisation, dreaming smartphone, Android 6 %R 10.1145/3067695.3082519 %U http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/bokhari2017_deep_parameter_optimisation.pdf %U http://dx.doi.org/10.1145/3067695.3082519 %P 1501-1508 %0 Conference Proceedings %T In-Vivo and Offline Optimisation of Energy Use in the Presence of Small Energy Signals: A Case Study on a Popular Android Library %A Bokhari, Mahmoud A. %A Alexander, Brad %A Wagner, Markus %Y Schulzrinne, Henning %Y Li, Pan %S Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2018 %D 2018 %8 May 7 nov %I ACM %C New York %F Bokhari:2018:MobiQuitous %X Energy demands of applications on mobile platforms are increasing. As a result, there has been a growing interest in optimising their energy efficiency. As mobile platforms are fast-changing, diverse and complex, the optimisation of energy use is a non-trivial task. To date, most energy optimisation methods either use models or external meters to estimate energy use. Unfortunately, it becomes hard to build widely applicable energy models, and external meters are neither cheap nor easy to set up. To address this issue, we run application variants in-vivo on the phone and use a precise internal battery monitor to measure energy use. We describe a methodology for optimising a target application in-vivo and with application-specific models derived from the device’s own internal meter based on jiffies and lines of code. We demonstrate that this process produces a significant improvement in energy efficiency with limited loss of accuracy. %K genetic algorithms, genetic programming, genetic improvement, electrical batteries, software engineering, Search-based software engineering, SBSE, energy consumption, mobile applications, multi-objective optimisation, Android, Non-functional properties %R 10.1145/3286978.3287014 %U https://cs.adelaide.edu.au/~markus/pub/2018mobiquitous-smallEnergySignals.pdf %U http://dx.doi.org/10.1145/3286978.3287014 %P 207-215 %0 Conference Proceedings %T The Quest for Non-Functional Property Optimisation in Heterogeneous and Fragmented Ecosystems: a Distributed Approach %A Bokhari, Mahmoud A. %A Wagner, Markus %A Alexander, Brad %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 Bokhari:2019:GI7 %X The optimisation of non-functional properties of software is of growing importance in all scales of modern computing (from embedded systems to data-centres). In mobile computing, smart devices have complex interactions between their hardware and software components. Small changes in the environment can greatly impact the measurements of non-functional properties of software. In-vivo optimisation of applications on a platform can be used to evolve robust new solutions. However, the portability of such solutions performance across different platforms is questionable. In this paper we discuss the issue of optimising the non-functional properties of applications running in the Android ecosystem. We also propose a distributed framework that can mitigate such issues. %K genetic algorithms, genetic programming, genetic improvement, smartphone, Hardware, Batteries, Software maintenance tools, Non-functional properties, energy consumption, mobile applications, Android %R 10.1145/3319619.3326877 %U https://cs.adelaide.edu.au/~markus/pub/2019gecco-islands.pdf %U http://dx.doi.org/10.1145/3319619.3326877 %P 1705-1706 %0 Conference Proceedings %T Genetic Improvement of Software Efficiency: The Curse of Fitness Estimation %A Bokhari, Mahmoud A. %A Wagner, Markus %A Alexander, Brad %Y Alexander, Brad %Y Brownlee, Alexander (Sandy) %Y Haraldsson, Saemundur O. %Y Wagner, Markus %Y Woodward, John R. %S 9th edition of GI @ GECCO 2020 %D 2020 %8 jul 8 12 %I ACM %C Internet %F Bokhari:2020:GI9 %X Many challenges arise in the application of Genetic Improvement (GI) of Software to improve non-functional requirements of software such as energy use and run-time. These challenges are mainly centred around the complexity of the search space and the estimation of the desired fitness function. For example, such fitness function are expensive, noisy and estimating them is not a straight-forward task. we illustrate some of the challenges incomputing such fitness functions and propose a synergy between in-vivo evaluation and machine learning approaches to overcome such issues. %K genetic algorithms, genetic programming, genetic improvement, SBSE, Machine Learning, Non-Functional Properties, batteries, Energy Consumption, Mobile Applications, Android %R 10.1145/3377929.3398109 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp145s2-file1.pdf %U http://dx.doi.org/10.1145/3377929.3398109 %P 1926-1927 %0 Conference Proceedings %T Towards Rigorous Validation of Energy Optimisation Experiments %A Bokhari, Mahmoud A. %A Alexander, Brad %A Wagner, Markus %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 Bokhari:2020:GECCO %X The optimisation of software energy consumption is of growing importance across all scales of modern computing, i.e., from embedded systems to data-centres. Practitioners in the field of Search-Based Software Engineering and Genetic Improvement of Software acknowledge that optimising software energy consumption is difficult due to noisy and expensive fitness evaluations. However, it is apparent from results to date that more progress needs to be made in rigorously validating optimisation results. This problem is pressing because modern computing platforms have highly complex and variable behaviour with respect to energy consumption. To compare solutions fairly we propose in this paper a new validation approach called R3-validation which exercises software variants in a rotated-round-robin order. Using a case study, we present an in-depth analysis of the impacts of changing system states on software energy usage, and we show how R3-validation mitigates these. We compare it with current validation approaches across multiple devices and operating systems, and we show that it aligns best with actual platform behaviour. %K genetic algorithms, genetic programming, genetic improvement, Android, non-functional properties, energy consumption, mobile applications %R 10.1145/3377930.3390245 %U https://arxiv.org/abs/2004.04500 %U http://dx.doi.org/10.1145/3377930.3390245 %P 1232-1240 %0 Thesis %T Genetic Improvement of Software for Energy Efficiency in Noisy and Fragmented Eco-Systems %A Bokhari, Mahmoud Abdulwahab K. %D 2020 %8 July %C Australia %C School of Computer Science, University of Adelaide %F Bokhari2020_PhD %X Software has made its way to every aspect of our daily life. Users of smart devices expect almost continuous availability and uninterrupted service. However, such devices operate on restricted energy resources. As energy efficiency of software is relatively a new concern for software practitioners, there is a lack of knowledge and tools to support the development of energy efficient software. Optimising the energy consumption of software requires measuring or estimating its energy use and then optimising it. Generalised models of energy behaviour suffer from heterogeneous and fragmented eco-systems (i.e. diverse hardware and operating systems). The nature of such optimisation environments favours in-vivo optimisation which provides the ground-truth for energy behaviour of an application on a given platform. One key challenge in in-vivo energy optimisation is noisy energy readings. This is because complete isolation of the effects of software optimisation is simply infeasible, owing to random and systematic noise from the platform. we explore in-vivo optimisation using Genetic Improvement of Software (GI) for energy efficiency in noisy and fragmented eco-systems. First, we document expected and unexpected technical challenges and their solutions when conducting energy optimisation experiments. This can be used as guidelines for software practitioners when conducting energy related experiments. Second, we demonstrate the technical feasibility of in-vivo energy optimisation using GI on smart devices. We implement a new approach for mitigating noisy readings based on simple code rewrite. Third, we propose a new conceptual framework to determine the minimum number of samples required to show significant differences between software variants competing in tournaments. We demonstrate that the number of samples can vary drastically between different platforms as well as from one point of time to another within a single platform. It is crucial to take into consideration these observations when optimising in the wild or across several devices in a control environment. Finally, we implement a new validation approach for energy optimisation experiments. Through experiments, we demonstrate that the current validation approaches can mislead software practitioners to draw wrong conclusions. Our approach outperforms the current validation techniques in terms of specificity and sensitivity in distinguishing differences between validation solutions. %K genetic algorithms, genetic programming, genetic improvement, search based software engineering, SBSE, genetic improvement of software, non-functional properties, energy efficiency, battery optimisation, deep parameter optimisation, mobile computing, Android, validation approach, R-3 validation approch %9 Ph.D. thesis %U http://hdl.handle.net/2440/130174 %0 Conference Proceedings %T Design of Scenario-based Application-optimized Data Replication Strategies through Genetic Programming %A Bokhari, Syed Mohtashim Abbas %A Theel, Oliver E. %Y Rocha, Ana Paula %Y Steels, Luc %Y van den Herik, H. Jaap %S Proceedings of the 12th International Conference on Agents and Artificial Intelligence, ICAART 2020 %D 2020 %8 feb 22 24 %V 2 %I SCITEPRESS %C Valletta, Malta %F DBLP:conf/icaart/BokhariT20 %X A distributed system is a paradigm which is indispensable to the current world due to countless requests with every passing second. Therefore, in distributed computing, high availability is very important. In a dynamic environment due to the scalability and complexity of the resources and components, systems are fault-prone because millions of computing devices are connected to each other via communication links. Distributed systems allow many users to access shared computing resources which makes faults inevitable. Replication plays its role in masking failures in order to achieve a fault-tolerant distributed environment. Data replication is an appropriate means to provide highly available data access operations at relatively low operation costs. Although there are several contemporary data replication strategies being used, the question still stands which strategy is the best for a given scenario or application class assuming a certain workload, its distribution across a network, av ailability of the individual replicas, and cost of the access operations. In this regard, research focuses on analysis, simulation, and machine learning approaches to automatically identify and design such replication strategies that are optimized for a given application scenario based on predefined constraints and properties exploiting a so-called voting structure. %K genetic algorithms, genetic programming, Distributed Systems, Fault Tolerance, Data Replication, Quorum Protocols, Operation Availability, Operation Cost, Voting Structures, Optimization, Machine Leaning, Evolutionary Strategies %R 10.5220/0008955301200129 %U https://doi.org/10.5220/0008955301200129 %U http://dx.doi.org/10.5220/0008955301200129 %P 120-129 %0 Conference Proceedings %T A Genetic Programming-based Multi-objective Optimization Approach to Data Replication Strategies for Distributed Systems %A Bokhari, Syed Mohtashim Abbas %A Theel, Oliver %Y Jin, Yaochu %S 2020 IEEE Congress on Evolutionary Computation, CEC 2020 %D 2020 %8 19 24 jul %I IEEE Press %C internet %F Bokhari:2020:CEC %X Data replication is the core of distributed systems to enhance their fault tolerance and make services highly available to the end-users. Data replication masks run-time failures and hence, makes the system more reliable. There are many contemporary data replication strategies for this purpose, but the decision to choose an appropriate strategy for a certain environment and a specific scenario is a challenge and full of compromises. There exists a potentially indefinite number of scenarios that cannot be covered entirely by contemporary strategies. It demands designing new data replication strategies optimized for the given scenarios. The constraints of such scenarios are often conflicting in a sense that an increase in one objective could be sacrificial to the others, which implies there is no best solution to the problem but what serves the purpose. In this regard, this research provides a genetic programming-based multi-objective optimization approach that endeavors to not only identify, but also design new data replication strategies and optimize their conflicting objectives as a single-valued metric. The research provides an intelligent, automatic mechanism to generate new replication strategies as well as easing up the decision making so that relevant strategies with satisfactory trade-offs of constraints can easily be picked and used from the generated solutions at run-time. Moreover, it makes the notion of hybrid strategies easier to accomplish which otherwise would have been very cumbersome to achieve, therefore, to optimize. %K genetic algorithms, genetic programming %R 10.1109/CEC48606.2020.9185598 %U http://dx.doi.org/10.1109/CEC48606.2020.9185598 %P paperid24298 %0 Conference Proceedings %T Introducing Novel Crossover and Mutation Operators into Data Replication Strategies for Distributed Systems %A Bokhari, Syed Mohtashim Abbas %A Theel, Oliver %S IEEE 25th Pacific Rim International Symposium on Dependable Computing, PRDC 2020 %D 2020 %8 January 4 dec %C Perth, Australia %F Bokhari:2020:PRDC %X In a distributed paradigm, data replication plays a vital role in achieving high availability and fault tolerance of a system. In a connected network, faults are inevitable, replication masks those faults at run-time while users are unaware of it and the system continues to work as expected. There are different strategies to enforce such fault-tolerant behavior on a system. However, there are numerous scenarios reflecting different trade-offs between several quality metrics and to identify a relevant strategy for a specific scenario is quite cumbersome since there could exist potentially infinite scenarios and solutions are limited. This requires designing new solutions satisfying the constraints of such scenarios, which may be left unaddressed otherwise. In this regard, this paper develops a mechanism to automatically design new replication strategies (up-to-now unknown), optimized for given scenarios. The paper uses genetic programming to explore unknown replication strategies. It evolves the population of replication strategies (representing each a computer program) gradually, but consistently to make them optimized to eventually meet the desired criteria. Furthermore, it introduces strong multi-crossover and multi-mutation operators into replication, which strengthens our machine learning framework, at the same time guaranteeing consistency of the solutions, to generate innovative hybrid replication strategies. %K genetic algorithms, genetic programming %R 10.1109/PRDC50213.2020.00013 %U http://dx.doi.org/10.1109/PRDC50213.2020.00013 %P 21-30 %0 Conference Proceedings %T Use of Genetic Programming Operators in Data Replication and Fault Tolerance %A Bokhari, Syed Mohtashim Abbas %A Theel, Oliver %S 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) %D 2020 %8 dec %F Bokhari:2020:ICPADS %X Distributed systems are a need of the current times to balance the workload since providing highly accessible data objects is of utmost importance. Faults hinder the availability of the data, thereby leading systems to fail. In this regard, data replication in distributed systems is a means to mask failures and mitigate any such possible hindrances in the availability of the data. This replicated behavior is then controlled by data replication strategies, but there are numerous scenarios reflecting different trade-offs between several quality metrics. It demands designing new replication strategies optimized for the given scenarios, which may be left unaddressed otherwise. This research, therefore, uses an automatic mechanism based on genetic programming to construct new optimized replication strategies (up-to-now) unknown. This mechanism uses a so-called voting structure of directed acyclic graphs (each representing a computer program) as a unified representation of replication strategies. These structures are interpreted by our general algorithm at run-time in order to derive respective quorums to manage replicated objects eventually. For this, the research particularly demonstrates the usefulness of various genetic operators through their instances, exploiting the heterogeneity between existing strategies, thereby creating innovative strategies flexibly. This mechanism creates new hybrid strategies and evolves them over several generations of evolution, to make them optimized while maintaining the consistency (validity) of the solutions. Our approach is very effective and extremely flexible to offer competitive results with respect to the contemporary strategies as well as generating novel strategies even with a slight use of relevant genetic operators. %K genetic algorithms, genetic programming %R 10.1109/ICPADS51040.2020.00047 %U http://dx.doi.org/10.1109/ICPADS51040.2020.00047 %P 290-299 %0 Journal Article %T An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach %A Bolandi, Hamed %A Banzhaf, Wolfgang %A Lajnef, Nizar %A Barri, Kaveh %A Alavi, Amir H. %J Technologies %D 2019 %8 jun %V 7 %N 2 %@ ISSN 2227-7080 %F olandi2019intelligent %X Accurate prediction of bond behaviour of fibre reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can formulate the bond strength by combining the capabilities of both standard genetic programming and classical regression. A number of parameters affecting the bond strength of FRP bars were identified and fed into the MGGP algorithm. The algorithm was trained using an experimental database including 223 test results collected from the literature. The proposed MGGP model accurately predicts the bond strength of FRP bars in concrete. The newly defined predictor variables were found to be efficient in characterizing the bond strength. The derived equation has better performance than the widely-used American Concrete Institute (ACI) model. %K genetic algorithms, genetic programming, multi-gene genetic programming, data mining, bond strength, FRP-bar %9 journal article %R 10.3390/technologies7020042 %U https://www.mdpi.com/2227-7080/7/2/42/pdf %U http://dx.doi.org/10.3390/technologies7020042 %P 42 %0 Conference Proceedings %T Bond strength prediction of FRP-bar reinforced concrete: a multi-gene genetic programming approach %A Bolandi, Hamed %A Banzhaf, Wolfgang %A Lajnef, Nizar %A Barri, Kaveh %A Alavi, Amir. H. %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 Bolandi:2019:GECCOcomp %K genetic algorithms, genetic programming %R 10.1145/3319619.3322066 %U http://dx.doi.org/10.1145/3319619.3322066 %P 364-364 %0 Generic %T The Environmental Discontinuity Hypothesis for Down-Sampled Lexicase Selection %A Boldi, Ryan %A Helmuth, Thomas %A Spector, Lee %D 2022 %8 31 may %I arXiv 2205.15931 %F boldi2022environmentaldiscontinuityhypothesisdownsampled %K genetic algorithms, genetic programming %U https://arxiv.org/abs/2205.15931 %0 Conference Proceedings %T The Problem Solving Benefits of Down-Sampling Vary by Selection Scheme %A Boldi, Ryan %A Bao, Ashley %A Briesch, Martin %A Helmuth, Thomas %A Sobania, Dominik %A Spector, Lee %A Lalejini, Alexander %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F boldi:2023:GECCOcomp %X Genetic programming systems often use large training sets to evaluate candidate solutions, which can be computationally expensive. Down-sampling training sets has long been used to decrease the computational cost of evaluation in a wide range of application domains. Indeed, recent studies have shown that both random and informed down-sampling can substantially improve problem-solving success for GP systems that use lexicase parent selection. We use the PushGP framework to experimentally test whether these down-sampling techniques can also improve problem-solving success in the context of two other commonly used selection methods, fitness-proportionate and tournament selection, across eight GP problems (four program synthesis and four symbolic regression). We verified that down-sampling can benefit the problem-solving success of both fitness-proportionate and tournament selection. However, the number of problems wherein down-sampling improved problem-solving success varied by selection scheme, suggesting that the impact of down-sampling depends both on the problem and choice of selection scheme. Surprisingly, we found that down-sampling was most consistently beneficial when combined with lexicase selection as compared to tournament and fitness-proportionate selection. Overall, our results suggest that down-sampling should be considered more often when solving test-based GP problems. %K genetic algorithms, genetic programming, program synthesis, down-sampling, selection, regression: Poster %R 10.1145/3583133.3590713 %U http://dx.doi.org/10.1145/3583133.3590713 %P 527-530 %0 Conference Proceedings %T A Static Analysis of Informed Down-Samples %A Boldi, Ryan %A Lalejini, Alexander %A Helmuth, Thomas %A Spector, Lee %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F boldi:2023:GECCOcomp2 %X We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection. We study recorded populations from the first generation of genetic programming runs, as well as entirely synthetic populations. Our findings verify the hypothesis that informed down-sampling better maintains population-level test coverage when compared to random down-sampling. Additionally, we show that both forms of down-sampling cause greater test coverage loss than standard lexicase selection with no down-sampling. However, given more information about the population, we found that informed down-sampling can further reduce its test coverage loss. We also recommend wider adoption of the static population analyses we present in this work. %K genetic algorithms, genetic programming, down-sampling, selection, program synthesis, regression: Poster %R 10.1145/3583133.3590751 %U http://dx.doi.org/10.1145/3583133.3590751 %P 531-534 %0 Conference Proceedings %T A Comprehensive Analysis of Down-sampling for Genetic Programming-based Program Synthesis %A Boldi, Ryan %A Bao, Ashley %A Briesch, Martin %A Helmuth, Thomas %A Sobania, Dominik %A Spector, Lee %A Lalejini, Alexander %Y Hu, Ting %Y Ekart, Aniko %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F boldi:2024:GECCOcomp3 %X Genetic programming systems typically require large computational resource investments for training-set evaluations. Down-sampling these sets has proven to decrease costs and improve problem-solving success, particularly with the lexicase parent selection algorithm. We investigated its effectiveness when applied to three other common selection methods and across various program synthesis problems. Our findings show that down-sampling notably enhances all three methods, indicating its potential broad applicability. Additionally, we found informed down-sampling to be more successful than its random counterpart, particularly in selection schemes maintaining diversity like lexicase selection. We conclude that down-sampling is a promising strategy for test-based genetic programming problems, irrespective of selection scheme.This paper is a comprehensive extension of a previous poster paper [1]. %K genetic algorithms, genetic programming: Poster %R 10.1145/3638530.3654134 %U http://dx.doi.org/10.1145/3638530.3654134 %P 487-490 %0 Conference Proceedings %T Untangling the Effects of Down-Sampling and Selection in Genetic Programming %A Boldi, Ryan %A Bao, Ashley %A Briesch, Martin %A Helmuth, Thomas %A Sobania, Dominik %A Spector, Lee %A Lalejini, Alexander %Y Faina, Andres %Y Risi, Sebastian %Y Medvet, Eric %Y Stoy, Kasper %Y Chan, Bert %Y Miras, Karine %Y Zahadat, Payam %Y Grbic, Djordje %Y Nadizar, Giorgia %S ALIFE 2024: Proceedings of the 2024 Artificial Life Conference %D 2024 %8 jul 22 26 %I MIT Press %C Copenhagen %F Boldi:2024:ALife %X the lexicase parent selection algorithm. We test whether these down-sampling techniques can also improve problem-solving success in the context of three other commonly used selection methods, fitness-proportionate, tournament, implicit fitness sharing plus tournament selection, across six program synthesis GP problems. We verified that down-sampling can significantly improve the problem-solving success for all three of these other selection schemes, demonstrating its general efficacy. We discern that the selection pressure imposed by the selection scheme does not interact with the down-sampling method. However, we find that informed down-sampling can improve problem solving success significantly over random down-sampling when the selection scheme has a mechanism for diversity maintenance like lexicase or implicit fitness sharing. Overall, our results suggest that down-sampling should be considered more often when solving test-based problems, regardless of the selection scheme in use. %K genetic algorithms, genetic programming, lexicase, PushGP, program synthesis benchmark problems, tournament, fitness proportional selection, mplicit Fitness Sharing, IFS, diversity maintenance %R 10.1162/isal_a_00832 %U https://direct.mit.edu/isal/proceedings/isal2024/36/88/123536 %U http://dx.doi.org/10.1162/isal_a_00832 %P 705-716 %0 Journal Article %T Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving %A Boldi, Ryan %A Briesch, Martin %A Sobania, Dominik %A Lalejini, Alexander %A Helmuth, Thomas %A Rothlauf, Franz %A Ofria, Charles %A Spector, Lee %J Evolutionary Computation %D 2024 %8 Winter %V 32 %N 4 %@ 1063-6560 %F Boldi:ECJ %X Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases, allowing for more individuals to be explored with the same number of program executions. However, sampling randomly can exclude important cases from the down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while still benefiting from reduced per-evaluation costs. %K genetic algorithms, genetic programming, lexicase selection, informed down-sampling %9 journal article %R 10.1162/evco_a_00346 %U https://arxiv.org/abs/2301.01488 %U http://dx.doi.org/10.1162/evco_a_00346 %P 307-337 %0 Thesis %T Ordenacao evolutiva de anuncios em publicidade computacional %A Bolelli Broinizi, Marcos Eduardo %D 2015 %8 aug %C Sao Paulo, Brazil %C Instituto de Matematica e Estatistica, Universidade de Sao Paulo, USP %G pt %F Bolelli-Broinizi:thesis %X Otimizar simultaneamente os interesses dos usuários, anunciantes e publicadores é um grande desafio na área de publicidade computacional. Mais precisamente, a ordenaÇão de anúncios, ou ad ranking, desempenha um papel central nesse desafio. Por outro lado, nem mesmo as melhores fórmulas ou algoritmos de ordenaÇão são capazes de manter seu status por um longo tempo em um ambiente que está em constante mudanÇa. Neste trabalho, apresentamos uma análise orientada a dados que mostra a importância de combinar diferentes dimensões de publicidade computacional por meio de uma abordagem evolutiva para ordenaÇão de anúncios afim de responder a mudanÇas de forma mais eficaz. Nós avaliamos as dimensões de valor comercial, desempenho histórico de cliques, interesses dos usuários e a similaridade textual entre o anúncio e a página. Nessa avaliaÇão, nós averiguamos o desempenho e a correlaÇão das diferentes dimensões. Como consequência, nós desenvolvemos uma abordagem evolucionária para combinar essas dimensões. Essa abordagem é composta por três partes: um repositório de configuraÇões para facilitar a implantaÇão e avaliaÇão de experimentos de ordenaÇão; um componente evolucionário de avaliaÇão orientado a dados; e um motor de programaÇão genética para evoluir fórmulas de ordenaÇão de anúncios. Nossa abordagem foi implementada com sucesso em um sistema real de publicidade computacional responsável por processar mais de quatorze bilhões de requisiÇões de anúncio por mês. De acordo com nossos resultados, essas dimensões se complementam e nenhuma delas deve ser neglicenciada. Além disso, nós mostramos que a combinaÇão evolucionária dessas dimensões não só é capaz de superar cada uma individualmente, como também conseguiu alcanÇar melhores resultados do que métodos estáticos de ordenaÇão de anúncios. %K genetic algorithms, genetic programming, computational advertising, contextual advertising, exploratory data analysis, learning to advertising, online advertising, principal component analysis %9 Ph.D. thesis %R 10.11606/T.45.2015.tde-09112015-104805 %U http://www.teses.usp.br/teses/disponiveis/45/45134/tde-09112015-104805/ %U http://dx.doi.org/10.11606/T.45.2015.tde-09112015-104805 %0 Conference Proceedings %T A GP Artificial Ant for image processing: preliminary experiments with EASEA %A Bolis, Enzo %A Zerbi, Christian %A Collet, Pierre %A Louchet, Jean %A Lutton, Evelyne %Y Miller, Julian F. %Y Tomassini, Marco %Y Lanzi, Pier Luca %Y Ryan, Conor %Y Tettamanzi, Andrea G. B. %Y Langdon, William B. %S Genetic Programming, Proceedings of EuroGP’2001 %S LNCS %D 2001 %8 18 20 apr %V 2038 %I Springer-Verlag %C Lake Como, Italy %@ 3-540-41899-7 %F bolis:2001:EuroGP %X This paper describes how animat-based food foraging techniques may be applied to the design of low-level image processing algorithms. First, we show how we implemented the food foraging application using the EASEA software package. We then use this technique to evolve an animat and learn how to move inside images and detect high-gradient lines with a minimum exploration time. The resulting animats do not use standard scanning + filtering techniques but develop other image exploration strategies close to contour tracking. Experimental results on grey level images are presented. %K genetic algorithms, genetic programming, Image processing, Contour detection, EASEA, Animat: Poster %R 10.1007/3-540-45355-5_19 %U http://minimum.inria.fr/evo-lab/Publications/EuroGPFinal.ps.gz %U http://dx.doi.org/10.1007/3-540-45355-5_19 %P 246-255 %0 Conference Proceedings %T RankDE: learning a ranking function for information retrieval using differential evolution %A Bollegala, Danushka %A Noman, Nasimul %A Iba, Hitoshi %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 Bollegala:2011:GECCO %X Learning a ranking function is important for numerous tasks such as information retrieval (IR), question answering, and product recommendation. For example, in information retrieval, a Web search engine is required to rank and return a set of documents relevant to a query issued by a user. We propose RankDE, a ranking method that uses differential evolution (DE) to learn a ranking function to rank a list of documents retrieved by a Web search engine. To the best of our knowledge, the proposed method is the first DE-based approach to learn a ranking function for IR. We evaluate the proposed method using LETOR dataset, a standard benchmark dataset for training and evaluating ranking functions for IR. In our experiments, the proposed method significantly outperforms previously proposed rank learning methods that use evolutionary computation algorithms such as Particle Swam Optimization (PSO) and Genetic Programming (GP), achieving a statistically significant mean average precision (MAP) of 0.339 on TD2003 dataset and 0.430 on the TD2004 dataset. Moreover, the proposed method shows comparable results to the state-of-the-art non-evolutionary computational approaches on this benchmark dataset. We analyze the feature weights learnt by the proposed method to better understand the salient features for the task of learning to rank for information retrieval. %K genetic algorithms, genetic programming, Real world applications %R 10.1145/2001576.2001814 %U http://dx.doi.org/10.1145/2001576.2001814 %P 1771-1778 %0 Generic %T Approximability and Non-Approximability by Binary Decision Diagrams (Extended Abstract) %A Bollig, Beate %A Sauerhoff, Martin %A Wegener, Ingo %D 1995 %G en %F oai:CiteSeerX.psu:10.1.1.36.6062 %X The usual applications of BDDs (binary decision diagrams), e. g., in verification and for CAD problems, require an exact representation of the considered Boolean functions. However, if BDDs are used for learning Boolean functions f on the basis of classified examples (e. g., in the environment of genetic programming), it is sufficient to produce the representation of a function g approximating f . This motivates the investigation of the size of the smallest BDD approximating f . Here exponential lower bounds for several BDD variants are proved and the relations between the size of approximating BDDs, randomised BDDs, communication complexity and general approximation techniques are revealed. %K genetic algorithms, genetic programming, subject classification, computational and structural complexity %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.36.6062 %0 Conference Proceedings %T Distributed and Persistent Evolutionary Algorithms: a Design Pattern %A Bollini, Alessandro %A Piastra, Marco %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 bollini:1999:dpEAdp %X In the scenario of distributed processing for evolutionary algorithms the adoption of object-oriented database management systems (ODBMS) may yield improvements in terms of both robustness and flexibility. Populations of evolvable individuals can be made persistent across several evolutionary runs, making it possible to devise incremental strategies. Moreover, virtually any number of evolutionary processes may be run in parallel on the same underlying population without explicit synchronisation beyond that provided by the locking mechanism of the ODBMS. This paper describes a design pattern for a genetic programming environment that allows combining existing techniques with persistent population storage and management. %K genetic algorithms, genetic programming: Poster %R 10.1007/3-540-48885-5_14 %U http://dx.doi.org/10.1007/3-540-48885-5_14 %P 173-183 %0 Conference Proceedings %T A persistent blackboard for distributed evolutionary computation %A Bollini, Alessandro %A Piastra, Marco %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 bollini:1999:A %K Java %P 48-56 %0 Journal Article %T Development of interpretable, data-driven plasticity models with symbolic regression %A Bomarito, G. F. %A Townsend, T. S. %A Stewart, K. M. %A Esham, K. V. %A Emery, J. M. %A Hochhalter, J. D. %J Computer & Structures %D 2021 %V 252 %@ 0045-7949 %F BOMARITO:2021:CS %X In many applications, such as those which drive new material discovery, constitutive models are sought that have three characteristics: (1) the ability to be derived in automatic fashion with (2) high accuracy and (3) an interpretable nature. Traditionally developed models are usually interpretable but sacrifice development time and accuracy. Purely data-driven approaches are usually fast and accurate but lack interpretability. In the current work, a framework for the rapid development of interpretable, data-driven constitutive models is pursued. The approach is characterized by the use of symbolic regression on data generated with micromechanical finite element models. Symbolic regression is the search for equations of arbitrary functional form which match a given dataset. Specifically, an implicit symbolic regression technique is developed to identify a plastic yield potential from homogenized finite element response data. Through three controlled test cases of varying complexity, the approach is shown to successfully produce interpretable plasticity models. The controlled test cases are used to investigate the robustness and scalability of the method and provide reasonable recommendations for more complex applications. Finally, the recommendations are used in the application of the method to produce a porous plasticity model from data corresponding to a representative volume element of voids within a metal matrix %K genetic algorithms, genetic programming, Plasticity, Homogenization, Symbolic regression %9 journal article %R 10.1016/j.compstruc.2021.106557 %U https://www.sciencedirect.com/science/article/pii/S0045794921000791 %U http://dx.doi.org/10.1016/j.compstruc.2021.106557 %P 106557 %0 Conference Proceedings %T Bayesian Model Selection for Reducing Bloat and Overfitting in Genetic Programming for Symbolic Regression %A Bomarito, Geoffrey %A Leser, Patrick %A Strauss, Nolan %A Garbrecht, Karl %A Hochhalter, Jacob %Y Trautmann, Heike %Y Doerr, Carola %Y Moraglio, Alberto %Y Bartz-Beielstein, Thomas %Y Filipic, Bogdan %Y Gallagher, Marcus %Y Ong, Yew-Soon %Y Gupta, Abhishek %Y Kononova, Anna V. %Y Wang, Hao %Y Emmerich, Michael %Y Bosman, Peter A. N. %Y Zaharie, Daniela %Y Caraffini, Fabio %Y Dreo, Johann %Y Auger, Anne %Y Dietric, Konstantin %Y Dufosse, Paul %Y Glasmachers, Tobias %Y Hansen, Nikolaus %Y Mersmann, Olaf %Y Posik, Petr %Y Tusar, Tea %Y Brockhoff, Dimo %Y Eftimov, Tome %Y Kerschke, Pascal %Y Naujoks, Boris %Y Preuss, Mike %Y Volz, Vanessa %Y Derbel, Bilel %Y Li, Ke %Y Li, Xiaodong %Y Zapotecas, Saul %Y Zhang, Qingfu %Y Coletti, Mark %Y Schuman, Catherine (Katie) %Y Scott, Eric “Siggy” %Y Patton, Robert %Y Wiegand, Paul %Y Bassett, Jeffrey K. %Y Gunaratne, Chathika %Y Chugh, Tinkle %Y Allmendinger, Richard %Y Hakanen, Jussi %Y Tauritz, Daniel %Y Woodward, John %Y Lopez-Ibanez, Manuel %Y McCall, John %Y Bacardit, Jaume %Y Brownlee, Alexander %Y Cagnoni, Stefano %Y Iacca, Giovanni %Y Walker, David %Y Toutouh, Jamal %Y O’Reilly, UnaMay %Y Machado, Penousal %Y Correia, Joao %Y Nesmachnow, Sergio %Y Ceberio, Josu %Y Villanueva, Rafael %Y Hidalgo, Ignacio %Y Fernandez de Vega, Francisco %Y Paolo, Giuseppe %Y Coninx, Alex %Y Cully, Antoine %Y Gaier, Adam %Y Wagner, Stefan %Y Affenzeller, Michael %Y Bruce, Bobby R. %Y Nowack, Vesna %Y Blot, Aymeric %Y Winter, Emily %Y Langdon, William B. %Y Petke, Justyna %Y Fernandez Alzueta, Silvino %Y Valledor Pellicer, Pablo %Y Stuetzle, Thomas %Y Paetzel, David %Y Wagner, Alexander %Y Heider, Michael %Y Veerapen, Nadarajen %Y Malan, Katherine %Y Liefooghe, Arnaud %Y Verel, Sebastien %Y Ochoa, Gabriela %Y Omidvar, Mohammad Nabi %Y Sun, Yuan %Y Tarantino, Ernesto %Y Ivanoe, De Falco %Y Della Cioppa, Antonio %Y Umberto, Scafuri %Y Rieffel, John %Y Mouret, Jean-Baptiste %Y Doncieux, Stephane %Y Nikolaidis, Stefanos %Y Togelius, Julian %Y Fontaine, Matthew C. %Y Georgescu, Serban %Y Chicano, Francisco %Y Whitley, Darrell %Y Kyriienko, Oleksandr %Y Dahl, Denny %Y Shir, Ofer %Y Spector, Lee %Y Rahat, Alma %Y Everson, Richard %Y Fieldsend, Jonathan %Y Wang, Handing %Y Jin, Yaochu %Y Hemberg, Erik %Y Elsayed, Marwa A. %Y Kommenda, Michael %Y La Cava, William %Y Kronberger, Gabriel %Y Gustafson, Steven %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F bomarito:2022:GECCOcomp %X When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and its impact on bloat and overfitting during population evolution is studied and compared to common alternatives in the literature. The proposed approach was found to be more robust to noise and data sparsity in numerical experiments, guiding evolution to a level of complexity appropriate to the dataset. Further evolution of the population resulted not in overfitting or bloat, but rather in slight simplifications in model form. The ability to identify an equation of complexity appropriate to the scale of noise in the training data was also demonstrated. In general, the Bayesian model selection algorithm was shown to be an effective means of regularization which resulted in less bloat and overfitting when any amount of noise was present in the training data.The efficacy of a Genetic Programming (GP) [1] solution is often characterized by its (1) fitness, i.e. ability to perform a training task, (2) complexity, and (3) generalizability, i.e. ability to perform its task in an unseen scenario. Bloat is a common phenomenon for GP in which continued training results in significant increases in complexity with minimal improvements in fitness. There are several theories for the prevalence of bloat in GP which postulate possible evolutionary benefits of bloat [2]; however, for most practical purposes bloat is a hindrance rather than a benefit. For example, bloated solutions are less interpretable and more computationally expensive. Overfitting is another common phenomena in GP and the broader machine learning field. Overfitting occurs when continued training results in better fitness but reduced generalizability. %K genetic algorithms, genetic programming %R 10.1145/3520304.3528899 %U http://dx.doi.org/10.1145/3520304.3528899 %P 526-529 %0 Journal Article %T Automated learning of interpretable models with quantified uncertainty %A Bomarito, G. F. %A Leser, P. E. %A Strauss, N. C. M. %A Garbrecht, K. M. %A Hochhalter, J. D. %J Computer Methods in Applied Mechanics and Engineering %D 2023 %V 403 %@ 0045-7825 %F BOMARITO:2023:cma %X Interpretability and uncertainty quantification in machine learning can provide justification for decisions, promote scientific discovery and lead to a better understanding of model behavior. Symbolic regression provides inherently interpretable machine learning, but relatively little work has focused on the use of symbolic regression on noisy data and the accompanying necessity to quantify uncertainty. A new Bayesian framework for genetic-programming-based symbolic regression (GPSR) is introduced that uses model evidence (i.e., marginal likelihood) to formulate replacement probability during the selection phase of evolution. Model parameter uncertainty is automatically quantified, enabling probabilistic predictions with each equation produced by the GPSR algorithm. Model evidence is also quantified in this process, and its use is shown to increase interpretability, improve robustness to noise, and reduce overfitting when compared to a conventional GPSR implementation on both numerical and physical experiments %K genetic algorithms, genetic programming, Interpretable machine learning, Symbolic regression, Bayesian model selection, Fractional Bayes factor %9 journal article %R 10.1016/j.cma.2022.115732 %U https://www.sciencedirect.com/science/article/pii/S0045782522006879 %U http://dx.doi.org/10.1016/j.cma.2022.115732 %P 115732 %0 Journal Article %T Open channel junction velocity prediction by using a hybrid self-neuron adjustable artificial neural network %A Bonakdari, Hossein %A Zaji, Amir Hossein %J Flow Measurement and Instrumentation %D 2016 %V 49 %@ 0955-5986 %F Bonakdari:2016:FMI %X Determining the appropriate hidden layers neuron number is one of the most important processes in modelling the Multi-Layer Perceptron Artificial Neural Network (MLP-ANN). Despite the significant effect of the MLP-ANN neurons number on predicting accuracy, there is no definite rule for its determination. In this study, a new self-neuron number adjustable, hybrid Genetic Algorithm-Artificial Neural Network (GA-ANN), is introduced and its application examined on the complex velocity field prediction of an open channel junction. The results of GA-ANN were compared with those got by the Genetic Programming (GP) method as two applications of the Genetic Algorithm (GA). The comparisons showed that the GA-ANN model can predict the open channel junction velocity with higher accuracy than the GP model, with Root Mean Squared Error (RMSE) of 0.086 and 0.156, respectively. Finally the equation, obtained by applying the GA-ANN model, predicting the velocity at the open channel junction is presented. %K genetic algorithms, genetic programming, Artificial neural network, Neuron number determination, Open channel junction, Velocity prediction %9 journal article %R 10.1016/j.flowmeasinst.2016.04.003 %U http://www.sciencedirect.com/science/article/pii/S0955598616300309 %U http://dx.doi.org/10.1016/j.flowmeasinst.2016.04.003 %P 46-51 %0 Journal Article %T Evaluating the apparent shear stress in prismatic compound channels using the Genetic Algorithm based on Multi-Layer Perceptron: A comparative study %A Bonakdari, Hossein %A Khozani, Zohreh Sheikh %A Zaji, Amir Hossein %A Asadpour, Navid %J Applied Mathematics and Computation %D 2018 %V 338 %@ 0096-3003 %F BONAKDARI:2018:AMC %X Apparent shear stress acting on a vertical interface between the main channel and floodplain in a compound channel is used to quantify the momentum transfer between these sub-areas of a cross section. In order to simulate the apparent shear stress, two soft computing techniques, including the Genetic Algorithm-Artificial Neural Network (GA-ANN) and Genetic Programming (GP) along with Multiple Linear Regression (MLR) were used. The proposed GA-ANN is a novel self-hidden layer neuron adjustable hybrid method made by combining the Genetic Algorithm (GA) with the Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) method. In order to find the optimum condition of the methods considered in modeling apparent shear stress, various input combinations, fitness functions, transfer functions (for the GAA method), and mathematical functions (for the GP method) were investigated. Finally, the results of the optimum GAA and GP methods were compared with the MLR as a basic method. The results show that the hybrid GAA method with RMSE of 0.5326 outperformed the GP method with RMSE of 0.6651. In addition, the results indicate that both GAA and GP methods performed significantly better than MLR with RMSE of 1.5409 in simulating apparent shear stress in symmetric compound channels %K genetic algorithms, genetic programming, Apparent shear stress, Artificial neural network, Compound channel, Hybrid method %9 journal article %R 10.1016/j.amc.2018.06.016 %U http://www.sciencedirect.com/science/article/pii/S0096300318305046 %U http://dx.doi.org/10.1016/j.amc.2018.06.016 %P 400-411 %0 Generic %T Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming %A Bonakdari, Hossein %A Ebtehaj, Isa %A Gharabaghi, Bahram %A Sharifi, Ali %A Mosavi, Amir %D 2020 %I arXiv %F journals/corr/abs-2002-02751 %K genetic algorithms, genetic programming, gene expression programming %U https://arxiv.org/abs/2002.02751 %0 Conference Proceedings %T A New Approach to Estimate the Discharge Coefficient in Sharp-Crested Rectangular Side Orifices Using Gene Expression Programming %A Bonakdari, Hossein %A Gharabaghi, Bahram %A Ebtehaj, Isa %A Sharifi, Ali %Y Arai, Kohei %Y Kapoor, Supriya %Y Bhatia, Rahul %S Intelligent Computing - Proceedings of the 2020 Computing Conference, Volume 3 %S Advances in Intelligent Systems and Computing %D 2020 %V 1230 %I Springer %F conf/sai/BonakdariGES20 %K genetic algorithms, genetic programming, gene expression programming %R 10.1007/978-3-030-52243-8_7 %U http://dx.doi.org/10.1007/978-3-030-52243-8_7 %P 77-96 %0 Journal Article %T A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle %A Bonakdari, Hossein %A Gholami, Azadeh %A Mosavi, Amir %A Kazemian-Kale-Kale, Amin %A Ebtehaj, Isa %A Azimi, Amir Hossein %J Entropy %D 2020 %V 22 %N 11 %@ 1099-4300 %F bonakdari:2020:Entropy %X This paper presents an extensive and practical study of the estimation of stable channel bank shape and dimensions using the maximum entropy principle. The transverse slope (St) distribution of threshold channel bank cross-sections satisfies the properties of the probability space. The entropy of St is subject to two constraint conditions, and the principle of maximum entropy must be applied to find the least biased probability distribution. Accordingly, the Lagrange multiplier (λ) as a critical parameter in the entropy equation is calculated numerically based on the maximum entropy principle. The main goal of the present paper is the investigation of the hydraulic parameters influence governing the mean transverse slope (St¯) value comprehensively using a Gene Expression Programming (GEP) by knowing the initial information (discharge (Q) and mean sediment size (d50)) related to the intended problem. An explicit and simple equation of the St¯ of banks and the geometric and hydraulic parameters of flow is introduced based on the GEP in combination with the previous shape profile equation related to previous researchers. Therefore, a reliable numerical hybrid model is designed, namely Entropy-based Design Model of Threshold Channels (EDMTC) based on entropy theory combined with the evolutionary algorithm of the GEP model, for estimating the bank profile shape and also dimensions of threshold channels. A wide range of laboratory and field data are used to verify the proposed EDMTC. The results demonstrate that the used Shannon entropy model is accurate with a lower average value of Mean Absolute Relative Error (MARE) equal to 0.317 than a previous model proposed by Cao and Knight (1997) (MARE = 0.98) in estimating the bank profile shape of threshold channels based on entropy for the first time. Furthermore, the EDMTC proposed in this paper has acceptable accuracy in predicting the shape profile and consequently, the dimensions of threshold channel banks with a wide range of laboratory and field data when only the channel hydraulic characteristics (e.g., Q and d50) are known. Thus, EDMTC can be used in threshold channel design and implementation applications in cases when the channel characteristics are unknown. Furthermore, the uncertainty analysis of the EDMTC supports the models high reliability with a Width of Uncertainty Bound (WUB) of ±0.03 and standard deviation (Sd) of 0.24. %K genetic algorithms, genetic programming, gene expression programming %9 journal article %R 10.3390/e22111218 %U https://www.mdpi.com/1099-4300/22/11/1218 %U http://dx.doi.org/10.3390/e22111218 %0 Conference Proceedings %T Comparing Reinforcement Learning Algorithms Applied to Crisp and Fuzzy Learning Classifier Systems %A Bonarini, Andrea %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 bonarini:1999:CRLAACFLCS %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-876.pdf %P 52-59 %0 Conference Proceedings %T FranksTree: A Genetic Programming Approach to Evolve Derived Bracketed L-systems %A Bonfim, Danilo Mattos %A de Castro, Leandro Nunes %Y Wang, Lipo %Y Chen, Ke %Y Ong, Yew-Soon %S Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part II %S Lecture Notes in Computer Science %D 2005 %8 aug 27 29 %V 3611 %I Springer %C Changsha, China %@ 3-540-28325-0 %F conf/icnc/BonfimC05 %X L-system is a grammar-like formalism introduced to simulate the development of organisms. The L-system grammar can be viewed as a sort of genetic information that will be used to generate a specific structure. However, throughout development, the string (genetic information) that will effectively be used to ’draw’ the phenotype of an individual is a result of the derivation of the L-system grammar. This work investigates the effect of applying a genetic programming approach to evolve derived L-systems instead of evolving the Lsystem grammar. The crossing over of plants from different species results in hybrid plants resembling a ’Frankstree’, i.e. plants resultant from phenotypically different parents that present unusual body structures. %K genetic algorithms, genetic programming, interactive evolution %R 10.1007/11539087_168 %U http://dx.doi.org/10.1007/11539087_168 %P 1275-1278 %0 Conference Proceedings %T Coevolutionary Dynamics of a Multi-population Genetic Programming System %A Bongard, Josh C. %Y Floreano, D. %Y Nicoud, J.-D. %Y Mondada, F. %S Advances in Artificial Life %S LNAI %D 1999 %8 13 17 sep %V 1674 %I Springer Verlag %C Lausanne %@ 3-540-66452-1 %F bongard:1999:ECAL %K genetic algorithms, genetic programming %U http://www.cs.uvm.edu/~jbongard/papers/s067.ps.gz %P 154 %0 Conference Proceedings %T The Legion System: A Novel Approach to Evolving Heterogeneity for Collective Problem Solving %A Bongard, Josh C. %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 bongard:2000:legion %X We investigate the dynamics of agent groups evolved to peform a collective task, and in which the behavioural heterogeneity of the group is under evolutionary control. Two task domains are studied: solutions are evolved for the two tasks using an evolutionary algorithm called the Legion system. A new metric of heterogeneity is also introduced, which measures the heterogeneity of evolved group behaviours. It was found that the amount of heterogeneity evolved in an agent group is dependent on the given problem domain: for the first task, the Legion system evolved heterogeneous groups; for the second task, primarily homogeneous groups evolved. We conclude that the proposed system, in conjunction with the introduced heterogeneity measure, can be used as a tool for investigating various issues concerning redundancy, robustness and division of labour in the context of evolutionary approaches to collective problem solving. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-46239-2_2 %U http://dx.doi.org/10.1007/978-3-540-46239-2_2 %P 16-28 %0 Journal Article %T Automated reverse engineering of nonlinear dynamical systems %A Bongard, Josh %A Lipson, Hod %J PNAS, Proceedings of the National Academy of Sciences of the United States of America %D 2007 %8 December %V 104 %N 24 %F Bongard:2007:PNAS %X Complex nonlinear dynamics arise in many fields of science and engineering, but uncovering the underlying differential equations directly from observations poses a challenging task. The ability to symbolically model complex networked systems is key to understanding them, an open problem in many disciplines. Here we introduce for the first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data. This method is applicable to any system that can be described using sets of ordinary nonlinear differential equations, and assumes that the (possibly noisy) time series of all variables are observable. Previous automated symbolic modeling approaches of coupled physical systems produced linear models or required a nonlinear model to be provided manually. The advance presented here is made possible by allowing the method to model each (possibly coupled) variable separately, intelligently perturbing and destabilising the system to extract its less observable characteristics, and automatically simplifying the equations during modelling. We demonstrate this method on four simulated and two real systems spanning mechanics, ecology, and systems biology. Unlike numerical models, symbolic models have explanatory value, suggesting that automated reverse engineering approaches for model-free symbolic nonlinear system identification may play an increasing role in our ability to understand progressively more complex systems in the future. %K genetic algorithms, genetic programming, Physical Sciences, Computer Sciences, coevolution, modelling, symbolic identification %9 journal article %R 10.1073/pnas.0609476104 %U http://dx.doi.org/10.1073/pnas.0609476104 %P 9943-9948 %0 Journal Article %T Accelerating Self-Modeling in Cooperative Robot Teams %A Bongard, Josh C. %J IEEE Transactions on Evolutionary Computation %D 2009 %8 apr %V 13 %N 2 %F Bongard:2009:TEC %X One of the major obstacles to achieving robots capable of operating in real-world environments is enabling them to cope with a continuous stream of unanticipated situations. In previous work, it was demonstrated that a robot can autonomously generate self-models, and use those self-models to diagnose unanticipated morphological change such as damage. In this paper, it is shown that multiple physical quadrupedal robots with similar morphologies can share self-models in order to accelerate modeling. Further, it is demonstrated that quadrupedal robots which maintain separate self-modeling algorithms but swap self-models perform better than quadrupedal robots that rely on a shared self-modeling algorithm. This finding points the way toward more robust robot teams: a robot can diagnose and recover from unanticipated situations faster by drawing on the previous experiences of the other robots. %K genetic algorithms, genetic programming, Robots, Robot sensing systems, Training data, Sensors, Data models, Service robots, Computational modeling, self-modeling, Collective robotics, evolutionary robotics %9 journal article %R 10.1109/TEVC.2008.927236 %U http://dx.doi.org/10.1109/TEVC.2008.927236 %P 321-332 %0 Book Section %T A Functional Crossover Operator for Genetic Programming %A Bongard, Josh %E Riolo, Rick L. %E O’Reilly, Una-May %E McConaghy, Trent %B Genetic Programming Theory and Practice VII %S Genetic and Evolutionary Computation %D 2009 %8 14 16 may %I Springer %C Ann Arbor %F Bongard:2009:GPTP %X Practitioners of evolutionary algorithms in general, and of genetic programming in particular, have long sought to develop variation operators that automatically preserve and combine useful genetic substructure. This is often pursued with crossover operators that swap genetic material between genotypes that have survived the selection process. However in genetic programming, crossover often has a large phenotypic effect, thereby drastically reducing the probability of a beneficial crossover event. In this paper we introduce a new crossover operator, Functional crossover (FXO), which swaps subtrees between parents based on the subtrees’ functional rather than structural similarity. FXO is employed in a genetic programming system identification task, where it is shown that FXO often outperforms standard crossover on both simulated and physically-generated data sets. %K genetic algorithms, genetic programming, homologous crossover, crossover operators, system identification %R 10.1007/978-1-4419-1626-6_12 %U http://dx.doi.org/10.1007/978-1-4419-1626-6_12 %P 195-210 %0 Conference Proceedings %T A probabilistic functional crossover operator for genetic programming %A Bongard, Josh C. %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 Bongard:2010:gecco %X The original mechanism by which evolutionary algorithms were to solve problems was to allow for the gradual discovery of sub-solutions to sub-problems, and the automated combination of these sub-solutions into larger solutions. This latter property is particularly challenging when recombination is performed on genomes encoded as trees, as crossover events tend to greatly alter the original genomes and therefore greatly reduce the chance of the crossover event being beneficial. A number of crossover operators designed for tree-based genetic encodings have been proposed, but most consider crossing genetic components based on their structural similarity. In this work we introduce a tree-based crossover operator that probabilistically crosses branches based on the behavioural similarity between the branches. It is shown that this method outperforms genetic programming without crossover, random crossover, and a deterministic form of the crossover operator in the symbolic regression domain. %K genetic algorithms, genetic programming %R 10.1145/1830483.1830649 %U http://dx.doi.org/10.1145/1830483.1830649 %P 925-932 %0 Journal Article %T Innocent Until Proven Guilty: Reducing Robot Shaping from Polynomial to Linear Time %A Bongard, Josh C. %J IEEE Transactions on Evolutionary Computation %D 2011 %8 aug %V 15 %N 4 %@ 1089-778X %F Bongard:2012:ieeetec %X In evolutionary algorithms, much time is spent evaluating inferior phenotypes that produce no offspring. A common heuristic to address this inefficiency is to stop evaluations early if they hold little promise of attaining high fitness. However, the form of this heuristic is typically dependent on the fitness function used, and there is a danger of prematurely stopping evaluation of a phenotype that may have recovered in the remainder of the evaluation period. Here a stopping method is introduced that gradually reduces fitness over the phenotype’s evaluation, rather than accumulating fitness. This method is independent of the fitness function used, only stops those phenotypes that are guaranteed to become inferior to the current offspring-producing phenotypes, and realises significant time savings across several evolutionary robotics tasks. It was found that for many tasks, time complexity was reduced from polynomial to sublinear time, and time savings increased with the number of training instances used to evaluate a phenotype as well as with task difficulty. %K genetic algorithms, genetic programming, Early stopping, Evolutionary computation, Joints, Manipulators, Neurons, Robot sensing systems, evolutionary robotics %9 journal article %R 10.1109/TEVC.2010.2096540 %U http://dx.doi.org/10.1109/TEVC.2010.2096540 %P 571-585 %0 Generic %T Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis %A Bongard, Joshua %A Kodali, Anuradha %A Szubert, Marcin %A Das, Kamalika %A Ganguly, Sangram %D 2017 %F Bongard:2017:ECML %X The tropical rainforests are the largest reserves of terrestrial carbon sink and therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and ongoing drought since 2000 in the Congo region there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. Contradicting research has claimed that these forests are resilient to such extreme climatic events. A significant reason behind these diverse conclusions is the lack of a holistic spatio-temporal analysis of the remote sensing data available for these regions. Small scale studies that use statistical correlation measure and simple linear regression to model the climate-vegetation interactions have suffered from the lack of complete data representation and the use of simple (linear) models that fail to represent physical processes accurately, thereby leading to inconclusive or incorrect predictions about the future. In this paper we use a genetic programming (GP) based approach called symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space and build a nonlinear GP model for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Overall it shows that the rainforests exhibit tremendous resiliency in the face of severe droughts. Based on the partitioning of the observed data points, we can conclude that in the absence of adequate precipitation, the trees adopt to reach a different steady state and recover as soon as precipitation is back to normal. %K genetic algorithms, genetic programming, earth resources and remote sensing %U http://hdl.handle.net/2060/20170011183 %0 Journal Article %T Quantifying the information lost in optimal covariance matrix cleaning %A Bongiorno, Christian %A Lamrani, Lamia %J Physica A: Statistical Mechanics and its Applications %D 2025 %V 657 %@ 0378-4371 %F Bongiorno:2025:physa %X Obtaining an accurate estimate of the underlying covariance matrix from finite sample size data is challenging due to sample size noise. In recent years, sophisticated covariance-cleaning techniques based on random matrix theory have been proposed to address this issue. Most of these methods aim to achieve an optimal covariance matrix estimator by minimizing the Frobenius norm distance as a measure of the discrepancy between the true covariance matrix and the estimator. However, this practice offers limited interpretability in terms of information theory. To better understand this relationship, we focus on the Kullback-Leibler divergence to quantify the information lost by the estimator. Our analysis centers on rotationally invariant estimators, which are state-of-art in random matrix theory, and we derive an analytical expression for their Kullback-Leibler divergence. Due to the intricate nature of the calculations, we use genetic programming regressors paired with human intuition. Ultimately, using this approach, we formulate a conjecture validated through extensive simulations, showing that the Frobenius distance corresponds to a first-order expansion term of the Kullback-Leibler divergence, thus establishing a more defined link between the two measures %K genetic algorithms, genetic programming, Random matrix theory, Covariance matrix estimation, Genetic regressor programming, High-dimension statistics, Information theory %9 journal article %R 10.1016/j.physa.2024.130225 %U https://www.sciencedirect.com/science/article/pii/S0378437124007349 %U http://dx.doi.org/10.1016/j.physa.2024.130225 %P 130225 %0 Conference Proceedings %T An Investigation of Exploration and Exploitation Within Cluster Oriented Genetic Algorithms (COGAs) %A Bonham, Christopher R. %A Parmee, Ian C. %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 bonham:1999:AIEEWCOGA %K real world applications %U http://gpbib.cs.ucl.ac.uk/gecco1999/RW-765.pdf %P 1491-1497 %0 Journal Article %T Andrew Adamatzky: Physarum Machines: Computers from Slime Mould %A Bonifaci, Vincenzo %J Genetic Programming and Evolvable Machines %D 2013 %8 mar %V 14 %N 1 %@ 1389-2576 %F Bonifaci:2013:GPEM %O Book Review %K genetic algorithms, genetic programming, evolvable life %9 journal article %R 10.1007/s10710-012-9169-2 %U https://rdcu.be/dR8fU %U http://dx.doi.org/10.1007/s10710-012-9169-2 %P 123-124 %0 Journal Article %T Genetic Algorithm for Boolean minimization in an FPGA cluster %A Pedraza, Cesar %A Castillo, Javier %A Martinez, Jose I. %A Huerta, Pablo %A Bosque, Jose L. %A Cano, Javier %J The Journal of Supercomputing %D 2011 %8 nov %V 58 %N 2 %I Springer %@ 0920-8542 %F Pedraza:2011:JSC %O Special issue on HPC in computational Science and Engineering. Part I %X Evolutionary algorithms are an alternative option to the Boolean synthesis due to that they allow one to create hardware structures that would not be able to be obtained with other techniques. This paper shows a parallel genetic programming (PGP) Boolean synthesis implementation based on a cluster of FPGAs that takes full advantage of parallel programming and hardware/software co-design techniques. The performance of our cluster of FPGAs implementation has been compared with an HPC implementation. The experimental results have shown an excellent behaviour in terms of speed up (up to x500) and in terms of solving the scalability problems of this algorithms present in previous works. %K genetic algorithms, genetic programming, Performance evaluation, FPGA, Boolean synthesis, Hardware co-design %9 journal article %R 10.1007/s11227-010-0401-7 %U http://dx.doi.org/10.1007/s11227-010-0401-7 %P 244-252 %0 Conference Proceedings %T Low Cost Platform for Evolvable-Based Boolean Synthesis %A Bonilla, Cesar Pedraza %A Camargo, Carlos Ivan %S IEEE Second Latin American Symposium on Circuits and Systems (LASCAS), 2011 %D 2011 %8 feb %F Bonilla:2011:LASCAS %X Evolutionary algorithms are another option for combinational synthesis because they allow for the generation of hardware structures that cannot be obtained with other techniques. This paper shows a parallel genetic programming (PGP) Boolean synthesis implementation based on a low cost cluster of an embedded platform called SIE, based on a 32-bit processor and a Spartan-3 FPGA. Some tasks of the PGP have been accelerated in hardware and results have been compared with an HPC implementation, resulting in speedup values up to approximately 180. %K genetic algorithms, genetic programming, 32-bit processor, HPC implementation, PGP Boolean synthesis implementation, SIE, combinational synthesis, embedded platform, evolutionary algorithms, evolvable-based Boolean synthesis, hardware structures, low cost cluster, low cost platform, parallel genetic programming, spartan-3 FPGA, speedup values, Boolean functions, combinational circuits, embedded systems, field programmable gate arrays, logic design, microprocessor chips %R 10.1109/LASCAS.2011.5750310 %U http://dx.doi.org/10.1109/LASCAS.2011.5750310 %0 Journal Article %T Sintesis booleanacon programacion genetica paralela en CPU y GPU %A Pedraza, Cesar A. %A Oyaga, Jaime V. %A Gomez, Ricardo C. %J Ingenium Revista de la facultad de ingenieria %D 2013 %8 ene. %V 14 %F Pedraza_Oyaga_Gomez_2013 %X The Boolean or combinational synthesis is a process that optimizes a logic gates net-work, in order to reduce power consumption, minimize costs, minimize area and increase the performance when it is implemented. Moreover genetic programming is an important alternative to generate interesting and efficient hardware structures. It has been shown that evolvable algorithms are faster when implemented in parallel systems. This paper presents the implementation of a parallel genetic programming (PGP) for boolean synthesis on a GPU-CPU based platform. Our implementation uses the island mode which allows the parallel and independent evolution of the PGP through the multiple processing units of the GPU and the multiple cores of a new generation desktop processors. We tested multiple mapping alternatives of the PGP on the platform in order to optimize the PGP response time. As a result we show that our approach achieves a speedup up to 41. %K genetic algorithms, genetic programming, Programacion paralela, sintesis booleana, GPU, algoritmo evolutivo, Parallel programming, Boolean synthesis, GPU, evolutionary algorithm %9 journal article %R 10.21500/01247492.1325 %U https://revistas.usb.edu.co/index.php/Ingenium/article/view/1325/1116 %U http://dx.doi.org/10.21500/01247492.1325 %P 117-130 %0 Journal Article %T Cellular geometric semantic genetic programming %A Bonin, Lorenzo %A Rovito, Luigi %A De Lorenzo, Andrea %A Manzoni, Luca %J Genetic Programming and Evolvable Machines %D 2024 %V 25 %@ 1389-2576 %F bonin:2024:GPEM %O Online first %K genetic algorithms, genetic programming, Geometric semantic genetic programming, Cellular genetic programming %9 journal article %R 10.1007/s10710-024-09480-8 %U http://dx.doi.org/10.1007/s10710-024-09480-8 %P Articleno:8 %0 Conference Proceedings %T On novelty driven evolution in Poker %A Bonson, J. P. C. %A McIntyre, A. R. %A Heywood, M. I. %S 2016 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2016 %8 dec %F Bonson:2016:SSCI %X This work asks the question as to whether ‘novelty as an objective’ is still beneficial under tasks with a lot of ambiguity, such as Poker. Specifically, Poker represents a task in which there is partial information (public and private cards) and stochastic changes in state (what card will be dealt next). In addition, bluffing plays a fundamental role in successful strategies for playing the game. On the face of it, it appears that multiple sources of variation already exist, making the additional provision of novelty as an objective unwarranted. Indeed, most previous work in which agent strategies are evolved with novelty appearing as an explicit objective are not rich in sources of ambiguity. Conversely, the task of learning strategies for playing Poker, even under the 2-player case of heads-up Limit Texas Hold’em, is widely considered to be particularly challenging on account of the multiple sources of uncertainty. We benchmark a form of genetic programming, both with and without (task independent) novelty objectives. It is clear that pursuing behavioural diversity, even under the heads-up Limit Texas Hold’em task is central to learning successful strategies. Benchmarking against static and Bayesian opponents illustrates the capability of the resulting Genetic Programming (GP) agents to bluff and vary their style of play. %K genetic algorithms, genetic programming %R 10.1109/SSCI.2016.7849968 %U http://dx.doi.org/10.1109/SSCI.2016.7849968 %0 Conference Proceedings %T Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming %A Bonte, Bert %A Wyns, Bart %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 Bonte:2010:AIAI %X Genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. In this contribution we use genetic programming to automatically evolve efficient robot controllers for a corridor following task. Based on tests executed in a simulation environment we show that very robust and efficient controllers can be obtained. Also, we stress that it is important to provide sufficiently diverse fitness cases, offering a sound basis for learning more complex behaviour. The evolved controller is successfully applied to real environments as well. Finally, controller and sensor morphology are co-evolved, clearly resulting in an improved sensor configuration. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-16239-8_14 %U http://dx.doi.org/10.1007/978-3-642-16239-8_14 %P 86-93 %0 Conference Proceedings %T Logic Optimization for Majority Gate-Based Nanoelectronic Circuits Based on Genetic Algorithm %A Bonyadi, M. R. %A Azghadi, S. M. R. %A Rad, N. M. %A Navi, K. %A Afjei, E. %S International Conference on Electrical Engineering, 2007. ICEE ’07 %D 2007 %8 November 12 apr %C Lahore %@ 1-4244-0893-8 %F Bonyadi:2007:ICEE %X In this paper we propose a novel and efficient method for majority gate-based design. The basic Boolean primitive in quantum cellular automata (QCA) is the majority gate. Method for reducing the number of majority gates required for computing Boolean functions is developed to facilitate the conversion of sum of products (SOP) expression into QCA majority logic. This method is based on genetic algorithm and can reduce the hardware requirements for a QCA design. We will show that the proposed approach is very efficient in deriving the simplified majority expression in QCA design. %K genetic algorithms, genetic programming, QCA %R 10.1109/ICEE.2007.4287307 %U http://dx.doi.org/10.1109/ICEE.2007.4287307 %0 Book Section %T Recombination %A Booker, Lashon B. %A Fogel, David B. %A Whitley, Darrell %A Angeline, Peter J. %A Eiben, A. E. %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 booker:2000:EC1 %K genetic algorithms, genetic programming %U http://www.crcpress.com/product/isbn/9780750306645 %P 256-307 %0 Journal Article %T Evolutionary computation in zoology and ecology %A Boone, Randall B. %J Current Zoology %D 2017 %8 dec %V 63 %N 6 %@ 1674-5507 %F Boone:2017:CZ %X Evolutionary computational methods have adopted attributes of natural selection and evolution to solve problems in computer science, engineering, and other fields. The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. In evolutionary computation, a population is represented in a way that allows for an objective function to be assessed that is relevant to the problem of interest. The poorest performing members are removed from the population, and remaining members reproduce and may be mutated. The fitness of the members is again assessed, and the cycle continues until a stopping condition is met. Case studies include optimizing: egg shape given different clutch sizes, mate selection, migration of wildebeest, birds, and elk, vulture foraging behavior, algal bloom prediction, and species richness given energy constraints. Other case studies simulate the evolution of species and a means to project shifts in species ranges in response to a changing climate that includes competition and phenotypic plasticity. This introduction concludes by citing other uses of evolutionary computation and a review of the flexibility of the methods. For example, representing species niche spaces subject to selective pressure allows studies on cladistics, the taxon cycle, neutral versus niche paradigms, fundamental versus realized niches, community structure and order of colonization, invasiveness, and responses to a changing climate. %K genetic algorithms, genetic programming, agent-based modeling, case studies, evolutionary programming, evolutionary strategies %9 journal article %R 10.1093/cz/zox057 %U https://doi.org/10.1093/cz/zox057 %U http://dx.doi.org/10.1093/cz/zox057 %P 675-686 %0 Conference Proceedings %T Coevolution of Algorithms and Deterministic Solutions of Equations in Free Groups %A Booth, Richard F. %A Borovik, Alexandre V. %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 booth:2004:eurogp %X We discuss the use of evolutionary algorithms for solving problems in combinatorial group theory, using a class of equations in free groups as a test bench. We find that, in this context, there seems to be a correlation between successful evolutionary algorithms and the existence of good deterministic algorithms. We also trace the convergence of co-evolution of the population of fitness functions to a deterministic solution. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-24650-3_2 %U http://dx.doi.org/10.1007/978-3-540-24650-3_2 %P 11-22 %0 Conference Proceedings %T Genetic Programming with Embedded Features of Symbolic Computations %A Borcheninov, Yaroslav V. %A Okulovsky, Yuri S. %Y Filipe, Joaquim %Y Fred, Ana L. N. %S KDIR International Conference on Knowledge Discovery and Information Retrieval %D 2011 %8 26 29 oct %I SciTePress %C Paris, France %F conf/ic3k/BorcheninovO11 %X Genetic programming is a methodology, widely used in data mining for obtaining the analytic form that describes a given experimental data set. In some cases, genetic programming is complemented by symbolic computations that simplify found expressions. We propose to unify the induction of genetic programming with the deduction of symbolic computations in one genetic algorithm. Our approach was implemented as the .NET library and successfully tested at various data mining problems: function approximation, invariants finding and classification. %K genetic algorithms, genetic programming: poster %R 10.5220/0003682004760479 %U http://dx.doi.org/10.5220/0003682004760479 %P 476-479 %0 Conference Proceedings %T Internal and online simplification in genetic programming: an experimental comparison %A Borcheninov, Yaroslav %A Okulovsky, Yuri %Y Kamkin, Alexander S. %Y Petrenko, Alexander K. %Y Terekhov, Andrey N. %S Proceedings of the Spring/Summer Young Researchers’ Colloquium on Software Engineering %D 2012 %8 may %V 6 %C Perm, Russia %G en %F Borcheninov:2012:SYRCoSE %X Genetic programming is an evolutionary algorithm, which allows performing symbolic regression — the important task of obtaining the analytical form of a model by the data, produced by the model. One of the known problems of genetic programming is expressions bloating that results in ineffectively long expressions. To prevent bloating, symbolic simplification of expression is used. We introduce a new approach to simplification in genetic programming, making it a uniform part of the evolutionary process. To do that, we develop a genetic programming on the basis of transformation rules, similarly to computer algebra systems. We compare our approach with existed solution, and prove its adequacy and effectiveness. %K genetic algorithms, genetic programming, symbolic computations, computer algebra systems %R 10.15514/SYRCOSE-2012-6-22 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.1191 %U http://dx.doi.org/10.15514/SYRCOSE-2012-6-22 %P 134-138 %0 Conference Proceedings %T Towards Automatic Extraction of Definitions from Text %A Borg, Claudia %A Rosner, Mike %A Pace, Gordon %Y Borg, Claudia %Y Spina, Sandro %Y Abela, Charlie %S 5th Computer Science Annual Workshop CSAW 2007 %D 2007 %8 May 6 nov %C Bighi, Malta %G en %F Borg:2007:CSAW %X Definition Extraction can be useful for the creation of glossaries and in Question Answering Systems. It is a tedious task to extract such sentences manually, and thus an automatic system is desirable. In this work we will review some attempts at rule-based approaches and discuss their results. We will then propose a novel experiment involving Genetic Programming and Genetic Algorithms, aimed at assisting the discovery of grammar rules which can be used for the task of Definition Extraction. %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.296.4714 %0 Conference Proceedings %T Model selection in genetic programming %A Borges, Cruz E. %A Alonso, Cesar L. %A Montana, Jose L. %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 Borges:2010:gecco %X In this paper we discuss the problem of model selection in Genetic Programming. We present empirical comparisons between classical statistical methods (AIC, BIC) adapted to Genetic Programming and the Structural Risk Minimisation method (SRM) based on Vapnik-Chervonenkis theory (VC), for symbolic regression problems with added noise. We also introduce a new model complexity measure for the SRM method that tries to measure the non-linearity of the model. The experimentation suggests practical advantages of using VC-based model selection with the new complexity measure, when using genetic training. %K genetic algorithms, genetic programming, Poster %R 10.1145/1830483.1830662 %U http://dx.doi.org/10.1145/1830483.1830662 %P 985-986 %0 Conference Proceedings %T Coevolutionary Architectures with Straight Line Programs for solving the Symbolic Regression Problem %A Borges, Cruz Enrique %A Alonso, Cesar L. %A Montana, Jose Luis %A de la Cruz Echeandia, Marina %A Ortega de la Puente, Alfonso %Y Filipe, Joaquim %Y Kacprzyk, Janusz %S Proceedings of the International Conference on Evolutionary Computation (ICEC 2010) %D 2010 %8 24 26 oct %I SciTePress %C Valencia, Spain %F Borges:2010:ICEC %X To successfully apply evolutionary algorithms to the solution of increasingly complex problems we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. In this paper we present an architecture which involves cooperative coevolution of two subcomponents: a genetic program and an evolution strategy. As main difference with work previously done, our genetic program evolves straight line programs representing functional expressions, instead of tree structures. The evolution strategy searches for good values for the numerical terminal symbols used by those expressions. Experimentation has been performed over symbolic regression problem instances and the obtained results have been compared with those obtained by means of Genetic Programming strategies without coevolution. The results show that our coevolutionary architecture with straight line programs is capable to obtain better quality individuals than traditional genetic programming using the same amount of computational effort. %K genetic algorithms, genetic programming %R 10.5220/0003075100410050 %U http://paginaspersonales.deusto.es/cruz.borges/Papers/10ICEC.pdf %U http://dx.doi.org/10.5220/0003075100410050 %P 41-50 %0 Thesis %T Programacion Genetica, Algoritmos Evolutivos y Aprendizaje Inductivo: Hacia una solucion al problema xvii de Smale en el caso real %A Borges Hernandez, Cruz Enrique %D 2010 %8 25 nov %C Santander, Spain %C Universidad de Cantabria Departamento de Matematicas, Estadistica y Computacion %F BorgesHernandez:thesis %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://paginaspersonales.deusto.es/cruz.borges/Papers/11Tesis.pdf %0 Journal Article %T An unsupervised heuristic-based approach for bibliographic metadata deduplication %A Borges, Eduardo N. %A de Carvalho, Moises G. %A Galante, Renata %A Goncalves, Marcos Andre %A Laender, Alberto H. F. %J Information Processing & Management %D 2011 %V 47 %N 5 %@ 0306-4573 %F Borges2011706 %O Managing and Mining Multilingual Documents %X Digital libraries of scientific articles contain collections of digital objects that are usually described by bibliographic meta data records. These records can be acquired from different sources and be represented using several metadata standards. These metadata standards may be heterogeneous in both, content and structure. All of this implies that many records may be duplicated in the repository, thus affecting the quality of services, such as searching and browsing. In this article we present an approach that identifies duplicated bibliographic metadata records in an efficient and effective way. We propose similarity functions especially designed for the digital library domain and experimentally evaluate them. Our results show that the proposed functions improve the quality of metadata de-duplication up to 188percent compared to four different baselines. We also show that our approach achieves statistical equivalent results when compared to a state-of-the-art method for replica identification based on genetic programming, without the burden and cost of any training process. %K genetic algorithms, genetic programming, Digital libraries, Metadata, Deduplication, Similarity %9 journal article %R 10.1016/j.ipm.2011.01.009 %U http://www.sciencedirect.com/science/article/pii/S0306457311000100 %U http://dx.doi.org/10.1016/j.ipm.2011.01.009 %P 706-718 %0 Conference Proceedings %T Genetic Programming-Based Clustering Using an Information Theoretic Fitness Measure %A Boric, Neven %A Estevez, Pablo A. %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 Boric:2007:cec %X A clustering method based on multitree genetic programming and an information theoretic fitness is proposed. A probabilistic interpretation is given to the output of trees that does not require a conflict resolution phase. The method can cluster data with irregular shapes, estimate the underlying models of the data for each class and use those models to classify unseen patterns. The proposed scheme is tested on several real and artificial data sets, outperforming k-means algorithm in all of them. %K genetic algorithms, genetic programming %R 10.1109/CEC.2007.4424451 %U 1285.pdf %U http://dx.doi.org/10.1109/CEC.2007.4424451 %P 31-38 %0 Conference Proceedings %T Evolving Classification Models for Prediction of Patient Recruitment in Multicentre Clinical Trials Using Grammatical Evolution %A Borlikova, Gilyana %A Phillips, Michael %A Smith, Louis %A O’Neill, Michael %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/BorlikovaPSO16 %X Successful and timely completion of prospective clinical trials depends on patient recruitment as patients are critical to delivery of the prospective trial data. There exists a pressing need to develop better tools/techniques to optimise patient recruitment in multi-centre clinical trials. In this study Grammatical Evolution (GE) is used to evolve classification models to predict future patient enrolment performance of investigators/site to be selected for the conduct of the trial. Prediction accuracy of the evolved models is compared with results of a range of machine learning algorithms widely used for classification. The results suggest that GE is able to successfully induce classification models and analysis of these models can help in our understanding of the factors providing advanced indication of a trial sites’ future performance. %K genetic algorithms, genetic programming, Grammatical evolution, Clinical trials, Enrolment, Grammar-based genetic programming %R 10.1007/978-3-319-31204-0_4 %U https://link.springer.com/chapter/10.1007/978-3-319-31204-0_4 %U http://dx.doi.org/10.1007/978-3-319-31204-0_4 %P 46-57 %0 Conference Proceedings %T Development of a Multi-model System to Accommodate Unknown Misclassification Costs in Prediction of Patient Recruitment in Multicentre Clinical Trials %A Borlikova, Gilyana %A O’Neill, Michael %A Smith, Louis %A Phillips, Michael %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 Borlikova:2017:GECCO %X Clinical trials are an essential step in a new drug’s approval process. Optimisation of patient recruitment is one of the major challenges facing pharma and contract research organisations (CRO) in conducting multicentre clinical trials. Improving the quality of selection of investigators/sites at the start of a trial can help to address this business problem. Grammatical Evolution (GE) was previously used to evolve classification models to predict the future patient enrolment performance of investigators/sites considered for a trial. However, the unknown target misclassification costs at the model development stage pose additional challenges. To address them we use a new composite fitness function to develop a multi-model system of decision-tree type classifiers that optimise a range of possible trade-offs between the correct classification and errors. The predictive power of the GE-evolved models is compared with a range of machine learning algorithms widely used for classification. The results of the study demonstrate that the GE-evolved multi-model system can help to circumvent uncertainty at the model development stage by providing a collection of customised models for rapid deployment in response to business needs of a clinical trial. %K genetic algorithms, genetic programming, Grammatical evolution %R 10.1145/3067695.3076062 %U http://doi.acm.org/10.1145/3067695.3076062 %U http://dx.doi.org/10.1145/3067695.3076062 %P 263-264 %0 Conference Proceedings %T Alternative Fitness Functions in the Development of Models for Prediction of Patient Recruitment in Multicentre Clinical Trials %A Borlikova, Gilyana %A Phillips, Michael %A Smith, Louis %A Nicolau, Miguel %A O’Neill, Michael %Y Fink, Andreas %Y Fuegenschuh, Armin %Y Geiger, Martin Josef %S Operations Research Proceedings 2016 %D 2018 %I Springer International Publishing %F 10.1007/978-3-319-55702-1_50 %X For a drug to be approved for human use, its safety and efficacy need to be evidenced through clinical trials. At present, patient recruitment is a major bottleneck in conducting clinical trials. Pharma and contract research organisations (CRO) are actively looking into optimisation of different aspects of patient recruitment. One of the avenues to approach this business problem is to improve the quality of selection of investigators/sites at the start of a trial. This study builds upon previous work that used Grammatical Evolution (GE) to evolve classification models to predict the future patient enrolment performance of investigators/sites considered for a trial. Selection of investigators/sites, depending on the business context, could benefit from the use of either especially conservative or more liberal predictive models. To address this business need, decision-tree type classifiers were evolved using different fitness functions to drive GE. The functions compared were classical accuracy, balanced accuracy and F-measure with different values of parameter beta. The issue of models’ generalisability was addressed by introduction of a validation procedure. The predictive power of the resultant GE-evolved models on the test set was compared with performance of a range of machine learning algorithms widely used for classification. The results of the study demonstrate that flexibility of GE induced classification models can be used to address business needs in the area of patient recruitment in clinical trials. %K genetic algorithms, genetic programming, Grammatical evolution %R 10.1007/978-3-319-55702-1_50 %U https://link.springer.com/chapter/10.1007/978-3-319-55702-1_50 %U http://dx.doi.org/10.1007/978-3-319-55702-1_50 %P 375-381 %0 Book Section %T Business Analytics and Grammatical Evolution for the Prediction of Patient Recruitment in Multicentre Clinical Trials %A Borlikova, Gilyana %A Smith, Louis %A Phillips, Michael %A O’Neill, Michael %E Ryan, Conor %E O’Neill, Michael %E Collins, J. J. %B Handbook of Grammatical Evolution %D 2018 %I Springer %F Borlikova:2018:hbge %X For a drug to be approved for human use, its safety and efficacy need to be evidenced through clinical trials. Optimisation of patient recruitment is an active area of business interest for pharma and contract research organisations (CRO) conducting clinical trials. The healthcare industry and CROs are gradually starting to adapt business analytics techniques to improve processes and help boost performance. Development of methods able to predict at the outset which prospective investigators/sites will succeed in patient recruitment can provide powerful tools for this business problem. In this chapter we describe the application of Grammatical Evolution to the prediction of patient recruitment in multicentre clinical trials. %K genetic algorithms, genetic programming, Grammatical Evolution %R 10.1007/978-3-319-78717-6_19 %U http://dx.doi.org/10.1007/978-3-319-78717-6_19 %P 461-486 %0 Journal Article %T Performance of genetic programming to extract the trend in noisy data series %A Borrelli, A. %A De Falco, I. %A Della Cioppa, A. %A Nicodemi, M. %A Trautteur, G. %J Physica A: Statistical and Theoretical Physics %D 2006 %8 January %V 370 %N 1 %F Borrelli:2006:PhysicaA %O Econophysics Colloquium - Proceedings of the International Conference ’Econophysics Colloquium’ %X In this paper an approach based on genetic programming for forecasting stochastic time series is outlined. To obtain a suitable test-bed some well-known time series are dressed with noise. The GP approach is endowed with a multiobjective scheme relying on statistical properties of the faced series, i.e., on their momenta. Finally, the method is applied to the MIB30 Index series. %K genetic algorithms, genetic programming, Multiobjective genetic programming, Stochastic time series %9 journal article %R 10.1016/j.physa.2006.04.025 %U http://dx.doi.org/10.1016/j.physa.2006.04.025 %P 104-108 %0 Journal Article %T A comparison of an evolvable hardware controller with an artificial neural network used for evolving the gait of a hexapod robot %A Borrett, Fraser %A Beckerleg, Mark %J Genetic Programming and Evolvable Machines %D 2023 %8 jun %V 24 %N 1 %@ 1389-2576 %F Borrett:2023:GPEM %O Online first %X investigates the implementation of a novel evolvable hardware controller used in evolutionary robotics. The evolvable hardware consists of a Cartesian based array of logic blocks comprised of multiplexers and logic elements. The logic blocks are configured by a bit stream which is evolved using a genetic algorithm. A comparison is performed between an evolvable hardware and an artificial neural network controller evolved using the same genetic algorithm to produce the gait of a hexapod robot. To compare the two controllers, differences in their evolutionary efficiency and robot performance are investigated. The evolutionary efficiency is measured by the required number of generations to achieve an optimal fitness. An optimal hexapod controller allows the robot to walk forward in a straight line maintaining a constant heading and body attitude. It was found that the evolutionary efficiency and performance of the evolvable hardware and artificial neural network were similar, however the EHW was the most evolutionary efficient requiring less generations on average to evolve. Both evolved controllers were evaluated in simulation, and on a physical robot using a softcore processor and custom hardware implemented on a FPGA. The implementation showed that the controllers performed equally well when deployed, allowing the hexapod to meet the optimal gait criteria. These findings have shown that the evolvable hardware controller is a valid option for robotic control of a multi-legged robot such as a hexapod as its evolutionary efficiency and deployed performance on a real robot is comparable to that of an artificial neural network. One future application of these evolvable controllers is in fault tolerance where the robot can dynamically adapt to a fault by evolving the controller to adjust to the fault conditions. %K genetic algorithms, evolvable hardware, Evolutionary robots, Artificial neural network, ANN, Hexapod robotic, Robot gait, MATLAB, EHW, FPGA, ARM %9 journal article %R 10.1007/s10710-023-09452-4 %U https://rdcu.be/c8Hyy %U http://dx.doi.org/10.1007/s10710-023-09452-4 %P Articlenumber:5 %0 Conference Proceedings %T The Virtual Programmable Logic Device, a Novel Machine Learning Architecture %A Borrett, Fraser %A Beckerleg, Mark %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F borrett:2024:CEC %X This paper introduces a novel architecture for robotic control, called a Virtual Programmable Logic Device (VPLD) where the operation of the device can be dynamically configured using machine learning methods. The VPLD is based on the structure of a Programable Logic Device (PLD) comprising of a two-dimensional feed-forward array of function blocks, with each block containing multiplexers and function elements. However, the VPLD is implemented in software rather than hardware allowing the VPLD to be run on a CPU based system, such as PC’s and ARM based embedded systems. The operation of the VPLD is determined by a configuration bitstream which configures the multiplexers (routing) and function elements of each block. A genetic algorithm is used to evolve the configuration bitstream to produce the walking gait of a hexapod robot. The controller performance and the evolutionary efficiency of the VPLD are compared with an evolved artificial neural network (ANN), and an evolvable hardware (EHW) device. It was found the VPLD and ANN had similar controller performance and evolutionary efficiency, while the EHW had a comparable controller performance however its evolutionary efficiency was poor. It is shown that the VPLD is a viable alternative to an ANN for evolutionary robotic control. %K genetic algorithms, genetic programming, Performance evaluation, Multiplexing, Legged locomotion, Evolutionary robotics, Programmable logic devices, Artificial neural networks, Machine learning, virtual programmable logic device, artificial neural network, evolvable hardware, evolutionary robots, hexapod robot, robot gait %R 10.1109/CEC60901.2024.10612129 %U http://dx.doi.org/10.1109/CEC60901.2024.10612129 %0 Conference Proceedings %T A Comparison of a Digital and Floating-Point Virtual Programmable Logic Device and an Artifical Neural Network Evolved for Robotic Navigation %A Borrett, Fraser %A Beckerleg, Mark %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F borrett:2024:CEC2 %X Two novel architectures, the digital and floatingpoint virtual programmable logic device (VPLD) are compared with an artificial neural network, evolved for the robotic navigational tasks of obstacle avoidance and light following for a two wheeled robot. The VPLD is based on a programmable logic device but is coded in software rather than in hardware. This allows the VPLD to be implemented on CPUs allowing it to be run on a wide range of platforms including PCs, mobile phones and ARM processors. The function of the VPLD is governed by a configuration bitstream which can be evolved by evolutionary computation. It was found that the digital and floating-point VPLDs performed well against the ANN in the navigational tasks, making the VPLD a viable alternative to an ANN for robotic navigation. %K genetic algorithms, genetic programming, Program processors, Navigation, Programmable logic devices, Artificial neural networks, Evolutionary computation, Software, Hardware, Virtual Programmable Logic Device, Evolvable Hardware, Artificial Neural Network, Robotic Navigation, Genetic Algorithm %R 10.1109/CEC60901.2024.10612180 %U http://dx.doi.org/10.1109/CEC60901.2024.10612180 %0 Journal Article %T Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming %A Borunda, Monica %A Rodriguez-Vazquez, Katya %A Garduno-Ramirez, Raul %A de la Cruz-Soto, Javier %A Antunez-Estrada, Javier %A Jaramillo, Oscar A. %J Energies %D 2020 %V 13 %N 8 %@ 1996-1073 %F borunda:2020:Energies %X Given the imminent threats of climate change, it is urgent to boost the use of clean energy, being wind energy a potential candidate. Nowadays, deployment of wind turbines has become extremely important and long-term estimation of the produced power entails a challenge to achieve good prediction accuracy for site assessment, economic feasibility analysis, farm dispatch, and system operation. We present a method for long-term wind power forecasting using wind turbine properties, statistics, and genetic programming. First, due to the high degree of intermittency of wind speed, we characterise it with Weibull probability distributions and consider wind speed data of time intervals corresponding to prediction horizons of 30, 25, 20, 15 and 10 days ahead. Second, we perform the prediction of a wind speed distribution with genetic programming using the parameters of the Weibull distribution and other relevant meteorological variables. Third, the estimation of wind power is obtained by integrating the forecasted wind velocity distribution into the wind turbine power curve. To demonstrate the feasibility of the proposed method, we present a case study for a location in Mexico with low wind speeds. Estimation results are promising when compared against real data, as shown by MAE and MAPE forecasting metrics. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/en13081885 %U https://www.mdpi.com/1996-1073/13/8/1885 %U http://dx.doi.org/10.3390/en13081885 %0 Conference Proceedings %T Solving Approximation Problems By Ant Colony Programming %A Boryczka, Mariusz %A Czech, Zbigniew J. %Y Langdon, W. B. %Y Cantú-Paz, E. %Y Mathias, K. %Y Roy, R. %Y Davis, D. %Y Poli, R. %Y Balakrishnan, K. %Y Honavar, V. %Y Rudolph, G. %Y Wegener, J. %Y Bull, L. %Y Potter, M. A. %Y Schultz, A. C. %Y Miller, J. F. %Y Burke, E. %Y Jonoska, N. %S GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %D 2002 %8 September 13 jul %I Morgan Kaufmann Publishers %C New York %@ 1-55860-878-8 %F boryczka:2002:gecco %K genetic algorithms, genetic programming, artificial life, adaptive behavior, agents, ant colony optimization, poster paper, ant colony programming, approximation problems, automatic programming %U http://gpbib.cs.ucl.ac.uk/gecco2002/aaaa288.ps %P 133 %0 Conference Proceedings %T Solving Approximation Problems by Ant Colony Programming %A Boryczka, Mariusz %A Czech, Zbigniew J. %Y Cantú-Paz, Erick %S Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002) %D 2002 %8 jul %I AAAI %C New York, NY %F boryczka:2002:gecco:lbp %X A method of automatic programming, called genetic programming, assumes that the desired program is found by using a genetic algorithm. We propose an idea of ant colony programming in which instead of a genetic algorithm an ant colony algorithm is applied to search for the program. The test results demonstrate that the proposed idea can be used with success to solve the approximation problems. %K genetic algorithms, genetic programming, automatic programming, ant colony programming, ACO, approximation problems %U http://www-zo.iinf.polsl.gliwice.pl/pub/zjc/bc02.ps.Z %P 39-46 %0 Conference Proceedings %T Verified Transformations and Hoare Logic: Beautiful Proofs for Ugly Assembly Language %A Bosamiya, Jay %A Gibson, Sydney %A Li, Yao %A Parno, Bryan %A Hawblitzel, Chris %Y Christakis, Maria %Y Polikarpova, Nadia %Y Duggirala, Parasara Sridhar %Y Schrammel, Peter %S Software Verification - 12th International Conference, VSTTE 2020, and 13th International Workshop, NSV 2020 %S Lecture Notes in Computer Science %D 2020 %8 jul 20 21 %V 12549 %I Springer %C Los Angeles, CA, USA %F DBLP:conf/vstte/BosamiyaGLPH20 %O Revised Selected Papers %X Hand-optimized assembly language code is often difficult to formally verify. This paper combines Hoare logic with verified code transformations to make it easier to verify such code. This approach greatly simplifies existing proofs of highly optimized OpenSSL-based AES-GCM cryptographic code. Furthermore, applying various verified transformations to the AES-GCM code enables additional platform-specific performance improvements. %K genetic algorithms, genetic programming, genetic improvement, Hoare Logic %R 10.1007/978-3-030-63618-0_7 %U http://dx.doi.org/10.1007/978-3-030-63618-0_7 %P 106-123 %0 Thesis %T A Principled Approach Towards Unapologetic Security %A Bosamiya, Jay %D 2024 %8 may %C USA %C Computer Science Department, Carnegie Mellon University %F bosamiya-thesis %X Software is incredibly difficult to write correctly, let alone safely. Prior work (quite successfully) has, amongst other things, relied on formal verification—a powerful hammer to achieve provable guarantees. However, improvements in the state-of-the-art of software security often come with significant apologies for other important objectives, such as development velocity, software performance, or loss of functionality. While many developers (and more so, users) would like their software to be secure, these apologies cause security to become underprioritized. To be adopted, advances in security thus need to not only improve the state-of-the-art in security, but also focus on other practical considerations that have historically inhibited widespread deployment, and indeed prevented building secure software from being the natural default choice. In this thesis, we argue that security objectives are achievable without apology, through the use of principled approaches and formalism. To validate this thesis, we look at a collection of case studies that span across a wide collection of different kinds of software systems: (i) high-performance cryptographic primitives, (ii) safe execution of arbitrary untrusted code, (iii) agile safety enforcement for code, (iv) low-level serializers and parsers for untrusted data, and (v) source-unavailable executable comprehension. In each, we demonstrate that principled approaches and formalism help remove the need for the apologies required by prior work. Our hope is that providing security without apology, even in the face of practical complexities, makes a big step towards the shared goal of security researchers—making security the natural default choice. %K genetic algorithms, genetic programming, genetic improvement, Formal Techniques, Unapologetic Security, Software Verification, Cryptographic Implementations, Sandboxing, WebAssembly, Parsing, decompilation, Computer Software, faster instruction orderings %9 Ph.D. thesis %R 10.1184/R1/25898734.v1 %U https://www.jaybosamiya.com/publications/2024/thesis/bosamiya-thesis.pdf %U http://dx.doi.org/10.1184/R1/25898734.v1 %0 Conference Proceedings %T Towards Multi-movement Hand Prostheses: Combining Adaptive Classification with High Precision Sockets %A Boschmann, Alexander %A Kaufmann, Paul %A Platzner, Marco %A Winkler, Michael %S Technically Assisted Rehabilitation (TAR) %D 2009 %8 mar 18 19 %C Berlin, Germany %F bo-ka-09 %X The acceptance of hand prostheses strongly depends on their user-friendliness and functionality. Current prostheses are limited to a few movements and their operation is all but intuitive. The development of practically applicable multi-movement prostheses requires the combination of modern classification methods with novel techniques for manufacturing high precision sockets. In this paper, we introduce an approach for classifying EMG signals taken from forearm muscles using support vector machines. This classifier technique is used in an adaptive operation mode and customized to the amputee, which allows us to recognize eleven different hand movements with high accuracy. Then, we present a novel manufacturing technique for prosthesis sockets enabling a precise amputee-specific fitting and EMG sensor placement. %K genetic algorithms, genetic programming, Electromyographic, EMG %U http://www.ige.tu-berlin.de/fileadmin/fg176/IGE_Printreihe/TAR_2009/paper/05_boschmann.pdf %0 Conference Proceedings %T Enhancing Software Runtime with Reinforcement Learning-Driven Mutation Operator Selection in Genetic Improvement %A Bose, Damien %A Hanna, Carol %A Petke, Justyna %S "14th International Workshop on Genetic Improvement %F bose:2025:GI %0 Journal Article %D 2025 %8 27 apr %C Ottawa %F 2025"c %X Genetic Improvement employs heuristic search algorithms to explore the search space of program variants by modifying code using mutation operators. This research focuses on operators that delete, insert and replace source code statements. Traditionally, in GI, an operator is chosen uniformly at random at each search iteration. Reinforcement Learning to intelligently guide the selection of these operators specifically to improve program runtime. We propose to integrate RL into the operator selection process. Four Multi-Armed bandit RL algorithms (Epsilon Greedy, UCB, Probability Matching, and Policy Gradient) were integrated within a GI framework, and their efficacy and efficiency were bench marked against the traditional GI operator selection approach. These RL-guided operator selection strategies have demonstrated empirical superiority over the traditional GI methods of randomly selecting a search operator, with UCB emerging as the top-performing RL algorithm. On average, the UCB-guided Hill Climbing search algorithm produced variants that compiled and passed all tests 44% of the time, while only 22% of the variants produced by the traditional uniform random selection strategies compiled and passed all tests. %K genetic algorithms, genetic programming, Genetic Improvement, Reinforcement learning, gcov %9 journal article %R 10.1109/GI66624.2025.00013 %U https://rps.ucl.ac.uk/viewobject.html?cid=1&id=2360068 %U http://dx.doi.org/10.1109/GI66624.2025.00013 %P 27-34 %0 Journal Article %T Quantitative models for direct marketing: A review from systems perspective %A Bose, Indranil %A Chen, Xi %J European Journal of Operational Research %D 2009 %8 16 may %V 195 %N 1 %@ 0377-2217 %F Bose20091 %X quantitative models for direct marketing models are reviewed from a systems perspective. A systems view consists of input, processing, and output and the six key activities of direct marketing that take place within these constituent parts. A discussion about inputs for direct marketing models is provided by describing the various types of data used, by determining the significance of the data, and by addressing the issue of selection of appropriate data. Two types of models, statistical and machine learning based, are popularly used for conducting direct marketing activities. The advantages and disadvantages of these two approaches are discussed along with enhancements to these models. The evaluation of output for direct marketing models is done on the basis of accuracy and profitability. Some challenges in conducting research in the area of quantitative direct marketing models are listed and some significant research questions are proposed. %K genetic algorithms, genetic programming, Marketing, Data mining, Customer profiling, Customer targeting, Statistical modelling, Performance evaluation %9 journal article %R 10.1016/j.ejor.2008.04.006 %U http://www.sciencedirect.com/science/article/B6VCT-4S7SV3H-3/2/39d97985eecf3aa2b863955e4227cbb0 %U http://dx.doi.org/10.1016/j.ejor.2008.04.006 %P 1-16 %0 Report %T Graduated Embodiment for Sophisticated Agent Evolution and Optimization %A Boslough, Mark %A Peters, Michael %A Pierson, Arthurine %D 2005 %8 jan %N SAND2005-0014 %I Sandia National Laboratories %C P.O. Box 5800, Albuquerque, NM 87185-0318, USA %F SAND2005-0014 %X We summarise the results of a project to develop evolutionary computing methods for the design of behaviours of embodied agents in the form of autonomous vehicles. We conceived and implemented a strategy called graduated embodiment. This method allows high-level behavior algorithms to be developed using genetic programming methods in a low-fidelity, disembodied modelling environment for migration to high-fidelity, complex embodied applications. This project applies our methods to the problem domain of robot navigation using adaptive waypoints, which allow navigation behaviors to be ported among autonomous mobile robots with different degrees of embodiment, using incremental adaptation and staged optimisation. Our approach to biomimetic behaviour engineering is a hybrid of human design and artificial evolution, with the application of evolutionary computing in stages to preserve building blocks and limit search space. The methods and tools developed for this project are directly applicable to other agent-based modeling needs, including climate-related conflict analysis, multiplayer training methods,and market-based hypothesis evaluation. %K genetic algorithms, genetic programming, Algorithms Design, Evaluation, Genetics, Hypothesis, Navigation, Optimization, Programming Robots, Simulation, Training, Intelligent agents (Computer software), Robots-Control systems, Evolutionary programming (Computer science) %R 10.2172/921610 %U https://www.sandia.gov/research/publications/details/graduated-embodiment-for-sophisticated-agent-evolution-and-optimization-2005-01-01/ %U http://dx.doi.org/10.2172/921610 %0 Conference Proceedings %T Autonomous dynamic soaring %A Boslough, Mark %S 2017 IEEE Aerospace Conference %D 2017 %8 April 11 mar %C Big Sky, MT, USA %F Boslough:2017:ieeeAero %X This project makes use of biomimetic behavioural engineering in which adaptive strategies used by animals in the real world are applied to the development of autonomous robots. The key elements of the biomimetic approach are to observe and understand a survival behaviour exhibited in nature, to create a mathematical model and simulation capability for that behaviour, to modify and optimise the behaviour for a desired robotics application, and to implement it. The application described in this report is dynamic soaring, a behaviour that certain sea birds use to extract flight energy from laminar wind velocity gradients in the shallow atmospheric boundary layer directly above the ocean surface. Theoretical calculations, computational proof-of-principle demonstrations, and the first instrumented experimental flight test data for dynamic soaring are presented to address the feasibility of developing dynamic soaring flight control algorithms to sustain the flight of unmanned airborne vehicles (UAVs). Both hardware and software were developed for this application. Eight-foot custom foam glider were built and flown in a steep shear gradient. A logging device was designed and constructed with custom software to record flight data during dynamic soaring manoeuvres. A computational tool kit was developed to simulate dynamic soaring in special cases and with a full 6-degree of freedom flight dynamics model in a generalised time-dependent wind field. Several 3-dimensional visualization tools were built to replay the flight simulations. A realistic aerodynamics model of an eight-foot sailplane was developed using measured aerodynamic derivatives. Genetic programming methods were developed and linked to the simulations and visualization tools. These tools can now be generalised for other biomimetic behaviour applications. This work was carried out in 2000 and 2001, and until now its results have only been available in an internal Sandia report. %K genetic algorithms, genetic programming %R 10.1109/AERO.2017.7943967 %U http://dx.doi.org/10.1109/AERO.2017.7943967 %0 Conference Proceedings %T Linkage Information Processing In Distribution Estimation Algorithms %A Bosman, Peter A. N. %A Thierens, Dirk %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 bosman:1999:LIPIDEA %X The last few years there has been an increasing amount of interest in the field of distribution estimation optimization algorithms. As more techniques are introduced, the variety in tested distribution structures increases. we analyze the implications of the form of such a structure. We show that learning the linkage relations alone and using them directly in a distribution estimation algorithm to generate new samples is not sufficient for building competent evolutionary algorithms. The information needs to be processed to identify and use the building blocks. %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-812.pdf %P 60-67 %0 Report %T Grammar Transformations in an EDA for Genetic Programming %A Bosman, Peter A. N. %A de Jong, Edwin D. %D 2004 %N UU-CS-2004-047 %I Department of Information and Computing Sciences, Utrecht University %C The Netherlands %F UUCS2004047 %X In this paper we present a new Estimation of Distribution Algorithm (EDA) for Genetic Programming (GP). We propose a probability distribution for the space of trees, based on a grammar. To introduce dependencies into the distribution, grammar transformations are performed that facilitate the description of specific subfunctions. We present some results from experiments on two benchmark problems and show some of the subfunctions that were introduced during optimization as a result of the transformations that were applied. %K genetic algorithms, genetic programming, EDA, grammar %U http://www.cs.uu.nl/research/techreps/repo/CS-2004/2004-047.pdf %0 Conference Proceedings %T Grammar Transformations in an EDA for Genetic Programming %A Bosman, Peter A. N. %A de Jong, Edwin D. %Y Poli, R. %Y Cagnoni, S. %Y Keijzer, M. %Y Costa, E. %Y Pereira, F. %Y Raidl, G. %Y Upton, S. C. %Y Goldberg, D. %Y Lipson, H. %Y de Jong, E. %Y Koza, J. %Y Suzuki, H. %Y Sawai, H. %Y Parmee, I. %Y Pelikan, M. %Y Sastry, K. %Y Thierens, D. %Y Stolzmann, W. %Y Lanzi, P. L. %Y Wilson, S. W. %Y O’Neill, M. %Y Ryan, C. %Y Yu, T. %Y Miller, J. F. %Y Garibay, I. %Y Holifield, G. %Y Wu, A. S. %Y Riopka, T. %Y Meysenburg, M. M. %Y Wright, A. W. %Y Richter, N. %Y Moore, J. H. %Y Ritchie, M. D. %Y Davis, L. %Y Roy, R. %Y Jakiela, M. %S GECCO 2004 Workshop Proceedings %D 2004 %8 26 30 jun %C Seattle, Washington, USA %F bosman:2004:obu:panbos %X we present a new Estimation-of-Distribution Algorithm (EDA) for Genetic Programming (GP). We propose a probability distribution for the space of trees, based on a grammar. To introduce dependencies into the distribution, grammar transformations are performed that facilitate the description of specific subfunctions. We present some results from experiments on two benchmark problems and show some of the subfunctions that were introduced during optimisation as a result of the transformations that were applied. %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2004/WOBU001.pdf %0 Conference Proceedings %T Learning Probabilistic Tree Grammars for Genetic Programming %A Bosman, Peter A. N. %A de Jong, Edwin D. %Y Yao, Xin %Y Burke, Edmund %Y Lozano, Jose A. %Y Smith, Jim %Y Merelo-Guervós, Juan J. %Y Bullinaria, John A. %Y Rowe, Jonathan %Y Kabán, Peter Tiňo Ata %Y Schwefel, Hans-Paul %S Parallel Problem Solving from Nature - PPSN VIII %S LNCS %D 2004 %8 18 22 sep %V 3242 %I Springer-Verlag %C Birmingham, UK %@ 3-540-23092-0 %F Bosman:PPSN:2004 %X Genetic Programming (GP) provides evolutionary methods for problems with tree representations. A recent development in Genetic Algorithms (GAs) has led to principled algorithms called Estimation-of-Distribution Algorithms (EDAs). EDAs identify and exploit structural features of a problems structure during optimization. Here, we investigate the use of a specific EDA for GP. We develop a probabilistic model that employs transformations of production rules in a context-free grammar to represent local structures. The results of performing experiments on two benchmark problems demonstrate the feasibility of the approach. %K genetic algorithms, genetic programming, EDA %R 10.1007/b100601 %U http://www.cs.uu.nl/~dejong/publications/edagpppsn.pdf %U http://dx.doi.org/10.1007/b100601 %P 192-201 %0 Thesis %T Cost-aware resource management in clusters and clouds %A Van den Bossche, Ruben %D 2014 %8 jun %C Antwerp, Belgium %C Departement Wiskunde-Informatica, Universiteit Antwerpen %F Van_den_Bossche:thesis %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://hdl.handle.net/10067/1174120151162165141 %0 Thesis %T Application of Genetic Programming to the Induction of Linear Programming Trees %A Bot, Martijn %D 1999 %8 January %C Amsterdam, The Netherlands %C Vrije Universiteit %F bot:1999:masters %K genetic algorithms, genetic programming, data mining %9 Masters thesis %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/verslag.ps.gz %0 Conference Proceedings %T Application of Genetic Programming to Induction of Linear Classification Trees %A Bot, Martijn %A Langdon, William B. %Y Postma, Eric %Y Gyssens, Marc %S Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC’99) %D 1999 %8 March 4 nov %C Kasteel Vaeshartelt, Maastricht, Holland %F bot:1999:GPilct %X A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a benchmark of classification problems. In particular, using GP we are able to induce decision trees with a linear combination of variables in each function node. The effects of techniques as limited error fitness, fitness sharing Pareto scoring and domination Pareto scoring are evaluated. Results indicate that GP can be applied successfully to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified. %K genetic algorithms, genetic programming, data mining %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/BNAIC99.bot.18aug99.ps.gz %P 107-114 %0 Conference Proceedings %T Application of Genetic Programming to Induction of Linear Classification Trees %A Bot, Martijn C. J. %A Langdon, William B. %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 bot:2000:GPilct %X A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a set of benchmark classification problems. Using GP we are able to induce decision trees with a linear combination of variables in each function node. A new representation of decision trees using strong typing in GP is introduced. With this representation it is possible to let the GP classify into any number o f classes. Results indicate that GP can be applied successfully to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified. %K genetic algorithms, genetic programming: Poster %R 10.1007/978-3-540-46239-2_18 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/bot.eurogp2000.19jan.ps.gz %U http://dx.doi.org/10.1007/978-3-540-46239-2_18 %P 247-258 %0 Conference Proceedings %T Improving Induction of Linear Classification Trees with Genetic Programming %A Bot, Martijn C. J. %Y Whitley, Darrell %Y Goldberg, David %Y Cantu-Paz, Erick %Y Spector, Lee %Y Parmee, Ian %Y Beyer, Hans-Georg %S Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000) %D 2000 %8 October 12 jul %I Morgan Kaufmann %C Las Vegas, Nevada, USA %@ 1-55860-708-0 %F Bot:2000:GECCO %X Decision trees are a well known technique in machine learning for describing the underlying structure of a dataset. In [Bot and Langdon, 2000] a new representation of decision trees using strong typing in GP was introduced. In the function nodes, a linear combination of variables is made. The effects of techniques such as limited error fitness, fitness sharing Pareto scoring and domination Pareto scoring are evaluated on a set of benchmark classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified. Results indicate that GP can be applied successfully to classification problems. Limited error fitness reduces runtime while maintaing equal accuracy. Pareto scoring works well against bloat. Fitness sharing Pareto works better than domination Pareto. %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2000/GP185.pdf %P 403-410 %0 Conference Proceedings %T Feature Extraction for the k-Nearest Neighbour Classifier with Genetic Programming %A Bot, Martijn C. J. %Y Miller, Julian F. %Y Tomassini, Marco %Y Lanzi, Pier Luca %Y Ryan, Conor %Y Tettamanzi, Andrea G. B. %Y Langdon, William B. %S Genetic Programming, Proceedings of EuroGP’2001 %S LNCS %D 2001 %8 18 20 apr %V 2038 %I Springer-Verlag %C Lake Como, Italy %@ 3-540-41899-7 %F bot:2001:EuroGP %X In pattern recognition the curse of dimensionality can be handled either by reducing the number of features, e.g. with decision trees or by extraction of new features. We propose a genetic programming (GP) framework for automatic extraction of features with the express aim of dimension reduction and the additional aim of improving accuracy of the k-nearest neighbour (k-NN) classifier. We will show that our system is capable of reducing most datasets to one or two features while k-NN accuracy improves or stays the same. Such a small number of features has the great advantage of allowing visual inspection of the dataset in a two-dimensional plot. Since k-NN is a non-linear classification algorithm, we compare several linear fitness measures. We will show the a very simple one, the accuracy of the minimal distance to means (mdm) classifier outperforms all other fitness measures. We introduce a stopping criterion gleaned from numeric mathematics. New features are only added if the relative increase in training accuracy is more than a constant d, for the mdm classifier estimated to be 3.3%. %K genetic algorithms, genetic programming, Feature Extraction, Machine Learning: Poster %R 10.1007/3-540-45355-5_20 %U http://dx.doi.org/10.1007/3-540-45355-5_20 %P 256-267 %0 Conference Proceedings %T Feature Extraction for the k-Nearest Neighbour Classifier with Genetic Programming %A Bot, Martijn C. J. %Y Ryan, Conor %S Graduate Student Workshop %D 2001 %8 July %C San Francisco, California, USA %F bot:2001:fencgp %K genetic algorithms, genetic programming %P 397-400 %0 Book Section %T Evolving Controllers for Miniature Robots %A Botros, Michael %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 Botros:2004:EMTP %K genetic algorithms, genetic programming %U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html %P 73-100 %0 Book Section %T Evolving Complex Robotic Behaviors Using Genetic Programming %A Botros, Michael %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 botros:2006:GSP %X In this chapter, two possible approaches for evolving complex behaviours were discussed. In the first approach, the GP is used to explore possible hierarchy in the solution through implementing ADF and maintaining a subroutine library or using neural networks as primitive functions. In the second approach, human programmer set the architecture of the robot controller and then the GP is used to evolve each module of this architecture. Two examples of architectures were discussed, the subsumption architecture and action selection architecture. Two experiments were presented to demonstrate this approach. The first used subsumption architecture to control a team of two robots with different capabilities to implement a cooperative behavior. The second experiment used action selection architecture to allow switching between the simpler behaviours that constitute the main behavior %K genetic algorithms, genetic programming %R 10.1007/3-540-32498-4_8 %U http://dx.doi.org/10.1007/3-540-32498-4_8 %P 175-194 %0 Conference Proceedings %T Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events %A Botwey, Ransford Henry %A Daskalaki, Elena %A Diem, Peter %A Mougiakakou, Stavroula G. %S 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014) %D 2014 %8 aug %C Chicago, IL, USA %F Botwey:2014:EMBC %X Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient’s safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction, cARX, and a recurrent neural network, RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25percent, and 100percent correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement. %K genetic algorithms, genetic programming %R 10.1109/EMBC.2014.6944708 %U http://dx.doi.org/10.1109/EMBC.2014.6944708 %P 4843-4846 %0 Conference Proceedings %T Model Identification by Bacterial Optimization %A Botzheim, J. %A Koczy, L. T. %S Proceedings of the 5th International Symposium of Hungarian Researchers on Computational Intelligence %D 2004 %8 nov %C Budapest, Hungary %G en %F Botzheim:2004:ishrCI %X In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as artificial neural networks and fuzzy systems is in progress. In this paper a recent kind of evolutionary method called bacterial algorithm is introduced. This method can be used for fuzzy rule extraction and optimization. Bacterial Programming is also proposed in this paper. This approach is the combination of the bacterial algorithm and the genetic programming techniques and can be applied for the optimization of the structure of Bspline neural networks. %K genetic algorithms, genetic programming %U http://www.bmf.hu/conferences/mtn/botzheim.pdf %P 91-102 %0 Journal Article %T Genetic and Bacterial Programming for B-Spline Neural Networks Design %A Botzheim, Janos %A Cabrita, Cristiano %A Koczy, Laszlo T. %A Ruano, Antonio E. %J Journal of Advanced Computational Intelligence and Intelligent Informatics %D 2007 %8 feb %V 11 %N 2 %@ 1343-0130 %F BotzheimCabritaKoczyRuano07 %X The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions. %K genetic algorithms, genetic programming, constructive algorithms, B-splines, bacterial programming %9 journal article %R 10.20965/jaciii.2007.p0220 %U http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=JACII001100020012.xml %U http://dx.doi.org/10.20965/jaciii.2007.p0220 %P 220-231 %0 Thesis %T Intelligens szamitastechnikai modellek identifiacioja evolucios es gradiens alapu tanulo algoritmusokkal %A Botzheim, Janos %D 2007 %8 November %C Hungary %C Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics %F Botzheim:thesis %X The thesis discusses identification techniques of soft computing models. Its goal is to develop identification methods based on numerical data that can produce results better in terms of quality criteria (e.g. mean square error) relevant for the given applications than other techniques known from the literature. The first statement proposes the Bacterial Evolutionary Algorithm for the extraction of Mamdani-type fuzzy rules with trapezoidal membership functions. The second statement proposes the application of the Levenberg-Marquardt algorithm for local optimisation of fuzzy rules. The third statement introduces the Bacterial Memetic Algorithm, a combination of the Bacterial Evolutionary and the Levenberg-Marquardt algorithm. The fourth statement deals with Takagi-Sugeno-type fuzzy systems. The fifth statement proposes a new technique called Bacterial Programming for the design process of B-spline neural networks. Finally, the sixth statement presents the application of Bacterial Evolutionary Algorithm for the feature selection problem. %K genetic algorithms, genetic programming %U http://www.sze.hu/~botzheim/hid/disszertacio.pdf %0 Journal Article %T A hybrid learning algorithm for evolving Flexible Beta Basis Function Neural Tree Model %A Bouaziz, Souhir %A Dhahri, Habib %A Alimi, Adel M. %A Abraham, Ajith %J Neurocomputing %D 2013 %V 117 %@ 0925-2312 %F Bouaziz:2013:Neurocomputing %X In this paper, a tree-based encoding method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimised based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimisation algorithm (OPSO). The performance of the proposed method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related methods. %K genetic algorithms, genetic programming, Flexible Beta Basis Function Neural Tree Model, Opposite-based particle swarm optimization algorithm, Time-series forecasting, Control system %9 journal article %R 10.1016/j.neucom.2013.01.024 %U http://www.sciencedirect.com/science/article/pii/S0925231213001975 %U http://dx.doi.org/10.1016/j.neucom.2013.01.024 %P 107-117 %0 Conference Proceedings %T PSO-Based Update Memory for Improved Harmony Search Algorithm to the Evolution of FBBFNT’ Parameters %A Bouaziz, Souhir %A Alimi, Adel M. %A Abraham, Ajith %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 Bouaziz:2014:CEC %X In this paper, a PSO-based update memory for Improved Harmony Search (PSOUM-IHS) algorithm is proposed to learn the parameters of Flexible Beta Basis Function Neural Tree (FBBFNT) model. These parameters are the Beta parameters of each flexible node and the connected weights of the network. Furthermore, the FBBFNT’s structure is generated and optimised by the Extended Genetic Programming (EGP) algorithm. The combination of the PSOUM-IHS and EGP in the same algorithm is so used to evolve the FBBFNT model. The performance of the proposed evolving neural network is evaluated for nonlinear systems of prediction and identification and then compared with those of related models. %K genetic algorithms, genetic programming, Extended Genetic Programming, Memetic, multi-meme and hybrid algorithms %R 10.1109/CEC.2014.6900304 %U http://dx.doi.org/10.1109/CEC.2014.6900304 %P 1951-1958 %0 Journal Article %T Evolving flexible beta basis function neural tree using extended genetic programmin & Hybrid Artificial Bee Colony %A Bouaziz, Souhir %A Dhahri, Habib %A Alimi, Adel M. %A Abraham, Ajith %J Applied Soft Computing %D 2016 %V 47 %@ 1568-4946 %F Bouaziz:2016:ASC %X In this paper, a new hybrid learning algorithm is introduced to evolve the flexible beta basis function neural tree (FBBFNT). The structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Hybrid Artificial Bee Colony algorithm. This hybridization is essentially based on replacing the random Artificial Bee Colony (ABC) position with the guided Opposite-based Particle Swarm Optimization (OPSO) position. Such modification can minimize the delay which might be lead by the random position, in reaching the global solution. The performance of the proposed model is evaluated for benchmark problems drawn from time series prediction area and is compared with those of related methods. %K genetic algorithms, genetic programming, Flexible beta basis function neural tree model, Hybrid Artificial Bee Colony algorithm, Time-series forecasting %9 journal article %R 10.1016/j.asoc.2016.03.006 %U http://www.sciencedirect.com/science/article/pii/S1568494616301156 %U http://dx.doi.org/10.1016/j.asoc.2016.03.006 %P 653-668 %0 Conference Proceedings %T Application of Artificial Bee Colony Programming to Two Trails of the Artificial Ant Problem %A Boudardara, Fateh %A Gorkemli, Beyza %S 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) %D 2018 %8 oct %F Boudardara:2018:ISMSIT %X Automatic navigation of mobile robots has an increasing importance in many application fields such as robotics, mining industry, underwater exploration, aerospace research, virtual environments and games. In this study, we use artificial bee colony programming (ABCP), which is a novel evolutionary computation based automatic programming method, to solve the artificial ant problem that is considered as one of the basic test problems in robotic path planning. In order to see the performance of this method, a series of experiments are carried out on Santa Fe and Los Altos Hills trails. The results are compared with those of genetic programming. %K genetic algorithms, genetic programming %R 10.1109/ISMSIT.2018.8567048 %U http://dx.doi.org/10.1109/ISMSIT.2018.8567048 %0 Journal Article %T Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search %A Boukhelifa, Nadia %A Bezerianos, Anastasia %A Cancino, Waldo %A Lutton, Evelyne %J Evolutionary Computation %D 2017 %8 mar %V 25 %N 1 %I HAL CCSD; Massachusetts Institute of Technology Press (MIT Press) %@ 1063-6560 %G en %F Boukhelifa:2016:EC %X We evaluate and analyse a framework for Evolutionary Visual Exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactive evolutionary algorithm to steer the exploration of multidimensional datasets towards two-dimensional projections that are interesting to the analyst. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. In this paper, we revisit this framework and a prototype application that was developed as a demonstrator, and summarise our previous study with domain experts and its main findings. We then report on results from a new user study with a clear predefined task, that examines how users leverage the system and how the system evolves to match their needs. While previously we showed that using EVE, domain experts were able to formulate interesting hypothesis and reach new insights when exploring freely, our new findings indicate that users, guided by the interactive evolutionary algorithm, are able to converge quickly to an interesting view of their data when a clear task is specified. We provide a detailed analysis of how users interact with an evolutionary algorithm and how the system responds to their exploration strategies and evaluation patterns. Our work aims at building a bridge between the domains of visual analytics and interactive evolution. The benefits are numerous, in particular for evaluating Interactive Evolutionary Computation (IEC) techniques based on user study methodologies. %K genetic algorithms, genetic programming, interactive evolutionary computation, visual analytics, information visualization, data mining, interactive evolutionary algorithms %9 journal article %R 10.1162/EVCO_a_00161 %U https://hal.inria.fr/hal-01218959 %U http://dx.doi.org/10.1162/EVCO_a_00161 %P 55-86 %0 Journal Article %T Guest editorial: Special issue on genetic programming, evolutionary computation and visualization %A Boukhelifa, Nadia %A Lutton, Evelyne %J Genetic Programming and Evolvable Machines %D 2018 %8 sep %V 19 %N 3 %@ 1389-2576 %F Boukhelifa:2018:GPEM %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-018-9333-4 %U https://doi.org/10.1007/s10710-018-9333-4 %U http://dx.doi.org/10.1007/s10710-018-9333-4 %P 313-315 %0 Conference Proceedings %T Ground Resistance Estimation using Genetic Programming %A Boulas, Konstantinos %A Androvitsaneas, Valilios P. %A Gonos, Ioannis F. %A Dounias, Georgios %A Stathopulos, Ioannis A. %Y Spyridakos, Athanasios %Y Vryzidis, Lazaros %S 5th International Symposium and 27th National Conference on Operation Research %D 2016 %8 jun %C Aigaleo, Athens %F boulas_ground_2016 %X The objective of this paper is to use genetic programming methodologies for the modelling and estimation of ground resistance with the use of field measurements related to weather data. Grounding is important for the safe operation of any electrical installation and protects it against lightning and fault currents. The work uses both, conventional and intelligent data analysis techniques, for ground resistance modeling from field measurements. Experimental data consist of field measurements that have been performed in Greece during the previous four years. Five linear regression models have been applied to a properly selected dataset, as well as an intelligent approach based on Gene Expression Programming (GEP). Every model corresponds to a specific grounding system. A heuristic approach using GEP was performed in order to produce more robust and general models for grounding estimation. The results show that evolutionary techniques such as those based on Genetic Programming (GP) are promising for the estimation of the ground resistance. %K genetic algorithms, genetic programming, gene expression programming, symbolic regression, ground resistance %U http://eeee2016.teipir.gr/ConferenceBookHELORS2016.pdf %P 66-71 %0 Book Section %T Approximating Throughput of Small Production Lines Using Genetic Programming %A Boulas, Konstantinos %A Dounias, Georgios %A Papadopoulos, Chrissoleon %E Grigoroudis, Evangelos %E Doumpos, Michael %B Operational Research in Business and Economics: 4th International Symposium and 26th National Conference on Operational Research, 2015 %D 2017 %8 April 6 jun %I Springer %C Chania, Greece %F boulas_approximating_2017 %X Genetic Programming (GP) has been used in a variety of fields to solve complicated problems. This paper shows that GP can be applied in the domain of serial production systems for acquiring useful measurements and line characteristics such as throughput. Extensive experimentation has been performed in order to set up the genetic programming implementation and to deal with problems like code bloat or over fitting. We improve previous work on estimation of throughput for three stages and present a formula for the estimation of throughput of production lines with four stations. Further work is needed, but so far, results are encouraging. %K genetic algorithms, genetic programming, production lines, symbolic regression, throughput %R 10.1007/978-3-319-33003-7_9 %U http://link.springer.com/10.1007/978-3-319-33003-7_9 %U http://dx.doi.org/10.1007/978-3-319-33003-7_9 %P 185-204 %0 Conference Proceedings %T Acquisition of Accurate or Approximate Throughput Formulas for Serial Production Lines through Genetic Programming %A Boulas, Konstantinos %A Dounias, Georgios %A Papadopoulos, Chrissoleon %A Tsakonas, Athanasios %S Proceedings of the 4th International Symposium & 26th National Conference on Operational Research, HELORS-2015 %D 2015 %8 jun %V 1 %I Hellenic Operational Research Society %C Chania, Greece %F boulas_acquisition_2015 %X Genetic Programming (GP) has been used in a variety of fields to solve complicated problems. This paper shows that GP can be applied in the domain of serial production systems for acquiring useful measurements and line characteristics as throughput. Extensive experimentation has been performed in order to set up the genetic programming implementation, and to deal with problems like code bloat or over fitting. Further work is needed, but so far, results are encouraging. %K genetic algorithms, genetic programming, serial production lines, symbolic regression %U http://mde-lab.aegean.gr/images/stories/docs/CC97.pdf %P 128-133 %0 Conference Proceedings %T Acquisition of approximate throughput formulas for serial production lines with parallel machines using intelligent techniques %A Boulas, Konstantinos %A Tzanetos, Alexandros %A Dounias, Georgios %S Proceedings of the 10th Hellenic Conference on Artificial Intelligence %D 2018 %8 jul %I ACM Press %C Rio Patras, Greece %G en %F boulas_acquisition_2018 %O Article No 18 %X Estimating the performance of a production line is a difficult problem because of the enormous number of states that exist when analysing such systems. In addition to the methods developed to address the problem, it is very useful to have a formula linking the characteristics of the line to its performance. Three cases of sort serial production lines with parallel and identical machines in each workstation are examined in this paper. By using a combinational method that applies genetic programming (GP) and an innovative nature inspired method, named sonar inspired optimization (SIO) to improve the results, three models are derived to obtain the throughput of the corresponding lines. Further work will take place because results derived in this paper are encouraging. %K genetic algorithms, genetic programming, parallel-machine stations, performance evaluation, serial production lines, Sonar Inspired Optimization %R 10.1145/3200947.3201028 %U http://dl.acm.org/citation.cfm?doid=3200947.3201028 %U http://dx.doi.org/10.1145/3200947.3201028 %P 18:1-18:7 %0 Journal Article %T Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic programming techniques %A Boumanchar, Imane %A Chhiti, Younes %A Alaoui, Fatima Ezzahrae M’hamdi %A Sahibed-Dine, Abdelaziz %A Bentiss, Fouad %A Jama, Charafeddine %A Bensitel, Mohammed %J Waste Management & Research %D 2018 %8 dec 19 %V 37 %N 6 %G en %F Boumanchar:2018:WMR %X Municipal solid waste (MSW) management presents an important challenge for all countries. In order to exploit them as a source of energy, a knowledge of their calorific value is essential. In fact, it can be experimentally measured by an oxygen bomb calorimeter. This process is, however, expensive. In this light, the purpose of this paper was to develop empirical models for the prediction of MSW higher heating value (HHV) from ultimate analysis. Two methods were used: multiple regression analysis and genetic programming formalism. Both techniques gave good results. Genetic programming, however, provides more accuracy compared to published works in terms of a great correlation coefficient (CC) and a low root mean square error (RMSE). %K genetic algorithms, genetic programming, energy, higher heating value, multiple regression, municipal solid waste, prediction, chemical sciences/material chemistry, chemical sciences/polymers %9 journal article %R 10.1177/0734242x18816797 %U https://hal.univ-lille.fr/hal-02922402 %U http://dx.doi.org/10.1177/0734242x18816797 %P 578-589 %0 Journal Article %T Multiple regression and genetic programming for coal higher heating value estimation %A Boumanchar, Imane %A Chhiti, Younes %A Alaoui, Fatima Ezzahrae M’Hamdi %A Sahibed-Dine, Abdelaziz %A Bentiss, Fouad %A Jama, Charafeddine %A Bensitel, Mohammed %J International Journal of Green Energy %D 2018 %V 15 %N 14-15 %I Taylor & Francis %@ 1543-5075 %G en %F Boumanchar:2018:IJGE %X The higher heating value (HHV) is an important characteristic for the determination of fuels quality. Nevertheless, its experimental measurement requires intricate technologies. In this work, the HHV of coal was predicted from ultimate composition using two methods: multiple regression and genetic programming. A dataset of 100 samples from literature was exploited (75percent for training and 25percent for testing). A comparative study was elaborated between the developed models and published ones in terms of correlation coefficient, root mean square error, and mean absolute percent error. The adopted models gave a good statistical performance. Abbreviations: C: Carbon; CC: Correlation coefficient; H: Hydrogen; HHV: Higher heating valueI; GT: Institute of gas technology; GP: Genetic programming; LHV: Lower heating value; MAPE: Mean absolute percent error; N: Nitrogen; O: Oxygen; RMSE: Root mean square error; S: sulfur; Wt: Weight percentage %K genetic algorithms, genetic programming, coal, higher heating value, multiple regression, prediction, life sciences %9 journal article %R 10.1080/15435075.2018.1529591 %U https://hal.inrae.fr/hal-02620955 %U http://dx.doi.org/10.1080/15435075.2018.1529591 %P 958-964 %0 Journal Article %T Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming %A Boumanchar, Imane %A Charafeddine, Kenza %A Chhiti, Younes %A Alaoui, Fatima Ezzahrae M’hamdi %A Sahibed-dine, Abdelaziz %A Bentiss, Fouad %A Jama, Charafeddine %A Bensitel, Mohammed %J Biomass Conversion and Biorefinery %D 2019 %8 sep %V 9 %N 3 %@ 2190-6815 %F boumanchar:BCaB %X The higher heating value (HHV) is a significant parameter for the determination of fuel quality. However, its measurement is time-consuming and requires sophisticated equipment. For this reason, several researches have been interested to develop mathematical models for the prediction of HHV from fundamental composition. The purpose of this study is to develop new correlations to determine the biomass HHV from ultimate analysis. As a result, two models were elaborated. The first was developed using multiple variable regression analysis while the second has adopted genetic programming formalism. Data of 171 from various types of biomass samples were randomly used for the development (75percent) and the validation (25percent) of new equations. The accuracy of the established models was compared to previous literature works in terms of correlation coefficient (CC), average absolute error (AAE), and average bias error (ABE). The proposed models were more performing with the highest CC and the smallest errors. %K genetic algorithms, genetic programming, Higher heating value, HHV prediction, Multiple variable regression %9 journal article %R 10.1007/s13399-019-00386-5 %U http://link.springer.com/article/10.1007/s13399-019-00386-5 %U http://dx.doi.org/10.1007/s13399-019-00386-5 %P 499-509 %0 Conference Proceedings %T Dynamic Flies: Using Real-Time Parisian Evolution in Robotics %A Boumaza, Amine M. %A Louchet, Jean %Y Boers, Egbert J. W. %Y Cagnoni, Stefano %Y Gottlieb, Jens %Y Hart, Emma %Y Lanzi, Pier Luca %Y Raidl, Gunther R. %Y Smith, Robert E. %Y Tijink, Harald %S Applications of Evolutionary Computing %S LNCS %D 2001 %8 18 apr %V 2037 %I Springer-Verlag %C Lake Como, Italy %@ 3-540-41920-9 %F Boumaza:2001:EvoWorks %O best paper award %X The Fly algorithm is a Parisian evolution strategy devised for parameter space exploration in computer vision applications, which has been applied to stereovision. The resulting scene model is a set of 3-D points which concentrate upon the surfaces of obstacles. In this paper, we present how the evolutionary scene analysis can be continuously updated and integrated into a specific real-time mobile robot navigation system. Simulation-based experimental results are presented. %K genetic algorithms, genetic programming, fly algorithm, robot %R 10.1007/3-540-45365-2_30 %U http://dx.doi.org/10.1007/3-540-45365-2_30 %P 288-297 %0 Conference Proceedings %T Mobile Robot Sensor Fusion Using Flies %A Boumaza, Amine M. %A Louchet, Jean %Y Raidl, Günther R. %Y Cagnoni, Stefano %Y Cardalda, Juan Jesús Romero %Y Corne, David W. %Y Gottlieb, Jens %Y Guillot, Agnès %Y Hart, Emma %Y Johnson, Colin G. %Y Marchiori, Elena %Y Meyer, Jean-Arcady %Y Middendorf, Martin %S Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM %S LNCS %D 2003 %8 14 16 apr %V 2611 %I Springer-Verlag %C University of Essex, England, UK %F Boumaza:evowks03 %X The Fly algorithm is a fast artificial evolution-based image processing technique. Previous work has shown how to process stereo image sequences and use the evolving population of ’flies’ as a continuously updated representation of the scene for obstacle avoidance in a mobile robot. In this paper, we show that it is possible to use several sensors providing independent information sources on the surrounding scene and the robot’s position, and fuse them through the introduction of corresponding additional terms into the fitness function. This sensor fusion technique keeps the main properties of the fly algorithm: asynchronous processing. no low-level image pre-processing or costly image segmentation, fast reaction to new events in the scene. Simulation test results are presented. %K genetic algorithms, genetic programming, evolutionary computation, applications %R 10.1007/3-540-36605-9_33 %U http://dx.doi.org/10.1007/3-540-36605-9_33 %P 357-367 %0 Journal Article %T Cameron Browne: Evolutionary game design, Springer briefs in computer science series %A Boumaza, Amine %J Genetic Programming and Evolvable Machines %D 2012 %8 sep %V 13 %N 3 %I Springer %@ 1389-2576 %F Boumaza:2012:GPEM %O Book review %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-012-9165-6 %U https://rdcu.be/dR8gF %U http://dx.doi.org/10.1007/s10710-012-9165-6 %P 407-409 %0 Conference Proceedings %T LLM-Assisted Crossover in Genetic Improvement of Software %A Bouras, Dimitrios Stamatios %A Mechtaev, Sergey %A Petke, Justyna %S "14th International Workshop on Genetic Improvement %F bouras:2025:GI %0 Journal Article %D 2025 %8 27 apr %C Ottawa %F 2025"d %O Best presentation %X We evaluated against five traditional crossover methods across seven benchmarks, measuring performance on four key metrics: average ranking, best variant execution time, efficiency in reaching performance milestones, and viable variant count. Results show that LLM-assisted crossover achieved an average ranking of 2.27 (on a scale where 1 is best and 6 is worst), making it the top-performing method across benchmarks based on the quality of the optimal variants produced. The LLM-based approach also improved the fitness (execution time) by an average of 8.5 percent over the best variant produced by the traditional methods. In terms of efficiency, the LLM-assisted crossover required on average 25.6 percent fewer variants to reach 25 percent, 50 percent, 75 percent, and 100 percent of the final performance improvement, compared to the traditional methods. Additionally, the LLM-assisted crossover produced 4.8 percent more viable variants across scenarios, including both source code modification and parameter tuning cases. These findings suggest that LLMs can significantly enhance genetic programming by guiding the crossover process toward more effective and viable solutions, providing motivation for further research in LLM-assisted evolutionary algorithms. %K genetic algorithms, genetic programming, Genetic Improvement, MAGPIE, Large Language Models, LLM, ANN, Linux runtime, Weka, GPT4.mini %9 journal article %R 10.1109/GI66624.2025.00012 %U https://gpbib.cs.ucl.ac.uk/gi2025/bouras_2025_GI.pdf %U http://dx.doi.org/10.1109/GI66624.2025.00012 %P 19-26 %0 Conference Proceedings %T Optimised Fitness Functions for Automated Improvement of Software’s Execution Time %A Bouras, Dimitrios Stamatios %A Hanna, Carol %A Petke, Justyna %Y Hong, Shin %Y Wagner, Markus %Y Zhang, Man %S Search-Based Software Engineering 2025 %S Lecture Notes in Computer Science %D 2025 %8 16 nov %I Springer Nature %C Seoul, South Korea %F bouras:2025:ssbse %X Precise measurement of software execution time is challenging due to environmental variability and measurement overheads, an issue critical for search-based software improvement systems that evaluate thousands of variants. While precise measurements offer precise fitness measures, they often introduce a significant time overhead. To understand which measures are most effective as fitness functions in searchbased software optimisation, we conducted an empirical study of 21 approximates of execution time. These included hardware-level counters from perf, RAPL energy, and a custom measure based on weighted instruction cycles. To improve reliability, we evaluated each fitness function up to five times, using medians to reduce noise. We integrated the 13 most promising measures into a search-based software optimisation framework called MAGPIE. We evaluated these fitness functions plus Time, already present in MAGPIE, on 7 benchmarks using both code-level and parameter-level mutations. To assess generalisability, we tested the best performing measures with the parameter tuning tool ParamILS and analyzed how tool and search strategy affect outcomes. Our results show that perf cycles measure yields the best overall performance, outperforming Time by 5.1 percent. Sampling three times balances reliability and exploration. Energy and the weight-based measure excel in specific scenarios, with weights being the best for parameter optimization on MAGPIE, but are better suited to longer searches due to their overhead. We highlight a trade-off: low-overhead measures like Time work well for short runs, while robust measures such as cycles and weights benefit longer ones. %K genetic algorithms, genetic programming, Genetic Improvement, ParamILS, Magpie, software Performance, Search-Based Software Engineering, SBSE, Linux perf, energy, Intel RAPL energy %U https://solar.cs.ucl.ac.uk/pdf/bouras_2025_ssbse.pdf %0 Conference Proceedings %T Control System Design Optimisation via Genetic Programming %A Bourmistrova, A. %A Khantsis, S. %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 Bourmistrova:2007:cec %X This paper describes a stochastic approach for comprehensive diagnostics and validation of control system architecture for Unmanned Aerial Vehicle (UAV). Mathematically based diagnostics of a 6 DoF system provides capability for a complex evaluation of system components behaviour, but are typically both memory and computationally expensive. Design and optimisation of the flight controllers is a demanding task which usually requires deep engineering knowledge of intrinsic aircraft behaviour. Evolutionary Algorithms (EAs) are known for their robustness for a wide range of optimising functions, when no a priori knowledge of the search space is available. Thus it makes evolutionary approach a promising technique to design the task controllers for complex dynamic systems such as an aircraft. In this study, EAs are used to design a controller for recovery (landing) of a small fixed-wing UAV on a frigate ship deck. The control laws are encoded in a way common for Evolutionary Programming. However, parameters (numeric coefficients in the control equations) are optimised independently using effective Evaluation Strategies, while structural changes occur at a slower rate. The fitness evaluation is made via test runs on a comprehensive 6 degree-of-freedom non-linear UAV model. The need of a well defined approach to the control system validation is dictated by the nature of UAV application, where the major source of mission success is based on autonomous control system architecture reliability. The results show that an effective controller can be designed with little knowledge of the aircraft dynamics using appropriate evolutionary techniques. An evolved controller is evaluated and a set of reliable algorithm parameters is validated. %K genetic algorithms, genetic programming %R 10.1109/CEC.2007.4424718 %U 1691.pdf %U http://dx.doi.org/10.1109/CEC.2007.4424718 %P 1993-2000 %0 Book Section %T Flight Control System Design Optimisation via Genetic Programming %A Bourmistrova, Anna %A Khantsis, Sergey %E Lam, Thanh Mung %B Aerial Vehicles %D 2009 %I InTech %G eng %F Bourmistrova:2009:AV %X In this chapter, an application of the Evolutionary Design (ED) is demonstrated. The aim of the design was to develop a controller which provides recovery of a fixed-wing UAV onto a ship under the full range of disturbances and uncertainties that are present in the real world environment. The controller synthesis is a multistage process. However, the approach employed for synthesis of each block is very similar. Evolutionary algorithm is used as a tool to evolve and optimise the control laws. One of the greatest advantages of this methodology is that minimum or no a priori knowledge about the control methods is used, with the synthesis starting from the most basic proportional control or even from ‘null’ control laws. During the evolution, more complex and capable laws emerge automatically. As the resulting control laws demonstrate, evolution does not tend to produce parsimonious solutions. The method demonstrating remarkable robustness in terms of convergence indicating that a near optimal solution can be found. In very limited cases, however, it may take too long time for the evolution to discover the core of a potentially optimal solution, and the process does not converge. More often than not, this hints at a poor choice of the algorithm parameters. The most important and difficult problem in Evolutionary Design is preparation of the fitness evaluation procedure with predefined special intermediate problems. Computational considerations are also of the utmost importance. Robustness of EAs comes at the price of computational cost, with many thousands of fitness evaluations required. The simulation testing covers the entire operational envelope and highlights several conditions under which recovery is risky. All environmental factors–sea wave, wind speed and turbulence–have been found to have a significant effect upon the probability of success. Combinations of several factors may result in very unfavourable conditions, even if each factor alone may not lead to a failure. For example, winds up to 12 m/s do not affect the recovery in a calm sea, and a severe ship motion corresponding to Sea State 5 also does not represent a serious threat in low winds. At the same time, strong winds in a high Sea State may be hazardous for the aircraft. %K genetic algorithms, genetic programming, mobile robotics %R 10.5772/6470 %U http://www.intechopen.com/download/pdf/pdfs_id/5969 %U http://dx.doi.org/10.5772/6470 %0 Book Section %T Genetic Programming in Application to Flight Control System Design Optimisation %A Bourmistrova, Anna %A Khantsis, Sergey %E Korosec, Peter %B New Achievements in Evolutionary Computation %D 2010 %8 feb %I InTech %G eng %F Bourmistrova:2010:naEC %K genetic algorithms, genetic programming, UAV %R 10.5772/8055 %U http://www.intechopen.com/articles/show/title/genetic-programming-in-application-to-flight-control-system-design-optimisation %U http://dx.doi.org/10.5772/8055 %0 Conference Proceedings %T Fully Three-Dimensional Tomographic Evolutionary Reconstruction in Nuclear Medicine %A Bousquet, Aurelie %A Louchet, Jean %A Rocchisani, Jean-Marie %Y Monmarche, Nicolas %Y Talbi, El-Ghazali %Y Collet, Pierre %Y Schoenauer, Marc %Y Lutton, Evelyne %S Artificial Evolution, 2007 %S Lecture Notes in Computer Science %D 2007 %8 oct 29 31 %V 4926 %I Springer %C Tours, France %F Bousquet:2007:EA %O Revised Selected Papers %X 3-D reconstruction in Nuclear Medicine imaging using complete Monte-Carlo simulation of trajectories usually requires high computing power. We are currently developing a Parisian Evolution Strategy in order to reduce the computing cost of reconstruction without degrading the quality of results. Our approach derives from the Fly algorithm which proved successful on real-time stereo image sequence processing. Flies are considered here as photon emitters. We developed the marginal fitness technique to calculate the fitness function, an approach usable in Parisian Evolution whenever each individual’s fitness cannot be calculated independently of the rest of the population. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-79305-2_20 %U http://dx.doi.org/10.1007/978-3-540-79305-2_20 %P 231-242 %0 Conference Proceedings %T Software Anti-patterns Detection Under Uncertainty Using A Possibilistic Evolutionary Approach %A Boutaib, Sofien %A Elarbi, Maha %A Bechikh, Slim %A Hung, Chih-Cheng %A Said, Lamjed Ben %Y Hu, Ting %Y Lourenco, Nuno %Y Medvet, Eric %S EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming %S LNCS %D 2021 %8 July 9 apr %V 12691 %I Springer Verlag %C Virtual Event %F Boutaib:2021:EuroGP %X Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that could deteriorate the software maintainability and evolution. Despite the high number of existing detection methods, the issue of class label uncertainty is usually omitted. Indeed, two human experts may have different degrees of uncertainty about the smelliness of a particular software class not only for the smell detection task but also for the smell type identification one. Thus, this uncertainty should be taken into account and then processed by detection tools. Unfortunately, these latter usually reject and/or ignore uncertain data that correspond to software classes (i.e. dataset instances) with uncertain labels. This practice could considerably degrade the detection/identification process effectiveness. Motivated by this observation and the interesting performance of the Possibilistic K-NN (PK-NN) classifier in dealing with uncertain data, we propose a new possibilistic evolutionary detection method, named ADIPOK (Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs), that is able to deal with label uncertainty using some concepts stemming from the Possibility theory. ADIPOK is validated using a possibilistic base of smell examples that simulates the subjectivity of software engineers’ opinions’ uncertainty. The statistical analysis of the obtained results on a set of comparative experiments with respect to four state-of-the-art methods show the merits of our proposed method. %K genetic algorithms, genetic programming, SBSE, code smells, code smells detection, Uncertain software class labels, PK-NN evolution, Possibility theory: Poster %R 10.1007/978-3-030-72812-0_12 %U http://dx.doi.org/10.1007/978-3-030-72812-0_12 %P 181-197 %0 Conference Proceedings %T Grammatical Evolution Association Rule Mining to Detect Gene-Gene Interaction %A Boutorh, Aicha %A Guessoum, Ahmed %Y Pastor, Oscar %Y Sinoquet, Christine %Y Plantier, Guy %Y Schultz, Tanja %Y Fred, Ana L. N. %Y Gamboa, Hugo %S BIOINFORMATICS 2014 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, ESEO, Angers, Loire Valley, France, 3-6 March, 2014 %D 2014 %I SciTePress %F conf/biostec/BoutorhG14 %K genetic algorithms, genetic programming, grammatical evolution %U http://dx.doi.org/10.5220/0004913702530258 %P 253-258 %0 Journal Article %T Computation as Material in Live Coding %A Bovermann, T. %A Griffiths, D. %J Computer Music Journal %D 2014 %8 mar %V 38 %N 1 %@ 0148-9267 %F Bovermann:2014:CMJ %X What does computation sound like, and how can computational processing be integrated into live-coding practice along with code? This article gives insights into three years of artistic research and performance practice with Betablocker, an imaginary central processing unit architecture, specifically designed and implemented for live-coding purposes. It covers the themes of algorithmic composition, sound generation, genetic programming, and autonomous coding in the light of self-manipulating code and artistic research practice. %K genetic algorithms, genetic programming %9 journal article %R 10.1162/COMJ_a_00228 %U http://dx.doi.org/10.1162/COMJ_a_00228 %P 40-53 %0 Thesis %T Alexander Blades Boyd %A of Correlations, Thermodynamics %A in Information Engines, Structure %D 2018 %C USA %C Physics, University of California, Davis %F Boyd:thesis %X Understanding structured information and computation in thermodynamics systems is crucial to progress in diverse fields, from biology at a molecular level to designed nano-scale information processors. Landauer’s principle puts a bound on the energy cost of erasing a bit of information. This suggests that devices which exchange energy and information with the environment, which we call information engines, can use information as a thermodynamic fuel to extract work from a heat reservoir, or dissipate work to erase information. However, Landauer’s Principle on its own neglects the detailed dynamics of physical information processing, the mechanics and structure between the start and end of a computation. Our work deepens our understanding of these nonequilibrium dynamics, leading to new principles of efficient thermodynamic control. We explore a particular type of information engine called an information ratchet, which processes a symbol string sequentially, transducing its input string to an output string. We derive a general energetic framework for these ratchets as they operate out of equilibrium, allowing us to exactly calculate work and heat production. We show that this very general form of computation must obey a Landauer-like bound, the Information Processing Second Law (IPSL), which shows that any form of temporal correlations are a potential thermodynamic fuel. We show that in order to leverage that fuel, the autonomous information ratchet must have internal states which match the predictive states of the information reservoir. This leads to a thermodynamic principle of requisite complexity, much like Ashby’s law of requisite variety in cybernetics. This is a result of the modularity of information transducers. We derive the modularity dissipation, which is an energetic cost beyond Landauer’s bound that predicts the structural energy costs of different implementations of the same computation. Applying the modularity dissipation to information ratchets establishes design principles for thermodynamically efficient autonomous information processors. They prescribe the ratchet’s structure such that the computation saturates the bound set by the IPSL and, thus, achieves maximum thermodynamic efficiency. %9 Ph.D. thesis %U https://www.proquest.com/pagepdf/2047668591 %0 Book Section %T Programmatic Compression of Video using Genetic Programming %A Bozarth, Bradley J. %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 bozarth:2000:PCVGP %K genetic algorithms, genetic programming %P 46-53 %0 Journal Article %T Discovering Stick-Slip-Resistant Servo Control Algorithm Using Genetic Programming %A Bozek, Andrzej %J Sensors %D 2022 %V 22 %N 1 %@ 1424-8220 %F bozek:2022:Sensors %X The stick-slip is one of negative phenomena caused by friction in servo systems. It is a consequence of complicated nonlinear friction characteristics, especially the so-called Stribeck effect. Much research has been done on control algorithms suppressing the stick-slip, but no simple solution has been found. In this work, a new approach is proposed based on genetic programming. The genetic programming is a machine learning technique constructing symbolic representation of programs or expressions by evolutionary process. In this way, the servo control algorithm optimally suppressing the stick-slip is discovered. The GP training is conducted on a simulated servo system, as the experiments would last too long in real-time. The feedback for the control algorithm is based on the sensors of position, velocity and acceleration. Variants with full and reduced sensor sets are considered. Ideal and quantized position measurements are also analysed. The results reveal that the genetic programming can successfully discover a control algorithm effectively suppressing the stick-slip. However, it is not an easy task and relatively large size of population and a big number of generations are required. Real measurement results in worse control quality. Acceleration feedback has no apparent impact on the algorithms performance, while velocity feedback is important. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/s22010383 %U https://www.mdpi.com/1424-8220/22/1/383 %U http://dx.doi.org/10.3390/s22010383 %0 Conference Proceedings %T Transfer Learning in Artificial Bee Colony Programming %A Bozogullarindan, Elif %A Bozogullarindan, Ceylan %A Ozturk, Celal %S 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) %D 2020 %8 15 17 oct %C Istanbul, Turkey %F Bozogullarindan:2020:ASYU %X Artificial Bee Colony Programming (ABCP) is a machine learning method based on Artificial Bee Colony (ABC) algorithm used for parametric and structured optimization problems. It is used for the solution of symbolic regression problems as Genetic Programming (GP). On the other hand, transfer learning is the approach of using the knowledge of a system trained for a particular problem in another problem having a similar distribution. There are a number of research studies in the literature reporting the successful applications of the transfer learning to machine learning and GP. the transfer learning approach is applied to ABCP for the first time and all of the new methods created this way are named as ABCP-T. As a result of the experiments conducted for the symbolic regression problems in the literature, it is observed that ABCP-T gives better results than the standard ABCP. %K genetic algorithms, genetic programming, artificial bee colony algorithm, learning, artificial intelligence, regression analysis, symbolic regression problems, machine learning, transfer learning, ABCP-T %R 10.1109/ASYU50717.2020.9259801 %U http://dx.doi.org/10.1109/ASYU50717.2020.9259801 %0 Journal Article %T Modeling Water-Quality Parameters Using Genetic Algorithm-Least Squares Support Vector Regression and Genetic Programming %A Bozorg-Haddad, Omid %A Soleimani, Shima %A Loaiciga, Hugo A. %J Journal of Environmental Engineering %D 2017 %8 jul %V 143 %N 7 %I American Society of Civil Engineers %F Bozorg-Haddad:2017:JEE %X The modeling and monitoring of water-quality parameters is necessary because of the ever increasing use of water resources and contamination caused by sewage disposal. This study employs two data-driven methods for modeling water-quality parameters. The methods are the least-squares support vector regression (LSSVR) and genetic programming (GP). Model inputs to the LSSVR algorithm and GP were determined using principal component analysis (PCA). The coefficients of the LSSVR were selected by sensitivity analysis employing statistical criteria. The results of the sensitivity analysis of the LSSVR showed that its accuracy depends strongly on the values of its coefficients. The value of the Nash-Sutcliffe (NS) statistic was negative for 60percent of the combinations of coefficients applied in the sensitivity analysis. That is, using the mean of a time series would produce a more accurate estimate of water-quality parameters than the LSSVR method in 60percent of the combinations of parameters tried. The genetic algorithm (GA) was combined with LSSVR to produce the GA-LSSVR algorithm with which to achieve improved accuracy in modeling water-quality parameters. The GA-LSSVR algorithm and the GP method were employed in modeling Na+, K+, Mg2+, SO2-4, Cl-, pH, electric conductivity (EC), and total dissolved solids (TDS) in the Sefidrood River, Iran. The results indicate that the GA-LSSVR algorithm has better accuracy for modeling water-quality parameters than GP judged by the coefficient of determination (R2) and the NS criterion. The NS static established, however, that the GA-LSSVR and GP methods have the capacity to model water-quality parameters accurately. %K genetic algorithms, genetic programming, Genetic algorithm-least squares support vector regression (GA-LSSVR) algorithm, Water quality, Modeling, Sensitivity analysis, Principal component analysis, PCA %9 journal article %R 10.1061/(ASCE)EE.1943-7870.0001217 %U https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29EE.1943-7870.0001217?src=recsys %U http://dx.doi.org/10.1061/(ASCE)EE.1943-7870.0001217 %0 Conference Proceedings %T Comparison of different PCA based Face Recognition algorithms using Genetic Programming %A Bozorgtabar, Behzad %A Noorian, Farzad %A Rad, Gholam Ali Rezai %S 5th International Symposium on Telecommunications (IST 2010) %D 2010 %8 dec %F Bozorgtabar:2010:IST %X Face Recognition plays a vital role in automation of security systems; therefore many algorithms have been invented with varying degrees of effectiveness. After successful try out of principal component analyses (PCA) in eigenfaces method, many different PCA based algorithms such as Two Dimensional PCA (2DPCA) and Multilinear PCA (MLPCA), combined with several classifying algorithms were studied. This paper uses Genetic Programming (GP) as a clustering tool, to classify features extracted by PCA, 2DPCA and MLPCA. Results of different algorithms are compared with each other and also previous studies and it is shown that Genetic Programming can be used in combination with PCA for face recognition problems. %K genetic algorithms, genetic programming, eigenfaces method, face recognition algorithms, multilinear PCA, principal component analyses, security systems automation, two dimensional PCA, eigenvalues and eigenfunctions, face recognition, principal component analysis %R 10.1109/ISTEL.2010.5734132 %U http://dx.doi.org/10.1109/ISTEL.2010.5734132 %P 801-805 %0 Conference Proceedings %T A Genetic Programming approach to face recognition %A Bozorgtabar, Behzad %A Noorian, Farzad %A Gholam Ali, Rezai Rad %S IEEE GCC Conference and Exhibition (GCC), 2011 %D 2011 %8 feb 19 22 %I IEEE %C Dubai, United Arab Emirates %F Bozorgtabar:2011:GCC %X Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), an acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further improve the results, a leveraging method is also used. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable to other Face recognition solutions. %K genetic algorithms, genetic programming, data mining, face recognition technology, feature extraction, image group classification, pattern recognition, principal component analysis, relation discovery methodology, data mining, face recognition, feature extraction, image classification, principal component analysis %R 10.1109/IEEEGCC.2011.5752477 %U http://dx.doi.org/10.1109/IEEEGCC.2011.5752477 %P 194-197 %0 Journal Article %T A Genetic Programming-PCA Hybrid Face Recognition Algorithm %A Bozorgtabar, Behzad %A Rad, Gholam Ali Rezai %J Journal of Signal and Information Processing %D 2011 %V 2 %N 3 %I Scientific Research Publishing %@ 21594465 %G eng %F Bozorgtabar:2011:JSIP %X Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further improve the results, a leveraging method is also used. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable to other Face recognition solutions. %K genetic algorithms, genetic programming, face recognition, principal component analysis, leveraging algorithm %9 journal article %R 10.4236/jsip.2011.23022 %U http://dx.doi.org/10.4236/jsip.2011.23022 %P 170-174 %0 Conference Proceedings %T Uncovering Technical Trading Rules Using Evolutionary Automatic Programming %A Brabazon, Tony %A O’Neill, M. %A Ryan, C. %A Collins, J. J. %S Proceedings of 2001 AAANZ Conference (Accounting Association of Australia and NZ) %D 2001 %8 January 3 jul %C Auckland, New Zealand %F brabazon:2001:AAANZ %K genetic algorithms, genetic programming, grammatical evolution, financial prediction %0 Conference Proceedings %T Evolving classifiers to model the relationship between strategy and corporate performance using grammatical evolution %A Brabazon, Anthony %A O’Neill, Michael %A Ryan, Conor %A Matthews, Robin %Y Foster, James A. %Y Lutton, Evelyne %Y Miller, Julian %Y Ryan, Conor %Y Tettamanzi, Andrea G. B. %S Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002 %S LNCS %D 2002 %8 March 5 apr %V 2278 %I Springer-Verlag %C Kinsale, Ireland %@ 3-540-43378-3 %F brabazon:2002:EuroGP %X This study examines the potential of grammatical evolution to construct a linear classifier to predict whether a firm’s corporate strategy will increase or decrease shareholder wealth. Shareholder wealth is measured using a relative fitness criterion, the change in a firm’s market-value-added ranking in the Stern-Stewart Performance 1000 list, over a four year period, 1992-1996. Model inputs and structure are selected by means of grammatical evolution. The best classifier correctly categorised the direction of performance ranking change in 66.38percent of the firms in the training set and 65percent in the out-of-sample validation set providing support for a hypothesis that changes in corporate strategy are linked to changes in corporate performance. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1007/3-540-45984-7_10 %U http://dx.doi.org/10.1007/3-540-45984-7_10 %P 103-112 %0 Conference Proceedings %T Grammatical Evolution And Corporate Failure Prediction %A Brabazon, Anthony %A O’Neill, Michael %A Matthews, Robin %A Ryan, Conor %Y Langdon, W. B. %Y Cantú-Paz, E. %Y Mathias, K. %Y Roy, R. %Y Davis, D. %Y Poli, R. %Y Balakrishnan, K. %Y Honavar, V. %Y Rudolph, G. %Y Wegener, J. %Y Bull, L. %Y Potter, M. A. %Y Schultz, A. C. %Y Miller, J. F. %Y Burke, E. %Y Jonoska, N. %S GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %D 2002 %8 September 13 jul %I Morgan Kaufmann Publishers %C New York %@ 1-55860-878-8 %F brabazon:2002:gecco %X This study examines the potential of Grammatical Evolution to uncover a series of useful rules which can assist in predicting corporate failure using information drawn from financial statements. A sample of 178 publically quoted, failed and non-failed Us firms, drawn from the period 1991 to 2000 are used to train and test the model. The preliminary findings indicate that the methodology has much potential. %K genetic algorithms, genetic programming, Grammatical Evolution, real world applications, corporate failure prediction, genotype to phenotype mapping, grammars %U http://gpbib.cs.ucl.ac.uk/gecco2002/RWA145.ps %P 1011-1018 %0 Conference Proceedings %T Trading Foreign Exchange Markets Using Evolutionary Automatic Programming %A Brabazon, Tony %A O’Neill, Michael %Y Barry, Alwyn M. %S GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference %D 2002 %8 August %I AAAI %C New York %F brabazon:2002:gecco:workshop %K genetic algorithms, genetic programming, grammatical evolution %U http://www.grammatical-evolution.org/gews2002/brabazon.ps %P 133-136 %0 Conference Proceedings %T A Grammar Model for Foreign-Exchange Trading %A Brabazon, Anthony %A O’Neill, Michael %Y Arabnia, Hamid R. %Y Joshua, Rose %Y Mun, Youngsong %S Proceedings of the International Conference on Artificial Intelligence %D 2003 %8 23 26 jun %V II %I CSREA Press %C Las Vegas, USA %@ 1-932415-13-0 %F Brabazon:2003:ICAI %X This study examines the potential of Grammatical Evolution to uncover a series of useful technical trading rules which can be used to trade foreign exchange markets. In this study, each of the evolved programs represents a market trading system and implicitly, a predictive model. The form of these programs is not specified ex-ante but emerges by means of an evolutionary process. Daily US Dollar-DM exchange rates for the period 9/3/93 to 13/10/97 are used to train and test the model. The preliminary findings suggest that the developed rules earn positive returns in hold-out sample test periods after allowing for trading and slippage costs. This suggests potential for future research to determine whether further refinement of the methodology adopted in this study could improve the returns earned by the developed rules. %K genetic algorithms, genetic programming %U https://www.tib.eu/en/search/id/BLCP%3ACN050261220/A-Grammar-Model-for-Foreign-Exchange-Trading/ %P 492-498 %0 Conference Proceedings %T Grammars, Representations, Mental Maps and Corporate Strategy %A Brabazon, Anthony %A Matthews, Robin %A O’Neill, Michael %Y Gardner, C. %Y Biberman, J. %Y Alkhafaji, A. %S Business Research Yearbook: Global Business Perspectives. Proceedings of the Fifteenth Annual International Conference of the International Academy of Business Disciplines %D 2004 %8 mar 24 27 %V 11 %C San Antonio, USA %F Brabazon:2004:BYB %K genetic algorithms, genetic programming, grammatical evolution %P 1054-1058 %0 Conference Proceedings %T Bond-Issuer Credit Rating with Grammatical Evolution %A Brabazon, Anthony %A O’Neill, Michael %Y Raidl, Guenther R. %Y Cagnoni, Stefano %Y Branke, Jurgen %Y Corne, David W. %Y Drechsler, Rolf %Y Jin, Yaochu %Y Johnson, Colin R. %Y Machado, Penousal %Y Marchiori, Elena %Y Rothlauf, Franz %Y Smith, George D. %Y Squillero, Giovanni %S Applications of Evolutionary Computing, EvoWorkshops2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC %S LNCS %D 2004 %8 May 7 apr %V 3005 %I Springer Verlag %C Coimbra, Portugal %@ 3-540-21378-3 %F brabazon:evows04 %X This study examines the utility of Grammatical Evolution in modelling the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data, and the associated Standard & Poor’s issuer-credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment-grade and junk bond ratings with an average accuracy of 87.59 (84.92)percent across a five-fold cross validation. The results suggest that the two classifications of credit rating can be predicted with notable accuracy from a relatively limited subset of firm-specific financial data, using Grammatical Evolution. %K genetic algorithms, genetic programming, grammatical evolution, evolutionary computation %R 10.1007/978-3-540-24653-4_28 %U http://dx.doi.org/10.1007/978-3-540-24653-4_28 %P 270-279 %0 Journal Article %T Grammar-mediated time-series prediction %A Brabazon, Anthony %A Meagher, Katrina %A Carty, Edward %A O’Neill, Michael %A Keenan, Peter %J Journal of Intelligent Systems %D 2004 %8 aug %V 14 %N 2–3 %@ 2191-026X %F brabazon:2005:GMTSP %X Grammatical Evolution is a data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study examines the potential of Grammatical Evolution to uncover useful technical trading rulesets for intra-day equity trading. The form of these rule-sets is not specified ex-ante but emerges by means of an evolutionary process. High-frequency price data drawn from United States stock markets is used to train and test the model. The findings suggest that the developed rules earn positive returns in holdout test periods, and that the sizes of these returns are critically impacted by the choice of position exit-strategy. %K genetic algorithms, genetic programming, grammatical evolution, time-series, high-frequency finance, intra-day stock trading %9 journal article %R 10.1515/JISYS.2005.14.2-3.123 %U http://dx.doi.org/10.1515/JISYS.2005.14.2-3.123 %P 123-143 %0 Journal Article %T Diagnosing Corporate Stability using Grammatical Evolution %A Brabazon, Anthony %A O’Neill, Michael %J International Journal of Applied Mathematics and Computer Science %D 2004 %V 14 %N 3 %@ 1641-876X %F BrabazonONeill:2004:IJAMCSDCSuGE %X Grammatical Evolution (GE) is a novel data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and demonstrates the methodology by applying it to construct a series of models for the prediction of bankruptcy, employing information drawn from financial statements. Unlike prior studies in this domain, the raw financial information is not preprocessed into pre-determined financial ratios. Instead, the ratios to be incorporated into the classification rule are evolved from the raw financial data. This allows the creation and subsequent evolution of alternative ratio-based representations of the financial data. A sample of 178 publicly quoted, US firms, drawn from the period 1991 to 2000 are used to train and test the model. The best evolved model correctly classified 86 (77)percent of the firms in the in-sample training set (out-of-sample validation set), one year prior to failure. %K genetic algorithms, genetic programming, Grammatical Evolution, corporate failure prediction %9 journal article %U http://eudml.org/doc/207703 %P 363-374 %0 Journal Article %T Evolving Technical Trading Rules for Spot Foreign-Exchange Markets Using Grammatical Evolution %A Brabazon, Anthony %A O’Neill, Michael %J Computational Management Science %D 2004 %8 oct %V 1 %N 3-4 %I Springer-Verlag %@ 1619-697X %F BrabazonONeill:2004:CMSETTRfSFEMuGE %X Grammatical Evolution (GE) is a novel, data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and applies the methodology in an attempt to uncover useful technical trading rules which can be used to trade foreign exchange markets. In this study, each of the evolved rules (programs) represents a market trading system. The form of these programs is not specified ex-ante, but emerges by means of an evolutionary process. Daily US-DM, US-Stg and US-Yen exchange rates for the period 1992 to 1997 are used to train and test the model. The findings suggest that the developed rules earn positive returns in hold-out sample test periods, after allowing for trading and slippage costs. This suggests potential for future research to determine whether further refinement of the methodology adopted in this study could improve the returns earned by the developed rules. It is also noted that this novel methodology has general utility for rule-induction, and data mining applications. %K genetic algorithms, genetic programming, Grammatical evolution, Foreign exchange prediction, Technical trading rules %9 journal article %R 10.1007/s10287-004-0018-5 %U https://rdcu.be/dO4Fe %U http://dx.doi.org/10.1007/s10287-004-0018-5 %P 311-327 %0 Journal Article %T Grammar-Mediated Time-Series Prediction %A Brabazon, A. %A Meagher, K. %A Carty, E. %A O’Neill, M. %A Keenan, P. %J Journal of Intelligent Systems %D 2005 %V 14 %N 2-3 %F Brabazon:2005:JIS %O Special Issue %9 journal article %P 123-143 %0 Conference Proceedings %T Credit Rating with pi Grammatical Evolution %A Brabazon, Anthony %A O’Neill, Michael %Y Tadeusiewicz, R. %Y Ligeza, A. %Y Szymkat, M. %S Proceedings of Computer Methods and Systems Conference %D 2005 %8 14 16 nov %V 1 %I Oprogramowanie Naukowo-Techniczne Tadeusiewicz %C Krakow, Poland %@ 83-916420-3-8 %F brabazon:2005:CRWpiGE %X This study examines the utility of pi Grammatical Evolution in modelling the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data, and the associated Standard and Poor’s issuer-credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment grade and junk bond ratings with an average accuracy of 86 (87)percent across a five-fold cross validation. %K genetic algorithms, genetic programming, grammatical evolution %P 253-260 %0 Book %T Biologically Inspired Algorithms for Financial Modelling %A Brabazon, Anthony %A O’Neill, Michael %S Natural Computing Series %D 2006 %I Springer %@ 3-540-26252-0 %F Brabazon:2006:BIAS %K genetic algorithms, genetic programming, ant colony systems, artificial immune systems, biologically inspired algorithms (BIAs), computer trading, evolutionary methodologies, financial markets, financial trading, grammatical evolution, (GE), multilayer perceptrons, neural networks (NNs), particle swarm optimisation (PSO) %R 10.1007/3-540-31307-9 %U http://dx.doi.org/10.1007/3-540-31307-9 %0 Journal Article %T Credit Classification Using Grammatical Evolution %A Brabazon, Anthony %A O’Neill, Michael %J Informatica %D 2006 %V 30 %N 3 %@ 0350-5596 %F Brabazon:2006:I %X Grammatical Evolution (GE) is a novel data driven, model induction tool, inspired by the biological genetoprotein mapping process. This study provides an introduction to GE, and demonstrates the methodology by applying it to model the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data and the associated Standard & Poor’s issuer credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment grade and junk bond ratings with an average accuracy of 87.59 (84.92)percent across a five-fold cross validation. %K genetic algorithms, genetic programming, grammatical evolution, Povzetek: Metoda gramaticne evolucije je uporabljena za klasificiranje kreditov. %9 journal article %U http://ai.ijs.si/informatica/PDF/30-3/07_Brabazon_Credit%20Classification%20Using.pdf %P 325-335 %0 Book Section %T Bond Rating with piGrammatical Evolution %A Brabazon, Anthony %A O’Neill, Michael %E Cotta, C. %E Reich, S. %E Schaefer, R. %E Ligeza, A. %B Knowledge Engineering and Intelligent Computations %S Studies in Computational Intelligence %D 2008 %V 102 %I Springer %F Brabazon:2008:K-DC %X Most large firms use both share and debt capital to provide long-term finance for their operations. The debt capital may be raised from a bank loan, or may be obtained by selling bonds directly to investors. As an example of the scale of US bond markets, the value of new bonds issued in 2004 totaled $5.48 trillion, and the total value of outstanding marketable bond debt at 31 December 2004 was $23.6 trillion [1]. In comparison, the total global market capitalisation of all companies quoted on the New York Stock Exchange (NYSE) at 31/12/04 was $19.8 trillion [2]. Hence, although company stocks attract most attention in the business press, bond markets are actually substantially larger. When a company issues traded debt (e.g. bonds), it must obtain a credit rating for the issue from at least one recognised rating agency (Standard and Poor’s (S&P), Moody’s and Fitches’). The credit rating represents an agency’s opinion, at a specific date, of the credit worthiness of a borrower in general (a bond-issuer credit-rating), or in respect of a specific debt issue (a bond credit rating). These ratings impact on the borrowing cost, and the marketability of issued bonds. Although several studies have examined the potential of both statistical and machine-learning methodologies for credit rating prediction [3-6], many of these studies used relatively small sample sizes, making it difficult to generalise strongly from their findings. This study by contrast, uses a large dataset of 791 firms, and introduces pi GE to this domain. %K genetic algorithms, genetic programming, Grammatical Evolution %R 10.1007/978-3-540-77475-4_2 %U http://dx.doi.org/10.1007/978-3-540-77475-4_2 %P 17-30 %0 Book %T Natural Computing in Computational Finance %E Brabazon, Anthony %E O’Neill, Michael %S Studies in Computational Intelligence %D 2008 %8 apr %V 100 %I Springer %F Brabazon:2008:edbook %X Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modelling in modern computational finance. Following an introductory chapter the book is organised into three sections. The first section deals with optimisation applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies, quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear description of the financial application addressed. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, in the fields of both natural computing and finance. %K genetic algorithms, genetic programming, computational finance, evolution strategies, differential evolution, bacterial foraging, quantum-inspired evolutionary algorithms %U http://www.springer.com/engineering/book/978-3-540-77476-1 %0 Journal Article %T An Introduction to Evolutionary Computation in Finance %A Brabazon, Anthony %A O’Neill, Michael %A Dempsey, Ian %J IEEE Computational Intelligence Magazine %D 2008 %8 nov %V 3 %N 4 %@ 1556-603X %F Brabazon:2008:IEEECIM %X The world of finance is an exciting and challenging environment. Recent years have seen an explosion in the application of computational intelligence methodologies in finance. In this article we provide an overview of some of these applications concentrating on those employing an evolutionary computation approach. %K genetic algorithms, genetic programming, grammatical evolution, finance, evolutionary computation, financial data processing computational intelligence methodologies, evolutionary computation approach, finance %9 journal article %R 10.1109/MCI.2008.929841 %U http://ieeexplore.ieee.org/xpl/tocresult.jsp?isYear=2008&isnumber=4625777&Submit32=Go+To+Issue %U http://dx.doi.org/10.1109/MCI.2008.929841 %P 42-55 %0 Book %T Natural Computing in Computational Finance (Volume 2) %E Brabazon, Anthony %E O’Neill, Michael %S Studies in Computational Intelligence %D 2009 %8 mar %V 185 %I Springer %F Brabazon:2009:book %X About this book Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws inspiration from phenomena in the natural world) for the purposes of financial modeling and optimisation. A related stream of work has also seen the application of learning mechanisms drawn from Natural Computing algorithms for the purposes of agent based modelling in finance and economics. In this book we have collected a series of chapters which illustrate these two faces of Natural Computing. The first part of the book illustrates how algorithms inspired by the natural world can be used as problem solvers to uncover and optimise financial models. The second part of the book examines a number agent-based simulations of financial systems. This book follows on from Natural Computing in Computational Finance (Volume 100 in Springer’s Studies in Computational Intelligence series) which in turn arose from the success of EvoFIN 2007, the very first European Workshop on Evolutionary Computation in Finance & Economics held in Valencia, Spain in April 2007. Written for: Engineers, researchers, and graduate students in Computational Intelligence and Computer Finance %K genetic algorithms, genetic programming, computational Finance, Computational Intelligence %U http://www.springer.com/engineering/book/978-3-540-95973-1 %0 Book %T Natural Computing in Computational Finance (Volume 3) %E Brabazon, A. %E O’Neill, M. %E Maringer, D. G. %S Studies in Computational Intelligence %D 2010 %V 293 %I Springer %F brabazon_oneill_maringer:2010:book %X This book consists of eleven chapters each of which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics. The inspiration for this book was due in part to the success of EvoFIN 2009, the 3rd European Workshop on Evolutionary Computation in Finance and Economics. This book follows on from Natural Computing in Computational Finance Volumes I \citeBrabazon:2008:edbook and II \citeBrabazon:2009:book %K genetic algorithms, genetic programming, natural computing, computational finance, computational intelligence %R 10.1007/978-3-642-13950-5 %U http://www.springer.com/engineering/book/978-3-642-13949-9 %U http://dx.doi.org/10.1007/978-3-642-13950-5 %0 Unpublished Work %T Natural Computing and Finance %A Brabazon, A. %A O’Neill, M. %D 2010 %8 November 15 sep %I PPSN 2010 11th International Conference on Parallel Problem Solving From Nature %C Krakow, Poland %F abrabazon_moneill:ppsn2010 %O Tutorial %K genetic algorithms, genetic programming, grammatical evolution, finance %9 unpublished %U http://ncra.ucd.ie/papers/PPSN_tutorial_2010_published.pdf %0 Book Section %T Natural Computing in Finance - A Review %A Brabazon, Anthony %A Dang, Jing %A Dempsey, Ian %A O’Neill, Michael %A Edelman, David %E Rozenberg, Grzegorz %E Baeck, Thomas %E Kok, Joost N. %B Handbook of Natural Computing %D 2012 %8 19 aug %V 2 %I Springer %F BrabazonDDOE:2012:HNCNCiFAR %X The field of natural computing (NC) has advanced rapidly over the past decade. One significant offshoot of this progress has been the application of NC methods in finance. This chapter provides an introduction to a wide range of financial problems to which NC methods have been usefully applied. The chapter also identifies open issues and suggests future directions for the application of NC methods in finance. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-92910-9_51 %U http://www.springer.com/computer/theoretical+computer+science/book/978-3-540-92911-6 %U http://dx.doi.org/10.1007/978-3-540-92910-9_51 %P 1707-1735 %0 Book %T Natural Computing Algorithms %A Brabazon, Anthony %A O’Neill, Michael %A McGarraghy, Sean %D 2015 %I Springer %F Brabazon:book:NCA %X The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design. This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science. %K genetic algorithms, genetic programming %R 10.1007/978-3-662-43631-8 %U http://www.springer.com/computer/theoretical+computer+science/book/978-3-662-43630-1 %U http://dx.doi.org/10.1007/978-3-662-43631-8 %0 Book Section %T Genetic Programming %A Brabazon, Anthony %A O’Neill, Michael %A McGarraghy, Sean %B Natural Computing Algorithms %S Natural Computing Series %D 2015 %I Springer %F Brabazon:book:NCA.7 %X Genetic programming (GP) was initially developed to allow the automatic creation of a computer program from a high-level statement of a problem’s requirements, by means of an evolutionary process. In GP, a computer program to solve a defined task is evolved from an initial population of random computer programs. An iterative evolutionary process is employed by GP, where better (fitter) programs for the task at hand are allowed to reproduce using recombination processes to recombine components of existing programs. The reproduction process is supplemented by incremental trial-and-error development, and both variety-generating mechanisms act to generate variants of existing good programs. Over time, the utility of the programs in the population improves as poorer solutions to the problem are replaced by better solutions. More generally, GP has been applied to evolve a wide range of structures (and their associated parameters) including electronic circuits, mathematical models, engineering designs, etc. %K genetic algorithms, genetic programming %R 10.1007/978-3-662-43631-8_7 %U http://dx.doi.org/10.1007/978-3-662-43631-8_7 %P 95-114 %0 Book Section %T An Introduction to Developmental and Grammatical Computing %A Brabazon, Anthony %A O’Neill, Michael %A McGarraghy, Sean %B Natural Computing Algorithms %S Natural Computing Series %D 2015 %I Springer %F Brabazon:book:NCA.17 %X To say that the knowledge uncovered by developmental biologists has been under-exploited in natural computing is perhaps an understatement. Curiously, despite the relative lack of research attention that has been paid to these important biological processes, one of the fathers of Computer Science, Alan Turing, recognised the power of developmental systems and developed reaction-diffusion models to understand the mechanisms behind morphogenesis (the development of biological form) [638]. In recent years it is heartening to see researchers beginning to close this gap and start to explore the power of developmental processes such as genetic regulatory networks for problem solving, and the use of approaches such as self-modification of phenotypes and developmental evaluation. The surge in interest in developmental computing is illustrated by the creation of a new track dedicated to Generative and Developmental Systems which began in 2007 at the ACM Genetic and Evolutionary Computation Conference [72, 321, 347, 526, 586, 623] and which has run every year since. A special issue of the journal IEEE Transactions on Evolutionary Computation was also dedicated to this topic in 2011 [639]. In this chapter the concept of developmental computing is introduced with particular emphasis on grammatical computing, which uses grammars as a generative process in order to create structures of interest. %K genetic algorithms, genetic programming %R 10.1007/978-3-662-43631-8_17 %U http://dx.doi.org/10.1007/978-3-662-43631-8_17 %P 335-343 %0 Book Section %T Grammar-Based and Developmental Genetic Programming %A Brabazon, Anthony %A O’Neill, Michael %A McGarraghy, Sean %B Natural Computing Algorithms %S Natural Computing Series %D 2015 %I Springer %F Brabazon:book:NCA.18 %X The use of grammars in genetic programming (GP) has a long tradition, and there are many examples of different approaches in the literature representing linear, tree-based and more generally graph-based forms. McKay et al. \citeMcKay:2010:GPEM presented a survey of grammar-based GP in the 10th Anniversary issue of the journal Genetic Programming and Evolvable Machines. In this and subsequent chapters, we highlight some of the more influential forms of grammar-based and developmental GP. %K genetic algorithms, genetic programming %R 10.1007/978-3-662-43631-8_18 %U http://dx.doi.org/10.1007/978-3-662-43631-8_18 %P 345-356 %0 Book Section %T Grammatical Evolution %A Brabazon, Anthony %A O’Neill, Michael %A McGarraghy, Sean %B Natural Computing Algorithms %S Natural Computing Series %D 2015 %I Springer %F Brabazon:book:NCA.19 %X Grammatical Evolution (GE), a form of grammar-based genetic programming (Chap. 18 \citeBrabazon:book:NCA.18 ), is an algorithm that can evolve computer programs, rulesets or, more generally, sentences in any language [150, 460, 470, 472, 547]. Rulesets could be as diverse as a regression model, a set of design instructions, or a trading system for a financial market. Rather than representing the programs as syntax trees, as in GP (Chap. 7 \citeBrabazon:book:NCA.7 ) [340, 514], a linear genome representation is used in conjunction with a grammar. %K genetic algorithms, genetic programming, Grammatical Evolution %R 10.1007/978-3-662-43631-8_19 %U http://dx.doi.org/10.1007/978-3-662-43631-8_19 %P 357-373 %0 Book Section %T Tree-Adjoining Grammars and Genetic Programming %A Brabazon, Anthony %A O’Neill, Michael %A McGarraghy, Sean %B Natural Computing Algorithms %S Natural Computing Series %D 2015 %I Springer %F Brabazon:book:NCA.20 %K genetic algorithms, genetic programming %R 10.1007/978-3-662-43631-8_20 %U http://dx.doi.org/10.1007/978-3-662-43631-8_20 %P 375-381 %0 Book Section %T Grammatical Evolution in Finance and Economics: A Survey %A Brabazon, Anthony %E Ryan, Conor %E O’Neill, Michael %E Collins, J. J. %B Handbook of Grammatical Evolution %D 2018 %I Springer %F Brabazon:2018:hbge %X Finance was one of the earliest application domains for Grammatical Evolution (GE). Since the first such study in 2001, well in excess of 100 studies have been published employing GE for a diverse range of purposes encompassing financial trading, credit-risk modelling, supply chain management, detection of tax non-compliance, and corporate strategy modelling. This chapter surveys a sample of this work and in doing so, suggests some future directions for the application of GE in finance and economics. %K genetic algorithms, genetic programming, Grammatical Evolution %R 10.1007/978-3-319-78717-6_11 %U http://dx.doi.org/10.1007/978-3-319-78717-6_11 %P 263-288 %0 Journal Article %T Applications of genetic programming to finance and economics: past, present, future %A Brabazon, Anthony %A Kampouridis, Michael %A O’Neill, Michael %J Genetic Programming and Evolvable Machines %D 2020 %8 jun %V 21 %N 1-2 %@ 1389-2576 %F Brabazon:GPEM20 %O Twentieth Anniversary Issue %X While the origins of genetic programming (GP) stretch back over 50 years, the field of GP was invigorated by John Koza popularisation of the methodology in the 1990s. A particular feature of the GP literature since then has been a strong interest in the application of GP to real-world problem domains. One application domain which has attracted significant attention is that of finance and economics, with several hundred papers from this subfield being listed in the GP bibliography. In this article we outline why finance and economics has been a popular application area for GP and briefly indicate the wide span of this work. However, despite this research effort there is relatively scant evidence of the usage of GP by the mainstream finance community in academia or industry. We speculate why this may be the case, describe what is needed to make this research more relevant from a finance perspective, and suggest some future directions for the application of GP in finance and economics. %K genetic algorithms, genetic programming, Finance, Economics, Quantitative trading %9 journal article %R 10.1007/s10710-019-09359-z %U http://dx.doi.org/10.1007/s10710-019-09359-z %P 33-53 %0 Conference Proceedings %T Automatic Repair of Concurrency Bugs %A Bradbury, Jeremy S. %A Jalbert, Kevin %Y Di Penta, Massimiliano %Y Poulding, Simon %Y Briand, Lionel %Y Clark, John %S Proceedings of the 2nd International Symposium on Search Based Software Engineering (SSBSE ’10) %D 2010 %8 July 9 sep %C Benevento, Italy %F BradburyJ10 %O Fast abstract %X Bugs in concurrent software are difficult to identify and fix since they may only exhibit abnormal behaviour on certain thread interleavings. We propose the use of genetic programming to incrementally create a solution that fixes a concurrency bug automatically. Bugs in a concurrent program are fixed by iteratively mutating the program and evaluating each mutation using a fitness function that compares the mutated program with the previous version. We propose three mutation operators that can fix concurrency bugs: synchronise an unprotected shared resource, expand synchronization regions to include unprotected source code, and interchange nested lock objects. %K genetic algorithms, genetic programming, genetic improvement, APR, SBSE, concurrency, mutation :poster? %U http://www.ssbse.org/2010/fastabstracts/ssbse2010_fastabstract_04.pdf %0 Book Section %T A simple Approach to Protein Structure Prediction using Genetic Algorithms %A Braden, Katie %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 braden:2002:AAPSPGA %K genetic algorithms %U http://www.genetic-programming.org/sp2002/Braden.pdf %P 36-44 %0 Conference Proceedings %T Evolving Trading Rule-Based Policies %A Bradley, Robert Gregory %A Brabazon, Anthony %A O’Neill, Michael %Y Di Chio, Cecilia %Y Brabazon, Anthony %Y Di Caro, Gianni A. %Y Ebner, Marc %Y Farooq, Muddassar %Y Fink, Andreas %Y Grahl, Jorn %Y Greenfield, Gary %Y Machado, Penousal %Y O’Neill, Michael %Y Tarantino, Ernesto %Y Urquhart, Neil %S EvoFIN %S LNCS %D 2010 %8 July 9 apr %V 6025 %I Springer %C Istanbul %F bradley:2010:evofin %X Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number of suggestions for future work. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1007/978-3-642-12242-2_26 %U http://dx.doi.org/10.1007/978-3-642-12242-2_26 %P 251-260 %0 Conference Proceedings %T Objective Function Design in a Grammatical Evolutionary Trading System %A Bradley, Robert %A Brabazon, Anthony %A O’Neill, Michael %S 2010 IEEE World Congress on Computational Intelligence %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F bradley_etal:cec2010 %X Designing a suitable objective function is an essential step in successfully applying an evolutionary algorithm to a problem. In this study we apply a grammar-based Genetic Programming algorithm called Grammatical Evolution to the problem of trading model induction. A number of experiments were performed to assess the effect of objective function design on the trading characteristics of the evolved trading strategies. Empirical results suggest that the choice of objective function has a significant impact. The paper concludes with in and out-of-sample results, and indicates a number of avenues of future work. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1109/CEC.2010.5586020 %U http://dx.doi.org/10.1109/CEC.2010.5586020 %P 3487-3494 %0 Thesis %T Active versus Passive Investing: An Investigation of Market Timing %A Bradley, Robert %C Ireland %C Michael Smurfit Graduate School of Business, University College Dublin %F Bradley:thesis %K genetic algorithms, genetic programming %9 Ph.D. thesis %U https://www.smurfitschool.ie/facultyresearch/phdresearch/phdgraduates/robertbradley/ %0 Journal Article %T genBRDF: discovering new analytic BRDFs with genetic programming %A Brady, Adam %A Lawrence, Jason %A Peers, Pieter %A Weimer, Westley %J ACM Transactions on Graphics %D 2014 %8 jul %V 33 %N 4 %I ACM %@ 0730-0301 %F Brady:2014:acmTG %X We present a framework for learning new analytic BRDF models through Genetic Programming that we call genBRDF. This approach to reflectance modelling can be seen as an extension of traditional methods that rely either on a phenomenological or empirical process. Our technique augments the human effort involved in deriving mathematical expressions that accurately characterise complex high-dimensional reflectance functions through a large-scale optimisation. We present a number of analysis tools and data visualisation techniques that are crucial to sifting through the large result sets produced by genBRDF in order to identify fruitful expressions. Additionally, we highlight several new models found by genBRDF that have not previously appeared in the BRDF literature. These new BRDF models are compact and more accurate than current state-of-the-art alternatives. %K genetic algorithms, genetic programming, GPU, BRDF, analytic, isotropic %9 journal article %R 10.1145/2601097.2601193 %U https://web.eecs.umich.edu/~weimerw/p/brady_sig14.pdf %U http://dx.doi.org/10.1145/2601097.2601193 %P 114:1-114:11 %0 Report %T Murphy’s law, the fitness of evolving species, and the limits of software reliability %A Brady, Robert M. %A Anderson, Ross J. %A Ball, Robin C. %D 1996? %I Computer Laboratory, Cambridge %F brady:murphy %X We tackle two problems of interest to the software assurance community. Firstly, existing models of software development (such as the waterfall and spiral models) are oriented towards one-off software development projects, while the growth of mass market computing has led to a world in which most software consists of packages which follow an evolutionary development model. This leads us to ask whether anything interesting and useful may be said about evolutionary development. We answer in the affirmative. Secondly, existing reliability growth models emphasise the Poisson distribution of individual software bugs, while the empirically observed reliability growth for large systems is asymptotically slower than this. We provide a rigorous explanation of this phenomenon. Our reliability growth model is inspired by statistical thermodynamics, but also applies to biological evolution. It is in close agreement with experimental measurements of the fitness of an evolving species and the reliability of commercial software products. However, it shows that there are significant differences between the evolution of software and the evolution of species. In particular, we establish maximisation properties corresponding to Murphy?s law which work to the advantage of a biological species, but to the detriment of software reliability. %U http://www.ftp.cl.cam.ac.uk/ftp/users/rja14/babtr.pdf %0 Journal Article %T A Hybrid Multi-gene Genetic Programming with Capuchin Search Algorithm for Modeling a Nonlinear Challenge Problem: Modeling Industrial Winding Process, Case Study %A Braik, Malik %J Neural Process. Lett. %D 2021 %V 53 %N 4 %F DBLP:journals/npl/Braik21 %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s11063-021-10530-w %U https://doi.org/10.1007/s11063-021-10530-w %U http://dx.doi.org/10.1007/s11063-021-10530-w %P 2873-2916 %0 Book %T Vehicles %A Braitenberg, Valentino %D 1984 %I MIT Press %C Cambridge MA, USA %@ 0-262-52112-1 %F Braitenberg:1984 %K NEURAL MOBILE SIMULATION EVOLUTION MOTOR-SCHEMA REACTIVE MODULAR %0 Conference Proceedings %T Automated design of combinatorial logic circuits %A Brajer, Iva %A Jakobovic, Domagoj %S Proceedings of the 35th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2012) %D 2012 %8 21 25 may %C Opatija, Croatia %F Brajer:2012:MIPRO %X This paper deals with automated design of combinatorial circuits with the use of Cartesian Genetic Programming (CGP). The synthesis is based on user specifications of network functionality, while the network structure may be predefined. The results show that CGP approach is able to match the desired functionality while preserving other performance criteria, such as latency and number of gates. Additionally, the evolution process may use Verilog network descriptions as input files, which facilitates the design for larger number of inputs and test patterns. %K genetic algorithms, genetic programming, Cartesian Genetic Programming %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6240757 %P 823-828 %0 Conference Proceedings %T Compensation of the size of the finished part for the PolyJet rapid prototyping procedure %A Brajlih, Tomaz %A Drstvensek, Igor %A Kovacic, Miha %A Balic, Joze %S Proceedings of the International Conference Polymers & Moulds Innovations PMI 2005 %D 2005 %8 apr 20 23 %C Gent, Belgium %F Brajlih:2005:PMI %X The main accuracy problem of rapid prototyping procedures, which are using polymers as a building material is shrinking of a finished layer in the phase of polymerization. Different procedures are using different approaches to handle the problem but none of them can actually reach the accuracy that users of traditional cutting techniques are used to. To achieve better quality of the PolyJet procedure, which originally employs a method of size compensation to reach a desired accuracy, we decided to improve the procedure’s performance by adjusting the compensation factor for every part separately. To this purpose some traditional methods of statistics were used, which were later combined with some newer, less traditional methods like genetic programming. The later enabled us to acquire a formula for compensation factor determination based upon the geometry of the actual part. It also showed the importance or unimportance of some influencing parameters respectively. The method resulted in a better compensation factor and better overall performance of the PolyJet procedure compared to other rapid prototyping techniques used nowadays. %K genetic algorithms, genetic programming, hitra izdelava prototipov, PolyJet postopek, izravnalni faktor, prototipi, rapid prototyping, polyjet procedure, compensation factor %0 Conference Proceedings %T Improving the Accuracy of Rapid Prototyping Procedures by Genetic Programming %A Brajlih, T. %A Drstvensek, I. %A Valentan, B. %A Balic, J. %Y Kyeener, R. %S Proceedings of the 5TH International conference of DAAAM Baltic – Industrial Engineering %D 2006 %8 20 22 apr %I DAAAM %C Tallinn, Estonia %F Brajlih:2006:DAAAM %X To achieve better quality of the PolyJet Rapid Prototyping procedure, which originally employs a method of size compensation by scale factors to reach a desired accuracy, we decided to improve the procedure’s performance by adjusting scale factors for every part separately. The main accuracy problem of rapid prototyping procedures that are using polymers as a building material is shrinking of a finished layer in the phase of polymerization. To this purpose we used genetic programming that enabled us to acquire a formula for scale factor’s determination based upon the geometry of the actual part. The method resulted in optimized scale factors and better overall performance of the PolyJet procedure compared to other rapid prototyping techniques used nowadays. %K genetic algorithms, genetic programming %U http://innomet.ttu.ee/daaam06/proceedings/Production%20Engineering/24brajilih.pdf %P 113-116 %0 Journal Article %T Optimizing scale factors of the PolyJet rapid prototyping procedure by genetic programming %A Brajlih, Tomaz %A Drstvensek, Igor %A Kovacic, Miha %A Balic, Joze %J Journal of achievements in materials and manufacturing engineering %D 2006 %8 may jun %V 16 %N 1-2 %@ 1734-8412 %F Brajlih:2006:AMME %O Special Issue of CAM3S’2005 %X The main problem of assuring a high dimensional accuracy of rapid prototyping procedures, that are using polymers as a building material, is shrinking of a finished layer during the phase of polymerisation. Therefore, the finished object is slightly smaller then the object’s CAD three-dimensional model, that was used to build the prototype. Commonly used method to minimise this problem is to scale (enlarge) the original CAD model in order to compensate for the material’s shrinkage during manufacturing. The scaling is usually done by the number factor (in percentages) that is recommended by the rapid prototyping machine’s manufacturer. With a long-term use of the certain rapid prototyping machine the end-users can determine their own scale factor’s values, which are more suited to their model’s properties. This research has established a method that enables the user of a PolyJet RP machine to determine the optimal scale factor regardless of his previous experience. For that purpose the genetic programming methods were used to establish a mathematical model that enables the user to calculate optimal scale factor values for each axis (X,Y,Z) regarding a certain object’s properties. This method was later tested on a series of prototypes that were scaled with factor values acquired with the established mathematical model. %K genetic algorithms, genetic programming, rapid prototyping, PolyJet %9 journal article %U http://jamme.acmsse.h2.pl/index.php?id=69 %P 101-106 %0 Report %T SYSGP – A C++ library of different GP variants %A Brameier, Markus %A Kantschik, Wolfgang %A Dittrich, Peter %A Banzhaf, Wolfgang %D 1998 %N CI-98/48 %I Collaborative Research Center 531, University of Dortmund %C Germany %G en %F oai:CiteSeerPSU:323834 %X In recent years different variants of genetic programming (GP) have emerged all following the basic idea of GP, the automatic evolution of computer programs. Today, three basic forms of representation for genetic programs are used, namely tree, graph and linear structures. We introduce a multi-representation system, SYSGP, that allows researchers to experiment with different representations with only a minimum implementation overhead. The system further offers the possibility to combine modules of different representation forms into one genetic program. SYSGP has been implemented as a C++ library using templates that operate with a generic data type. %K genetic algorithms, genetic programming %U https://eldorado.uni-dortmund.de/bitstream/2003/5345/2/ci4898_doc.pdf %0 Conference Proceedings %T Parallel Machine Code Genetic Programming %A Brameier, Markus %A Hoffmann, Frank %A Nordin, Peter %A Banzhaf, Wolfgang %A Francone, Frank %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 brameier:1999:PMCGP %X AIMGP is a very fast linear genetic programming approach that evolves machine code programs. We report on a parallelization of AIMGP for a parallel transputer system resulting in an almost linear speedup. %K genetic algorithms, genetic programming, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/GP-439.pdf %P 1228 %0 Report %T Effective Linear Genetic Programming %A Brameier, Markus %A Banzhaf, Wolfgang %D 2001 %8 29 oct %N Reihe CI 108/01, SFB 531 %I Department of Computer Science, University of Dortmund %C 44221 Dortmund, Germany %G en %F oai:CiteSeerPSU:488546 %X Different variants of genetic operators are introduced and compared for linear genetic programming including program induction without crossover. Variation strength of crossover and mutations is controlled based on the genetic code. Effectivity of genetic operations improves on code level and on fitness level. Thereby algorithms for creating code efficient solutions are presented. %K genetic algorithms, genetic programming %R 10.17877/DE290R-15250 %U http://hdl.handle.net/2003/5407 %U http://dx.doi.org/10.17877/DE290R-15250 %0 Report %T A Comparison of Genetic Programming and Neural Networks in Medical Data Analysis %A Brameier, Markus %A Banzhaf, Wolfgang %D 1998 %N CI 43/98, SFB 531 %I Dortmund University %C Germany %G en %F oai:CiteSeerPSU:324837 %X We apply an interpreting variant of linear genetic programming to several diagnosis problems in medicine. We compare our results to results obtained with neural networks and argue that genetic programming is able to show similar performances in classification and generalization even when using a relatively small number of generations. Finally, an efficient algorithm for the elimination of introns in linear genetic programs is presented %K genetic algorithms, genetic programming %9 Reihe %U https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5344/2/ci4398_doc.pdf %0 Journal Article %T A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining %A Brameier, Markus %A Banzhaf, Wolfgang %J IEEE Transactions on Evolutionary Computation %D 2001 %8 feb %V 5 %N 1 %F Brameier:2001:TEC %X We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring in real-world applications like medicine. We compare our results to those obtained with neural networks and argue that genetic programming is able to show similar performance in classification and generalization even within a relatively small number of generations. %K genetic algorithms, genetic programming, Data mining, evolutionary computation, neural networks %9 journal article %U http://web.cs.mun.ca/~banzhaf/papers/ieee_taec.pdf %P 17-26 %0 Journal Article %T Evolving Teams of Predictors with Linear Genetic Programming %A Brameier, Markus %A Banzhaf, Wolfgang %J Genetic Programming and Evolvable Machines %D 2001 %8 dec %V 2 %N 4 %@ 1389-2576 %F brameier:2001:GPEM %X This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a real numbered vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP. In particular, the processing time of linear genetic programs is reduced significantly by removing intron code before program execution. %K genetic algorithms, genetic programming, evolution of teams, combination of multiple predictors, linear genetic programming %9 journal article %R 10.1023/A:1012978805372 %U http://web.cs.mun.ca/~banzhaf/papers/teams.pdf %U http://dx.doi.org/10.1023/A:1012978805372 %P 381-407 %0 Report %T Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming %A Brameier, Markus %A Banzhaf, Wolfgang %D 2002 %8 feb 25 %I Dortmund University %G en %F oai:CiteSeerPSU:552561 %X We investigate structural and semantic distance metrics for linear genetic programs. Causal connections between changes of the genotype and fitness changes form a necessary condition for analyzing structural differences between genetic programs and for the two major objectives of this paper: (i) Distance information betweenin-dividuals is used to control structural diversity of population individuals actively by a two-level tournament selection. (ii) Variation distance of effective code is controlled for different genetic operators - including an effective variant of the mutation operator that works closely with the used distance metric. Numerous experiments have been performed for a regression problem, a classification task, and a Boolean problem %K genetic algorithms, genetic programming %U http://eldorado.uni-dortmund.de/0x81d98002_0x0004162d %0 Conference Proceedings %T Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming %A Brameier, Markus %A Banzhaf, Wolfgang %Y Foster, James A. %Y Lutton, Evelyne %Y Miller, Julian %Y Ryan, Conor %Y Tettamanzi, Andrea G. B. %S Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002 %S LNCS %D 2002 %8 March 5 apr %V 2278 %I Springer-Verlag %C Kinsale, Ireland %@ 3-540-43378-3 %F brameier:2002:EuroGP %X We have investigated structural distance metrics for linear genetic programs. Causal connections between changes of the genotype and changes of the phenotype form a necessary condition for analyzing structural differences between genetic programs and for the two objectives of this paper: (i) The distance information between individuals is used to control structural diversity of population individuals actively by a two-level tournament selection. (ii) Variation distance of effective code is controlled for different genetic operators - including a mutation operator that works closely with the applied distance measure. Numerous experiments have been performed for three benchmark problems. %K genetic algorithms, genetic programming %R 10.1007/3-540-45984-7_4 %U http://www.cs.mun.ca/~banzhaf/papers/eurogp02_dist.pdf %U http://dx.doi.org/10.1007/3-540-45984-7_4 %P 37-49 %0 Conference Proceedings %T Neutral Variations Cause Bloat in Linear GP %A Brameier, Markus %A Banzhaf, Wolfgang %Y Ryan, Conor %Y Soule, Terence %Y Keijzer, Maarten %Y Tsang, Edward %Y Poli, Riccardo %Y Costa, Ernesto %S Genetic Programming, Proceedings of EuroGP’2003 %S LNCS %D 2003 %8 14 16 apr %V 2610 %I Springer-Verlag %C Essex %@ 3-540-00971-X %F brameier03 %X In this contribution we investigate the influence of different variation effects on the growth of code. A mutation-based variant of linear GP is applied that operates with minimum structural step sizes. Results show that neutral variations are a direct cause for (and not only a result of) the emergence and the growth of intron code. The influence of non-neutral variations has been found to be considerably smaller. Neutral variations turned out to be beneficial by solving two classification problems more successfully. %K genetic algorithms, genetic programming: Poster %R 10.1007/3-540-36599-0_26 %U http://dx.doi.org/10.1007/3-540-36599-0_26 %P 286-296 %0 Thesis %T On Linear Genetic Programming %A Brameier, Markus %D 2004 %8 feb %C Germany %C Fachbereich Informatik, Universität Dortmund %F B2005OLGP %X The thesis is about linear genetic programming (LGP), a machine learning approach that evolves computer programs as sequences of imperative instructions. Two fundamental differences to the more common tree-based variant (TGP) may be identified. These are the graph-based functional structure of linear genetic programs, on the one hand, and the existence of structurally noneffective code, on the other hand. The two major objectives of this work comprise(1) the development of more advanced methods and variation operators to produce better and more compact program solutions and (2) the analysis of general EA/GP phenomena in linear GP, including intron code, neutral variations, and code growth, among others. First, we introduce efficient algorithms for extracting features of the imperative and functional structure of linear genetic programs.In doing so, especially the detection and elimination of noneffective code during runtime will turn out as a powerful tool to accelerate the time-consuming step of fitness evaluation in GP. Variation operators are discussed systematically for the linear program representation. We will demonstrate that so called effective instruction mutations achieve the best performance in terms of solution quality.These mutations operate only on the (structurally) effective code and restrict the mutation step size to one instruction. One possibility to further improve their performance is to explicitly increase the probability of neutral variations. As a second, more time-efficient alternative we explicitly control the mutation step size on the effective code (effective step size).Minimum steps do not allow more than one effective instruction to change its effectiveness status. That is, only a single node may be connected to or disconnected from the effective graph component. It is an interesting phenomenon that, to some extent, the effective code becomes more robust against destructions over the generations already implicitly. A special concern of this thesis is to convince the reader that there are some serious arguments for using a linear representation.In a crossover-based comparison LGP has been found superior to TGP over a set of benchmark problems. Furthermore, linear solutions turned out to be more compact than tree solutions due to (1) multiple usage of subgraph results and (2) implicit parsimony pressure by structurally noneffective code. The phenomenon of code growth is analysed for different linear genetic operators. When applying instruction mutations exclusively almost only neutral variations may be held responsible for the emergence and propagation of intron code. It is noteworthy that linear genetic programs may not grow if all neutral variation effects are rejected and if the variation step size is minimum.For the same reasons effective instruction mutations realize an implicit complexity control in linear GP which reduces a possible negative effect of code growth to a minimum.Another noteworthy result in this context is that program size is strongly increased by crossover while it is hardly influenced by mutation even if step sizes are not explicitly restricted. Finally, we investigate program teams as one possibility to increase the dimension of genetic programs. It will be demonstrated that much more powerful solutions may be found by teams than by individuals. Moreover, the complexity of team solutions remains surprisingly small compared to individual programs. Both is the result of specialisation and cooperation of team members. %K genetic algorithms, genetic programming, Evolutionary algorithms, Machine learning %9 Ph.D. thesis %R 10.17877/DE290R-253 %U https://eldorado.uni-dortmund.de/bitstream/2003/20098/2/Brameierunt.pdf %U http://dx.doi.org/10.17877/DE290R-253 %0 Journal Article %T Automatic discovery of cross-family sequence features associated with protein function %A Brameier, Markus %A Haan, Josien %A Krings, Andrea %A MacCallum, Robert M. %J BMC bioinformatics [electronic resource] %D 2006 %8 jan 12 %V 7 %N 16 %I BioMed Central Ltd. %@ 1471-2105 %G en %F oai:biomedcentral.com:1471-2105-7-16 %X Background Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. Results We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the ’transcription’ function than to the general ’nuclear’ function/location. Conclusion We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription. %K genetic algorithms, genetic programming %9 journal article %R 10.1186/1471-2105-7-16 %U http://www.biomedcentral.com/content/pdf/1471-2105-7-16.pdf %U http://dx.doi.org/10.1186/1471-2105-7-16 %0 Book %T Linear Genetic Programming %A Brameier, Markus %A Banzhaf, Wolfgang %S Genetic and Evolutionary Computation %D 2007 %N XVI %I Springer %@ 0-387-31029-0 %F Brameier:2006:book %X Table of contents Preface, About the Authors, Acknowledgments, Introduction, I Fundamental Analyses: Basic Concepts, Representation Characteristics, A Comparison with Neural Networks, II Method Design: Segment Variations, Instruction Mutations, Analysis of Control Parameters, A Comparison with Tree-Based GP, III Advanced Techniques and Phenomena: Control of Diversity and Step Size, Code Growth and Neutral Variations, Evolution of Program Teams, References, Index. %K genetic algorithms, genetic programming, Step Size Control, Syntax, algorithms, code growth, diversity control, evolutionary algorithm, genetic operators, learning, machine learning, neutral variations, optimisation, programming %R 10.1007/978-0-387-31030-5 %U http://dx.doi.org/10.1007/978-0-387-31030-5 %0 Journal Article %T NucPred Predicting nuclear localization of proteins %A Brameier, Markus %A Krings, Andrea %A MacCallum, Robert M. %J Bioinformatics %D 2007 %V 23 %N 9 %F NucPred-bioinformatics2007 %X NucPred analyses patterns in eukaryotic protein sequences and predicts if a protein spends at least some time in the nucleus or no time at all. Subcellular location of proteins represents functional information, which is important for understanding protein interactions, for the diagnosis of human diseases and for drug discovery. NucPred is a novel web tool based on regular expression matching and multiple program classifiers induced by genetic programming. A likelihood score is derived from the programs for each input sequence and each residue position. Different forms of visualisation are provided to assist the detection of nuclear localisation signals (NLSs). The NucPred server also provides access to additional sources of biological information (real and predicted) for a better validation and interpretation of results. Availability: The web interface to the NucPred tool is provided at http://www.sbc.su.se/ maccallr/nucpred. In addition, the Perl code is made freely available under the GNU Public Licence (GPL) for simple incorporation into other tools and web servers. %K genetic algorithms, genetic programming %9 journal article %R 10.1093/bioinformatics/btm066 %U http://dx.doi.org/10.1093/bioinformatics/btm066 %P 1159-1160 %0 Journal Article %T Ab initio identification of human microRNAs based on structure motifs %A Brameier, Markus %A Wiuf, Carsten %J BMC Bioinformatics %D 2007 %8 18 dec %V 8 %F Brameier:2007:BMCbinf %X BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNA molecules that are directly involved in post-transcriptional regulation of gene expression. The mature miRNA sequence binds to more or less specific target sites on the mRNA. Both their small size and sequence specificity make the detection of completely new miRNAs a challenging task. This cannot be based on sequence information alone, but requires structure information about the miRNA precursor. Unlike comparative genomics approaches, ab initio approaches are able to discover species-specific miRNAs without known sequence homology. RESULTS: MiRPred is a novel method for ab initio prediction of miRNAs by genome scanning that only relies on (predicted) secondary structure to distinguish miRNA precursors from other similar-sized segments of the human genome. We apply a machine learning technique, called linear genetic programming, to develop special classifier programs which include multiple regular expressions (motifs) matched against the secondary structure sequence. Special attention is paid to scanning issues. The classifiers are trained on fixed-length sequences as these occur when shifting a window in regular steps over a genome region. Various statistical and empirical evidence is collected to validate the correctness of and increase confidence in the predicted structures. Among other things, we propose a new criterion to select miRNA candidates with a higher stability of folding that is based on the number of matching windows around their genome location. An ensemble of 16 motif-based classifiers achieves 99.9 percent specificity with sensitivity remaining on an acceptable high level when requiring all classifiers to agree on a positive decision. A low false positive rate is considered more important than a low false negative rate, when searching larger genome regions for unknown miRNAs. 117 new miRNAs have been predicted close to known miRNAs on human chromosome 19. All candidate structures match the free energy distribution of miRNA precursors which is significantly shifted towards lower free energies. We employed a human EST library and found that around 75 percent of the candidate sequences are likely to be transcribed, with around 35 percent located in introns. CONCLUSION: Our motif finding method is at least competitive to state-of-the-art feature-based methods for ab initio miRNA discovery. In doing so, it requires less previous knowledge about miRNA precursor structures while programs and motifs allow a more straightforward interpretation and extraction of the acquired knowledge. %K genetic algorithms, genetic programming, linear genetic programming %9 journal article %R 10.1186/1471-2105-8-478 %U http://www.biomedcentral.com/content/pdf/1471-2105-8-478.pdf %U http://dx.doi.org/10.1186/1471-2105-8-478 %P 478 %0 Journal Article %T Using FE Calculations and Data-Based System Identification Techniques to Model the Nonlinear Behavior of PMSMs %A Bramerdorfer, Gerd %A Winkler, Stephan M. %A Kommenda, Michael %A Weidenholzer, Guenther %A Silber, Siegfried %A Kronberger, Gabriel %A Affenzeller, Michael %A Amrhein, Wolfgang %J IEEE Transactions on Industrial Electronics %D 2014 %8 nov %V 61 %N 11 %@ 0278-0046 %F Bramerdorfer:2014:ieeeIE %X This article investigates the modelling of brushless permanent magnet synchronous machines (PMSMs). The focus is on deriving an automatable process for obtaining dynamic motor models that take nonlinear effects, such as saturation, into account. The modelling is based on finite element (FE) simulations for different current vectors in the dq plane over a full electrical period. The parameters obtained are the stator flux in terms of the direct and quadrature component and the air gap torque, both modelled as functions of the rotor angle and the current vector. The data is preprocessed according to theoretical results on potential harmonics in the targets as functions of the rotor angle. A variety of modelling strategies were explored: linear regression, support vector machines, symbolic regression using genetic programming, random forests, and artificial neural networks. The motor models were optimised for each training technique and their accuracy was then compared on the basis of the initially available FE data and further FE simulations for additional current vectors. Artificial neural networks and symbolic regression using genetic programming achieved the highest accuracy especially with additional test data. %K genetic algorithms, genetic programming, brushless machine, permanent magnet, cogging torque, torque ripple, modelling, field-oriented control, symbolic regression, artificial neural network, random forests %9 journal article %R 10.1109/TIE.2014.2303785 %U http://dx.doi.org/10.1109/TIE.2014.2303785 %P 6454-6462 %0 Conference Proceedings %T Identification of a nonlinear PMSM model using symbolic regression and its application to current optimization scenarios %A Bramerdorfer, Gerd %A Amrhein, Wolfgang %A Winkler, Stephan M. %A Affenzeller, Michael %S 40th Annual Conference of the IEEE Industrial Electronics Society, IECON 2014 %D 2014 %8 oct %F Bramerdorfer:2014:IECON %X This article presents the nonlinear modelling of the torque of brushless PMSMs by using symbolic regression. It is still popular to characterise the operational behaviour of electrical machines by employing linear models. However, nowadays most PMSMs are highly used and thus a linear motor model does not give an adequate accuracy for subsequently derived analyses, e.g., for the calculation of the maximum torque per ampere (MTPA) trajectory. This article focuses on modelling PMSMs by nonlinear white-box models derived by symbolic regression methods. An optimised algebraic equation for modelling the machine behaviour is derived using genetic programming. By using a Fourier series representation of the motor torque a simple to handle model with high accuracy can be derived. A case study is provided for a given motor design and the motor model obtained is used for deriving the MTPA-trajectory for sinusoidal phase currents. The model is further applied for determining optimised phase current waveforms ensuring zero torque ripple. %K genetic algorithms, genetic programming %R 10.1109/IECON.2014.7048566 %U http://dx.doi.org/10.1109/IECON.2014.7048566 %P 628-633 %0 Conference Proceedings %T Nonlinear system identification by GPA-ES %A Brandejsky, Thomas %S 13th International Carpathian Control Conference (ICCC 2012) %D 2012 %8 may %F Brandejsky:2012:ICCC %X The paper discusses application of Genetic Programming Algorithm - Evolutionary Strategy (GPA-ES) algorithm to symbolic regression of chaotic systems. In the paper, Van der Pol oscillator and especially Rabinovich-Fabrikant equations are analysed and regressed. On the base of these experiments, novel improvements of GPA-ES algorithm are suggested. %K genetic algorithms, genetic programming, GPA-ES algorithm, Rabinovich-Fabrikant equations, Van der Pol oscillator, chaotic systems, genetic programming algorithm-evolutionary strategy algorithm, nonlinear system identification, regression analysis, symbolic regression, chaos, identification, nonlinear systems, oscillators, regression analysis %R 10.1109/CarpathianCC.2012.6228616 %U http://dx.doi.org/10.1109/CarpathianCC.2012.6228616 %P 58-62 %0 Journal Article %T Specific modification of a GPA-ES evolutionary system suitable for deterministic chaos regression %A Brandejsky, Tomas %J Computer & Mathematics with Applications %D 2013 %V 66 %N 2 %@ 0898-1221 %F Brandejsky:2013:CMA %O Nostradamus 2012 %X The paper deals with symbolic regression of deterministic chaos systems using a GPA-ES system. A Lorenz attractor, Roessler attractor, Rabinovich-Fabrikant equations and a van der Pol oscillator are used as examples of deterministic chaos systems to demonstrate significant differences in the efficiency of the symbolic regression of systems described by equations of similar complexity. Within the paper, the source of this behaviour is identified in presence of structures which are hard to be discovered during the evolutionary process due to the low probability of their occurrence in the initial population and by the low chance to produce them by standard evolutionary operators given by small probability to form them in a single step and low fitness function magnitudes of inter-steps when GPA tries to form them in more steps. This low magnitude of fitness function for particular solutions tends to eliminate them, thus increasing the number of needed evolutionary steps. As the solution of identified problems, modification of terminals and related crossover and mutation operators are suggested. %K genetic algorithms, genetic programming, Evolutionary strategy, Optimisation, Symbolic regression, Deterministic chaos %9 journal article %R 10.1016/j.camwa.2013.01.011 %U http://www.sciencedirect.com/science/article/pii/S089812211300028X %U http://dx.doi.org/10.1016/j.camwa.2013.01.011 %P 106-112 %0 Book Section %T The Use of Local Models Optimized by Genetic Programming Algorithms in Biomedical-Signal Analysis %A Brandejsky, Tomas %E Zelinka, Ivan %E Snasel, Vaclav %E Abraham, Ajith %B Handbook of Optimization %S Intelligent Systems Reference Library %D 2013 %V 38 %I Springer %F Brandejsky:2013:HBO %X Today researchers need to solve vague defined problems working with huge data sets describing signals close to chaotic ones. Common feature of such signals is missing algebraic model explaining their nature. Genetic Algorithms and Evolutionary Strategies are suitable to optimise such models and Genetic Programming Algorithms to develop them. Hierarchical GPA-ES algorithm presented herein is used to build compact models of difficult signals including signals representing deterministic chaos. Efficiency of GPA-ES is presented in the paper. Specific group of non-linearly composed functions similar to real biomedical signals is studied in the paper. On the base of these prerequisites, models applicable in complex biomedical signals like EEG modelling are formed and studied within the contribution. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-30504-7_28 %U http://dx.doi.org/10.1007/978-3-642-30504-7_28 %P 697-716 %0 Conference Proceedings %T Influence of Two Different Crossover Operators Use Onto GPA Efficiency %A Brandejsky, Tomas %S 2018 International Conference on Control, Artificial Intelligence, Robotics Optimization (ICCAIRO) %D 2018 %8 may %F Brandejsky:2018:ICCAIRO %X Increasing capabilities of today computers, especially size of memory and computational power open new application areas to Genetic Programming Algorithms [1]. Unfortunately, efficiency of these algorithms is not big and decreases with solved problem complexity. Thus, its increase is extremely important for opening of new application domains. There exists three main areas that should potentially influence GPA efficiency. They are algorithms, pseudo-random number generator behaviours and evolutionary operators. Genetic programming algorithms use two basic evolutionary operators - mutation and crossover in the sense of Darwinian evolution. Non-looking to the fact, that it is possible to define additional operators like e.g. application defined operators [2], there are many different implementations of both basic evolutionary operators [3] and each of them is sometimes useful in artificial evolutionary process. Thus, the main question solved in this paper is that it might bring some advance to use two randomly executed different crossover operators in GPA. The study is focused to symbolic regression problem and as GPA is used GPA-ES, because it is capable to eliminate influence of solution parameters (constants) identification and thus to produce more clear results. %K genetic algorithms, genetic programming %R 10.1109/ICCAIRO.2018.00029 %U http://dx.doi.org/10.1109/ICCAIRO.2018.00029 %P 127-132 %0 Conference Proceedings %T Floating Data Window Movement Influence to Genetic Programming Algorithm Efficiency %A Brandejsky, Tomas %S Computational Statistics and Mathematical Modeling Methods in Intelligent Systems %D 2019 %I Springer %F brandejsky:2019:CSMMMIS %K genetic algorithms, genetic programming %R 10.1007/978-3-030-31362-3_4 %U http://link.springer.com/chapter/10.1007/978-3-030-31362-3_4 %U http://dx.doi.org/10.1007/978-3-030-31362-3_4 %0 Conference Proceedings %T Versatile Function GPA %A Brandejsky, Tomas %A Merta, Jan %S 2024 IEEE 17th International Scientific Conference on Informatics (Informatics) %D 2024 %8 nov %F Brandejsky:2024:Informatics %X continuous versatile function Genetic Programming Algorithm (GPA) developed with respect to BigData processing. The basic structure of this hierarchical evolutionary algorithm and examples of versatile functions are presented.On the basis of experiments with the hybrid evolutionary algorithm (hybrid EA) providing symbolic regression of precomputed Lorenz attractor system data representing hybrid EA’s behaviour, a discussion of examples of an obtained solution is presented. The versatile function concept GPA is applicable, but it requires the hybrid evolutionary algorithm application, as is demonstrated in the paper. %K genetic algorithms, genetic programming, Graphics processing units, Evolutionary computation, Big Data applications, Data models, Nonlinear dynamical systems, Informatics, Tuning, Optimisation, hybrid evolutionary algorithm, genetic programming algorithm, versatile function GPA, BigData, symbolic regression %R 10.1109/Informatics62280.2024.10900916 %U http://dx.doi.org/10.1109/Informatics62280.2024.10900916 %P 448-452 %0 Conference Proceedings %T Reactive control of Ms. Pac Man using Information Retrieval based on Genetic Programming %A Brandstetter, Matthias F. %A Ahmadi, Samad %S Computational Intelligence and Games (CIG), 2012 IEEE Conference on %D 2012 %F Brandstetter:2012:CIG %X During the last years the well-known Ms. Pac Man video game has been - and still is - an interesting test bed for the research on various concepts from the broad area of Artificial Intelligence (AI). Among these concepts is the use of Genetic Programming (GP) to control the game from a human player’s perspective. Several GP-based approaches have been examined so far, where traditionally they define two types of GP terminals: one type for information retrieval, the second type for issuing actions (commands) to the game world. However, by using these action terminals the controller has to manage actions issued to the game during their runtime and to monitor their outcome. In order to avoid the need for active task management this paper presents a novel approach for the design of a GP-based Ms. Pac Man controller: the proposed approach solely relies on information retrieval terminals in order to rate all possible directions of movement at every time step during a running game. Based on these rating values the controller can move the agent through the mazes of the the game world of Ms. Pac Man. With this design, which forms the main contribution of our work, we decrease the overall GP solution complexity by removing all action control management tasks from the system. It is demonstrated that by following the proposed approach such a system can successfully control an autonomous agent in a computer game environment on the level of an amateur human player. %K genetic algorithms, genetic programming, computer games, information retrieval, AI, GP solution complexity, GP-based approaches, Ms. Pac Man video game, action control management task removal, action terminals, artificial intelligence, autonomous agent control, game mazes, human player perspective, information retrieval, rating values, reactive control, task management, Abstracts, Computational intelligence, Games, Humans, Information retrieval, Sociology, Statistics %R 10.1109/CIG.2012.6374163 %U http://dx.doi.org/10.1109/CIG.2012.6374163 %P 250-256 %0 Conference Proceedings %T Reducing Genetic Drift in Steady State Evolutionary Algorithms %A Branke, Jurgen %A Cutaia, Massimo %A Dold, Heinrich %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 branke:1999:RGDSSEA %K genetic algorithms and classifier systems %P 68-74 %0 Conference Proceedings %T Evolving En-Route Caching Strategies for the Internet %A Branke, Juergen %A Funes, Pablo %A Thiele, Frederik %Y Deb, Kalyanmoy %Y Poli, Riccardo %Y Banzhaf, Wolfgang %Y Beyer, Hans-Georg %Y Burke, Edmund %Y Darwen, Paul %Y Dasgupta, Dipankar %Y Floreano, Dario %Y Foster, James %Y Harman, Mark %Y Holland, Owen %Y Lanzi, Pier Luca %Y Spector, Lee %Y Tettamanzi, Andrea %Y Thierens, Dirk %Y Tyrrell, Andy %S Genetic and Evolutionary Computation – GECCO-2004, Part II %S Lecture Notes in Computer Science %D 2004 %8 26 30 jun %V 3103 %I Springer-Verlag %C Seattle, WA, USA %@ 3-540-22343-6 %F Branke:EEC:gecco2004 %X Nowadays, large distributed databases are commonplace. Client applications increasingly rely on accessing objects from multiple remote hosts. The Internet itself is a huge network of computers, sending documents point-to-point by routing packetized data over multiple intermediate relays. As hubs in the network become overladen, slowdowns and timeouts can disrupt the process. It is thus worth to think about ways to minimise these effects. Caching, i.e. storing replicas of previously-seen objects for later reuse, has the potential for generating large bandwidth savings and in turn a significant decrease in response time. En-route caching is the concept that all nodes in a network are equipped with a cache, and may opt to keep copies of some documents for future reuse [18]. The rules used for such decisions are called caching strategies. Designing such strategies is a challenging task, because the different nodes interact, resulting in a complex, dynamic system. In this paper, we use genetic programming to evolve good caching strategies, both for specific networks and network classes. An important result is a new innovative caching strategy that outperforms current state-of-the-art methods. %K genetic algorithms, genetic programming %R 10.1007/b98645 %U http://dx.doi.org/10.1007/b98645 %P 434-446 %0 Journal Article %T Evolutionary design of en-route caching strategies %A Branke, Jurgen %A Funes, Pablo %A Thiele, Frederik %J Applied Soft Computing %D 2006 %8 jun %V 7 %N 3 %F Branke:2006:ASC %X Nowadays, large distributed databases are commonplace. Client applications increasingly rely on accessing objects from multiple remote hosts. The Internet itself is a huge network of computers, sending documents point-to-point by routing packeted data over multiple intermediate relays. As hubs in the network become over used, slowdowns and timeouts can disrupt the process. It is thus worth to think about ways to minimise these effects. Caching, i.e. storing replicas of previously-seen objects for later reuse, has the potential for generating large bandwidth savings and in turn a significant decrease in response time. En-route caching is the concept that all nodes in a network are equipped with a cache, and may opt to keep copies of some documents for future reuse [X. Tang, S.T. Chanson, Coordinated en-route web caching, IEEE Transact. Comput. 51 6 (2002) 595-607]. The rules used for such decisions are called caching strategies. Designing such strategies is a challenging task, because the different nodes interact, resulting in a complex, dynamic system. In this paper, we use genetic programming to evolve good caching strategies, both for specific networks and network classes. An important result is a new innovative caching strategy that outperforms current state-of-the-art methods. %K genetic algorithms, genetic programming, En-route caching, Robustness %9 journal article %R 10.1016/j.asoc.2006.04.003 %U http://dx.doi.org/10.1016/j.asoc.2006.04.003 %P 890-898 %0 Conference Proceedings %T GECCO ’10: Proceedings of the 12th annual conference on Genetic and evolutionary computation %E Branke, Juergen %E Pelikan, Martin %E Alba, Enrique %E Arnold, Dirk V. %E Bongard, Josh %E Brabazon, Anthony %E Butz, Martin V. %E Clune, Jeff %E Cohen, Myra %E Deb, Kalyanmoy %E Engelbrecht, Andries P. %E Krasnogor, Natalio %E Miller, Julian F. %E O’Neill, Michael %E Sastry, Kumara %E Thierens, Dirk %E van Hemert, Jano %E Vanneschi, Leonardo %E Witt, Carsten %D 2010 %8 jul 07 11 %I ACM %C Portland, OR, USA %F Branke:2010:GECCO %X These proceedings contain the papers presented at the 12th Annual Genetic and Evolutionary Computation Conference (GECCO-2010), held in Portland, USA, July 7-11, 2010. This year, we received 373 submissions, of which 168 were accepted as full eight-page publication with 25 minute presentation during the conference. This corresponds to an acceptance rate of 45percent. In addition, 110 submissions (29percent) have been accepted for poster presentation with two-page abstracts in the proceedings. GECCO works according to the motto one conference, many mini-conferences. This year, there were 15 separate tracks that operated independently from each other. Each track had its own track chair(s) and individual program committee. To ensure an unbiased reviewing process, all reviews were conducted double blind; no authors’ names were revealed to the reviewers. About 560 researchers participated in the reviewing process. We want to thank them for all their work, which is highly appreciated and absolutely vital to ensure the high quality of the conference. In addition to the presentation of the papers contained in these proceedings, GECCO-2010 also included free tutorials, workshops, a series of sessions on Evolutionary Computation in Practice, various competitions, and late-breaking papers. %K genetic algorithms, genetic programming, Ant Colony Optimisation and Swarm Intelligence, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, Evolvable Hardware, Bioinformatics and Computational Biology, Combinatorial Optimisation and Metaheuristics, Estimation of Distribution Algorithms, Evolution Strategies and Evolutionary Programming, Evolutionary Multiobjective, Optimisation, Generative and Developmental Systems, Genetics-Based Machine Learning, Parallel Evolutionary Systems, Real World Application, Search Based Software Engineering, Theory %U http://portal.acm.org/citation.cfm?id=1830483&coll=DL&dl=ACM&CFID=12039329&CFTOKEN=58660565 %0 Journal Article %T Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations %A Branke, Juergen %A Hildebrandt, Torsten %A Scholz-Reiter, Bernd %J Evolutionary Computation %D 2015 %8 Summer %V 23 %N 2 %@ 1063-6560 %F Branke:2015:EC %X Dispatching rules are frequently used for real-time, on-line scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighbourhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on Artificial Neural Networks, and a tree representation. Using appropriate Evolutionary Algorithms (CMA-ES for the Neural Network and linear representations, Genetic Programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualise what the rules do in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of Genetic Programming gives the best results if many candidate rules can be evaluated, closely followed by the Neural Network representation that leads to good results already for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets. %K genetic algorithms, genetic programming, Job Shop Scheduling, Dispatching Rule, Representation, CMA-ES, Artificial Neural Network %9 journal article %R 10.1162/EVCO_a_00131 %U http://dx.doi.org/10.1162/EVCO_a_00131 %P 249-277 %0 Journal Article %T Automated Design of Production Scheduling Heuristics: A Review %A Branke, Juergen %A Nguyen, Su %A Pickardt, Christoph %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2016 %8 feb %V 20 %N 1 %@ 1089-778X %F Branke:2015:ieeeTEC %X Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyperheuristics have been developed and shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasised in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This review strives to fill this gap by summarising the state of the art, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work. %K genetic algorithms, genetic programming, Evolutionary design, hyper-heuristic, scheduling. %9 journal article %R 10.1109/TEVC.2015.2429314 %U http://wrap.warwick.ac.uk/88212/1/WRAP-automated-design-production-scheduling-heuristics-Branke-2015.pdf %U http://dx.doi.org/10.1109/TEVC.2015.2429314 %P 110-124 %0 Conference Proceedings %T Evolving control rules for a dual-constrained job scheduling scenario %A Branke, Juergen %A Groves, Matthew J. %A Hildebrandt, Torsten %S 2016 Winter Simulation Conference (WSC) %D 2016 %8 dec %F Branke:2016:WSC %X Dispatching rules are often used for scheduling in semiconductor manufacturing due to the complexity and stochasticity of the problem. In the past, simulation-based Genetic Programming has been shown to be a powerful tool to automate the time-consuming and expensive process of designing such rules. However, the scheduling problems considered were usually only constrained by the capacity of the machines. In this paper, we extend this idea to dual-constrained flow shop scheduling, with machines and operators for loading and unloading to be scheduled simultaneously. We show empirically on a small test problem with parallel workstations, re-entrant flows and dynamic stochastic job arrival that the approach is able to generate dispatching rules that perform significantly better than benchmark rules from the literature. %K genetic algorithms, genetic programming %R 10.1109/WSC.2016.7822295 %U http://dx.doi.org/10.1109/WSC.2016.7822295 %P 2568-2579 %0 Conference Proceedings %T Simulation optimisation: tutorial %A Branke, Juergen %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 Branke:2019:GECCOcomp %O Tutorial %K genetic algorithms, genetic programming %R 10.1145/3319619.3323385 %U http://dx.doi.org/10.1145/3319619.3323385 %P 862-889 %0 Conference Proceedings %T SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networks %A Branquinho, Henrique %A Lourenco, Nuno %A Costa, Ernesto %Y Tarantino, Ernesto %Y Galvan, Edgar %Y De Falco, Ivanoe %Y Della Cioppa, Antonio %Y Umberto, Scafuri %Y Zhang, Mengjie %S Neuroevolution at work %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F branquinho:2023:NEWK %X Spiking Neural Networks (SNNs) have attracted recent interest due to their energy efficiency and biological plausibility. However, the performance of SNNs still lags behind traditional Artificial Neural Networks (ANNs), as there is no consensus on the best learning algorithm for SNNs. Best-performing SNNs are based on ANN to SNN conversion or learning with spike-based backpropagation through surrogate gradients. The focus of recent research has been on developing and testing different learning strategies, with hand-tailored architectures and parameter tuning. Neuroevolution (NE), has proven successful as a way to automatically design ANNs and tune parameters, but its applications to SNNs are still at an early stage. DENSER is a NE framework for the automatic design and parametrization of ANNs, based on the principles of Genetic Algorithms (GA) and Structured Grammatical Evolution (SGE). In this paper, we propose SPENSER, a NE framework for SNN generation based on DENSER, for image classification on the MNIST and Fashion-MNIST datasets. SPENSER generates competitive performing networks with a test accuracy of 99.42% and 91.65% respectively. %K genetic algorithms, genetic programming, grammatical evolution, neuroevolution, computer vision, spiking neural networks, DENSER %R 10.1145/3583133.3596399 %U http://dx.doi.org/10.1145/3583133.3596399 %P 2115-2122 %0 Conference Proceedings %T A Fuzzy Entropy Algorithm For Data Extrapolation In Multi-Compressor System %A Brar, Gursewak S. %A Brar, Yadwinder S. %A Singh, Yaduvir %S Proceedings of the World Congress on Engineering, WCE 2007 %D 2007 %8 jul 2 4 %V I %C London %G en %F Brar:2007:WCE %X In this paper incomplete quantitative data has been dealt by using the concept of fuzzy entropy. Fuzzy entropy has been used to extrapolate the data pertaining to the compressor current. Certain attributes related to the compressor current have been considered. Test data of compressor current used in this knowledge discovery algorithm knows the entire attribute clearly. The developed algorithm is very effective and can be used in the various application related to knowledge discovery and machine learning. The developed knowledge discovery algorithm using fuzzy entropy has been tested on a multi-compressor system for incomplete compressor current data and it is found that the error level is merely 4.40percent, which is far better than other available knowledge discovery algorithms %K genetic algorithms, genetic programming, fuzzy entropy, incomplete data, classification, knowledge discovery, multi-compressor system %U http://www.iaeng.org/publication/WCE2007/WCE2007_pp105-110.pdf %P 105-110 %0 Journal Article %T Multi-gene genetic programming to building up fuzzy rule-base in Neo-Fuzzy-Neuron networks %A Bras, Glender %A Marques Silva, Alisson %A Wanner, Elizabeth Fialho %J J. Intell. Fuzzy Syst. %D 2021 %V 41 %N 1 %F DBLP:journals/jifs/BrasSW21 %K genetic algorithms, genetic programming %9 journal article %R 10.3233/JIFS-202146 %U https://doi.org/10.3233/JIFS-202146 %U http://dx.doi.org/10.3233/JIFS-202146 %P 499-516 %0 Journal Article %T A genetic algorithm for rule extraction in fuzzy adaptive learning control networks %A Bras, Glender %A Marques Silva, Alisson %A Wanner, Elizabeth F. %J Genetic Programming and Evolvable Machines %D 2024 %V 25 %@ 1389-2576 %F Bras:2024:GPEM %O Online first %X Falcon-GA, for rule extraction in a Fuzzy Adaptive Learning Control Network (FALCON) using a Genetic Algorithm (GA). The FALCON-GA combines multiple techniques to establish the relationships and connections among fuzzy rules, including the use of a GA for rule extraction and a Gradient-based method for fine-tuning the membership function parameters. The learning algorithm of FALCON-GA incorporates three key components: the ART (Adaptive Resonance Theory) clustering algorithm for initial membership function identification, the Genetic Algorithm for rule extraction, and the Gradient method for adjusting membership function parameters. Moreover, FALCON-GA offers flexibility by allowing the incorporation of different rule types within the FALCON architecture, making it flexible and expansible. The proposed model has been evaluated in various forecasting problems reported in the literature and compared to alternative models. Computational experiments demonstrate the effectiveness of FALCON-GA in forecasting tasks and reveal significant performance improvements compared to the original FALCON. These results indicate that Genetic Algorithms efficiently extract rules for Fuzzy Adaptive Learning Control Networks. %K genetic algorithms, Fuzzy systems, Rule extraction, Forecasting %9 journal article %R 10.1007/s10710-024-09486-2 %U http://dx.doi.org/10.1007/s10710-024-09486-2 %P Articleno11 %0 Journal Article %T A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems %A Braune, Roland %A Benda, Frank %A Doerner, Karl F. %A Hartl, Richard F. %J International Journal of Production Economics %D 2022 %V 243 %@ 0925-5273 %F BRAUNE:2022:IJPE %X This paper deals with a Genetic Programming (GP) approach for solving flexible shop scheduling problems. The adopted approach aims to generate priority rules in the form of an expression tree for dispatching jobs. Therefore, in a list-scheduling algorithm, the available jobs can be ranked using the tree-based priority rules generated using GP. These priority rules were tested on benchmark problem settings, such as those of Brandimarte and Lawrence, in a static and dynamic environment. The GP approaches were then applied to a special case based on the problem setting of an industrial partner. The goal of these approaches was to minimize the maximum completion time of all jobs, which is known as the makespan. To reach this goal, we considered job assignment and machine sequencing decisions simultaneously in a single-tree representation and compared this single tree with a multi-tree approach, where the terminal sets (job- and machine-related) were strictly separated. This resulted in two parallel GP populations; they were used to first decide which job to choose and then which machine it should be assigned to. Furthermore, for both tree approaches, we implemented an iterative variant that stores recorded information of past schedules to achieve further improvements. Computational experiments revealed a consistent advantage compared to the existing advanced priority rules from the literature with considerably increased performance under the presence of unrelated parallel machines and larger instances in general %K genetic algorithms, genetic programming, Flexible shop scheduling, Machine learning, Iterative dispatching rule, Multi-tree representation %9 journal article %R 10.1016/j.ijpe.2021.108342 %U https://www.sciencedirect.com/science/article/pii/S0925527321003182 %U http://dx.doi.org/10.1016/j.ijpe.2021.108342 %P 108342 %0 Book Section %T Evolution of Planning: Using recursive techniques in Genetic Planning %A Brave, Scott %E Koza, John R. %B Artificial Life at Stanford 1994 %D 1994 %8 jun %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-182105-2 %F brave:1994:recursive %K genetic algorithms, genetic programming %P 1-10 %0 Conference Proceedings %T Using Genetic Programming to Evolve Recursive Programs for Tree Search %A Brave, Scott %Y Louis, Sushil J. %S Fourth Golden West Conference on Intelligent Systems %D 1995 %8 December 14 jun %I International Society for Computers and their Applications - ISCA %@ 1-880843-12-9 %F brave:1994:recursiveGW %K genetic algorithms, genetic programming %P 60-65 %0 Conference Proceedings %T Using Genetic Programming to Evolve Mental Models %A Brave, Scott %Y Louis, Sushil J. %S Fourth Golden West Conference on Intelligent Systems %D 1995 %8 December 14 jun %I International Society for Computers and their Applications - ISCA %@ 1-880843-12-9 %F brave:1994:mmGW %K genetic algorithms, genetic programming, memory %P 91-96 %0 Book Section %T Evolving Recursive Programs for Tree Search %A Brave, Scott %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 brave:1996:aigp2 %X This article compares basic genetic programming, genetic programming with automatically defined functions (ADFs), and genetic programming with ADFs using a restricted form of recursion on a planning problem involving tree search. The results show that evolution of a recursive program is possible and further that, of the three techniques explored, genetic programming with recursive ADFs performs the best for the tree search problem. Additionally, genetic programming using ADFs (recursive and non-recursive) outperforms genetic programming without ADFs. The scalability of these techniques is also investigated. The computational effort required to reach a solution using ADFs with recursion is shown to remain essentially constant with world size, while genetic programming with non-recursive ADFs scales linearly at best, and basic genetic programming scales exponentially. Finally, many solutions were found using genetic programming with recursive ADFs which generalised to any world size. %K genetic algorithms, genetic programming %R 10.7551/mitpress/1109.003.0015 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.3005 %U http://dx.doi.org/10.7551/mitpress/1109.003.0015 %P 203-220 %0 Conference Proceedings %T Evolving Deterministic Finite Automata Using Cellular Encoding %A Brave, Scott %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 brave:1996:dface %X his paper presents a method for the evolution of deterministic finite automata that combines genetic programming and cellular encoding. Programs are evolved that specify actions for the incremental growth of a deterministic finite automata from an initial single-state zygote. The results show that, given a test bed of positive and negative samples, the proposed method is successful at inducing automata to recognise several different languages. 1. Introduction The automatic creation of finite... %K genetic algorithms, genetic programming %U http://citeseer.ist.psu.edu/cache/papers/cs/1745/http:zSzzSzbrave.www.media.mit.eduzSzpeoplezSzbravezSzpublicationszSzautomata.pdf/brave96evolving.pdf %P 39-44 %0 Conference Proceedings %T The Evolution of Memory and Mental Models Using Genetic Programming %A Brave, Scott %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 brave:1996:emmmGP %X his paper applies genetic programming to the evolution of intelligent agents that gradually build internal representations of their surroundings for later use in planning. The method used allows for the creation of dynamically determined representations that are not pre-designed by the human creator of the system. In an illustrative path-planning problem, evolved programs learn a model of their world and use this internal representation to plan their successive actions. The results show that... %K genetic algorithms, genetic programming, memory %U http://citeseer.ist.psu.edu/cache/papers/cs/1745/http:zSzzSzbrave.www.media.mit.eduzSzpeoplezSzbravezSzpublicationszSzmodels.pdf/brave96evolution.pdf %P 261-266 %0 Conference Proceedings %T Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference %E Brave, Scott %E Wu, Annie S. %D 1999 %8 13 jul %C Orlando, Florida, USA %F brave:1999:gecco99lb %K genetic algorithms, genetic programming, Evolutionary Programming, fuzzy rules %0 Conference Proceedings %T Evolving Game-Specific UCB Alternatives for General Video Game Playing %A Bravi, Ivan %A Khalifa, Ahmed %A Holmgard, Christoffer %A Togelius, Julian %Y Squillero, Giovanni %S 20th European Conference on the Applications of Evolutionary Computation %S LNCS %D 2017 %8 19 21 apr %V 10199 %I Springer %C Amsterdam %F Bravi:2017:evoApplications %X At the core of the most popular version of the Monte Carlo Tree Search (MCTS) algorithm is the UCB1 (Upper Confidence Bound) equation. This equation decides which node to explore next, and therefore shapes the behaviour of the search process. If the UCB1 equation is replaced with another equation, the behavior of the MCTS algorithm changes, which might increase its performance on certain problems (and decrease it on others). In this paper, we use genetic programming to evolve replacements to the UCB1 equation targeted at playing individual games in the General Video Game AI (GVGAI) Framework. Each equation is evolved to maximize playing strength in a single game, but is then also tested on all other games in our test set. For every game included in the experiments, we found a UCB replacement that performs significantly better than standard UCB1. Additionally, evolved UCB replacements also tend to improve performance in some GVGAI games for which they are not evolved, showing that improvements generalize across games to clusters of games with similar game mechanics or algorithmic performance. Such an evolved portfolio of UCB variations could be useful for a hyper-heuristic game-playing agent, allowing it to select the most appropriate heuristics for particular games or problems in general. %K genetic algorithms, genetic programming, General AI, MTCS, Monte-Carlo Tree Search %R 10.1007/978-3-319-55849-3_26 %U http://dx.doi.org/10.1007/978-3-319-55849-3_26 %P 393-406 %0 Conference Proceedings %T Implicit Fitness Sharing Speciation and Emergent Diversity in Tree Classifier Ensembles %A Brazier, Karl J. %A Richards, Graeme %A Wang, Wenjia %Y Yang, Zheng Rong %Y Everson, Richard M. %Y Yin, Hujun %S Intelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, Proceedings %S Lecture Notes in Computer Science %D 2004 %8 aug 25 27 %V 3177 %I Springer %C Exeter, UK %@ 3-540-22881-0 %F DBLP:conf/ideal/BrazierRW04 %X Implicit fitness sharing is an approach to the stimulation of speciation in evolutionary computation for problems where the fitness of an individual is determined as its success rate over a number trials against a collection of succeed/fail tests. By fixing the reward available for each test, individuals succeeding in a particular test are caused to depress the size of one another’s fitness gain and hence implicitly co-operate with those succeeding in other tests. An important class of problems of this form is that of attribute-value learning of classifiers. Here, it is recognised that the combination of diverse classifiers has the potential to enhance performance in comparison with the use of the best obtainable individual classifiers. However, proposed prescriptive measures of the diversity required have inherent limitations from which we would expect the diversity emergent from the self-organisation of speciating evolutionary simulation to be free. The approach was tested on a number of the popularly used real-world data sets and produced encouraging results in terms of accuracy and stability. %K genetic algorithms, genetic programming, gene expression programming %R 10.1007/b99975 %U http://dx.doi.org/10.1007/b99975 %P 333-338 %0 Conference Proceedings %T On-Line, On-Board Evolution of Robot Controllers %A Bredeche, N. %A Haasdijk, E. %A Eiben, A. E. %Y Collet, Pierre %Y Monmarche, Nicolas %Y Legrand, Pierrick %Y Schoenauer, Marc %Y Lutton, Evelyne %S 9th International Conference, Evolution Artificielle, EA 2009 %S Lecture Notes in Computer Science %D 2009 %8 oct 26 28 %V 5975 %I Springer %C Strasbourg, France %F Bredeche:2009:EA %O Revised Selected Papers %X This paper reports on a feasibility study into the evolution of robot controllers during the actual operation of robots (on-line), using only the computational resources within the robots themselves (on-board). We identify the main challenges that these restrictions imply and propose mechanisms to handle them. The resulting algorithm is evaluated in a hybrid system, using the actual robots’ processors interfaced with a simulator that represents the environment. The results show that the proposed algorithm is indeed feasible and the particular problems we encountered during this study give hints for further research. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-14156-0_10 %U http://www.cs.vu.nl/~gusz/papers/2009-bredeche09ea_final2-LNCS.pdf %U http://dx.doi.org/10.1007/978-3-642-14156-0_10 %P 110-121 %0 Conference Proceedings %T Using an optimization toolkit for Java to evolve market strategies for European seeds %A Breeden, Joseph L. %A Allen, Todd W. %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 breeden:1999:UJE %K Genetic Algorithms %P 57-64 %0 Conference Proceedings %T Evolving Art using Aesthetic Analogies - Evolutionary Supervised Learning to Generate Art with Grammatical Evolution %A Breen, Aidan %A O’Riordan, Colm %Y Guervos, Juan Julian Merelo %Y Melicio, Fernando %Y Cadenas, Jose Manuel %Y Dourado, Antonio %Y Madani, Kurosh %Y Ruano, Antonio E. %Y Filipe, Joaquim %S Proceedings of the 8th International Joint Conference on Computational Intelligence, IJCCI 2016 %D 2016 %8 nov 9 11 %I SciTePress %C Porto, Portugal %F conf/ijcci/BreenO16 %K genetic algorithms, genetic programming %R 10.5220/0006048400590068 %U http://dx.doi.org/10.5220/0006048400590068 %P 59-68 %0 Conference Proceedings %T An Evolutionary Approach for Performing Structural Unit-Testing on Third-Party Object-Oriented Java Software %A Ribeiro, Jose Carlos %A Zenha-Rela, Mario %A Fernandez de Vega, Francisco %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 RibeiroZV07a %X Evolutionary Testing is an emerging methodology for automatically generating high quality test data. The focus of this paper is on presenting an approach for generating test cases for the unit-testing of object-oriented programs, with basis on the information provided by the structural analysis and interpretation of Java bytecode and on the dynamic execution of the instrumented test object. The rationale for working at the bytecode level is that even when the source code is unavailable, insight can still be obtained and used to guide the search-based test case generation process. Test cases are represented using the Strongly Typed Genetic Programming paradigm, which effectively mimics the polymorphic relationships, inheritance dependences and method argument constraints of object-oriented programs. %K genetic algorithms, genetic programming, SBSE %R 10.1007/978-3-540-78987-1_34 %U http://jcbribeiro.googlepages.com/NICSO2007-053.pdf %U http://dx.doi.org/10.1007/978-3-540-78987-1_34 %P 379-388 %0 Conference Proceedings %T eCrash: a framework for performing evolutionary testing on third-party Java components %A Bregieiro Ribeiro, Jose Carlos %A Zenha-Rela, Mario Alberto %A Fernandez de Vega, Francisco %Y Alba, Enrique %Y Herrera, Francisco %S I Jornadas sobre Algoritmos Evolutivos y Metaheuristicas (JAEM 2007) %D 2007 %8 November 14 sep %C Zaragoza, Spain %F Bregieiro-Ribeiro:2008:JAEM %X The focus of this paper is on presenting a tool for generating test data by employing evolutionary search techniques, with basis on the information provided by the structural analysis and interpretation of the Java bytecode of third-party Java components, and on the dynamic execution of the instrumented test object. The main objective of this approach is that of evolving a set of test cases that yields full structural code coverage of the test object. Such a test set can be used for effectively performing the testing activity, providing confidence in the quality and robustness of the test object. The rationale of working at the bytecode level is that even when the source code is unavailable structural testing requirements can still be derived, and used to assess the quality of a test set and to guide the evolutionary search towards reaching specific test goals. %K genetic algorithms, genetic programming, SBSE, STGP %U http://jcbribeiro.googlepages.com/jribeiro_jaem07.pdf %P 137-144 %0 Conference Proceedings %T A strategy for evaluating feasible and unfeasible test cases for the evolutionary testing of object-oriented software %A Bregieiro Ribeiro, Jose Carlos %A Zenha-Rela, Mario Alberto %A Fernandez de Vega, Francisco %S AST ’08: Proceedings of the 3rd international workshop on Automation of software test %D 2008 %I ACM %C Leipzig, Germany %F Bregieiro-Ribeiro:2008:AST %X Evolutionary Testing is an emerging methodology for automatically producing high quality test data. The focus of our on-going work is precisely on generating test data for the structural unit-testing of object-oriented Java programs. The primary objective is that of efficiently guiding the search process towards the definition of a test set that achieves full structural coverage of the test object. However, the state problem of object-oriented programs requires specifying carefully ne-tuned methodologies that promote the traversal of problematic structures and difficult controlflow paths - which often involves the generation of complex and intricate test cases, that dene elaborate state scenarios. This paper proposes a methodology for evaluating the quality of both feasible and unfeasible test cases - i.e., those that are effectively completed and terminate with a call to the method under test, and those that abort prematurely because a runtime exception is thrown during test case execution. With our approach, unfeasible test cases are considered at certain stages of the evolutionary search, promoting diversity and enhancing the possibility of achieving full coverage. %K genetic algorithms, genetic programming, SBSE, Search-Based Test Case Generation, Evolutionary Testing, Object-Orientation, Strongly-Typed Genetic Programming, Software Engineering, Testing and Debugging| Testing tools, Verification %R 10.1145/1370042.1370061 %U http://jcbribeiro.googlepages.com/ast12-ribeiro.pdf %U http://dx.doi.org/10.1145/1370042.1370061 %P 85-92 %0 Conference Proceedings %T Strongly-typed genetic programming and purity analysis: input domain reduction for evolutionary testing problems %A Bregieiro Ribeiro, Jose Carlos %A Zenha-Rela, Mario Alberto %A Fernandez de Vega, 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 Bregieiro-Ribeiro:2008:gecco %X Search-based test case generation for object-oriented software is hindered by the size of the search space, which encompasses the arguments to the implicit and explicit parameters of the test object’s public methods. The performance of this type of search problems can be enhanced by the definition of adequate Input Domain Reduction strategies. The focus of our on-going work is on employing evolutionary algorithms for generating test data for the structural unit-testing of Java programs. Test cases are represented and evolved using the Strongly-Typed Genetic Programming paradigm; Purity Analysis is particularly useful in this situation because it provides a means to automatically identify and remove Function Set entries that do not contribute to the definition of interesting test scenarios. Categories and Subject Descriptors %K genetic algorithms, genetic programming, Input domain reduction, search-based test case generation, strongly-Typed genetic programming, Search-based software engineering: Poster, Testing, Debugging, Testing tools, data generators, coverage testing, stack, bitset, STGP, EMCDG, IDR %R 10.1145/1389095.1389439 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1783.pdf %U http://dx.doi.org/10.1145/1389095.1389439 %P 1783-1784 %0 Conference Proceedings %T Search-based test case generation for object-oriented java software using strongly-typed genetic programming %A Bregieiro Ribeiro, Jose Carlos %Y Ebner, Marc %Y Cattolico, Mike %Y van Hemert, Jano %Y Gustafson, Steven %Y Merkle, Laurence D. %Y Moore, Frank W. %Y Congdon, Clare Bates %Y Clack, Christopher D. %Y Rand, William %Y Ficici, Sevan G. %Y Riolo, Rick %Y Bacardit, Jaume %Y Bernado-Mansilla, Ester %Y Butz, Martin V. %Y Smith, Stephen L. %Y Cagnoni, Stefano %Y Hauschild, Mark %Y Pelikan, Martin %Y Sastry, Kumara %S GECCO-2008 Graduate Student Workshops %D 2008 %8 December 16 jul %I ACM %C Atlanta, GA, USA %F Bregieiro-Ribeiro:2008:geccocomp %K genetic algorithms, genetic programming, dynect-orientation, evolutionary testing, search-based test case generation, strongly-Typed genetic programming %R 10.1145/1388969.1388979 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1819.pdf %U http://dx.doi.org/10.1145/1388969.1388979 %P 1819-1822 %0 Journal Article %T Test Case Evaluation and Input Domain Reduction Strategies for the Evolutionary Testing of Object-Oriented Software %A Bregieiro Ribeiro, Jose Carlos %A Zenha-Rela, Mario Alberto %A Fernandez de Vega, Francisco %J Information and Software Technology %D 2009 %8 nov %V 51 %N 11 %@ 0950-5849 %F BregieiroRibeiro2009 %X In Evolutionary Testing, meta-heuristic search techniques are used for generating test data. The focus of our research is on employing evolutionary algorithms for the structural unit-testing of object-oriented programs. Relevant contributions include the introduction of novel methodologies for automation, search guidance and input domain reduction; the strategies proposed were empirically evaluated with encouraging results.Test cases are evolved using the Strongly-Typed Genetic Programming technique. Test data quality evaluation includes instrumenting the test object, executing it with the generated test cases, and tracing the structures traversed in order to derive coverage metrics. The methodology for efficiently guiding the search process towards achieving full structural coverage involves favouring test cases that exercise problematic structures. Purity Analysis is employed as a systematic strategy for reducing the search space.’ %K genetic algorithms, genetic programming, Evolutionary Testing, Search-Based Software Engineering, Test Case Evaluation, Input Domain Reduction %9 journal article %R 10.1016/j.infsof.2009.06.009 %U http://www.sciencedirect.com/science/article/B6V0B-4WP47MR-2/2/798c73c2b9c5e1e9389b8a3491eac4f2 %U http://dx.doi.org/10.1016/j.infsof.2009.06.009 %P 1534-1548 %0 Conference Proceedings %T An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing %A Bregieiro Ribeiro, Jose Carlos %A Zenha Rela, Mario %A Fernandez de Vega, Francisco %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/RibeiroRV09 %X This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the algorithm’s efficiency considerably, while introducing a negligible computational overhead. %K genetic algorithms, genetic programming, Poster %R 10.1145/1569901.1570253 %U http://dx.doi.org/10.1145/1569901.1570253 %P 1949-1950 %0 Journal Article %T Test Case Evaluation and Input Domain Reduction strategies for the Evolutionary Testing of Object-Oriented software %A Bregieiro Ribeiro, Jose Carlos %A Zenha-Rela, Mario Alberto %A Fernandez de Vega, Francisco %J Information and Software Technology %D 2009 %V 51 %N 11 %@ 0950-5849 %F Ribeiro20091534 %O Third IEEE International Workshop on Automation of Software Test (AST 2008); Eighth International Conference on Quality Software (QSIC 2008) %X In Evolutionary Testing, meta-heuristic search techniques are used for generating test data. The focus of our research is on employing evolutionary algorithms for the structural unit-testing of Object-Oriented programs. Relevant contributions include the introduction of novel methodologies for automation, search guidance and Input Domain Reduction; the strategies proposed were empirically evaluated with encouraging results. Test cases are evolved using the Strongly-Typed Genetic Programming technique. Test data quality evaluation includes instrumenting the test object, executing it with the generated test cases, and tracing the structures traversed in order to derive coverage metrics. The methodology for efficiently guiding the search process towards achieving full structural coverage involves favouring test cases that exercise problematic structures. Purity Analysis is employed as a systematic strategy for reducing the search space. %K genetic algorithms, genetic programming, SBSE, Evolutionary Testing, Search-Based Software Engineering, Test Case Evaluation, Input Domain Reduction %9 journal article %R 10.1016/j.infsof.2009.06.009 %U http://www.sciencedirect.com/science/article/B6V0B-4WP47MR-2/2/798c73c2b9c5e1e9389b8a3491eac4f2 %U http://dx.doi.org/10.1016/j.infsof.2009.06.009 %P 1534-1548 %0 Conference Proceedings %T Enabling Object Reuse on Genetic Programming-based Approaches to Object-Oriented Evolutionary Testing %A Bregieiro Ribeiro, Jose Carlos %A Zenha-Rela, Mario Alberto %A Fernandez de Vega, Francisco %Y Esparcia-Alcazar, Anna Isabel %Y Ekart, Aniko %Y Silva, Sara %Y Dignum, Stephen %Y Uyar, A. Sima %S Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010 %S LNCS %D 2010 %8 July 9 apr %V 6021 %I Springer %C Istanbul %F Ribeiro:2010:EuroGP %X Recent research on search-based test data generation for Object-Oriented software has relied heavily on typed Genetic Programming for representing and evolving test data. However, standard typed Genetic Programming approaches do not allow Object Reuse; this paper proposes a novel methodology to overcome this limitation. Object Reuse means that one instance can be passed to multiple methods as an argument, or multiple times to the same method as arguments. In the context of Object-Oriented Evolutionary Testing, it enables the generation of test programs that exercise structures of the software under test that would not be reachable otherwise. Additionally, the experimental studies performed show that the proposed methodology is able to effectively increase the performance of the test data generation process. %K genetic algorithms, genetic programming, SBSE %R 10.1007/978-3-642-12148-7_19 %U http://dx.doi.org/10.1007/978-3-642-12148-7_19 %P 220-231 %0 Thesis %T Contributions for Improving Genetic Programming-Based Approaches to the Evolutionary Testing of Object-Oriented Software %A Bregieiro Ribeiro, Jose Carlos %D 2010 %8 December %C Merida, Spain %C Departamento de Tecnologa de los Computadores y de las Comunicaciones, Universidad de Extremadura %F Bregieiro-Ribeiro:thesis %K genetic algorithms, genetic programming, SBSE %9 Ph.D. thesis %U https://sites.google.com/site/jcbribeiro/jose.ribeiro_phdthesis_final.pdf %0 Book Section %T eCrash: a Genetic Programming-Based Testing Tool for Object-Oriented Software %A Bregieiro Ribeiro, Jose Carlos %A Nogueira, Ana Filipa %A Fernandez de Vega, Francisco %A Zenha-Rela, Mario Alberto %E Gandomi, Amir H. %E Alavi, Amir H. %E Ryan, Conor %B Handbook of Genetic Programming Applications %D 2015 %I Springer %F Bregieiro-Ribeiro:2015:hbgpa %X This paper describes the methodology, architecture and features of the eCrash framework, a Java-based tool which employs Strongly-Typed Genetic Programming to automate the generation of test data for the structural unit testing of Object-Oriented programs. The application of Evolutionary Algorithms to Test Data generation is often referred to as Evolutionary Testing. eCrash implements an Evolutionary Testing strategy developed with three major purposes: improving the level of performance and automation of the Software Testing process; minimising the interference of the tool’s users on the Test Object analysis to a minimum; and mitigating the impact of users decisions in the Test Data generation process. %K genetic algorithms, genetic programming, SBSE, Evolutionary Testing, Object-Orientation, Search-Based Software Engineering, Unit Testing %R 10.1007/978-3-319-20883-1_23 %U http://dx.doi.org/10.1007/978-3-319-20883-1_23 %P 575-593 %0 Conference Proceedings %T Towards Evolutionary Emergence %A Bremer, Joerg %A Lehnhoff, Sebastian %Y Ganzha, Maria %Y Maciaszek, Leszek %Y Paprzycki, Marcin %Y Slezak, Dominik %S Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems %D 2021 %8 February 5 sep %I FedCSIS %C Online %F Bremer:2021:CSIS %X With the upcoming era of large-scale, complex cyber-physical systems, also the demand for decentralized and self-organising algorithms for coordination rises. Often such algorithms rely on emergent behavior; local observations and decisions aggregate to some global behavior without any apparent, explicitly programmed rule. Systematically designing these algorithms targeted for a new orchestration or optimisation task is, at best, tedious and error prone. Suitable and widely applicable design patterns are scarce so far. We opt for a machine learning based approach that learns the necessary mechanisms for targeted emergent behavior automatically. To achieve this,we use Cartesian genetic programming. As an example that demonstrates the general applicability of this idea, we trained a swarm-based optimization heuristics and present first results showing that the learned swarm behavior is significantly better than just random search. We also discuss the encountered pitfalls and remaining challenges on the research agenda. %K genetic algorithms, genetic programming, Cartesian genetic programming, PSO %R 10.15439/2021F111 %U https://annals-csis.org/Volume_26/pliks/position.pdf %U http://dx.doi.org/10.15439/2021F111 %P 55-60 %0 Conference Proceedings %T Fully Distributed Cartesian Genetic Programming %A Bremer, Joerg %A Lehnhoff, Sebastian %Y Dignum, Frank %Y Mathieu, Philippe %Y Corchado, Juan Manuel %Y De La Prieta, Fernando %S Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection %S LNAI %D 2022 %V 13616 %I Springer %F bremer:2022:PAAMS %X Cartesian genetic programming is a popular version of genetic programming and has meanwhile proven its performance in many use cases. This paper introduces an algorithmic level decomposition of program evolution that can be solved by a multi-agent system in a fully distributed manner. A heuristic for distributed combinatorial problem solving is adapted to evolve programs. The applicability of the approach and the effectiveness of the multi-agent approach as well as of the evolved genetic programs are demonstrated using symbolic regression, n-parity, and classification problems. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, parallel computing, Multi-agent system, COHDA, Distributed optimization %R 10.1007/978-3-031-18192-4_4 %U https://rdcu.be/c7nZL %U http://dx.doi.org/10.1007/978-3-031-18192-4_4 %P 36-49 %0 Journal Article %T Enhancing Local Decisions in Agent-Based Cartesian Genetic Programming by CMA-ES %A Bremer, Joerg %A Lehnhoff, Sebastian %J Systems %D 2023 %V 11 %N 4 %@ 2079-8954 %F bremer:2023:Systems %X Cartesian genetic programming is a popular version of classical genetic programming, and it has now demonstrated a very good performance in solving various use cases. Originally, programs evolved by using a centralized optimisation approach. Recently, an algorithmic level decomposition of program evolution has been introduced that can be solved by a multi-agent system in a fully distributed manner. A heuristic for distributed combinatorial problem-solving was adapted to evolve these programs. The applicability of the approach and the effectiveness of the used multi-agent protocol as well as of the evolved genetic programs for the case of full enumeration in local agent decisions has already been successfully demonstrated. Symbolic regression, n-parity, and classification problems were used for this purpose. As is typical of decentralized systems, agents have to solve local sub-problems for decision-making and for determining the best local contribution to solving program evolution. So far, only a full enumeration of the solution candidates has been used, which is not sufficient for larger problem sizes. We extend this approach by using CMA-ES as an algorithm for local decisions. The superior performance of CMA-ES is demonstrated using Koza’s computational effort statistic when compared with the original approach. In addition, the distributed modality of the local optimisation is scrutinized by a fitness landscape analysis. %K genetic algorithms, genetic programming, cartesian genetic programming %9 journal article %R 10.3390/systems11040177 %U https://www.mdpi.com/2079-8954/11/4/177 %U http://dx.doi.org/10.3390/systems11040177 %P ArticleNo.177 %0 Conference Proceedings %T Hybridizing Levy Flights and Cartesian Genetic Programming for Learning Swarm-Based Optimization %A Bremer, Joerg %A Lehnhoff, Sebastian %S UK Workshop on Computational Intelligence %D 2023 %8 June 8 sep %I Springer %C Birmingham %F bremer:2023:UKCI %K genetic algorithms, genetic programming, Cartesian Genetic Programming %R 10.1007/978-3-031-47508-5_24 %U http://link.springer.com/chapter/10.1007/978-3-031-47508-5_24 %U http://dx.doi.org/10.1007/978-3-031-47508-5_24 %P 299-310 %0 Conference Proceedings %T Learning Parameterizable Decoders with Cartesian Genetic Programming %A Bremer, Joerg %A Lehnhoff, Sebastian %Y Hart, Emma %Y Horvath, Tomas %Y Tan, Zhiyuan %Y Thomson, Sarah %S 24th UK Workshop on Computational Intelligence (UKCI 2025) %S Advances in Intelligent Systems and Computing %D 2025 %8 March %V 1468 %I Springer %C Edinburgh Napier University %F Bremer:2025:UKCI %X For optimization in the smart grid, distributed algorithms based on decoders for handling individual constraints of different energy resources are a promising approach to tackle the scalability and versatility of controlled devices. Decoders based on machine learning can capture the operational capabilities and serve as a means for systematically ensuring the feasibility of solution candidates during optimization. Currently, decoders are trained based on a training set for a specific initial state predicted for the start time of the optimization period. Thus, a new decoder has to be trained for any new initial operational state of the energy resource. This paper explores a new approach based on Cartesian genetic programming to train a decoder that can be parameterized with different initial states. We train decoders for co-generation plants with a range of different states of charge for the thermal buffer store and demonstrate that such decoders can be obtained in a reasonable training time and with sufficiently good performance over the whole range of temperatures. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, Decoder, Constraint Handling, Solution Repair, Predictive Scheduling %R 10.1007/978-3-032-07938-1_7 %U http://dx.doi.org/10.1007/978-3-032-07938-1_7 %P 79-90 %0 Conference Proceedings %T Evolving Digital Circuits Using Complex Building Blocks %A Bremner, Paul %A Samie, Mohammad %A Dragffy, Gabriel %A Pipe, Tony %A Walker, James Alfred %A Tyrrell, Andy M. %Y Tempesti, Gianluca %Y Tyrrell, Andy M. %Y Miller, Julian F. %S Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010 %S Lecture Notes in Computer Science %D 2010 %8 sep 6 8 %V 6274 %I Springer %C York %F Bremner:2010:ICES %X This work is a study of the viability of using complex building blocks (termed molecules) within the evolutionary computation paradigm of CGP; extending it to MolCGP. Increasing the complexity of the building blocks increases the design space that is to be explored to find a solution; thus, experiments were undertaken to find out whether this change affects the optimum parameter settings required. It was observed that the same degree of neutrality and (greedy) 1+4 evolution strategy gave optimum performance. The Computational Effort used to solve a series of benchmark problems was calculated, and compared with that used for the standard implementation of CGP. Significantly less Computational Effort was exerted by MolCGP in 3 out of 4 of the benchmark problems tested. Additionally, one of the evolved solutions to the 2-bit multiplier problem was examined, and it was observed that functionality present in the molecules, was exploited by evolution in a way that would be highly unlikely if using standard design techniques. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-15323-5_4 %U http://dx.doi.org/10.1007/978-3-642-15323-5_4 %P 37-48 %0 Conference Proceedings %T Evolving Cell Array Configurations Using CGP %A Bremner, Paul %A Samie, Mohammad %A Pipe, Anthony G. %A Dragffy, Gabriel %A Liu, Yang %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 Bremner:2011:EuroGP %X A cell array is a proposed type of custom FPGA, where digital circuits can be formed from interconnected configurable cells. In this paper we have presented a means by which CGP might be adapted to evolve configurations of a proposed cell array. As part of doing so, we have suggested an additional genetic operator that exploits modularity by copying sections of the genome within a solution, and investigated its efficacy. Additionally, we have investigated applying selection pressure for parsimony during functional evolution, rather than in a subsequent stage as proposed in other work. Our results show that solutions to benchmark problems can be evolved with a good degree of efficiency, and that compact solutions can be found with no significant impact on the required number of circuit evaluations. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-20407-4_7 %U http://dx.doi.org/10.1007/978-3-642-20407-4_7 %P 73-84 %0 Conference Proceedings %T Multi-Objective Optimisation of Cell-Array Circuit Evolution %A Bremner, Paul %A Samie, Mohammad %A Pipe, Anthony %A Tyrrell, Andy %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 Bremner:2011:MOoCCE %X In this paper we have investigated the efficacy of applying multi-objective optimisation to Cartesian genetic programming (CGP) when used for evolution of cell-array configurations. A cell-array is a proposed type of custom FPGA, where digital circuits can be formed from interconnected configurable cells; thus, the CGP nodes are more complex than in its standard implementation. We have described modifications to a previously described optimisation algorithm that has led to significant improvements in performance; circuits close to a hand designed equivalent have been found, in terms of the optimised objectives. Additionally we have investigated the effect of circuit decomposition techniques on evolutionary performance. We found that using a hybrid of input and output decomposition techniques substantial reductions in evolution time were observed. Further, while the number of circuit inputs is the key factor for functional evolution time, the number of circuit outputs is the key factor for optimisation time. %K genetic algorithms, genetic programming, cartesian genetic programming, cell-array circuit evolution, circuit decomposition technique, custom FPGA, digital circuit, interconnected configurable cell, multiobjective optimisation, field programmable gate arrays %R 10.1109/CEC.2011.5949651 %U http://dx.doi.org/10.1109/CEC.2011.5949651 %P 440-446 %0 Journal Article %T Spatiotemporal Effect of Land Use on Water Quality in a Peri-Urban Watershed in a Brazilian Metropolitan Region: An Approach Considering GEP-Based Artificial Intelligence %A Bressane, Adriano %A Loureiro, Anna Isabel Silva %A Gomes, Raissa Caroline %A Ribeiro, Admilson Irio %A Longo, Regina Marcia %A Negri, Rogerio Galante %J Pollutants %D 2023 %V 3 %N 1 %@ 2673-4672 %F bressane:2023:Pollutants %X The suppression of natural spaces due to urban sprawl and increases in built and agricultural environments has affected water resource quality, especially in areas with high population densities. Considering the advances in the Brazilian environmental legal framework, the present study aimed to verify whether land use has still affected water quality through a case study of a peri-urban watershed in a Brazilian metropolitan region. Analyses of physical–chemical indicators, collected at several sample points with various land-use parameters at different seasons of the year, were carried out based on an approach combining variance analysis and genetic programming. As a result, some statistically significant spatiotemporal effects on water quality associated with the land use, such as urban areas and thermotolerant coliform (R = −0.82, p < 0.01), mixed vegetation and dissolved oxygen (R = 0.80, p < 0.001), agriculture/pasture and biochemical oxygen demand (R = 0.40, p < 0.001), and sugarcane and turbidity (R = 0.65, p < 0.001), were verified. In turn, gene expression programming allowed for the computing of the importance of land-use typologies based on their capability to explain the variances of the water quality parameter. In conclusion, in spite of the advances in the Brazilian law, land use has still significantly affected water quality. Public policies and decisions are required to ensure effective compliance with legal guidelines. %K genetic algorithms, genetic programming, gene expression programming %9 journal article %R 10.3390/pollutants3010001 %U https://www.mdpi.com/2673-4672/3/1/1 %U http://dx.doi.org/10.3390/pollutants3010001 %P 1-11 %0 Book Section %T Location Independent Pattern Recognition using Genetic Programming %A Breunig, Markus M. %E Koza, John R. %B Genetic Algorithms and Genetic Programming at Stanford 1995 %D 1995 %8 November %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-195720-5 %F breunig:1995:LIPRGP %X This paper describes an application of genetic programming. Programs able of recognising a pattern independent of its location are evolved. Usually the evolution of programs is controlled primarily by the fitness evaluation function. This paper demonstrates how genetic programming can be encouraged to evolve programs with properties not being explicitly considered in the fitness measure like location independence. The measurements taken include the use of automatically defined functions allowing the problem to be decomposed into sub-functions, a special implementation of iteration and carefully chosen function and terminal sets. A main purpose was to minimise the restrictions imposed on the solution, i.e. giving the genetic programming as much freedom as possible while still encouraging the desired properties. %K genetic algorithms, genetic programming, ADF %U http://www.dbs.informatik.uni-muenchen.de/~breunig/HomepageResearch/Papers/PatternRecog.pdf %P 29-38 %0 Conference Proceedings %T System for discovering and optimizung mathematical models using genetic programming and genetic algorithms %A Brezocnik, Miran %A Balic, Joze %Y Katalinic, Branko %S Proceedings of the 8th International DAAAM Symposium %D 1997 %8 23 25 oct %I DAAAM International %C Dubrovnik, Croatia %@ 3-901509-04-6 %F Brezocnik:1997:DAAAM %X In this paper, we propose a system for discovering and optimising of various mathematical models. The system consists of two parts. In the first part, we discover unknown mathematical models on the basis of empirical given data (learning data). In the second part, we optimise parameters of the discovered mathematical models. Genetic programming (GP) and genetic algorithm (GA) are used for discovering and optimizing of models, respectively. GP and GA are evolutionary optimization methods based on the Darwinian natural selection and survival of the fittest. %K genetic algorithms, genetic programming, adaptive systems, evolutionary computation %P 37-38 %0 Conference Proceedings %T Comparison of genetic programming with genetic algorithm %A Brezocnik, Miran %A Balic, Joze %Y Jezernik, Anton %Y Dolsak, Bojan %S 3rd International Conference Design to Manufacture in Modern Industry: Design to manufacture in modern industry %D 1997 %8 sep %I University of Maribor, Faculty of Mechanical Engineering %@ 86-435-0192-1 %F Brezocnik:1997:ICDMMI %X The paper is concerned about the conventional genetic algorithm (GA) and, particularly, the recently proposed paradigm: genetic programming (GP). The well-known basic knowledge of the conventional GA is briefly presented, but only for comparison with GP. On the contrary, the GP method is discussed in detail. The GP is an evolutionary process, where the fittest computer program in the space of possible computer programs is searched for. The fittest computer program represents a solution in the observed problem domain. One of the most powerful abilities of the GP that allows inclusion of rich information into computer programs is clearly presented and emphasised. Our personal views concerning the complementary nature of the conventional GA and GP are discussed. Finally, we briefly presented the implementation of the GP on the wide variety of different problems. %K genetic algorithms, genetic programming %P 150-156 %0 Conference Proceedings %T A genetic programming approach for modelling of self-organizing assembly systems %A Brezocnik, Miran %A Balic, Joze %Y Kopacek, Peter %Y Noe, Dragica %S Intelligent assembly and disassembly - IAD’98: A proceedings volume from the IFAC Workshop %D 1998 %8 21 23 may %I Pergamon %C Bled, Slovenia %@ 0-08-043042-2 %F Brezocnik:1998:IAD %X The paper proposes a genetic programming approach to the modelling of the assembly of the basic components (cells) into an integrated (whole) organism. The concept is based on the simulation of self-organising uniting of live cells into tissues, organs and individuals. The assembly is treated as the basic and general principle, therefore, the basic cells can be very different. Assembling takes place on the basis of the genetic content in the basic components and is influenced by the environmental conditions. The genetic content can be topological, geometrical, technological, ecological, economical, etc. The simulation of the self-organizing genetic assembly of the variant product consisting of many basic components is given. The basic feature of genetic assembly is that it takes place in a distributed, nondeterministical, bottom-up, and self-organising manner. %K genetic algorithms, genetic programming, self-organising systems, intelligent manufacturing systems, assembly, simulation %U http://www.amazon.co.uk/Intelligent-Assembly-Disassembly-IAD-Proceedings/dp/0080430422 %P 47-52 %0 Thesis %T MODELING OF TECHNOLOGICAL SYSTEMS BY THE USE OF GENETIC METHODS %A Brezocnik, Miran %D 1998 %C Smetanova ulica 17, SI-2000 Maribor, Slovenia %C University of Maribor, Faculty of Mechanical Engineering %F Brezocnik:thesis %X In this work we propose modelling of different technological systems by a general approach. The research starts with searching for common characteristics of the technological systems. After they have been found out, they are synthesised into uniform principle serving for conceiving a general method for their modeling. The method imitates associating of living cells into tissues, organs and organisms. The disturbances resulting from limited human knowledge, unpredictability of technological systems, and unexpected events in production environment are automatically eliminated during the evolutionary process. More and more intelligent behaviour of the individual technological system, which is expressed as an increasingly successful synchronisation of the material, energy and information, is obtained gradually with self-organization and without centralised instruction. In order to support the theoretical researches a system for genetic programming is developed. It is successfully used for genetic modeling of: 1. forming efficiency, 2. assembly and classification, and 3. trajectories of robots in the production environment. The results of modeling of forming efficiency show excellent correspondence between analytically obtained models, experimental results, and genetically developed models. In case of genetic modeling of assembly the basic components are integrated into the final product in a self-organising manner. Genetic modelling the trajectory of the robot, striving to arrive at the aim through a dynamic production environment, discovers the intelligent robot navigation formed during the evolutionary process. %K genetic algorithms, genetic programming, intelligent manufacturing systems, technological system, forming, assembly, robots, self-organisation, genetic methods, modelling, optimisation %9 phdthesis %9 Ph.D. thesis %0 Journal Article %T Artificial intelligence approach to determination of flow curve %A Brezocnik, Miran %A Balic, Joze %A Gusel, Leo %J Journal for technology of plasticity %D 2000 %V 25 %N 1-2 %@ 0350-2368 %F Brezocnik:2000:JTP %X For the control of the forming process it is necessary to know as precisely as possible the flow curve of the material formed. The paper presents the determination of the equation for the flow curve with the artificial intelligence approach. The genetic programming method (GP) was used. It is an evolutionary optimisation technique based on the Darwinian natural selection and the survival of the fittest organisms. The comparison between the experimental results, the analytical solution and the solution obtained genetically clearly shows that the genetic programming method is a very promising approach. %K genetic algorithms, genetic programming, forming, flow curve, artificial intelligence %9 journal article %P 1-7 %0 Book %T Uporaba genetskega programiranja v inteligentnih proizvodnih sistemih %A Brezocnik, Miran %D 2000 %I University of Maribor, Faculty of mechanical engineering %C Maribor, Slovenia %@ 86-435-0306-1 %F Brezocnik:book %O In Slovenian %K genetic algorithms, genetic programming, manufacturing, intelligent manufacturing systems, modelling, assembly, metal forming, autonomous robot, evolutionary algorithms %U http://www.isbns.net/isbn/9788643503065/ %0 Journal Article %T Modeling of forming efficiency using genetic programming %A Brezocnik, Miran %A Balic, Joze %A Kampus, Zlatko %J Journal of Materials Processing Technology %D 2001 %8 January %V 109 %N 1-2 %@ 0924-0136 %F Brezocnik:2001:MPT %X This paper proposes new approach for modeling of various processes in metal-forming industry. As an example, we demonstrate the use of genetic programming (GP) for modeling of forming efficiency. The forming efficiency is a basis for determination of yield stress which is the fundamental characteristic of metallic materials. Several different genetically evolved models for forming efficiency on the basis of experimental data for learning were discovered. The obtained models (equations) differ in size, shape, complexity and precision of solutions. In one run out of many runs of our GP system the well-known equation of Siebel was obtained. This fact leads us to opinion that GP is a very powerful evolutionary optimization method appropriate not only for modeling of forming efficiency but also for modeling of many other processes in metal-forming industry. [COBISS.SI-ID 5979414] %K genetic algorithms, genetic programming, Metal-forming, Yield stress, Forming efficiency, Modeling, Adaptation, Artificial intelligence %9 journal article %R 10.1016/S0924-0136(00)00783-4 %U http://www.sciencedirect.com/science/article/B6TGJ-423HM9M-5/1/bcc93a13fbb04521236d3a8e16f8850b %U http://dx.doi.org/10.1016/S0924-0136(00)00783-4 %P 20-29 %0 Journal Article %T A genetic-based approach to simulation of self-organizing assembly %A Brezocnik, Miran %A Balic, Joze %J Robotics and Computer-Integrated Manufacturing %D 2001 %8 feb %V 17 %N 1-2 %@ 0736-5845 %F Brezocnik:2001:RCIM %X The paper proposes a new and innovative biologically oriented idea in conceiving intelligent systems in modern factories of the future. The intelligent system is treated as an autonomous organization structure efficiently adapting itself to the dynamic changes in the production environment and the environment in a wider sense. Simulation of self-organizing assembly of mechanical parts (basic components) into the product is presented as an example of the intelligent system. The genetic programming method is used. The genetic-based assembly takes place on the basis of the genetic content in the basic components and the influence of the environment. The evolution of solutions happens in a distributed way, nondeterministically, bottom-up, and in a self-organizing manner. The paper is also a contribution to the international research and development program intelligent manufacturing systems, which is one of the biggest projects ever introduced. %K genetic algorithms, genetic programming, Intelligent manufacturing systems, Self-organizing assembly, Evolution %9 journal article %R 10.1016/S0736-5845(00)00044-2 %U http://www.sciencedirect.com/science/article/B6V4P-42DP1Y1-J/1/175033beb3ddb787b75c22253e5534c2 %U http://dx.doi.org/10.1016/S0736-5845(00)00044-2 %P 113-120 %0 Conference Proceedings %T Survey of the evolutionary computation and its application in manufacturing systems %A Brezocnik, Miran %A Kovacic, Miha %Y Jurkovic, Milan %Y Karabegovic, Isak %S 3rd International Conference on Revitalization and Modernization of Production RIM 2001 %D 2001 %8 sep %C University of Bihac, Bihacu, Bosnia and Herzegovina %@ 9958-624-10-9 %F Brezocnik:2001:RIM %K genetic algorithms, genetic programming %P 501-508 %0 Journal Article %T Genetic programming approach to determining of metal materials properties %A Brezocnik, Miran %A Balic, Joze %A Kuzman, Karl %J Journal of Intelligent Manufacturing %D 2002 %8 feb %V 13 %N 1 %@ 0956-5515 %F Brezocnik:2002:JIM %X The paper deals with determining metal materials properties by use of genetic programming (GP). As an example, the determination of the flow stress in bulk forming is presented. The flow stress can be calculated on the basis of known forming efficiency. The experimental data obtained during pressure test serve as an environment to which models for forming efficiency have to be adapted during simulated evolution as much as possible. By performing four experiments, several different models for forming efficiency are genetically developed. The models are not a result of the human intelligence but of intelligent evolutionary process. With regard to their precision, the successful models are more or less equivalent; they differ mainly in size, shape, and complexity of solutions. The influence of selection of different initial model components (genes) on the probability of successful solution is studied in detail. In one especially successful run of the GP system the Siebel’s expression was genetically developed. In addition, redundancy of the knowledge hidden in the experimental data was detected and eliminated without the influence of human intelligence. Researches showed excellent agreement between the experimental data, existing analytical solutions, and models obtained genetically. %K genetic algorithms, genetic programming, materials properties, metal forming, modeling, self-organisation %9 journal article %R 10.1023/A:1013693828052 %U http://dx.doi.org/10.1023/A:1013693828052 %P 5-17 %0 Conference Proceedings %T Prediction of surface roughness with genetic programming %A Brezocnik, Miran %A Kovacic, Miha %Y Dobrzanski, Leszek A. %S Proceedings of the 11th International Scientific Conference Achievements in Mechanical and Materials Engineering, AMME’2002 %D 2002 %@ 83-914458-7-9 %F Brezocnik:2002:AMME %K genetic algorithms, genetic programming %P 23-26 %0 Book Section %T On intelligent learning systems for next-generation manufacturing %A Brezocnik, Miran %E Katalinic, Branko %B DAAAM International Scientific Book 2002 %D 2002 %8 oct %V 1 %I DAAAM International %C Vienna %@ 3-901509-30-5 %F Brezocnik:2002:DAAAM %X In the first part of the paper we analyse the basic scientific and philosophical facts, as well as social circumstances, that have a great impact on manufacturing concepts. Then we propose a shift from the present manufacturing paradigm favouring particularly determinism, rationalism, and top-down organisational principles towards intelligent systems in next-generation manufacturing involving phenomena such as non-determination, emergence, learning, complexity, self-organization, bottom-up organisation, and co-existence with natural environment. In the second part we give two examples from metal forming industry and autonomous intelligent vehicles. Both systems are based on learning and imitate some excellent properties of living systems. The stable global order (i.e. the solution) of each presented system gradually emerges as a result of interactions between basic entities of which the system consists and the environment. %K genetic algorithms, genetic programming, manufacturing systems, artificial intelligence, learning, evolutionary computation, emergence %U http://www.daaam.com/ %P 39-48 %0 Conference Proceedings %T Integrated evolutionary computation environment for optimizing and modeling of manufacturing processes %A Brezocnik, Miran %A Kovacic, Miha %Y Brdarevia, Safet %Y Ekinovia, Sabahudin %Y Compamys Pascual, Ramon %Y Calvet Vivancos, Joan %S 6th International Research/Expert Conference ’Trends in the development of Machinery and Associated Technology’ %D 2002 %8 18 22 sep %C Neum, Bosnia and Herzegovina %@ 9958-617-11-0 %F Brezocnik:TMT2002 %K genetic algorithms, genetic programming, Poster %U http://www.mf.unze.ba/tmt2002/tmt2002-1.htm %P TMT02-073 %0 Journal Article %T Emergence of intelligence in next-generation manufacturing systems %A Brezocnik, Miran %A Balic, Joze %A Brezocnik, Zmago %J Robotics and Computer-Integrated Manufacturing %D 2003 %8 feb apr %V 19 %N 1-2 %F Brezocnik:2003:RCIM %X In the paper we propose a fundamental shift from the present manufacturing concepts and problem solving approaches towards new manufacturing paradigms involving phenomena such as emergence, intelligence, non-determinism, complexity, self-organisation, bottom-up organization, and coexistence with the ecosystem. In the first part of the paper we study the characteristics of the past and the present manufacturing concepts and the problems they caused. According to the analogy with the terms in cognitive psychology four types of problems occurring in complex manufacturing systems are identified. Then, appropriateness of various intelligent systems for solving of these four types of problems is analysed. In the second part of the paper, we study two completely different problems. These two problems are (1) identification of system in metal forming industry and (2) autonomous robot system in manufacturing environment. A genetic-based approach that imitates integration of living cells into tissues, organs, and organisms is used. The paper clearly shows how the state of the stable global order (i.e., the intelligence) of the overall system gradually emerges as a result of low-level interactions between entities of which the system consists and the environment. %K genetic algorithms, genetic programming, Intelligent manufacturing systems, Emergence, Learning %9 journal article %R 10.1016/S0736-5845(02)00062-5 %U http://www.sciencedirect.com/science/article/B6V4P-47XW4VG-1/2/f88aada395a16da3031d89d272dae207 %U http://dx.doi.org/10.1016/S0736-5845(02)00062-5 %P 55-63 %0 Book Section %T Modelling of intelligent mobility for next-generation manufacturing systems %A Brezocnik, Miran %A Kovacic, Miha %E Katalinic, B. %B DAAAM International Scientific Book 2003 %D 2003 %8 jul %V 2 %I DAAAM International Vienna %C Vienna %@ 3-901509-30-5 %F Brezocnik:2003:DAAAM %X We present the modelling of the intelligent mobility for next-generation manufacturing systems. The modelling took place in the simplified dynamic manufacturing environment with several loads, obstacles and one robot placed in it. Each agent is freely movable on the floor. The aim of the robot is to pick up all loads and to come to the goal point. For optimisation of the robot path between loads and for planning of the robot travel the genetic algorithm and the genetic programming were used, respectively. The research showed that intelligent behaviour of the robot results from the interactions of the robot with the dynamic environment. %K genetic algorithms, genetic programming %P 95-102 %0 Conference Proceedings %T Genetic-based approach to predict surface roughness in end milling %A Brezocnik, Miran %A Kovacic, Miha %A Ficko, Mirko %S 7th International Research/Expert Conference ’Trends in the Development Machinery and Associated Technology’ %D 2003 %8 15 16 sep %C Barcelona, Spain %@ 9958-617-18-8 %F Brezocnik:2003:tmt %K genetic algorithms, genetic programming %P 529-532 %0 Journal Article %T Intelligent systems for next-generation manufacturing %A Brezocnik, Miran %A Kovacic, Miha %A Ficko, Mirko %J Academic Journal of Manufacturing Engineering %D 2004 %V 2 %N 1 %@ 1583-7904 %F Brezocnik:2004:AJME %X In this paper we propose a fundamental shift from the present manufacturing paradigm favouring particularly determinism, rationalism, and top-down organizational principles towards intelligent systems in next-generation manufacturing involving phenomena such as non-determinism, emergence, learning, complexity, self-organization, and bottom-up organization. The problem types and different intelligent systems for solving of problems were studied. Two examples of the intelligent systems from the areas of metal forming industry and autonomous intelligent vehicles are given. Both systems are based on learning and imitate some excellent properties of the living systems. Genetic programming and genetic algorithms were used. The stable global order (i.e., solution) of each presented system gradually emerges as a result of interactions between entities of which the system consists and the environment %K genetic algorithms, genetic programming %9 journal article %U http://www.worldcat.org/title/intelligent-systems-for-next-generation-manufacturing/oclc/440013859 %P 34-37 %0 Journal Article %T Predicting stress distribution in cold-formed material with genetic programming %A Brezocnik, Miran %A Gusel, Leo %J International journal of advanced manufacturing technology %D 2004 %V 23 %N 7-8 %@ 0268-3768 %F Brezocnik:2004:IJAMT %X In this paper we propose a genetic programming approach to predict radial stress distribution in cold-formed material. As an example, cylindrical specimens of copper alloy were forward extruded and analysed by the visioplasticity method. They were extruded with different coefficients of friction. The values of three independent variables (i.e., radial and axial position of measured stress node, and coefficient of friction) were collected after each extrusion. These variables influence the value of the dependent variable, i.e., radial stress. On the basis of training data set, various different prediction models for radial stress distribution were developed during simulated evolution. Accuracy of the best models was proved with the testing data set. The research showed that by proposed approach the precise prediction models can be developed; therefore, it is widely used also in other areas in metal-forming industry, where the experimental data on the process are known. %K genetic algorithms, genetic programming, metal forming, stress distribution, modelling %9 journal article %R 10.1007/s00170-003-1649-3 %U http://dx.doi.org/10.1007/s00170-003-1649-3 %P 467-474 %0 Conference Proceedings %T Genetic based approach to predict surface roughness %A Brezocnik, Miran %A Ficko, Mirko %A Kovacic, Miha %S 8th International Research/Expert Conference Trends in the Development Machinery and Associated Technology %D 2004 %8 15 19 sep %C Neum, Bosnia and Herzegovina %@ 9958-617-21-8 %F Brezocnik:2004:TMT %X In this paper we propose genetic programming to predict surface roughness in end milling. Two independent data sets were obtained from measurements: the training data set and the testing data set. Spindle speed, feed rate, depth of cut and vibrations were used as independent input variables (parameters), while surface roughness was the output variable. Different surface roughness models were obtained with the training data set and genetic programming. The testing data set was used to prove the accuracy of the best model. The conclusion is that surface roughness is most influenced by the feed rate, while vibrations increase the prediction accuracy. %K genetic algorithms, genetic programming, celno frezanje, povrsinska hrapavost, napoved hrapavosti, genetsko programiranje, end milling, surface roughness, prediction of surface roughness %U http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9009686 %P 91-94 %0 Journal Article %T Prediction of surface roughness with genetic programming %A Brezocnik, M. %A Kovacic, M. %A Ficko, M. %J Journal of Materials Processing Technology %D 2004 %8 20 dec 2004 %V 157-158 %@ 0924-0136 %F Brezocnik:2004:JMPT %X In this paper, we propose genetic programming to predict surface roughness in end-milling. Two independent data sets were obtained on the basis of measurement: training data set and testing data set. Spindle speed, feed rate, depth of cut, and vibrations are used as independent input variables (parameters), while surface roughness as dependent output variable. On the basis of training data set, different models for surface roughness were developed by genetic programming. Accuracy of the best model was proved with the testing data. It was established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy. %K genetic algorithms, genetic programming, Manufacturing systems, Surface roughness %K Milling, Evolutionary algorithms %9 journal article %R 10.1016/j.jmatprotec.2004.09.004 %U http://dx.doi.org/10.1016/j.jmatprotec.2004.09.004 %P 28-36 %0 Conference Proceedings %T Genetic Programming Approach for Autonomous Vehicles %A Kovacic, Miha %A Brezocnik, Miran %A Balic, Joze %S Mechatronics 2004 9th Mechatronics Forum International Conference %D 2004 %8 30 aug 1 sep %C METU, Ankara, Turkey %F Mechatronics2004_Abstract_026 %X GP was used for intelligent path planning of an autonomous vehicle in 2D production environment. Robot had to find loads, to avoid all the obstacles and to reach the target point. The production environment (robot, loads and obstacles) are represented as free 2D shapes. The robot discretely rotates for 30 degrees left and right and moves forward by two different steps. Step decreases if the sensor detects the load or obstacle. The GP system tries to find gradually optimal program for robot navigation through production environment as a consequence of interactions between the robot and detected environment. Program for navigation can be randomly constructed of logical operators (IFLOAD, IF-OBSTACLE), basic commands (MOVE, RIGHT, LEFT), and connection functions (CF2, CF3). Each program is run several times until 100 time units for the robot’s task are used or the target point is reached. The system for genetic programming was run 50-times. Robot travelled safely with all collected loads to the target point 2-times, which means that the probability of the finding successful navigation program is 4 percent. In future the researches will be oriented particularly towards conceiving an improved GP system with the possibility of use 3D models of the production environment. Preliminary results of the concept are encouraging. %K genetic algorithms, genetic programming %U http://mechatronics.atilim.edu.tr/mechatronics2004/papers/Mechatronics2004_Abstract_026.pdf %0 Journal Article %T Programming CNC measuring machines by genetic algorithms %A Brezocnik, Miran %A Kovacic, Miha %A Balic, Joze %A Sovilj, Bogdan %J Academic Journal of Manufacturing Engineering %D 2004 %V 2 %N 4 %@ 1583-7904 %F brezocnik_2004_AJME %X The need for efficient and reliable tools for programming of CNC coordinate measuring machine is rapidly increasing in modern production. The proposed concept based on genetic algorithms assures generation and optimization of NC programs for measuring machine. Therefore the structure, undergoing simulated evolution, is the population of NC programs. The NC programs control the tactile probe which performs simple elementary motions in the discretized measuring area. During the simulated evolution the probe movement becomes more and more optimized and intelligent solutions emerge gradually as a result of the low level interaction between the simple probe movements and the measuring environment. Example of CNC programming of measuring machine is given. Results show universality and inventiveness of the approach %K genetic algorithms, genetic programming, optimisation, coordinate measuring machines, computer aided quality control, evolutionary computation %9 journal article %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/brezocnik_2004_AJME.pdf %P 15-20 %0 Journal Article %T Integrated genetic programming and genetic algorithm approach to predict surface roughness %A Brezocnik, Miran %A Kovacic, Miha %J Materials and Manufacturing Processes %D 2003 %8 may %V 18 %N 3 %F Brezocnik:2003:MMP %X we propose a new integrated genetic programming and genetic algorithm approach to predict surface roughness in end-milling. Four independent variables, spindle speed, feed rate, depth of cut, and vibrations, were measured. Those variables influence the dependent variable (i.e., surface roughness). On the basis of training data set, different models for surface roughness were developed by genetic programming. The floating-point constants of the best model were additionally optimised by a genetic algorithm. Accuracy of the model was proved on the testing data set. By using the proposed approach, more accurate prediction of surface roughness was reached than if only modelling by genetic programming had been carried out. It was also established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy. %K genetic algorithms, genetic programming, Manufacturing systems, Surface roughness, Milling %9 journal article %R 10.1081/AMP-120022023 %U http://dx.doi.org/10.1081/AMP-120022023 %P 475-491 %0 Conference Proceedings %T Cost estimation for punch dies by genetic programming %A Brezocnik, Miran %A Vaupotic, Bostjan %A Fridrih, Janez %A Pahole, Ivo %Y Jurkovic, Milan %Y Dolecek, Vlatko %S RIM 2005 / 5th International scientific conference on Production engineering %D 2005 %8 14 17 sep %I Faculty of Technical Engineering, Bihac, Bosnia and Hercegovina %@ 9958-9262-0-2 %F Brezocnik:2005:RIM %X The paper presents a new approach for cost estimation of punch dies used in metal-forming industry. In the modern business world fast and accurate information is the principal advantage in securing orders and establishing the company’s renowned. Often, the offer for the manufacturing and supply of the tool must be sent within a short time. However, precise preparation of the offer requires much work. The paper presents an approach ensuring fast determination of the relatively precise cost estimate of the punch dies on the basis of the tool input parameters (e.g., outside dimensions, number of blades, number of directions of cutting). The proposed approach is based on the evolutionary searching for the adequate general equation describing the influence of the tool input parameters on punch die manufacturing cost. Evolutionary development of the equation was performed by the genetic programming and the base of the punch dies already made. %K genetic algorithms, genetic programming, punch dies, cost estimation %P 167-172 %0 Journal Article %T Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution %A Brezocnik, Miran %A Kovacic, Miha %A Gusel, Leo %J Materials and Manufacturing Processes %D 2005 %8 may %V 20 %N 3 %@ 1042-6914 %F brezocnik:2005:MMP %X We compare genetic algorithm (GA) and genetic programming (GP) for system modelling in metal forming. As an example, the radial stress distribution in a cold-formed specimen (steel X6Cr13) was predicted by GA and GP. First, cylindrical workpieces were forward extruded and analysed by the visioplasticity method. After each extrusion, the values of independent variables (radial position of measured stress node, axial position of measured stress node, and coefficient of friction) were collected. These variables influence the value of the dependent variable, radial stress. On the basis of training data, different prediction models for radial stress distribution were developed independently by GA and GP. The obtained models were tested with the testing data. The research has shown that both approaches are suitable for system modeling. However, if the relations between input and output variables are complex, the models developed by the GP approach are much more accurate. %K genetic algorithms, genetic programming, Metal forming, Stress distribution, System modelling %9 journal article %R 10.1081/AMP-200053541 %U http://journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=1042-6914&volume=20&issue=3&spage=497 %U http://dx.doi.org/10.1081/AMP-200053541 %P 497-508 %0 Journal Article %T Prediction of steel machinability by genetic programming %A Brezocnik, Miran %A Kovacic, Miha %A Psenicnik, Matej %J Journal of achievements in materials and manufacturing engineering %D 2006 %8 may jun %V 16 %N 1-2 %F Brezocnik:2006:AMME %O Special Issue of CAM3S’2005 %X The steels with extra machinability are made according to a special technological process. Such steels can be machined at high cutting speeds. In addition, the resistance of the tools used for machining, is higher than in the case of ordinary steels. It depends on several parameters, particularly on the steel chemical composition, whether the steel will meet the criterion of extra machinability. Special tests for each batch separately show whether the steel has extra machinability or not. In our research, the prediction of machinability of steels, depending on input parameters, was performed by genetic programming and data on the batches of steel already made. The model developed during the simulated evolution was tested also with the testing data set. The results show that the proposed concept can be successfully used in practice. %K genetic algorithms, genetic programming, Steel machinability, Extra machinability, Modelling %9 journal article %U http://jamme.acmsse.h2.pl/index.php?id=69 %P 107-113 %0 Journal Article %T Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic Programming–An Industrial Study %A Brezocnik, Miran %A Zuperl, Uros %J Metals %D 2021 %V 11 %N 6 %@ 2075-4701 %F brezocnik:2021:Metals %X Store Steel Ltd. is one of the major flat spring steel producers in Europe. Until 2016 the company used a three-strand continuous casting machine with 6 m radius, when it was replaced by a completely new two-strand continuous caster with 9 m radius. For the comparison of the tensile strength of 41 hypoeutectoid steel grades, we conducted 1847 tensile strength tests during the first period of testing using the old continuous caster, and 713 tensile strength tests during the second period of testing using the new continuous caster. It was found that for 11 steel grades the tensile strength of the rolled material was statistically significantly lower (t-test method) in the period of using the new continuous caster, whereas all other steel grades remained the same. To improve the new continuous casting process, we decided to study the process in more detail using the Multiple Linear Regression method and the Genetic Programming approach based on 713 items of empirical data obtained on the new continuous casting machine. Based on the obtained models of the new continuous casting process, we determined the most influential parameters on the tensile strength of a product. According to the model’s analysis, the secondary cooling at the new continuous caster was improved with the installation of a self-cleaning filter in 2019. After implementing this modification, we performed an additional 794 tensile tests during the third period of testing. It was found out that, after installation of the self-cleaning filter, in 6 steel grades out of 19, the tensile strength in rolled condition improved statistically significantly, whereas all the other steel grades remained the same. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/met11060972 %U https://www.mdpi.com/2075-4701/11/6/972 %U http://dx.doi.org/10.3390/met11060972 %0 Journal Article %T Using genetic algorithms for early schedulability analysis and stress testing in real-time systems %A Briand, Lionel C. %A Labiche, Yvan %A Shousha, Marwa %J Genetic Programming and Evolvable Machines %D 2006 %8 aug %V 7 %N 2 %@ 1389-2576 %F Briand:2006:GPEM %O Special Issue: Best of GECCO 2005 %X Reactive real-time systems have to react to external events within time constraints: Triggered tasks must execute within deadlines. It is therefore important for the designers of such systems to analyse the schedulability of tasks during the design process, as well as to test the system’s response time to events in an effective manner once it is implemented. This article explores the use of genetic algorithms to provide automated support for both tasks. Our main objective is then to automate, based on the system task architecture, the derivation of test cases that maximise the chances of critical deadline misses within the system; we refer to this testing activity as stress testing. A second objective is to enable an early but realistic analysis of tasks’ schedulability at design time. We have developed a specific solution based on genetic algorithms and implemented it in a tool. Case studies were run and results show that the tool (1) is effective at identifying test cases that will likely stress the system to such an extent that some tasks may miss deadlines, (2) can identify situations that were deemed to be schedulable based on standard schedulability analysis but that, nevertheless, exhibit deadline misses. %K genetic algorithms, Software verification and validation, Schedulability theory %9 journal article %R 10.1007/s10710-006-9003-9 %U http://dx.doi.org/10.1007/s10710-006-9003-9 %P 145-170 %0 Journal Article %T A Genetic Programming Approach to Engineering MRI Reporter Genes %A Bricco, Alexander R. %A Miralavy, Iliya %A Bo, Shaowei %A Perlman, Or %A Korenchan, David E. %A Farrar, Christian T. %A McMahon, Michael T. %A Banzhaf, Wolfgang %A Gilad, Assaf A. %J ACS Synthetic Biology %D 2023 %V 12 %N 4 %@ 2161-5063 %F Bricco:2023:ACSsynbio %X Here we develop a mechanism of protein optimization using a computational approach known as genetic programming. We developed an algorithm called Protein Optimisation Engineering Tool (POET). Starting from a small library of literature values, the use of this tool allowed us to develop proteins that produce four times more MRI contrast than what was previously state-of-the-art. Interestingly, many of the peptides produced using POET were dramatically different with respect to their sequence and chemical environment than existing CEST producing peptides, and challenge prior understandings of how those peptides function. While existing algorithms for protein engineering rely on divergent evolution, POET relies on convergent evolution and consequently allows discovery of peptides with completely different sequences that perform the same function with as good or even better efficiency. Thus, this novel approach can be expanded beyond developing imaging agents and can be used widely in protein engineering. %K genetic algorithms, genetic programming, protein engineering, CEST MRI %9 journal article %R 10.1021/acssynbio.2c00648 %U https://www.sciencedirect.com/science/article/pii/S2161506323002140 %U http://dx.doi.org/10.1021/acssynbio.2c00648 %P 1154-1163 %0 Conference Proceedings %T On the Trade-Off between Population Size and Number of Generations in GP for Program Synthesis %A Briesch, Martin %A Sobania, Dominik %A Rothlauf, Franz %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F briesch:2023:GECCOcomp %X When using genetic programming for program synthesis, we are usually constrained by a computational budget measured in program executions during evolution. The computational budget is influenced by the choice of population size and number of generations per run leading to a trade-off between both possibilities. To better understand this trade-off, we analyze the effects of different combinations of population sizes and number of generations on performance. Further, we analyze how the use of different variation operators affects this trade-off. We conduct experiments on a range of common program synthesis benchmarks and find that using larger population sizes lead to a better search performance. Additionally, we find that using high probabilities for crossover and mutation lead to higher success rates. Focusing on only crossover or using only mutation usually leads to lower search performance. In summary, we find that large populations combined with high mutation and crossover rates yield highest GP performance for program synthesis approaches. %K genetic algorithms, genetic programming, program synthesis, crossover, population size, generations, mutation: Poster %R 10.1145/3583133.3590681 %U http://dx.doi.org/10.1145/3583133.3590681 %P 535-538 %0 Conference Proceedings %T Improving Lexicase Selection with Informed Down-Sampling %A Briesch, Martin %A Boldi, Ryan %A Sobania, Dominik %A Lalejini, Alexander %A Helmuth, Thomas %A Rothlauf, Franz %A Ofria, Charles %A Spector, Lee %Y Gallagher, Marcus %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F briesch:2024:GECCOcomp %X This short paper presents the main findings of our work titled Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving, which was recently published in the Evolutionary Computation Journal. In this work, we introduce informed down-sampled lexicase selection to dynamically build diverse subsets of training cases during evolution using population statistics. We evaluate our method on a set of program synthesis problems in two genetic programming systems and find that informed down-sampling improves performance in both systems compared to random down-sampling when using lexicase selection. Additionally, we investigate the constructed down-samples and find that informed down-sampling can identify important training cases and does so across different evolutionary runs and systems. %K genetic algorithms, genetic programming, lexicase selection, informed down-sampling %R 10.1145/3638530.3664068 %U http://dx.doi.org/10.1145/3638530.3664068 %P 25-26 %0 Conference Proceedings %T On the Effects of Down-Sampling for Tournament and Lexicase Selection in Program Synthesis %A Briesch, Martin %Y Manzoni, Luca %Y Cussat-Blanc, Sylvain %Y Chen, Qi %S European Conference on Genetic Programming, EuroGP 2026 %D 2026 %8 August 10 apr %I Springer Nature %C Toulouse %F briesch:2026:EuroGP %K genetic algorithms, genetic programming %0 Conference Proceedings %T Functional Genetic Programming with Combinators %A Briggs, Forrest %A O’Neill, Melissa %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 Briggs:2006:ASPGP %X Prior program representations for genetic programming that incorporated features of modern programming languages solved harder problems than earlier representations, but required more complex genetic operators. We develop the idea of using combinator expressions as a program representation for genetic programming. This representation makes it possible to evolve programs with a variety of programming language constructs using simple genetic operators. We investigate the effort required to evolve combinator-expression solutions to several problems: linear regression, even parity on N inputs, and implementation of the stack and queue data structures. Genetic programming with combinator expressions compares favourably to prior approaches, namely the works of Yu [37], Kirshenbaum [18], Agapitos and Lucas [1], Wong and Leung [35], Koza [20], Langdon \citeLangdon:1995:GPdata, and Katayama [17]. %K genetic algorithms, genetic programming %U http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/forrest/fsb-meo-combs.pdf %P 110-127 %0 Journal Article %T Functional Genetic Programming and Exhaustive Program Search with Combinator Expressions %A Briggs, Forrest %A O’Neill, Melissa %J International Journal of Knowledge-Based and Intelligent Engineering Systems %D 2008 %V 12 %N 1 %I IOS Press %@ 1327-2314 %F Briggs:2008:IJKBIES %X Using a strongly typed functional programming language for genetic programming has many advantages, but evolving functional programs with variables requires complex genetic operators with special cases to avoid creating ill-formed programs. We introduce combinator expressions as an alternative program representation for genetic programming, providing the same expressive power as strongly typed functional programs, but in a simpler format that avoids variables and other syntactic clutter. We outline a complete genetic-programming system based on combinator expressions, including a novel generalised genetic operator, and also show how it is possible to exhaustively enumerate all well-typed combinator expressions up to a given size. Our experimental evidence shows that combinator expressions compare favourably with prior representations for functional genetic programming and also offers insight into situations where exhaustive enumeration outperforms genetic programming and vice versa. %K genetic algorithms, genetic programming, Lambda-expressions %9 journal article %R 10.3233/KES-2008-12105 %U http://content.iospress.com/articles/international-journal-of-knowledge-based-and-intelligent-engineering-systems/kes00140 %U http://dx.doi.org/10.3233/KES-2008-12105 %P 47-68 %0 Conference Proceedings %T An Interdisciplinary Investigation of the Evolution and Maintenance of Conditional Strategies in Chthamalus anisopoma, using Genetic Programming and a Quantitative Genetic Model %A Briney, Kristin %A Karpinski, Tod %Y Barry, Alwyn M. %S GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference %D 2003 %8 November %I AAAI %C Chigaco %F briney+karpinski:2003:gecco:workshop %K genetic algorithms, genetic programming %P 258-261 %0 Conference Proceedings %T Evaluation of stochastic algorithm performance on antenna optimization benchmarks %A Brinster, Irina %A De Wagter, Philippe %A Lohn, Jason %S Antennas and Propagation Society International Symposium (APSURSI), 2012 IEEE %D 2012 %C Chicago, IL, USA %F Brinster:2012:APSURSI %X This paper evaluates performance of ten stochastic search algorithms on a benchmark suite of four antenna optimisation problems. Hill climbers (HC) serve as baseline algorithms. We implement several variants of genetic algorithms, evolution strategies, and genetic programming as examples of competitive strategy for achieving optimal solution. Ant colony and particle-swarm optimisation represent cooperative strategy. Static performance is measured in terms of success rates and mean hit time, while dynamic performance is evaluated from the development of the mean solution quality. Among the evaluated algorithms, steady-state GA provides the best trade-off between efficiency and effectiveness. PSO is recommended for noisy problems, while ACO and GP should be avoided for antenna optimisations because of their low efficiencies. %K genetic algorithms, genetic programming, ant colony optimisation, antennas, particle swarm optimisation, search problems, stochastic processes, Hill climbers, ant colony optimisation, antenna optimisation benchmark, cooperative strategy, evolution strategies, particle swarm optimisation, steady-state GA, stochastic algorithm performance, stochastic search algorithm, Antennas, Arrays, Benchmark testing, Electromagnetics, Heuristic algorithms, Optimisation %R 10.1109/APS.2012.6348758 %U http://dx.doi.org/10.1109/APS.2012.6348758 %0 Conference Proceedings %T A Diversity Study in Genetic Algorithms for Job Shop Scheduling Problems %A Brizuela, Carlos A. %A Sannomiya, Nobuo %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 brizuela:1999:ADSGAJSSP %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-333.pdf %P 75-82 %0 Book Section %T Evolving Reusable Subroutines for Genetic Programming %A Brock, Oliver %E Koza, John R. %B Artificial Life at Stanford 1994 %D 1994 %8 jun %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-182105-2 %F brock:1994:ers %X Although automatically defined functions (ADFs) are able to significantly reduce the computational effort required in genetic programming, reasonably diÆcult problems still require large amounts of computation time. However, every time genetic programming evolves a program to solve a problem those ADFs have to be rediscovered from scratch. If the ADFs of a correct program contain partial solutions that are generally useful, they can be used to solve similar problems. This paper proposes a technique to make the information of successful ADFs accessible to genetic programming in order to reduce the computational costs of solving related problems with less computational effort and demonstrates its utility using the example of the even n-parity function. %K genetic algorithms, genetic programming %U http://robotics.stanford.edu/users/oli/PAPERS/a-life.ps %P 11-19 %0 Book Section %T Evolution in Nanomaterio: The NASCENCE Project %A Broersma, Hajo %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 Broersma:2017:miller %X This chapter describes some of the work carried out by members of the NASCENCE project, an FP7 project sponsored by the European Community. After some historical notes and background material, the chapter explains how nanoscale material systems have been configured to perform computational tasks by finding appropriate configuration signals using artificial evolution. Most of this exposition is centred around the work that has been carried out at the MESA+ Institute for Nanotechnology at the University of Twente using disordered networks of nanoparticles. The interested reader will also find many pointers to references that contain more details on work that has been carried out by other members of the NASCENCE consortium on composite materials based on single-walled carbon nanotubes. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-67997-6_4 %U http://dx.doi.org/10.1007/978-3-319-67997-6_4 %P 87-111 %0 Generic %T Evolutionary NAS with Gene Expression Programming of Cellular Encoding %A Broni-Bediako, Clifford %A Murata, Yuki %A Mormille, Luiz Henrique %A Atsumi, Masayasu %D 2020 %I arXiv %F journals/corr/abs-2005-13110 %K genetic algorithms, genetic programming, gene expression programming %U https://arxiv.org/abs/2005.13110 %0 Conference Proceedings %T Evolutionary NAS with Gene Expression Programming of Cellular Encoding %A Broni-Bediako, Cliford %A Murata, Yuki %A Mormille, Luiz H. B. %A Atsumi, Masayasu %S 2020 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2020 %8 dec %F Broni-Bediako:2020:SSCI %X The renaissance of neural architecture search (NAS) has seen classical methods such as genetic algorithms (GA) and genetic programming (GP) being exploited for convolutional neural network (CNN) architectures. While recent work have achieved promising performance on visual perception tasks, the direct encoding scheme of both GA and GP has functional complexity deficiency and does not scale well on large architectures like CNN. To address this, we present a new generative encoding scheme-symbolic linear generative encoding (SLGE)-simple, yet a powerful scheme which embeds local graph transformations in chromosomes of linear fixed-length string to develop CNN architectures of variant shapes and sizes via an evolutionary process of gene expression programming. In experiments, the effectiveness of SLGE is shown in discovering architectures that improve the performance of the state-of-the-art handcrafted CNN architectures on CIFAR-10 and CFAR-100 image classification tasks; and achieves a competitive classification error rate with the existing NAS methods using fewer GPU resources. %K genetic algorithms, genetic programming %R 10.1109/SSCI47803.2020.9308346 %U http://dx.doi.org/10.1109/SSCI47803.2020.9308346 %P 2670-2676 %0 Conference Proceedings %T Working with OpenCL to speed up a genetic programming financial forecasting algorithm: initial results %A Brookhouse, James %A Otero, Fernando E. B. %A Kampouridis, Michael %Y Wagner, Stefan %Y Affenzeller, Michael %S GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft) %D 2014 %8 December 16 jul %I ACM %C Vancouver, BC, Canada %F Brookhouse:2014:GECCOcomp %X The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device. %K genetic algorithms, genetic programming, GPU %R 10.1145/2598394.2605689 %U https://kar.kent.ac.uk/42144/ %U http://dx.doi.org/10.1145/2598394.2605689 %P 1117-1124 %0 Conference Proceedings %T Artificial Life and Real Robots %A Brooks, Rodney A. %Y Varela, Francisco J. %Y Bourgine, Paul %S Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life %D 1991 %8 November 13 dec %I MIT Press %C Paris %@ 0-262-72019-1 %F Brooks92RR9 %X The first part of this paper explores the general issues in using Artificial Life techniques to program actual mobile robots. In particular it explores the difficulties inherent in transferring programs evolved in a simulated environment to run on an actual robot. It examines the dual evolution of organism morphology and nervous systems in biology. It proposes techniques to capture some of the search space pruning that dual evolution offers in the domain of robot programming. It explores the relationship between robot morphology and program structure, and techniques for capturing regularities across this mapping. The second part of the paper is much more specific. It proposes techniques which could allow realistic explorations concerning the evolution of programs to control physically embodied mobile robots. In particular we introduce a new abstraction for behaviour-based robot programming which is specially tailored to be used with genetic programming techniques. To compete with hand coding techniques it will be necessary to automatically evolve programs that are one to two orders of magnitude more complex than those previously reported in any domain. Considerable extensions to previously reported approaches to genetic programming are necessary in order to achieve this goal. %K genetic algorithms, genetic programming %U http://people.csail.mit.edu/brooks/papers/real-robots.pdf %P 3-10 %0 Conference Proceedings %T Progressive Insular Cooperative GP %A Brotto Rebuli, Karina %A Vanneschi, Leonardo %Y Hu, Ting %Y Lourenco, Nuno %Y Medvet, Eric %S EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming %S LNCS %D 2021 %8 July 9 apr %V 12691 %I Springer Verlag %C Virtual Event %F Brotto-Rebuli:2021:EuroGP %X This work presents a novel genetic programming system for multi-class classification, called progressively insular cooperative genetic programming (PIC GP). Based on the idea that effective multiclass classification can be obtained by appropriately joining classifiers that are highly specialized on the single classes, PIC GP evolves, at the same time, two populations. The first population contains individuals called specialists, and each specialist is optimized on one specific target class. The second population contains higher-level individuals, called teams, that join specialists to obtain the final algorithm prediction. By means of three simple parameters, PIC GP can tune the amount of cooperation between specialists of different classes. The first part of the paper is dedicated to a study of the influence of these parameters on the evolution dynamics. The obtained results indicate that PIC GP achieves the best performance when the evolution begins with a high level of cooperation between specialists of different classes, and then this type of cooperation is progressively decreased, until only specialists of the same class can cooperate between each other. The last part of the work is dedicated to an experimental comparison between PIC GP and a set of state-of-the-art classification algorithms. The presented results indicate that PIC GP outperforms the majority of its competitors on the studied test problems. %K genetic algorithms, genetic programming, Multiclass classification, Cooperative evolution %R 10.1007/978-3-030-72812-0_2 %U http://dx.doi.org/10.1007/978-3-030-72812-0_2 %P 19-35 %0 Conference Proceedings %T A preliminary study of Prediction Interval Methods with Genetic Programming %A Brotto Rebuli, Karina %A Giacobini, Mario %A Tallone, Niccolo %A Vanneschi, Leonardo %Y Trautmann, Heike %Y Doerr, Carola %Y Moraglio, Alberto %Y Bartz-Beielstein, Thomas %Y Filipic, Bogdan %Y Gallagher, Marcus %Y Ong, Yew-Soon %Y Gupta, Abhishek %Y Kononova, Anna V. %Y Wang, Hao %Y Emmerich, Michael %Y Bosman, Peter A. N. %Y Zaharie, Daniela %Y Caraffini, Fabio %Y Dreo, Johann %Y Auger, Anne %Y Dietric, Konstantin %Y Dufosse, Paul %Y Glasmachers, Tobias %Y Hansen, Nikolaus %Y Mersmann, Olaf %Y Posik, Petr %Y Tusar, Tea %Y Brockhoff, Dimo %Y Eftimov, Tome %Y Kerschke, Pascal %Y Naujoks, Boris %Y Preuss, Mike %Y Volz, Vanessa %Y Derbel, Bilel %Y Li, Ke %Y Li, Xiaodong %Y Zapotecas, Saul %Y Zhang, Qingfu %Y Coletti, Mark %Y Schuman, Catherine (Katie) %Y Scott, Eric “Siggy” %Y Patton, Robert %Y Wiegand, Paul %Y Bassett, Jeffrey K. %Y Gunaratne, Chathika %Y Chugh, Tinkle %Y Allmendinger, Richard %Y Hakanen, Jussi %Y Tauritz, Daniel %Y Woodward, John %Y Lopez-Ibanez, Manuel %Y McCall, John %Y Bacardit, Jaume %Y Brownlee, Alexander %Y Cagnoni, Stefano %Y Iacca, Giovanni %Y Walker, David %Y Toutouh, Jamal %Y O’Reilly, UnaMay %Y Machado, Penousal %Y Correia, Joao %Y Nesmachnow, Sergio %Y Ceberio, Josu %Y Villanueva, Rafael %Y Hidalgo, Ignacio %Y Fernandez de Vega, Francisco %Y Paolo, Giuseppe %Y Coninx, Alex %Y Cully, Antoine %Y Gaier, Adam %Y Wagner, Stefan %Y Affenzeller, Michael %Y Bruce, Bobby R. %Y Nowack, Vesna %Y Blot, Aymeric %Y Winter, Emily %Y Langdon, William B. %Y Petke, Justyna %Y Fernandez Alzueta, Silvino %Y Valledor Pellicer, Pablo %Y Stuetzle, Thomas %Y Paetzel, David %Y Wagner, Alexander %Y Heider, Michael %Y Veerapen, Nadarajen %Y Malan, Katherine %Y Liefooghe, Arnaud %Y Verel, Sebastien %Y Ochoa, Gabriela %Y Omidvar, Mohammad Nabi %Y Sun, Yuan %Y Tarantino, Ernesto %Y Ivanoe, De Falco %Y Della Cioppa, Antonio %Y Umberto, Scafuri %Y Rieffel, John %Y Mouret, Jean-Baptiste %Y Doncieux, Stephane %Y Nikolaidis, Stefanos %Y Togelius, Julian %Y Fontaine, Matthew C. %Y Georgescu, Serban %Y Chicano, Francisco %Y Whitley, Darrell %Y Kyriienko, Oleksandr %Y Dahl, Denny %Y Shir, Ofer %Y Spector, Lee %Y Rahat, Alma %Y Everson, Richard %Y Fieldsend, Jonathan %Y Wang, Handing %Y Jin, Yaochu %Y Hemberg, Erik %Y Elsayed, Marwa A. %Y Kommenda, Michael %Y La Cava, William %Y Kronberger, Gabriel %Y Gustafson, Steven %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F rebuli:2022:GECCOcomp %X This article presents an exploratory study on modelling Prediction Intervals (PI) with two Genetic Programming (GP) methods. A PI is the range of values in which the real target value is expected to fall into. It should combine two contrasting properties: to be as narrow as possible and to include as many data observations as possible. One proposed GP method, called CWC-GP, evolves simultaneously the lower and upper boundaries of the PI using a single fitness measure that combines the width and the probability coverage of the PI. The other proposed GP method, called LUBE-GP, evolves independently the boundaries of the PI with a multi-objective approach, in which one fitness aims to minimise the width and the other aims to maximise the probability coverage of the PI. Both methods were applied with Direct and Sequential approaches. In the former, the PI is assessed without the crisp prediction of the model. In the latter, the method makes use of the crisp prediction to find the PI boundaries. The proposed methods showed to have good potential on assessing PIs and the presented preliminary results pave the way to further investigations. The most promising results were observed with the Sequential CWC-GP. %K genetic algorithms, genetic programming, modelling uncertainty, crisp prediction, prediction interval %R 10.1145/3520304.3528806 %U http://dx.doi.org/10.1145/3520304.3528806 %P 530-533 %0 Conference Proceedings %T Single and Multi-objective Genetic Programming Methods for Prediction Intervals %A Brotto Rebuli, Karina %A Giacobini, Mario %A Tallone, Niccolo %A Vanneschi, Leonardo %Y De Stefano, Claudio %Y Fontanella, Francesco %Y Vanneschi, Leonardo %S WIVACE 2022, XVI International Workshop on Artificial Life and Evolutionary Computation %S Computer and Information Science %D 2022 %8 sep 14 16 %V 1780 %I Springer %C Gaeta (LT), Italy %F Brotto-Rebuli:2022:WIVACE %X A PI is the range of values in which the real target value of a supervised learning task is expected to fall into, and it should combine two contrasting properties: to be as narrow as possible, and to include as many data observations as possible. This article presents an study on modeling Prediction Intervals (PI) with two Genetic Programming(GP) methods. The first proposed GP method is called CWC-GP, and it evolves simultaneously the lower and upper boundaries of the PI using a single fitness measure. This measure is the Coverage Width-based Criterion (CWC), which combines the width and the probability coverage of the PI. The second proposed GP method is called LUBE-GP, and it evolves independently the lower and upper boundaries of the PI. This method applies a multi-objective approach, in which one fitness aims to minimise the width and the other aims to maximise the probability coverage of the PI. Both methods were applied both with the Direct and the Sequential approaches. In the former, the PI is assessed without the crisp prediction of the model. In the latter, the method makes use of the crisp prediction to find the PI boundaries. The proposed methods showed to have good potential on assessing PIs and the results pave the way to further investigations. %K genetic algorithms, genetic programming %R 10.1007/978-3-031-31183-3_17 %U http://dx.doi.org/10.1007/978-3-031-31183-3_17 %P 205-218 %0 Conference Proceedings %T A Comparison of Structural Complexity Metrics for Explainable Genetic Programming %A Brotto Rebuli, Karina %A Giacobini, Mario %A Silva, Sara %A Vanneschi, Leonardo %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F brotto-rebuli:2023:GECCOcomp %X Genetic Programming (GP) has the potential to generate intrinsically explainable models. Despite that, in practice, this potential is not fully achieved because the solutions usually grow too much during the evolution. The excessive growth together with the functional and structural complexity of the solutions increase the computational cost and the risk of overfitting. Thus, many approaches have been developed to prevent the solutions to grow excessively in GP. However, it is still an open question how these approaches can be used for improving the interpretability of the models. This article presents an empirical study of eight structural complexity metrics that have been used as evaluation criteria in multi-objective optimisation. Tree depth, size, visitation length, number of unique features, a proxy for human interpretability, number of operators, number of non-linear operators and number of consecutive nonlinear operators were tested. The results show that potentially the best approach for generating good interpretable GP models is to use the combination of more than one structural complexity metric. %K genetic algorithms, genetic programming, complexity metrics, explainable AI, XAI, interpretable models: Poster %R 10.1145/3583133.3590595 %U https://novaresearch.unl.pt/en/publications/a-comparison-of-structural-complexity-metrics-for-explainable-gen %U http://dx.doi.org/10.1145/3583133.3590595 %P 539-542 %0 Report %T Exploring 3D design worlds using Lindenmayer systems and Genetic Programming %A Broughton, T. %A Coates, P. %A Jackson, H. %D 1998 %I University of East London %F broughton:1998:e3DwlsGPwww %K genetic algorithms, genetic programming %0 Book Section %T Exploring Three-dimensional design worlds using Lindenmeyer Systems and Genetic Programming %A Broughton, T. %A Coates, Paul S. %A Jackson, Helen %E Bentley, Peter %B Evolutionary Design Using Computers %D 1999 %I Academic press %C London, UK %@ 0-12-089070-4 %F broughton:1999:e3DwlsGPwww %X The raw Lindenmeyer-system (L-system) generates random branching structures in the isospatial grid. Using a three dimensional L-system, early experiments (reported CAAD Futures 97, \citecoates:1997:GPx3dw ) showed that globally defined useful form (the flytrap) can evolve quite quickly using one fitness function This paper will describe further experiments undertaken using an improved L-system and multigoal evolution to evolve space/enclosure systems that satisfy both the requirements of space use and those of enclosure. This is implemented as symbiotic coevolution between: 1) L-system branching tree system whose goal is to surround the largest volume of empty space (defined as space which is ’invisible’ to an outside observer). 2) Circulation system using walking three dimensional turtles to measure the spatial property of the enclosed space. The resulting enclosure phenotypes can be realised using the occupied isospatial grid points as nodes of a nurbs surface. The chapter covers: 1.0 Introduction to Genetic Programming, L-Systems and the Isospatial Grid 2.0 Three dimensional L-systems, production rules and s-expressions 3.0 Evolutionary Experiments in Simple Environments 4.0 Symbiotic Coevolution %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/P.Bentley/evdes.html %P 323-341 %0 Journal Article %T AI, Teamwork is Goal of Robot Soccer Tourney %A Brown, Janelle %J Wired News %D 1997 %8 March %V 5 %N 10 %F brown:1997:GPsoccer %X It’s got all the excitement of real soccer, but without the bad haircuts and big egos. This week the Robot Soccer World Cup debuts at the International Joint Conferences on Artificial Intelligence in Japan. Matching robot against robot, RoboCup is making breakthroughs in artificial life and multi-agent collaboration, while providing a few kicks in the process. %K genetic algorithms, genetic programming %9 journal article %0 Conference Proceedings %T Using Evolvable Regressors to Partition Data %A Brown, Joseph A. %A Ashlock, Daniel %Y Dagli, Cihan H. %S ANNIE 2010, Intelligent Engineering Systems through Artificial Neural Networks %D 2010 %8 nov 1 3 %V 20 %I ASME %C St. Louis, Mo, USA %F Brown:2010:ANNIE %X This manuscript examines permitting multiple populations of evolvable regressors to compete to be the best model for the largest number of data points. Competition between populations enables a natural process of specialisation that implicitly partitions the data. This partitioning technique uses function-stack based regressors and has the ability to discover the natural number of clusters in a data set via a process of sub-population collapse. %K genetic algorithms, genetic programming %R 10.1115/1.859599.paper24 %U http://www.uoguelph.ca/~jbrown16/EvolRegress.pdf %U http://dx.doi.org/10.1115/1.859599.paper24 %P 187-194 %0 Thesis %T Regression and Classification from Extinction %A Brown, Joseph Alexander %D 2014 %8 October %C Canada %C School of Computer Science, The University of Guelph %F Brown:thesis %X Evolutionary Algorithms use the principles of natural selection and biological evolution to act as search and optimisation tools. Two novel Spatially Structured Evolutionary Algorithms: the Multiple Worlds Model (MWM) and Multiple Agent Genetic Networks (MAGnet) are presented. These evolutionary algorithms create evolved unsupervised classifiers for data. Both have a property of subpopulation collapse, where a population/node receives little or no fitness implying the number of classes is too large. This property has the best biological analog of extinction. MWM has a number of evolving populations of candidate solutions. The novel fitness function selects one member from each population, and fitness is divided between. Each of these populations meets with the biological definition of a separate species; each is a group of organisms which produces offspring within their type, but not outside of it. This fitness function creates an unsupervised classification by partitioning the data, based on which population is of highest fitness, and creates an evolved classifier for that partition. MAGnet involves a number of evolving agents spread about a graph, the nodes of which contain individual data members or problem instances. The agents will in turn test their fitness on each of the neighbouring nodes in the graph, moving to the one where they have the highest fitness. During this move they may choose to take one of these problem instances with them. The agent then undergoes evolutionary operations based on which neighbours are on the node. The locations of the problem instances over time are sorted by the evolving agents, and the agents on a node act as a classifier %K genetic algorithms, genetic programming Bioinformatics %9 Ph.D. thesis %U http://hdl.handle.net/10214/7793 %0 Journal Article %T Tile Based Genetic Programming Generation for Diablo-like games %A Brown, Joseph Alexander %A Valtchanov, Valtchan %J Seeds %D 2017 %V 2 %F Brown:2017:GP_Diablo %X Diablo’s initial pitch document highlights the use of Procedural Content as a prominent feature of the game: The heart of Diablo is the randomly created dungeon. A new dungeon level is generated... %K genetic algorithms, genetic programming, game %9 journal article %U http://www.procjam.com/seeds/issues/2.pdf %P 89-93 %0 Journal Article %T Efficient hybrid evolutionary optimization of interatomic potential models %A Brown, W. Michael %A Thompson, Aidan P. %A Schultz, Peter A. %J Journal of Chemical Physics %D 2010 %V 132 %N 2 %@ 1089-7690 %F Brown:2010:JCP %X The lack of adequately predictive atomistic empirical models precludes meaningful simulations for many materials systems. We describe advances in the development of a hybrid, population based optimisation strategy intended for the automated development of material specific inter atomic potentials. We compare two strategies for parallel genetic programming and show that the Hierarchical Fair Competition algorithm produces better results in terms of transferability, despite a lower training set accuracy. We evaluate the use of hybrid local search and several fitness models using system energies and/or particle forces. We demonstrate a drastic reduction in the computation time with the use of a correlation-based fitness statistic. We show that the problem difficulty increases with the number of atoms present in the systems used for model development and demonstrate that vectorisation can help to address this issue. Finally, we show that with the use of this method, we are able to ’rediscover’ the exact model for simple known two- and three-body interatomic potentials using only the system energies and particle forces from the supplied atomic configurations. %K genetic algorithms, genetic programming, potential energy functions, search problems %9 journal article %R 10.1063/1.3294562 %U http://dx.doi.org/10.1063/1.3294562 %P 024108 %0 Conference Proceedings %T Classifier System Renaissance: New Analogies, New Directions %A Brown Cribbs III, H. %A Smith, Robert E. %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 browncribbs:1996:nand %K Classifier Systems, Genetic Algorithms %U http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap89.pdf %P 547-552 %0 Thesis %T Automatic Generation and Evaluation of Recombination Games %A Browne, Cameron %D 2008 %8 feb %C Australia %C Faculty of Information Technology, Queensland University of Technology %F CameronBrowne:thesis %X Many new board games are designed each year, ranging from the unplayable to the truly exceptional. For each successful design there are untold numbers of failures; game design is something of an art. Players generally agree on some basic properties that indicate the quality and viability of a game, however these properties have remained subjective and open to interpretation. The aims of this thesis are to determine whether such quality criteria may be precisely defined and automatically measured through self-play in order to estimate the likelihood that a given game will be of interest to human players, and whether this information may be used to direct an automated search for new games of high quality. Combinatorial games provide an excellent test bed for this purpose as they are typically deep yet described by simple well defined rule sets. To test these ideas, a game description language was devised to express such games and a general game system implemented to play, measure and explore them. Key features of the system include modules for measuring statistical aspects of self-play and synthesising new games through the evolution of existing rule sets. Experiments were conducted to determine whether automated game measurements correlate with rankings of games by human players, and whether such correlations could be used to inform the automated search for new high quality games. The results support both hypotheses and demonstrate the emergence of interesting new rule combinations. %K genetic algorithms, genetic programming, Combinatorial, Games, Design, Aesthetics, Evolutionary, Search, Yavalath %9 Ph.D. thesis %U http://www.cameronius.com/cv/publications/thesis-2.47.zip %0 Book %T Evolutionary Game Design %A Browne, Cameron %D 2011 %I Springer %F CameronBrowne:book %X This book tells the story of Yavalath, the first computer-generated board game to be commercially released... Table of contents Introduction Games in General The Ludi System Measuring Games Evolving Games Viable Games Yavalath Conclusion %K genetic algorithms, genetic programming %R 10.1007/978-1-4471-2179-4 %U http://www.springer.com/computer/ai/book/978-1-4471-2178-7 %U http://dx.doi.org/10.1007/978-1-4471-2179-4 %0 Journal Article %T Yavalath: Sample chapter from Evolutionary Game Design %A Browne, Cameron %J ICGA Journal %D 2012 %V 35 %N 1 %F Browne:2012:ICGA.Yavalath %K genetic algorithms, genetic programming %9 journal article %U https://chessprogramming.wikispaces.com/ICGA+Journal %0 Journal Article %T Evolutionary Game Design: Automated Game Design Comes of Age %A Browne, Cameron %J SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation %D 2012 %V 6 %N 2 %@ 1931-8499 %F Browne_2012_sigevolution %X The HUMIES awards are an annual competition held in conjunction with the Genetic and Evolutionary Computation Conference (GECCO), in which cash prizes totalling 10,000 USA dollars are awarded to the most human-competitive results produced by any form of evolutionary computation published in the previous year. This article describes the gold medal-winning entry from the 2012 Humies competition, based on the LUDI system for playing, evaluating and creating new board games. LUDI was able to demonstrate human competitive results in evolving novel board games that have gone on to be commercially published, one of which, Yavalath, has been ranked in the top 2.5percent of abstract board games ever invented. Further evidence of human-competitiveness was demonstrated in the evolved games implicitly capturing several principles of good game design, outperforming human designers in at least one case, and going on to inspire a new subgenre of games. %K genetic algorithms, genetic programming, LUDI, game description language GDL %9 journal article %R 10.1145/2597453.2597454 %U http://www.sigevolution.org/issues/pdf/SIGEVOlution0602.pdf %U http://dx.doi.org/10.1145/2597453.2597454 %P 3-15 %0 Generic %T Vision-Based Obstacle Avoidance: A Coevolutionary Approach %A Browne, David %D 1996 %8 oct %C Australia %F browne:1996:bsc %X This thesis investigates the design of robust obstacle avoidance strategies. Specifically, simulated coevolution is used to breed steering agents and obstacle courses in a ‘computational arms race’. Both steering agent strategies and obstacle courses are represented by computer programs, and are coevolved according to the genetic programming paradigm. Previous research has found it difficult to evolve robust vision based obstacle avoidance agents. By independently evolving obstacle avoidance agents against a competing evolving species (ie the obstacle courses), it is hypothesised that the robustness of the agents will be increased. The simon system, an existing genetic programming tool, is modified and used to evolve both the obstacle avoidance agents and the obstacle courses. A comparison is made between the robustness of coevolved obstacle avoidance agents and traditionally evolved (non-coevolved) agents. Robustness is measured by average performance in a series of randomly generated obstacle courses. Experimental results show that the average robustness of the coevolved oa agents is greater than that of the traditionally evolved, and statistically it is shown that this data is representative of all cases. It is therefore concluded that coevolution is applicable to oa type problems, and can be used to evolve more robust, general purpose Vision-Based Obstacle Avoidance agents. %K genetic algorithms, genetic programming %U http://www.csse.monash.edu.au/hons/projects/1996/David.Browne/ %0 Journal Article %T Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming %A Browne, Nigel P. A. %A dos Santos, Marcus V. %J Applied Computational Intelligence and Soft Computing %D 2010 %V 2010 %F Browne:2010:ACISC %X Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP’s representation. %K genetic algorithms, genetic programming, gene expression programming %9 journal article %R 10.1155/2010/409045 %U http://downloads.hindawi.com/journals/acisc/2010/409045.pdf %U http://dx.doi.org/10.1155/2010/409045 %P ArticleID409045 %0 Conference Proceedings %T Code Fragments: Past and Future use in Transfer Learning %A Browne, Will N. %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 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation %D 2016 %8 20 24 jul %I ACM %C Denver, Colorado, USA %F Browne:2016:GECCOcomp %X Code Fragments (CFs) have existed as an extension to Evolutionary Computation, specifically Learning Classifiers Systems (LCSs), for half a decade. Through the scaling, abstraction and reuse of both knowledge and functionality that CFs enable, interesting problems have been solved beyond the capability of any other technique. This paper traces the development of the different CF-based systems and outlines future research directions that will form the basis for advanced Transfer Learning in LCSs. %K genetic algorithms, genetic programming %R 10.1145/2908961.2931737 %U http://dx.doi.org/10.1145/2908961.2931737 %P 1405-1405 %0 Journal Article %T Search-Based Energy Optimization of Some Ubiquitous Algorithms %A Brownlee, Alexander Edward Ian %A Burles, Nathan %A Swan, Jerry %J IEEE Transactions on Emerging Topics in Computational Intelligence %D 2017 %8 jun %V 1 %N 3 %@ 2471-285X %F Brownlee:2017:ieeeETCI %X Reducing computational energy consumption is of growing importance, particularly at the extremes (i.e., mobile devices and datacentres). Despite the ubiquity of the Java virtual machine (JVM), very little work has been done to apply search-based software engineering (SBSE) to minimize the energy consumption of programs that run on it. We describe OPACITOR, a tool for measuring the energy consumption of JVM programs using a bytecode level model of energy cost. This has several advantages over time-based energy approximations or hardware measurements. It is 1) deterministic, 2) unaffected by the rest of the computational environment, 3) able to detect small changes in execution profile, making it highly amenable to metaheuristic search, which requires locality of representation. We show how generic SBSE approaches coupled with OPACITOR achieve substantial energy savings for three widely used software components. Multilayer perceptron implementations minimizing both energy and error were found, and energy reductions of up to 70percent and 39.85percent were obtained over the original code for Quicksort and object-oriented container classes, respectively. These highlight three important considerations for automatically reducing computational energy: tuning software to particular distributions of data; trading off energy use against functional properties; and handling internal dependencies that can exist within software that render simple sweeps over program variants sub-optimal. Against these, global search greatly simplifies the developer’s job, freeing development time for other tasks. %K genetic algorithms, genetic programming, genetic improvement, SBSE, Energy, Java %9 journal article %R 10.1109/TETCI.2017.2699193 %U http://eprints.whiterose.ac.uk/117916/1/07935484_1.pdf %U http://dx.doi.org/10.1109/TETCI.2017.2699193 %P 188-201 %0 Conference Proceedings %T Relating training instances to automatic design of algorithms for bin packing via features %A Brownlee, Alexander E. I. %A Woodward, John R. %A Veerapen, Nadarajen %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 Brownlee:2018:GECCOcomp %X Automatic Design of Algorithms (ADA) treats algorithm choice and design as a machine learning problem, with problem instances as training data. However, this paper reveals that, as with classification and regression, for ADA not all training sets are equally valuable. We apply genetic programming ADA for bin packing to several new and existing benchmark sets. Using sets with narrowly-distributed features for training results in highly specialised algorithms, whereas those with well-spread features result in very general algorithms. Variance in certain features has a strong correlation with the generality of the trained policies. %K genetic algorithms, genetic programming, automatic design of algorithms, bin packing, features %R 10.1145/3205651.3205748 %U http://dx.doi.org/10.1145/3205651.3205748 %P 135-136 %0 Conference Proceedings %T Gin: genetic improvement research made easy %A Brownlee, Alexander E. I. %A Petke, Justyna %A Alexander, Brad %A Barr, Earl T. %A Wagner, Markus %A White, David R. %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 Brownlee:2019:GECCO %X Genetic improvement (GI) is a young field of research on the cusp of transforming software development. GI uses search to improve existing software. Researchers have already shown that GI can improve human-written code, ranging from program repair to optimising run-time, from reducing energy-consumption to the transplantation of new functionality. Much remains to be done. The cost of re-implementing GI to investigate new approaches is hindering progress. Therefore, we present Gin, an extensible and modifiable toolbox for GI experimentation, with a novel combination of features. Instantiated in Java and targeting the Java ecosystem, Gin automatically transforms, builds, and tests Java projects. Out of the box, Gin supports automated test-generation and source code profiling. We show, through examples and a case study, how Gin facilitates experimentation and will speed innovation in GI. %K genetic algorithms, genetic programming, genetic improvement, Search-based Software Engineering, SBSE, Software engineering, Software notations and tools, GI %R 10.1145/3321707.3321841 %U https://cs.adelaide.edu.au/users/markus/pub/2019gecco-gintool.pdf %U http://dx.doi.org/10.1145/3321707.3321841 %P 985-993 %0 Conference Proceedings %T Injecting Shortcuts for Faster Running Java Code %A Brownlee, Alexander %A Petke, Justyna %A Rasburn, Anna F. %Y Brownlee, Alexander (Sandy) %Y Haraldsson, Saemundur O. %Y Petke, Justyna %Y Woodward, John R. %S 2020 IEEE Congress on Evolutionary Computation (CEC) %D 2020 %8 jul 19 24 %I IEEE %C Internet %F Brownlee:2020:CEC %O Special Session on Genetic Improvement %X Genetic Improvement of software applies search methods to existing software to improve the target program in some way. Impressive results have been achieved, including substantial speedups, using simple operations that replace, swap and delete lines or statements within the code. Often this is achieved by specialising code, removing parts that are unnecessary for particular use-cases. Previous work has shown that there is a great deal of potential in targeting more specialised operations that modify the code to achieve the same functionality in a different way. We propose six new edit types for Genetic Improvement of Java software, based on the insertion of break, continue and return statements. The idea is to add shortcuts that allow parts of the program to be skipped in order to speed it up. 10000 randomly-generated instances of each edit were applied to three open-source applications taken from GitHub. The key findings are: (1) compilation rates for inserted statements without surrounding if statements are 1.5 to 18.3percent; (2) edits where the insert statement is embedded within an if have compilation rates of 3.2 to 55.8percent; (3) of those that compiled, all 6 edits have a high rate of passing tests (Neutral Variant Rate), >60percent in all but one case, and so have the potential to be performance improving edits. Finally, a preliminary experiment based on local search shows how these edits might be used in practice. %K genetic algorithms, genetic programming, genetic improvement, GI, SBSE, GIN %R 10.1109/CEC48606.2020.9185708 %U http://geneticimprovementofsoftware.com/paper_pdfs/E-24667.pdf %U http://dx.doi.org/10.1109/CEC48606.2020.9185708 %0 Conference Proceedings %T Exploring the Accuracy – Energy Trade-off in Machine Learning %A Brownlee, Alexander E. I. %A Adair, Jason %A Haraldsson, Saemundur O. %A Jabbo, John %Y Petke, Justyna %Y Bruce, Bobby R. %Y Huang, Yu %Y Blot, Aymeric %Y Weimer, Westley %Y Langdon, W. B. %S GI @ ICSE 2021 %D 2021 %8 30 may %I IEEE %C internet %F Brownlee:2021:GI %X Machine learning accounts for considerable global electricity demand and resulting environmental impact, as training a large deep-learning model produces 284000 kgs of the greenhouse gas carbon dioxide. In recent years, search-based approaches have begun to explore improving software to consume less energy. Machine learning is a particularly strong candidate for this because it is possible to trade off functionality (accuracy) against energy consumption, whereas with many programs functionality is simply a pass-or-fail constraint. We use a grid search to explore hyperparameter configurations for a multilayer perceptron on five classification data sets, considering trade-offs of classification accuracy against training or inference energy. On one data set, we show that 77 percent of energy consumption for inference can saved by reducing accuracy from 94.3 percent to 93.2 percent. Energy for training can also be reduced by 30-50 percent with minimal loss of accuracy. We also find that structural parameters like hidden layer size is a major driver of the energy-accuracy trade-off, but there is some evidence that non-structural hyperparameters influence the trade-off too. We also show that a search-based approach has the potential to identify these trade-offs more efficiently than the grid search. %K genetic algorithms, genetic programming, genetic improvement, AI, ML, PyRAPL, NSGA-II jMetalPy %R 10.1109/GI52543.2021.00011 %U https://geneticimprovementofsoftware.com/paper_pdfs/gi2021icse/brownlee_gi-icse_2021.pdf %U http://dx.doi.org/10.1109/GI52543.2021.00011 %P 11-18 %0 Journal Article %T Genetic Improvement @ ICSE 2021: Personal reflection of a Workshop Participant %A Brownlee, Alexander E. I. %J SIGSOFT Software Engineering Notes %D 2021 %8 oct %V 46 %N 4 %I Association for Computing Machinery %@ 0163-5948 %F Brownlee:2021:SEN %X Following Dr. Stephanie Forrest of Arizona State University keynote \citeForrest:2021:GI presentation there was a wide ranging discussion at the tenth international Genetic Improvement workshop, GI-2021 @ ICSE (held as part of the International Conference on Software Engineering on Sunday 30th May 2021) \citePetke:2021:ICSEworkshop. Topics included a growing range of target systems and applications, algorithmic improvements, wide-ranging questions about how other fields (especially evolutionary computation) can inform advances in GI, and about how GI is branded to other disciplines. We give a personal perspective on the workshop proceedings, the discussions that took place, and resulting prospective directions for future research. %K genetic algorithms, genetic programming, genetic improvement %9 journal article %R 10.1145/3485952.3485960 %U https://doi.org/10.1145/3485952.3485960 %U http://dx.doi.org/10.1145/3485952.3485960 %P 28-30 %0 Conference Proceedings %T Enhancing Genetic Improvement Mutations Using Large Language Models %A Brownlee, Alexander E. I. %A Callan, James %A Even-Mendoza, Karine %A Geiger, Alina %A Hanna, Carol %A Petke, Justyna %A Sarro, Federica %A Sobania, Dominik %Y Arcaini, Paolo %Y Yue, Tao %Y Fredericks, Erik %S SSBSE 2023: Challenge Track %S LNCS %D 2023 %8 August %V 14415 %I Springer %C San Francisco, USA %F Brownlee:2023:SSBSE %X Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI tool kit to call OpenAI API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75percent higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI %K genetic algorithms, genetic programming, genetic improvement, GI, GIN, SBSE, LLM, AI, ANN, OpenAI, Langchain4J, Java, jCodec %R 10.1007/978-3-031-48796-5_13 %U https://arxiv.org/pdf/2310.19813.pdf %U http://dx.doi.org/10.1007/978-3-031-48796-5_13 %P 153-159 %0 Conference Proceedings %T Genetic Improvement: Taking real-world source code and improving it using computational search methods %A Brownlee, Alexander Edward Ian %A Haraldsson, Saemundur Oskar %A Woodward, John Robert %A Wagner, Markus %Y Zhang, Mengjie %Y Hart, Emma %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F brownlee:2024:GECCOcomp %O Tutorial %K genetic algorithms, genetic programming, genetic improvement %R 10.1145/3638530.3648418 %U http://dx.doi.org/10.1145/3638530.3648418 %P 1197-1230 %0 Journal Article %T Large Language Model Based Mutations in Genetic Improvement %A Brownlee, Alexander Edward Ian %A Callan, James %A Even-Mendoza, Karine %A Geiger, Alina %A Hanna, Carol %A Petke, Justyna %A Sarro, Federica %A Sobania, Dominik %J Automated Software Engineering %D 2025 %V 15 %@ 0928-8910 %F Brownlee:2024:ASE %O Special Issue on Advances in Search-Based Software %X we evaluate the use of LLMs as mutation operators for genetic improvement (GI), an SBSE approach, to improve the GI search process. In a preliminary work, we explored the feasibility of combining the Gin Java GI toolkit with OpenAI LLMs in order to generate an edit for the JCodec tool. Here we extend this investigation involving three LLMs and three types of prompt, and five real-world software projects. We sample the edits at random, as well as using local search. Our results show that, compared with conventional statement GI edits, LLMs produce fewer unique edits, but these compile and pass tests more often, with the OpenAI model finding test-passing edits 77percent of the time. The OpenAI and Mistral LLMs were roughly equal in finding the best run-time improvements. Simpler prompts were more successful than those providing more context and examples. Qualitative analysis revealed a wide variety of areas where LLMs typically failed to produce valid edits: commonly including inconsistent formatting, generating non-Java syntax, or refusing to provide a solution %K genetic algorithms, genetic programming, Genetic Improvement, SBSE, GIN, LLM, AI, ANN, JavaParser, JVM, stochastic local search %9 journal article %R 10.1007/s10515-024-00473-6 %U https://rdcu.be/d67YW %U http://dx.doi.org/10.1007/s10515-024-00473-6 %P articlenumber15 %0 Conference Proceedings %T Reducing Energy Consumption Using Genetic Improvement %A Bruce, Bobby R. %A Petke, Justyna %A Harman, 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, Terrence %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 Keswsentini, 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 bruce2015reducing %X Genetic Improvement (GI) is an area of Search Based Software Engineering which seeks to improve software’s non-functional properties by treating program code as if it were genetic material which is then evolved to produce more optimal solutions. Hitherto, the majority of focus has been on optimising program’s execution time which, though important, is only one of many non-functional targets. The growth in mobile computing, cloud computing infrastructure, and ecological concerns are forcing developers to focus on the energy their software consumes. We report on investigations into using GI to automatically find more energy efficient versions of the MiniSAT Boolean satisfiability solver when specialising for three downstream applications. Our results find that GI can successfully be used to reduce energy consumption by up to 25percent %K genetic algorithms, genetic programming, Genetic Improvement, GI, SBSE, Search-Based Software Engineering and Self-* Search, Software Engineering, optimisation, energy optimisation, energy efficiency, energy consumption, Boolean satisfiability, SAT %R 10.1145/2739480.2754752 %U http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Bruce_2015_GECCO.pdf %U http://dx.doi.org/10.1145/2739480.2754752 %P 1327-1334 %0 Conference Proceedings %T Energy Optimisation via Genetic Improvement A SBSE technique for a new era in Software Development %A Bruce, Bobby R. %Y Langdon, William B. %Y Petke, Justyna %Y White, David R. %S Genetic Improvement 2015 Workshop %D 2015 %8 November 15 jul %I ACM %C Madrid %F Bruce:2015:gi %X The discipline of Software Engineering has arisen during a time in which developers rarely concerned themselves with the energy efficiency of their software. Due to the growth in both mobile devices and large server clusters this period is undoubtedly coming to an end and, as such, new tools for creating energy-efficient software are required. This paper takes the position that Genetic Improvement, a Search-Based Software Engineering technique, has the potential to aid developers in refactoring their software to a more energy-efficient state; allowing focus to remain on functional requirements while leaving concerns over energy consumption to an automated process. %K genetic algorithms, genetic programming, Genetic Improvement, SBSE, GI, Search Based Software En-gineering, energy efficiency, energy consumption, energy optimisation %R 10.1145/2739482.2768420 %U http://gpbib.cs.ucl.ac.uk/gi2015/energy_optimisation_via_genetic_improvement.pdf %U http://dx.doi.org/10.1145/2739482.2768420 %P 819-820 %0 Journal Article %T A Report on the Genetic Improvement Workshop@GECCO 2016 %A Bruce, Bobby %J SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation %D 2016 %8 aug %V 2 %N 9 %@ 1931-8499 %F Bruce:2016:sigevolution %K genetic algorithms, genetic programming, Genetic Improvement %9 journal article %R 10.1145/3066157.3066159 %U http://www.sigevolution.org/issues/pdf/SIGEVOlution0902.pdf %U http://dx.doi.org/10.1145/3066157.3066159 %P 7 %0 Conference Proceedings %T Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV %A Bruce, Bobby R. %A Aitken, Jonathan M. %A Petke, Justyna %Y Sarro, Federica %Y Deb, Kalyanmoy %S Proceedings of the 8th International Symposium on Search Based Software Engineering, SSBSE 2016 %S LNCS %D 2016 %8 August 10 oct %V 9962 %I Springer %C Raleigh, North Carolina, USA %F Bruce:2016:SSBSE %X OpenCV is a commonly used computer vision library containing a wide variety of algorithms for the AI community. This paper uses deep parameter optimisation to investigate improvements to face detection using the Viola-Jones algorithm in OpenCV, allowing a trade-off between execution time and classification accuracy. Our results show that execution time can be decreased by 48 percent if a 1.80 percent classification inaccuracy is permitted (compared to 1.04 percent classification inaccuracy of the original, unmodified algorithm). Further execution time savings are possible depending on the degree of inaccuracy deemed acceptable by the user. %K genetic algorithms, genetic programming, genetic improvement, SBSE, OpenCV, NSGA-II, Deep parameter optimisation, Automated parameter tuning, Multi-objective optimisation, GI %R 10.1007/978-3-319-47106-8_18 %U http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Bruce_2016_SSBSE.pdf %U http://dx.doi.org/10.1007/978-3-319-47106-8_18 %P 238-243 %0 Report %T Approximate Oracles and Synergy in Software Energy Search Spaces %A Bruce, Bobby R. %A Petke, Justyna %A Harman, Mark %A Barr, Earl T. %D 2017 %8 25 jan %N RN/17/01 %I University College, London %C London, UK %F bruce:RN1701 %X There is a growing interest in using evolutionary computation to reduce software systems’ energy consumption by using techniques such as genetic improvement. However, efficient and effective evolutionary optimisation of software systems requires a better understanding of the energy search landscape. One important choice practitioners have is whether to preserve the system’s original output or permit approximation; each of which has its own search space characteristics. When output preservation is a hard constraint, we report that the maximum energy reduction achievable by evolutionary mutation is 2.69percent (0.76percent on average). By contrast, this figure increases dramatically to 95.60percent (33.90percent on average) when approximation is permitted, indicating the critical importance of approximate output quality assessment for effective evolutionary optimisation. We investigate synergy, a phenomenon that occurs when simultaneously applied evolutionary mutations produce a effect greater than their individual sum. Our results reveal that 12.0percent of all joint code modifications produced such a synergistic effect though 38.5percent produce an antagonistic interaction in which simultaneously applied mutations are less effective than when applied individually. This highlights the need for an evolutionary approach over more greedy alternatives. %K genetic algorithms, genetic programming, genetic improvement %U http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_17_01.PDF %0 Report %T Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV : A Correction %A Bruce, Bobby R. %D 2017 %8 July %N RN/17/07 %I University College, London %C London, UK %F bruce:RN1707 %X In our 2016 paper ’Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV’ \citeBruce:2016:SSBSE we reported on an evolutionary, multi-objective approach to deep parameter optimisation that we reported could reduce execution time of a face detection algorithm by 48percent if a 1.90percent classification inaccuracy were permitted (compared to the 1.04percent classification inaccuracy of the original, unmodified algorithm) and that further execution time savings were possible depending on the degree of inaccuracy permitted by the user. However, after publication we found an error in our experimental setup; instead of running the deep parameter optimisation framework using an evolutionary search-based approach we had been using a systematic one. We therefore re-ran the experiments using the intended evolutionary implementation alongside the systematic implementation for 1000 evaluations and again for 10000 evaluations. We found that the systematic setup is superior to the intended evolutionary setup in that it produces solutions which, when run on the test set, produce a richer Pareto frontier. The evolutionary approach, in the 10000 evaluation setup, produced a better Pareto frontier on the training set. However, the majority of these solutions were infeasible or not Pareto optimal when run on the test set. We suspect this may be due to the evolutionary approach over-fitting to the training set. %K genetic algorithms, genetic programming, genetic improvement %U http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_17_07.pdf %0 Report %T Towards automatic generation and insertion of OpenACC directives %A Bruce, Bobby R. %A Petke, Justyna %D 2018 %8 December %N RN/18/04 %I University College, London %C London, UK %F Bruce:RN1804 %X While the use of hardware accelerators, like GPUs, can significantly improve software performance, developers often lack the expertise or time to properly translate source code to do so. In this paper we highlight two approaches to automatically offload computationally intensive tasks to a system’s GPU by generating and inserting OpenACC directives; one using grammar-based genetic programming, and another using a bespoke four stage process. We find that the grammar-based genetic programming approach reduces execution time by 2.60percent on average, across the applications studied, while the bespoke four-stage approach reduces execution time by 2.44percent. Despite this, our investigation shows a handwritten OpenACC implementation is capable of reducing execution time by 65.68percent. Comparing the differences, we identified a promising avenue for future research: combining genetic improvement with better handling of data to and from the GPU. %K genetic algorithms, genetic programming, genetic improvement, GPU, parallel computing %U http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_18_04.pdf %0 Thesis %T The Blind Software Engineer: Improving the Non-Functional Properties of Software by Means of Genetic Improvement %A Bruce, Bobby R. %D 2018 %8 December %C UK %C Computer Science, University College, London %F bruce_bobby_r_thesis %X Life, even in its most basic of forms, continues to amaze mankind with the complexity of its design. When analysing this complexity it is easy to see why the idea of a grand designer has been such a prevalent idea in human history. If it is assumed intelligence is required to undertake a complex engineering feat, such as developing a modern computer system, then it is logical to assume a creature, even as basic as an earthworm, is the product of an even greater intelligence. Yet, as Darwin observed, intelligence is not a requirement for the creation of complex systems. Evolution, a phenomenon without consciousness or intellect can, over time, create systems of grand complexity and order. From this observation a question arises: is it possible to develop techniques inspired by Darwinian evolution to solve engineering problems without engineers? The first to ask such a question was Alan Turing, a person considered by many to be the father of computer science. In 1948 Turing proposed three approaches he believed could solve complex problems without the need for human intervention. The first was a purely logicdriven search. This arose a decade later in the form of general problem-solving algorithms. Though successful in solving toy problems which could be sufficiently formalised, solving real-world problems was found to be infeasible. The second approach Turing called cultural search. This approach would store libraries of information to then reference and provide solutions to particular problems in accordance to this information. This is similar to what we would now refer to as an expert system. Though the first expert system is hard to date due to differences in definition, the development is normally attributed to Feigenbaum, Bachanan, Lederberg, and Sutherland for their work, originating in the 1960s, on the DENRAL system. Turings last proposal was an iterative, evolutionary technique which he later expanded on stating: We cannot expect to find a good child-machine at the first attempt. One must experiment with teaching one machine and see how well it learns. One can then try another and see if it is better or worse. There is an obvious connection between this process and evolution. Though a primitive proposal in comparison to modern techniques, Turing clearly identified the foundation of what we now refer to as Evolutionary Computation (EC). EC borrows principles from biological evolution and adapts them for use in computer systems. Despite EC initially appearing to be an awkward melding between the two perpendicular disciplines of biology and computer science, useful ideas from evolutionary theory can be used in engineering processes. Just as man dreamt of flight from watching birds, EC researchers dream of self-improving systems from observing evolutionary processes. Despite these similarities, evolutionary inspired techniques in computer science have yet to build complex software systems from scratch. Though they have been successfully used to solve complex problems, such as classification and clustering, there is a general acceptance that, as in nature, these evolutionary processes take vast amounts of time to create complex structures from simple starting points. Even the best computer systems cannot compete with natures ability to evaluate many millions of variants in parallel over the course of millennia. It is for this reason research into modifying and optimising already existing software, a process known as Genetic Improvement, has blossomed. Genetic Improvement (commonly referred to as GI) modifies existing software using search-based techniques with respect to some objective. These search-based techniques are typically evolutionary and, if not, are based on iterative improvement which we may view as a form of evolution. GI sets out to solve the last mile problems of software development; problems that arise in software engineering close to completion, such as bugs or sub-optimal performance. It is the genetic improvement of non-functional properties, such as execution time and energy consumption, which we concern ourselves with in this thesis, as we find it to be the area of research which is the most interesting, and the most exciting. It is hoped that those referencing this thesis may share the same vision: that the genetic improvement of non-functional properties has the potential to transform software development, and that the work presented here is a step towards that goal. The thesis is divided into six chapters (inclusive of this Introduction chapter). In Chapter 2 we explain the background material necessary to understand the content discussed later in the following chapters. From this, in Chapter 3, we highlight our investigations into the novel nonfunctional property of energy consumption which, in part, includes a study in how energy may be reduced via the approximation of output. We then expand on this in Chapter 4 by discussing our investigations into the applicability of GI in the domain of approximate computing, which covers a study into optimising the non-functional properties of software running on novel hardware: in this case, Android tablet devices. We then show, in Chapter 5, early research into how GI may be used to specialise software for specific hardware targets; in particular, how GI may automatically modify sequential code to run on GPUs. Finally, in Chapter 6 we discuss what relevant work is currently being undertaken by using the area of genetic improvement, and provide the reader with clear and concise take-away messages from this thesis. %K genetic algorithms, genetic programming, Genetic Improvement, Search-based Software Engineering, OpenCV, Deep Parameter Optimisation, Android Smartphones, GB-GP-Parallelisation, OpenACC, DawnCC, NAS-NPB Suite, SNU-NPB %9 Ph.D. thesis %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bruce_bobby_r_thesis.pdf %0 Journal Article %T Approximate Oracles and Synergy in Software Energy Search Spaces %A Bruce, Bobby R. %A Petke, Justyna %A Harman, Mark %A Barr, Earl T. %J IEEE Transactions on Software Engineering %D 2019 %8 nov %V 45 %N 11 %@ 0098-5589 %F Bruce:TSE %X Reducing the energy consumption of software systems though optimisations techniques such as genetic improvement is gaining interest. However, efficient and effective improvement of software systems requires a better understanding of the code-change search space. One important choice practitioners have is whether to preserve the system’s original output or permit approximation with each scenario having its own search space characteristics. When output preservation is a hard constraint, we report that the maximum energy reduction achievable by the modification operators is 2.69percent (0.76percent on average). By contrast, this figure increases dramatically to 95.60percent (33.90percent on average) when approximation is permitted, indicating the critical importance of approximate output quality assessment for code optimisation. We investigate synergy, a phenomenon that occurs when simultaneously applied source code modifications produce an effect greater than their individual sum. Our results reveal that 12.0percent of all joint code modifications produced such a synergistic effect though 38.5percent produce an antagonistic interaction in which simultaneously applied modifications are less effective than when applied individually. This highlights the need for more advanced search-based approaches. %K genetic algorithms, genetic programming, genetic improvement, search-based software engineering, SBSE, synergy, Energy consumption, Energy measurement, antagonism, oracle, approximation, MAGEEC, Raspberry Pi %9 journal article %R 10.1109/TSE.2018.2827066 %U http://www.bobbybruce.net/assets/pdfs/publications/bruce-2019-approximate.pdf %U http://dx.doi.org/10.1109/TSE.2018.2827066 %P 1150-1169 %0 Conference Proceedings %T Automatically Exploring Computer System Design Spaces %A Bruce, Bobby R. %Y Bruce, Bobby R. %Y Nowack, Vesna %Y Blot, Aymeric %Y Winter, Emily %Y Langdon, W. B. %Y Petke, Justyna %S GI @ GECCO 2022 %D 2022 %8 September %I Association for Computing Machinery %C Boston, USA %F Bruce:2022:GI %X While much research has focused on using search to optimize software, it is possible to use these same approaches to optimise other parts of the computer system. With decreased costs in silicon customisation, and the return of centralized systems carrying out specialized tasks, the time is right to begin thinking about tools to optimise systems for the needs of specific software targets. In the approach outlined, the standard Genetic Improvement process is flipped with source code considered static and the remainder of the computer system altered to the needs of the software. The project proposed is preliminary research into incorporating grammar-based GP with an advanced computer architecture simulator to automatically design computer systems. I argue this approach has the potential to significantly improve the design of computer systems while reducing manual effort. %K genetic algorithms, genetic programming, genetic improvement, SBSE, search, computer architecture, ISA, gem5 %R 10.1145/3520304.3534021 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Bruce_2022_GI.pdf %U http://dx.doi.org/10.1145/3520304.3534021 %P 1926-1927 %0 Conference Proceedings %T Automatic Generation of Object-Oriented Programs Using Genetic Programming %A Bruce, Wilker Shane %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 bruce:1996:agOOpGP %X This research addresses the application of genetic programming to the generation of object-oriented programs. An extended chromosome data structure is presented where the set of methods associated with an object is stored as an array of program trees. Modified genetic operators are defined to manipulate this structure. Indexed memory is used to allow the programs generated by the system to access and modify object memory. These extensions to the standard genetic programming... %K genetic algorithms, genetic programming, memory %U http://citeseer.ist.psu.edu/cache/papers/cs/12859/http:zSzzSzwww.scis.nova.eduzSz~brucewszSzPUBLICATIONSzSzgp96.pdf/bruce96automatic.pdf %P 267-272 %0 Thesis %T The Application of Genetic Programming to the Automatic Generation of Object-Oriented Programs %A Bruce, Wilker Shane %D 1995 %8 Dec %C 3100 SW 9th Avenue, Fort Lauderdale, Florida 33315, USA %C School of Computer and Information Sciences, Nova Southeastern University %F bruce:thesis %X Genetic programming is an automatic programming method that creates computer programs to satisfy a software designer’s input/output specification through the application of principles from genetics and evolutionary biology. A population of programs is maintained where each program is represented in the chromosome data structure as a tree. Programs are evaluated to determine their fitness in solving the specified task. Simulated genetic operations like crossover and mutation are probabilistically applied to the more highly fit programs in the population to generate new programs. These programs then replace existing programs in the population according to the principles of natural selection. The process repeats until a correct program is found or an iteration limit is reached. This research concerns itself with the application of genetic programming to the generation of object-oriented programs. A new chromosome data structure is presented in which the entire set of methods associated with an object are stored as a set of program trees. Modified genetic operators that manipulate this new structure are defined. Indexed memory methods are used to allow the programs generated by the system to access and modify object memory. The result of these modifications to the standard genetic programming paradigm is a system that can simultaneously generate all of the methods associated with an object. Experiments were performed to compare the sequential generation of object methods with two variants of simultaneous generation. The first variant used information about both method return values and object internal memory state in its fitness function. The second variant only used information about method return values. It was found that simultaneous generation of methods is possible in the domain of simple collection objects both with and without the availability of internal memory state in the fitness function. It was also found that this technique is up to four orders of magnitude more computationally expensive in terms of number of individuals generated in the search than the sequential generation of the same set of methods on an individual basis. %K genetic algorithms, genetic programming, memory %9 Ph.D. thesis %U https://nsuworks.nova.edu/gscis_etd/430/ %0 Conference Proceedings %T The Lawnmower Problem Revisited: Stack-Based Genetic Programming and Automatically Defined Functions %A Bruce, Wilker Shane %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 %@ 1-55860-483-9 %F bruce:1997:lprsbGPADF %X Stack-based genetic programming is an alternative to Koza-style tree-based genetic programming that generates linear programs that are executed on a virtual machine using a FORTH-style operand stack instead of tree-based function calls. A stack-based genetic programming system was extended to include the ability to generate programs containing automatically defined functions. Experiments were run to test the system using Koza’s lawnmower problem. The stack-based system using automatically... %K genetic algorithms, genetic programming, ADF %U http://citeseer.ist.psu.edu/cache/papers/cs/12859/http:zSzzSzwww.scis.nova.eduzSz~brucewszSzPUBLICATIONSzSzgp97.pdf/bruce97lawnmower.pdf %P 52-57 %0 Conference Proceedings %T An Indirect Block-Oriented Representation for Genetic Programming %A Brucherseifer, Eva %A Bechtel, Peter %A Freyer, Stephan %A Marenbach, Peter %Y Miller, Julian F. %Y Tomassini, Marco %Y Lanzi, Pier Luca %Y Ryan, Conor %Y Tettamanzi, Andrea G. B. %Y Langdon, William B. %S Genetic Programming, Proceedings of EuroGP’2001 %S LNCS %D 2001 %8 18 20 apr %V 2038 %I Springer-Verlag %C Lake Como, Italy %@ 3-540-41899-7 %F brucherseifer:2001:EuroGP %X When Genetic Programming (GP) is applied to system identification or controller design different codings can be used for internal representation of the individuals. One common approach is a block-oriented representation where nodes of the tree structure directly correspond to blocks in a block diagram. In this paper we present an indirect block-oriented representation, which adopts some aspects of the way humans perform the modelling in order to increase the GP system’s performance. A causality measure based on an edit distance is examined to compare the direct an the indirect representation. Finally, results from a real world application of the indirect block-oriented representation are presented. %K genetic algorithms, genetic programming, Block-oriented representation, Biotechnology, Process modelling, Controller design, Causality: Poster %R 10.1007/3-540-45355-5_21 %U http://dx.doi.org/10.1007/3-540-45355-5_21 %P 268-279 %0 Thesis %T Der Artbegriff in der Genetischen Programmierung %A Brucherseifer, Eva %D 2010 %8 sep %C Germany %C Department of Electrical Engineering and Information Technology, TU Darmstadt %F Brucherseifer:thesis %K genetic algorithms, genetic programming, GP theory, Genetische Programmierung, Evolutionaere Algorithmen, Art Analyse, Visualisierung, Artengraph, Clustering, Strukturoptimierung, Heuristische Optimierungsverfahren %9 Ph.D. thesis %U http://tubiblio.ulb.tu-darmstadt.de/54187/ %0 Journal Article %T Genetic Programming over Context-Free Languages with Linear Constraints for the Knapsack Problem: First Results %A Bruhn, Peter %A Geyer-Schulz, Andreas %J Evolutionary Computation %D 2002 %8 Spring %V 10 %N 1 %F bruhn:2002:ECJ %X we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the multidimensional knapsack problem, and discuss its performance relative to Michalewicz’s genetic algorithm with penalty functions. With respect to Michalewicz’s approach, we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over context-free languages with linear constraints is ideally suited to modeling complementarities between items in a knapsack problem: The more complementarities in the problem, the stronger the performance in comparison to its competitors. %K genetic algorithms, genetic programming, grammatical evolution, grammar-based genetic, programming, combinatorial, optimization, context-free grammars, with linear constraints, knapsack problems %9 journal article %R 10.1162/106365602317301772 %U http://dx.doi.org/10.1162/106365602317301772 %P 51-74 %0 Generic %T Evolving Shepherding Behavior with Genetic Programming Algorithms %A Brule, Joshua %A Engel, Kevin %A Fung, Nick %A Julien, Isaac %D 2016 %I ArXiv %F Brule:2016:ArXiv %K genetic algorithms, genetic programming %U http://arxiv.org/abs/1603.06141 %0 Conference Proceedings %T Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time %A Brum, Artur %A Ritt, Marcus %Y Liefooghe, Arnaud %Y Lopez-Ibanez, Manuel %S The 18th European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2018 %S LNCS %D 2018 %8 April 6 apr %V 10782 %I Springer %C Parma, Italy %F Brum:2018:evocop %X Automatic algorithm configuration aims to automate the often time-consuming task of designing and evaluating search methods. We address the permutation flow shop scheduling problem minimizing total completion time with a context-free grammar that defines how algorithmic components can be combined to form a full heuristic search method. We implement components from various works from the literature, including several local search procedures. The search space defined by the grammar is explored with a racing-based strategy and the algorithms obtained are compared to the state of the art %K genetic algorithms, genetic programming, Automatic algorithm configuration, Iterated greedy algorithm, Iterated local search, Flow shop scheduling problem, Total completion time %R 10.1007/978-3-319-77449-7_6 %U http://dx.doi.org/10.1007/978-3-319-77449-7_6 %P 85-100 %0 Thesis %T Automatic Algorithm Configuration for Flow Shop Scheduling Problems %A Brum, Artur Ferreira %D 2020 %8 aug %C Porto Alegre, Brazil %C Instituto de Informatica, Universidade Federal do Rio Grande do Sul %F Brum:thesis %X Scheduling problems have been a subject of interest to the optimization researchers for many years. Flow shop problems, in particular, are one of the most widely studied scheduling problems due to their application to many production environments. A large variety of solution methods can be found in the literature and, since many flow shop problems are NP-hard, the most frequently found approaches are heuristic methods. Heuristic search methods are often complex and hard to design, requiring a significant amount of time and manual work to perform such a task, which can be tedious and prone to human biases. Automatic algorithm configuration (AAC) comprises techniques to automate the design of algorithms by selecting and calibrating algorithmic components. It provides a more robust approach which can contribute to improving the state of the art. In this thesis we present a study on the permutation and the non-permutation flow shop scheduling problems. We follow a grammar-based AAC strategy to generate iterated local search or iterated greedy algorithms. We implement several algorithmic components from the literature in a parameterised solver, and explore the search space defined by the grammar with a racing-based strategy. New efficient algorithms are designed with minimal manual effort and are evaluated against benchmarks from the literature. The results show that the automatically designed algorithms can improve the state of the art in many cases, as evidenced by comprehensive computational and statistical testing. %K genetic algorithms, genetic programming, Informatica, Automatic algorithm configuration, Flow shop scheduling problem, Iterated greedy algorithm, Iterated local search, Problema de agendamento em flow shop, Configuracao automatica dealgoritmos, Busca local iterada, Algoritmo guloso iterado %9 Ph.D. thesis %U https://www.inf.ufrgs.br/site/eventos/evento/tese-de-doutorado-de-artur-ferreira-brum/ %0 Conference Proceedings %T Investigation of image feature extraction by a genetic algorithm %A Brumby, Steven P. %A Theiler, James %A Perkins, Simon J. %A Harvey, Neal %A Szymanski, John J. %A Bloch, Jeffrey J. %A Mitchell, Melanie %Y Bosacchi, Bruno %Y Fogel, David B. %Y Bezdek, James C. %S Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, Proceedings of SPIE %D 1999 %8 19 20 jul %V 3812 %F Brumby:1999:SPIE %X We describe the implementation and performance of a genetic algorithm (GA) which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of our analysis of the efficiency of the classic genetic operations of crossover and mutation for our application, and discuss our choice of evolutionary control parameters. We exhibit some of our evolved algorithms, and discuss possible avenues for future progress. %K genetic algorithms, genetic programming, Evolutionary computation, image analysis, multi-spectral analysis %R 10.1117/12.367697 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.8210 %U http://dx.doi.org/10.1117/12.367697 %P 24-31 %0 Conference Proceedings %T A genetic algorithm for combining new and existing image processing tools for multispectral imagery %A Brumby, Steven P. %A Harvey, Neal R. %A Perkins, Simon %A Porter, Reid B. %A Szymanski, John J. %A Theiler, James %A Bloch, Jeffrey J. %Y Shen, Sylvia S. %Y Descour, Michael R. %S Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI. Proceedings of SPIE %D 2000 %V 4049 %F Brumby:2000:SPIE %X We describe the implementation and performance of a genetic algorithm (GA) which evolves and combines image processing tools for multispectral imagery (MSI) datasets. Existing algorithms for particular features can also be ’re-tuned’ and combined with the newly evolved image processing tools to rapidly produce customised feature extraction tools. First results from our software system were presented previously. We now report on work extending our system to look for a range of broad-area features in MSI datasets. These features demand an integrated spatiospectral approach, which our system is designed to use. We describe our chromosomal representation of candidate image processing algorithms, and discuss our set of image operators. Our application has been geospatial feature extraction using publicly available MSI and hyperspectral imagery (HSI). We demonstrate our system on NASA/Jet Propulsion Laboratory’s Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) HSI which has been processed to simulate MSI data from the Department of Energy’s Multispectral Thermal Imager (MTI) instrument. We exhibit some of our evolved algorithms, and discuss their operation and performance. %K genetic algorithms, genetic programming, Evolutionary Computation, Image Processing, Remote Sensing, Multispectral Imagery, Hyperspectral Imagery %R 10.1117/12.410371 %U http://spiedigitallibrary.org/data/Conferences/SPIEP/35048/480_1.pdf %U http://dx.doi.org/10.1117/12.410371 %P 480-490 %0 Conference Proceedings %T Evolving forest fire burn severity classification algorithms for multi-spectral imagery %A Brumby, S. P. %A Bloch, J. J. %A Harvey, N. R. %A Theiler, J. %A Perkins, S. %A Young, A. C. %A Szymanski, J. J. %Y Shen, Sylvia S. %Y Descour, Michael R. %S In Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, Proceedings of SPIE %D 2001 %V 4381 %F Brumby:2001:SPIE %X Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100percent tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial colour/infrared photography. %K genetic algorithms, genetic programming, Multispectral imagery, Supervised classification, Forest fire, Wildfire, GENIE, Aladdin %R 10.1117/12.437013 %U http://public.lanl.gov/perkins/webdocs/brumby.aerosense01.pdf %U http://dx.doi.org/10.1117/12.437013 %P 236-245 %0 Conference Proceedings %T Genetic programming approach to extracting features from remotely sensed imagery %A Brumby, Steven P. %A Theiler, James %A Perkins, Simon %A Harvey, Neal R. %A Szymanski, John J. %S FUSION 2001: Fourth International Conference on Image Fusion %D 2001 %8 July 10 aug %C Montreal, Quebec, Canada %F Brumby:2001:FUSION %X Multi-instrument data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problems of spatial co-registration, the remote sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. We describe a genetic programming/supervised classifier software system, called Genie, which evolves and combines spatio-spectral image processing tools for remotely sensed imagery. We describe our representation of candidate image processing pipelines, and discuss our set of primitive image operators. Our primary application has been in the field of geospatial feature extraction, including wildfire scars and general land-cover classes, using publicly available multi-spectral imagery (MSI) and hyper-spectral imagery (HSI). Here, we demonstrate our system on Landsat 7 Enhanced Thematic Mapper (ETM+) MSI. We exhibit an evolved pipeline, and discuss its operation and performance. %K genetic algorithms, genetic programming, Evolutionary Computation, Image Processing, Remote Sensing, Multispectral Imagery, Panchromatic imagery %U http://public.lanl.gov/perkins/webdocs/brumbyFUSION2001.pdf %0 Conference Proceedings %T Evolving land cover classification algorithms for multispectral and multitemporal imagery %A Brumby, Steven P. %A Theiler, James %A Bloch, Jeffrey J. %A Harvey, Neal R. %A Perkins, Simon %A Szymanski, John J. %A Young, A. Cody %Y Descour, Michael R. %Y Shen, Sylvia S. %S Proc. SPIE Imaging Spectrometry VII %D 2002 %V 4480 %F oai:CiteSeerPSU:445835 %X The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multi-spectral and multi-temporal imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before, during, and after the wildfire. Using an existing land cover classification based on a 1992 Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, and an algorithm to mask out clouds and cloud shadows. We report preliminary results of combining individual classification results using a K-means clustering approach. The details of our evolved classification are compared to the manually produced land-cover classification. %K genetic algorithms, genetic programming, Feature Extraction, Supervised classification, K-means clustering, Multi-spectral imagery, Land cover, Wildfire %R 10.1117/12.453331 %U http://public.lanl.gov/jt/Papers/brumby_SPIE4480-14.pdf %U http://dx.doi.org/10.1117/12.453331 %P 120-129 %0 Report %T Evolution vs. Intelligent Design in Program Patching %A Brun, Yuriy %A Barr, Earl %A Xiao, Ming %A Le Goues, Claire %A Devanbu, P. %D 2013 %8 Fall %I Dept. of Computer Science, University of California, Davis %C USA %F Brun:2013:TR %X While Fixing bugs requires significant manual effort, recent research has shown that genetic programming (GP) can be used to search through a space of programs to automatically Find candidate bugfixing patches. Given a program, and a set of test cases (some of which fail), a GP-based repair technique evolves a patch or a patched program using program mutation and selection operators. We evaluate GenProg, a well-known GP-based patch generator, using a large, diverse dataset of over a thousand simple (both buggy and correct) student-written homework programs, using two different test sets: a white-box test set constructed to achieve edge coverage on an oracle program, and a black-box test set developed to exercise the desired specification. We Find that GenProg often succeeds at Finding a patch that will cause student programs to pass supplied white-box test cases; however, that the solution quite often overfits to the supplied tests and doesn’t pass all the black-box tests. In contrast, when students patch their own buggy programs, these patches tend to pass the black-box tests as well. We also Find that the GenProg-generated patches lack enough diversity to benefit from a kind of bagging, in which a plurality vote over a population of GP-generated patches outperforms a randomly chosen individual patch. We report these results and additional relationships between GenProg’s success and the size and complexity of the manual and automatic patches. %K genetic algorithms, genetic programming, genetic improvement, APR, SBSE %U https://escholarship.org/uc/item/3z8926ks.pdf %0 Journal Article %T Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments %A Brunello, Andrea %A Urgolo, Andrea %A Pittino, Federico %A Montvay, Andras %A Montanari, Angelo %J Sensors %D 2021 %V 21 %N 8 %@ 1424-8220 %F brunello:2021:Sensors %X Real-time estimation of temperatures in indoor environments is critical for several reasons, including the upkeep of comfort levels, the fulfillment of legal requirements, and energy efficiency. Unfortunately, setting an adequate number of sensors at the desired locations to ensure a uniform monitoring of the temperature in a given premise may be troublesome. Virtual sensing is a set of techniques to replace a subset of physical sensors by virtual ones, allowing the monitoring of unreachable locations, reducing the sensors deployment costs, and providing a fallback solution for sensor failures. In this paper, we deal with temperature monitoring in an open space office, where a set of physical sensors is deployed at uneven locations. Our main goal is to develop a black-box virtual sensing framework, completely independent of the physical characteristics of the considered scenario, that, in principle, can be adapted to any indoor environment. We first perform a systematic analysis of various distance metrics that can be used to determine the best sensors on which to base temperature monitoring. Then, following a genetic programming approach, we design a novel metric that combines and summarizes information brought by the considered distance metrics, outperforming their effectiveness. Thereafter, we propose a general and automatic approach to the problem of determining the best subset of sensors that are worth keeping in a given room. Leveraging the selected sensors, we then conduct a comprehensive assessment of different strategies for the prediction of temperatures observed by physical sensors based on other sensors’ data, also evaluating the reliability of the generated outputs. The results show that, at least in the given scenario, the proposed black-box approach is capable of automatically selecting a subset of sensors and of deriving a virtual sensing model for an accurate and efficient monitoring of the environment. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/s21082728 %U https://www.mdpi.com/1424-8220/21/8/2728 %U http://dx.doi.org/10.3390/s21082728 %0 Journal Article %T A genetic programming approach to WiFi fingerprint meta-distance learning %A Brunello, Andrea %A Montanari, Angelo %A Saccomanno, Nicola %J Pervasive and Mobile Computing %D 2022 %V 85 %@ 1574-1192 %F BRUNELLO:2022:pmcj %X Driven by the continuous growth in the number of mobile smart devices, location-based services are becoming a fundamental aspect in the ubiquitous computing domain. In this work, we focus on indoor scenarios, where positioning supports tasks such as navigation, logistics, and access management and control. Most indoor positioning solutions are based on WiFi fingerprinting, thanks to its ease of deployment. Such a technique often requires the adoption of a suitable distance metric to compare the currently observed WiFi access points with those pertaining to fingerprints contained in a database, and whose position is already known. Results from the literature make it evident that classical distance functions among WiFi fingerprints do not preserve spatial information in its entirety. Here, we explore the possibility of addressing such a shortcoming by combining a selection of fingerprint distance functions into a meta-distance, using a genetic programming approach to solve a symbolic regression problem. The outcomes of the investigation, based on 16 publicly available datasets, show that a small, but statistically relevant, improvement can be achieved in preserving spatial information, and that the developed meta-distance has a generalization capability no worse than top-performing classical fingerprint distance functions when trained on a dataset and tested on the others. In addition, when used within a k-nearest-neighbor positioning framework, the meta-distance outperforms all the contenders, despite not being expressly designed to support position estimation. This sheds a light on a significant relationship between preservation of spatial information and localization performance. The achieved results pave the way for the development of more advanced metric learning solutions, that go beyond the limitations of currently-employed fingerprint distance functions %K genetic algorithms, genetic programming, Indoor positioning, Wi-Fi fingerprinting, Metric, Machine learning %9 journal article %R 10.1016/j.pmcj.2022.101681 %U https://www.sciencedirect.com/science/article/pii/S1574119222000980 %U http://dx.doi.org/10.1016/j.pmcj.2022.101681 %P 101681 %0 Journal Article %T Monitors That Learn From Failures: Pairing STL and Genetic Programming %A Brunello, Andrea %A Monica, Dario Della %A Montanari, Angelo %A Saccomanno, Nicola %A Urgolo, Andrea %J IEEE Access %D 2023 %V 11 %@ 2169-3536 %F Brunello:2023:ACC %X In several domains, systems generate continuous streams of data during their execution, including meaningful telemetry information, that can be used to perform tasks like preemptive failure detection. Deep learning models have been exploited for these tasks with increasing success, but they hardly provide guarantees over their execution, a problem which is exacerbated by their lack of interpretability. In many critical contexts, formal methods, which ensure the correct behaviour of a system, are thus necessary. However, specifying in advance all the relevant properties and building a complete model of the system against which to check them is often out of reach in real-world scenarios. To overcome these limitations, we design a framework that resorts to monitoring, a lightweight runtime verification technique that does not require an explicit model specification, and pairs it with machine learning. Its goal is to automatically derive relevant properties, related to a bad behaviour of the considered system, encoded by means of formulas of Signal Temporal Logic ( $\mathsf STL$ ). Results based on experiments performed on well-known benchmark datasets show that the proposed framework is able to effectively anticipate critical system behaviours in an online setting, providing human-interpretable results. %K genetic algorithms, genetic programming, Monitoring, Machine learning, Task analysis, Feature extraction, Runtime, Data mining, Telemetry, Failure analysis, Machine learning, formal methods, runtime verification, monitoring, failure detection, explainable AI, XAI %9 journal article %R 10.1109/ACCESS.2023.3277620 %U http://dx.doi.org/10.1109/ACCESS.2023.3277620 %P 57349-57364 %0 Journal Article %T Learning of complex event processing rules with genetic programming %A Bruns, Ralf %A Dunkel, Jurgen %A Offel, Norman %J Expert Systems with Applications %D 2019 %V 129 %@ 0957-4174 %F BRUNS:2019:ESA %X Complex Event Processing (CEP) is an established software technology to extract relevant information from massive data streams. Currently, domain experts have to determine manually CEP rules that define a situation of interest. However, often CEP rules cannot be formulated by experts, because the relevant interdependencies and relations between the data are not explicitly known, but inherently hidden in the data streams. To cope with this problem, we present a new learning approach for CEP rules, which is based on Genetic Programming. We discuss in detail the different building blocks of Genetic Programming and how to adjust them to CEP rule learning. Extensive evaluations with synthetic and real world data demonstrate the high potential of the approach and give some hints about the choice of suitable process parameters %K genetic algorithms, genetic programming, Complex event processing, Rule learning, Pattern mining %9 journal article %R 10.1016/j.eswa.2019.04.007 %U http://www.sciencedirect.com/science/article/pii/S0957417419302386 %U http://dx.doi.org/10.1016/j.eswa.2019.04.007 %P 186-199 %0 Journal Article %T Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes %A Bryant, C. H. %A Muggleton, S. H. %A Oliver, S. G. %A Kell, D. B. %A Reiser, P. G. K. %A King, R. D. %J Electronic Transactions in Artificial Intelligence %D 2001 %8 30 aug %V 6 %N 12 %I Linkoping University Electronic Press, Sweden %@ 1401-9841 %F Bryant:2001:ETAI %X We aim to partially automate some aspects of scientific work, namely the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. We have developed ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. We have developed a novel form of learning curve, which in contrast to the form of learning curve normally used in Active Learning, allows one to compare the costs incurred by different leaning strategies. We plan to combine ASE-Progol with a standard laboratory robot to create a general automated approach to Functional Genomics. As a first step towards this goal, we are using ASE-Progol to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. Our approach involves auxotrophic mutant trials. To date, ASE-Progol has conducted such trials in silico. However we describe how they will be performed automatically in vitro by a standard laboratory robot designed for these sorts of liquid handling tasks, namely the Beckman/Coulter Biomek 2000. Although our work to date has been limited to trials conducted in silico, the results have been encouraging. Parts of the model were removed and the ability of ASE-Progol to efficiently recover the performance of the model was measured. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy in the range 46-88% was reduced if trials were selected by ASE-Progol rather than if they were sampled at random (without replacement). To reach an accuracy in the range 46-80%, ASE-Progol incurs five orders of magnitude less experimental costs than random sampling. ASE-Progol requires less time to converge upon a hypothesis with an accuracy in the range 74-87percent than if trials are sampled at random (without replacement) or selected using the naive strategy of always choosing the cheapest trial from the set of candidate trials. For example to reach an accuracy of 80%, ASE-Progol requires 4 days while random sampling requires 6 days and the naive strategy requires 10 days. %K ILP %9 journal article %U http://www.ep.liu.se/ea/cis/2001/012/ %0 Journal Article %T Graph-Based Algorithms for Boolean Function Manipulation %A Bryant, Randal E. %J IEEE Transactions on Computers %D 1986 %8 aug %V C-35 %N 8 %@ 0018-9340 %F 1676819 %X In this paper we present a new data structure for representing Boolean functions and an associated set of manipulation algorithms. Functions are represented by directed, acyclic graphs in a manner similar to the representations introduced by Lee [1] and Akers [2], but with further restrictions on the ordering of decision variables in the graph. Although a function requires, in the worst case, a graph of size exponential in the number of arguments, many of the functions encountered in typical applications have a more reasonable representation. Our algorithms have time complexity proportional to the sizes of the graphs being operated on, and hence are quite efficient as long as the graphs do not grow too large. We present experimental results from applying these algorithms to problems in logic design verification that demonstrate the practicality of our approach. %K DEC VAX, Boolean functions, binary decision diagrams, logic design verification, symbolic manipulation %9 journal article %R 10.1109/TC.1986.1676819 %U http://dx.doi.org/10.1109/TC.1986.1676819 %P 677-691 %0 Conference Proceedings %T Digital Evolution Exhibits Surprising Robustness to Poor Design Decisions %A Bryson, David M. %A Ofria, Charles %S ALIFE 2012: The Thirteenth International Conference on the Synthesis and Simulation of Living Systems %D 2012 %8 jul 19 22 %I MIT %C East Lansing, Michigan, USA %F 10.1162/978-0-262-31050-5-ch003 %X When designing an evolving software system, a researcher must set many aspects of the representation and inevitably make arbitrary decisions. Here we explore the consequences of poor design decisions in the development of a virtual instruction set in digital evolution systems. We evaluate the introduction of three different severities of poor choices. (1) functionally neutral instructions that water down mutational options, (2) actively deleterious instructions, and (3) a lethal die instruction. We further examine the impact of a high level of neutral bloat on the short term evolutionary potential of genotypes experiencing environmental change. We observed surprising robustness to these poor design decisions across all seven environments designed to analyse a wide range challenges. Analysis of the short term evolutionary potential of genotypes from the principal line of descent of case study populations demonstrated that the negative effects of neutral bloat in a static environment are compensated by retention of evolutionary potential during environmental change. %K genetic algorithms, genetic programming %R 10.1162/978-0-262-31050-5-ch003 %U https://doi.org/10.1162/978-0-262-31050-5-ch003 %U http://dx.doi.org/10.1162/978-0-262-31050-5-ch003 %P 19-26 %0 Journal Article %T Brains, Bodies, and Beyond: Competitive Co-Evolution of Robot Controllers, Morphologies and Environments %A Buason, Gunnar %A Bergfeldt, Nicklas %A Ziemke, Tom %J Genetic Programming and Evolvable Machines %D 2005 %8 mar %V 6 %N 1 %@ 1389-2576 %F buason:2005:GPEM %X We present a series of simulation experiments that incrementally extend previous work on neural robot controllers in a predator-prey scenario, in particular the work of Floreano and Nolfi, and integrates it with ideas from work on the co-evolution of robot morphologies and control systems. The aim of these experiments has been to further systematically investigate the tradeoffs and interdependencies between morphological parameters and behavioral strategies through a series of predator-prey experiments in which increasingly many aspects are subject to self-organization through competitive co-evolution. Motivated by the fact that, despite the emphasis of the interdependence of brain, body and environment in much recent research, the environment has actually received relatively little attention, the last set of experiments lets robots/species actively adapt their environments to their own needs, rather than just adapting themselves to a given environment. This paper is an extended version of: Buason and Ziemke. ’Co-evolving task-dependent visual morphologies in predator-prey experiments,’ in Genetic and Evolutionary Computation Conference, Cantu-Paz et al. (Eds.), Springer Verlag: Berlin, 2003, pp. 458-469. %K genetic algorithms, neuronal robot controller, CCE, khepera, YAKS simulator %9 journal article %R 10.1007/s10710-005-7618-x %U http://dx.doi.org/10.1007/s10710-005-7618-x %P 25-51 %0 Journal Article %T Artificial intelligence in health data analysis: The Darwinian evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes %A Buccheri, Enrico %A Dell’Aquila, Daniele %A Russo, Marco %J Diabetes Research and Clinical Practice %D 2021 %8 January %V 174 %@ 0168-8227 %F BUCCHERI2021108722 %X Aims The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and validate a novel zero-cost tool that significantly simplifies the screening of undiagnosed dysglycemia. Methods We use NHANES cross-sectional data over 10 years (2007 to 2016) to derive an equation that links non-laboratory exposure variables to the possible presence of undetected dysglycemia. For the first time, we adopt a novel artificial intelligence approach based on the Darwinian evolutionary theory to analyze health data. We collected data for 47 variables. Results Age and waist circumference are the only variables required to use the model. To identify undetected dysglycemia, we obtain an area under the curve (AUC) of 75.3 percent. Sensitivity and specificity are 0.65 and 0.73 by using the optimal threshold value determined from external validation data. Conclusions The use of uniquely two variables allows to obtain a zero-cost screening tool of analogous precision than that of more complex tools widely adopted in the literature. The newly developed tool has clinical use as it significantly simplifies the screening of dysglycemia. Furthermore, we suggest that the definition of an age-related waist circumference cut-off might help to improve existing diabetes risk factors. %K genetic algorithms, genetic programming, BP, Type 2 diabetes, Zero-cost dysglycemia screening, Artificial intelligence %9 journal article %R 10.1016/j.diabres.2021.108722 %U https://pubmed.ncbi.nlm.nih.gov/33647331/ %U http://dx.doi.org/10.1016/j.diabres.2021.108722 %P 108722 %0 Journal Article %T Stratified analysis of the age-related waist circumference cut-off model for the screening of dysglycemia at zero-cost %A Buccheri, Enrico %A Dell’Aquila, Daniele %A Russo, Marco %J Obesity Medicine %D 2022 %8 may %V 31 %@ 2451-8476 %F BUCCHERI2022100398 %X Aims We perform a stratified analysis of the recently published age-related waist circumference cut-off model to validate its performance in the screening of dysglycemia in the US population. Methods We use NHANES data as representative of the US population. Data were subdivided into sex, ethnic and glycemia groups. We evaluate the performance of the model separately in each group through the Wilcox statistic area under the (ROC) curve, AUC. We also discuss the calibration of the model. Results For the sex-stratified analysis, we obtain AUC = 0.69–0.71 (95percent confidence interval) for male individuals and AUC = 0.75–0.78 (95percent C.I.) for female individuals. The stratified analysis is performed in different ethnic groups, namely Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black and Other Race – Including Multi-Racial. We obtain, respectively, AUC = 0.74–0.75, AUC = 0.76–0.78, AUC = 0.73–0.75, AUC = 0.74–0.77 and AUC = 0.71–0.73 (95percent C.I.). The model achieves AUC = 0.70–0.73 (95percent C.I.) in the identification of individuals with prediabetes and AUC = 0.70–0.80 (95percent C.I.) in the identification of individuals with diabetes. Conclusions The accuracy of the model turns out to be similar in each group considered in the stratified analysis, indicating that the model is suitable to be used as a screening tool for dysglycemia in the US population %K genetic algorithms, genetic programming, type 2 diabetes, Dysglycemia screening tool, Stratified analysis, Artificial intelligence, AI %9 journal article %R 10.1016/j.obmed.2022.100398 %U https://www.sciencedirect.com/science/article/pii/S2451847622000100 %U http://dx.doi.org/10.1016/j.obmed.2022.100398 %P 100398 %0 Journal Article %T Appendicular Skeletal Muscle Mass in Older Adults Can Be Estimated With a Simple Equation Using a Few Zero-Cost Variables %A Buccheri, Enrico %A Dell’Aquila, Daniele %A Russo, Marco %A Chiaramonte, Rita %A Vecchio, Michele %J Journal of Geriatric Physical Therapy %D 2024 %8 oct / dec %V 47 %N 4 %F Buccheri:2024:JPT %K genetic algorithms, genetic programming, Brain Project, medicine, appendicular skeletal muscle mass, artificial intelligence, dual-energy X-ray absorptiometer, muscle mass loss %9 journal article %R 10.1519/JPT.0000000000000420 %U https://pubmed.ncbi.nlm.nih.gov/39079022/ %U http://dx.doi.org/10.1519/JPT.0000000000000420 %P E149-E158 %0 Conference Proceedings %T A Quality Diversity Study in EvoDevo Processes for Engineering Design %A Buchanan, Edgar %A Hickinbotham, Simon %A Dubey, Rahul %A Friel, Imelda %A Colligan, Andrew %A Price, Mark %A Tyrrell, Andy M. %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F buchanan:2024:CEC %X For a long time engineering design has relied on human engineers manually crafting and refining designs using their expertise and experience. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are employed to investigate a broader design space that may go beyond what human engineers have considered. Previous literature has demonstrated the use of quality and diversity (QD) algorithms in evolutionary approaches to drive the process to better quality solutions. This paper provides a study to understand the effects of using QD algorithms in EvoDevo processes for engineering design. This paper also analyses the impact of using different behavioural characterisations (BC) in the performance of the quality of the solutions found. The results demonstrate that quality and diversity algorithms can find better solutions than other EAs for engineering design problems. It was also found that the characterisation of the BC is important to get the best results. %K genetic algorithms, genetic programming, Measurement, Dimensionality reduction, Refining, Evolutionary computation, Calibration, evodevo, generative design, structural engineering, quality diversity, neural networks %R 10.1109/CEC60901.2024.10612076 %U http://dx.doi.org/10.1109/CEC60901.2024.10612076 %0 Journal Article %T An Artificial-Intelligence-Based Method to Automatically Create Interpretable Models from Data Targeting Embedded Control Applications %A Buchner, Jens S. %A Boblest, Sebastian %A Engel, Patrick %A Junginger, Andrej %A Ulmer, Holger %J IFAC-PapersOnLine %D 2020 %V 53 %N 2 %@ 2405-8963 %F BUCHNER:2020:IFAC-PapersOnLine %O 21st IFAC World Congress %X The development of new automotive drivetrain layouts requires modeling of the involved components to allow for ideal control strategies. The creation of these models is both costly and challenging, specifically because interpretability, accuracy, and computational effort need to be balanced. In this study, a method is put forward which supports experts in the modeling process and in making an educated choice to balance these constraints. The method is based on the artificial intelligence technique of genetic programming. By solving a symbolic regression problem, it automatically identifies equation-based models from data. To address possible system complexities, data-based expressions like curves and maps can additionally be employed for the model identification. The performance of the method is demonstrated based on two examples: 1. Identification of a pure equation based model, demonstrating the benefit of interpretability. 2. Creation of a hybrid-model, combining a base equation with data-based expressions. Possible applications of the method are model creation, system identification, structural optimization, and model reduction. The existing implementation in ETAS ASCMO-MOCA also offers a high efficiency increase by combining and automating the two procedural steps of embedded function engineering and calibration %K genetic algorithms, genetic programming, Nonlinear, optimal automotive control, Automotive system identification, modeling, Modeling, supervision, control, diagnosis of automotive systems %9 journal article %R 10.1016/j.ifacol.2020.12.887 %U https://www.sciencedirect.com/science/article/pii/S240589632031226X %U http://dx.doi.org/10.1016/j.ifacol.2020.12.887 %P 13789-13796 %0 Conference Proceedings %T Information-Dependent Switching of Identification Criteria in a Genetic Programming System for System Identification %A Buchsbaum, Thomas %A Vössner, Siegfried %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:BuchsbaumVossner %X Genetic Programming (GP) can be used to identify the nonlinear differential equations of dynamical systems. If, however, the fitness function is chosen in a classical way, the optimisation will not work very well. In this article, we explain the reasons for the failure of the GP approach and present a solution strategy for improving performance. Using more than one identification criterion (fitness function) and switching based on the information content of the data enable standard GP algorithms to find better solutions in shorter times. A computational example illustrates that identification criteria switching has a bigger influence on the results than the choice of the GP parameters has. %K genetic algorithms, genetic programming %R 10.1007/11729976_27 %U http://dx.doi.org/10.1007/11729976_27 %P 300-309 %0 Conference Proceedings %T Toward a Winning GP Strategy for Continuous Nonlinear Dynamical System Identification %A Buchsbaum, Thomas %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 Buchsbaum:2007:cec %X System identification is the scientific art of building models from data. Good models are of essential importance in many areas of science and industry. Models are used to analyse, simulate, and predict systems and their states. Model structure selection and estimation of the model parameters with respect to a chosen criterion of fit are essential parts of the identification process. In this article, we investigate the suitability of genetic programming for creating continuous nonlinear state-space models from noisy time series data. We introduce methodologies from the field of chaotic time series estimation and present concepts for integrating them into a genetic programming system. We show that even small changes of the fitness evaluation approach may lead to a significantly improved performance. In combination with multiobjective optimisation, a multiple shooting approach is able to create powerful models from noisy data. %K genetic algorithms, genetic programming %R 10.1109/CEC.2007.4424616 %U 1490.pdf %U http://dx.doi.org/10.1109/CEC.2007.4424616 %P 1269-1275 %0 Thesis %T Improvement of Evolutionary Computation Approaches for Continuous Dynamical System Identification - Robustness and Performance Improvement of Standard Genetic Programming by Approximation, Multiple Shooting Methods, and Iterative Approaches %A Buchsbaum, Thomas %D 2007 %C Kopernikusgasse 24 Graz, Austria %C Institute of Engineering and Business Informatics, Graz University of Technology %G English %F Buchsbaum:thesis %X The objective of a mathematical model is to describe certain aspects of a real system; the aim of system identification is to create models from, usually noisy, measurement data. Genetic Programming (GP) is a biology-inspired method for optimising structured representations in general, and dynamical model structures and their parameters in particular. It has been applied to continuous dynamical system identification, but suffered from weak performance and premature convergence behavior. This thesis investigates GP’s suitability for creating nonlinear continuous state-space models from noisy time series data. Methodologies are introduced that improve GP’s performance and robustness. For the considered test problem, it is shown that instead of solving a dynamical problem by an initial value method, a static problem can be approximated, which can be solved by symbolic regression. This approximation approach speeds up evolution considerably. Fitness evaluation using multiple shooting methods, known from the field of chaotic time series, simplifies the optimization problem by smoothing the fitness landscape; the GP algorithm finds useful building blocks more easily. Three concepts for integrating multiple shooting into GP systems are developed and compared. This thesis offers a concept for automatically switching the identification approach based on the information content of the training data. Computational studies showed that automatic switching combined the advantages of different identification approaches: Better models were created in shorter times. Further, multi-objective methods for regularisation were shown to improve the evolved models generalization abilities substantially. Investigations of model-based input signal optimisation by evolutionary computation methods complete this dissertation. The developed methodologies improve GPs performance and robustness on continuous dynamical system identification tasks. This makes GP a useful tool that assists human modelers in finding building blocks for model synthesis. By applying the introduced methods, the chance of finding hidden information in complex signals can be increased. Medicine, natural sciences, technology, and business could benefit from the improved prediction qualities of the resulting models and the cost savings due to data-efficient modeling procedures. %K genetic algorithms, genetic programming, System Identification, Evolutionary Computation, Design of Experiments, Input Signal Shaping, Multiple shooting Approximation Time series modeling, Dynamical systems, Continuous state space models, System Identifikation, Evolutionaere Algorithmen, Zeitreihenanalyse, Versuchsplanung, Eingangsgroessenoptimierung %9 Ph.D. thesis %U https://online.tugraz.at/tug_online/pl/ui/$ctx;lang=DE/wbAbs.showThesis?pThesisNr=24591&pOrgNr=13706 %0 Journal Article %T Inductive machine learning for improved estimation of catchment-scale snow water equivalent %A Buckingham, David %A Skalka, Christian %A Bongard, Josh %J Journal of Hydrology %D 2015 %V 524 %@ 0022-1694 %F Buckingham:2015:JH %X Summary Infrastructure for the automatic collection of single-point measurements of snow water equivalent (SWE) is well-established. However, because SWE varies significantly over space, the estimation of SWE at the catchment scale based on a single-point measurement is error-prone. We propose low-cost, lightweight methods for near-real-time estimation of mean catchment-wide SWE using existing infrastructure, wireless sensor networks, and machine learning algorithms. Because snowpack distribution is highly nonlinear, we focus on Genetic Programming (GP), a nonlinear, white-box, inductive machine learning algorithm. Because we did not have access to near-real-time catchment-scale SWE data, we used available data as ground truth for machine learning in a set of experiments that are successive approximations of our goal of catchment-wide SWE estimation. First, we used a history of maritime snowpack data collected by manual snow courses. Second, we used distributed snow depth (HS) data collected automatically by wireless sensor networks. We compared the performance of GP against linear regression (LR), binary regression trees (BT), and a widely used basic method (BM) that naively assumes non-variable snowpack. In the first experiment set, GP and LR models predicted SWE with lower error than BM. In the second experiment set, GP had lower error than LR, but outperformed BT only when we applied a technique that specifically mitigated the possibility of over-fitting. %K genetic algorithms, genetic programming, Snow water equivalent, Machine learning, Wireless sensor network, Snowpack modelling %9 journal article %R 10.1016/j.jhydrol.2015.02.042 %U http://www.sciencedirect.com/science/article/pii/S0022169415001547 %U http://dx.doi.org/10.1016/j.jhydrol.2015.02.042 %P 311-325 %0 Book Section %T An Application of Genetic Programming to Forecasting Foreign Exchange Rates %A Buckley, Muneer %A Michalewicz, Zbigniew %A Zurbruegg, Ralf %E Chiong, Raymond %B Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering %D 2010 %I IGI Global %F Buckley:2010:Chiong %K genetic algorithms, genetic programming %R 10.4018/978-1-60566-705-8 %U http://hdl.handle.net/2440/54525 %U http://dx.doi.org/10.4018/978-1-60566-705-8 %P 26-48 %0 Book Section %T An Application of Genetic Programming to Forecasting Foreign Exchange Rates %A Buckley, Muneer %A Michalewicz, Zbigniew %A Zurbruegg, Ralf %E Chiong, Raymond %B Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering %D 2009 %I IGI Global %F Buckley:2009:niiiakd %X There is a great need for accurate predictions of foreign exchange rates. Many industries participate in foreign exchange scenarios with little idea where the exchange rate is moving, and what the optimum decision to make at any given time is. Although current economic models do exist for this purpose, improvements could be made in both their flexibility and adaptability. This provides much room for models that do not suffer from such constraints. This chapter proposes the use of a genetic program (GP) to predict future foreign exchange rates. The GP is an extension of the DyFor GP tailored for forecasting in dynamic environments. The GP is tested on the Australian / US (AUD/USD) exchange rate and compared against a basic economic model. The results show that the system has potential in forecasting long term values, and may do so better than established models. Further improvements are also suggested. %K genetic algorithms, genetic programming %U http://www.igi-global.com/bookstore/chapter.aspx?titleid=36310 %P 26-48 %0 Journal Article %T The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation %A Bucur, Doina %A Iacca, Giovanni %A Squillero, Giovanni %A Tonda, Alberto %J Applied Soft Computing %D 2014 %8 mar %V 16 %@ 1568-4946 %F Bucur:2014:ASC %X The analysis of worst-case behaviour in wireless sensor networks is an extremely difficult task, due to the complex interactions that characterize the dynamics of these systems. In this paper, we present a new methodology for analysing the performance of routing protocols used in such networks. The approach exploits a stochastic optimization technique, specifically an evolutionary algorithm, to generate a large, yet tractable, set of critical network topologies; such topologies are then used to infer general considerations on the behaviors under analysis. As a case study, we focused on the energy consumption of two well-known ad hoc routing protocols for sensor networks: the multi-hop link quality indicator and the collection tree protocol. The evolutionary algorithm started from a set of randomly generated topologies and iteratively enhanced them, maximizing a measure of how interesting such topologies are with respect to the analysis. In the second step, starting from the gathered evidence, we were able to define concrete, protocol-independent topological metrics which correlate well with protocols poor performances. Finally, we discovered a causal relation between the presence of cycles in a disconnected network, and abnormal network traffic. Such creative processes were made possible by the availability of a set of meaningful topology examples. Both the proposed methodology and the specific results presented here, that is, the new topological metrics and the causal explanation, can be fruitfully reused in different contexts, even beyond wireless sensor networks. %K genetic algorithms, genetic programming, genetic improvement, Collection tree protocol (CTP), MultiHopLQI (MHLQI), Wireless sensor networks (WSN), Evolutionary algorithms (EA), Routing protocols, Verification, Energy consumption %9 journal article %R 10.1016/j.asoc.2013.12.002 %U http://www.sciencedirect.com/science/article/pii/S1568494613004213 %U http://dx.doi.org/10.1016/j.asoc.2013.12.002 %P 210-222 %0 Conference Proceedings %T All-Implicants Neural Networks for Efficient Boolean Function Representation %A Buffoni, Federico %A Gianini, Gabriele %A Damiani, Ernesto %A Granitzer, Michael %S 2018 IEEE International Conference on Cognitive Computing (ICCC) %D 2018 %8 jul %F Buffoni:2018:ICCC %X Boolean classifiers can be evolved by means of genetic algorithms. This can be done within an intercommunicating island system, of evolutionary niches, undergoing cycles that alternate long periods of isolation to short periods of information exchange. In these settings, the efficiency of the communication is a key requirement. In the present work, we address this requirement by providing a technique for efficiently representing and transmitting differential encodings of Boolean functions. We introduce a new class of Boolean Neural Networks (BNN), the all-implicants BNN, and show that this representation supports efficient update communication, better than the classical representation, based on truth tables. %K genetic algorithms, genetic programming %R 10.1109/ICCC.2018.00019 %U http://dx.doi.org/10.1109/ICCC.2018.00019 %P 82-86 %0 Conference Proceedings %T Water Resource Engineers and Environmental Hydraulics %A Bui, Tai D. %A Smith, Alan A. %Y Phelps, Don %Y Sehlke, Gerald %S World Water Congress 2001 %D 2001 %8 20 24 may %V 111 %I ASCE %C Orlando, Florida, USA %F bui:286 %X In past decades, the fundamental notion of employing a multi-disciplinary approach to water resource projects was well received and promoted. According to this approach, water resource practitioners (especially engineers) should change their solution techniques and evaluation so that a solution would encompass a plethora of issues ? both structural and non-structural ? related to the project. It was recognized that solutions could not be based solely on mathematical models of flow conditions. Aspects such as ecology and non-technical issues such as recreational and societal needs should all be considered in the solution derivation process. Undoubtedly, sophisticated technical and mathematical tools (such as artificial neural network and genetic programming, and other tools related to Hydro-informatics) are essential to implement the approach. Added to this is the involvement of various professionals in certain projects. Planners, biologists, limnologists, economists, landscape architects, etc. are some of the other disciplines, besides engineers, involved in dealing with water resource projects. To address the issues, a distinct branch of engineering is imperative. The International Association of Hydraulic Engineering and Research initiated the Eco-hydraulic branch, the American Society of Civil Engineers formed the Environmental Hydraulic Technical Committee and the Canadian Society of Civil Engineers has the Hydrotechnical branch. All in all, these efforts are intended to ensure not only that levels of awareness are elevated but also those levels of engineering practice are adjusted to suit. As a result, solutions would be environmentally friendly and/or sympathetic. %K genetic algorithms, genetic programming %R 10.1061/40569(2001)286 %U http://dx.doi.org/10.1061/40569(2001)286 %P 286-286 %0 Book Section %T Solving the 8-Puzzle with Genetic Programming %A Bui, Thai %E Koza, John R. %B Genetic Algorithms and Genetic Programming at Stanford 1997 %D 1997 %8 17 mar %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-205981-2 %F Bui:1997:s8p %K genetic algorithms, genetic programming %P 11-17 %0 Conference Proceedings %T NEAT in HyperNEAT Substituted with Genetic Programming %A Buk, Zdenek %A Koutnik, Jan %A Snorek, Miroslav %Y Kolehmainen, Mikko %Y Toivanen, Pekka %Y Beliczynski, Bartlomiej %S 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009 %S Lecture Notes in Computer Science %D 2009 %8 23 25 apr %V 5495 %I Springer %C Kuopio, Finland %F Buk:2009:ICANNGA %O Revised selected papers %X In this paper we present application of genetic programming (GP) [1] to evolution of indirect encoding of neural network weights. We compare usage of original HyperNEAT algorithm with our implementation, in which we replaced the underlying NEAT with genetic programming. The algorithm was named HyperGP. The evolved neural networks were used as controllers of autonomous mobile agents (robots) in simulation. The agents were trained to drive with maximum average speed. This forces them to learn how to drive on roads and avoid collisions. The genetic programming lacking the NEAT complexification property shows better exploration ability and tends to generate more complex solutions in fewer generations. On the other hand, the basic genetic programming generates quite complex functions for weights generation. Both approaches generate neural controllers with similar abilities. %K genetic algorithms, genetic programming, HyperGP, ANN %R 10.1007/978-3-642-04921-7_25 %U http://dx.doi.org/10.1007/978-3-642-04921-7_25 %P 243-252 %0 Conference Proceedings %T Comprehensive evolutionary approach for neural network ensemble automatic design %A Bukhtoyarov, Vladimir V. %A Semenkina, Olga E. %S IEEE Congress on Evolutionary Computation (CEC 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Bukhtoyarov:2010:cec %X Neural network ensemble is an approach based on cooperative usage of many neural networks for problem solving. Often this approach enables to solve problem more efficiently than approach where only one network is used. The two major stages of the neural network ensemble construction are: design and training component networks, combining of the component networks predictions to produce the ensemble output. In this paper, a probability-based method is proposed to accomplish the first stage. Although this method is based on the genetic algorithm, it requires fewer parameters to be tuned. A method based on genetic programming is proposed for combining the predictions of component networks. This method allows us to build nonlinear combinations of component networks predictions providing more flexible and adaptive solutions. To demonstrate robustness of the proposed approach, its results are compared with the results obtained using other methods. %K genetic algorithms, genetic programming %R 10.1109/CEC.2010.5586516 %U http://dx.doi.org/10.1109/CEC.2010.5586516 %0 Journal Article %T Design of Computational Models for Hydroturbine Units Based on a Nonparametric Regression Approach with Adaptation by Evolutionary Algorithms %A Bukhtoyarov, Vladimir Viktorovich %A Tynchenko, Vadim Sergeevich %J Computation %D 2021 %V 9 %N 8 %@ 2079-3197 %F bukhtoyarov:2021:Computation %X This article deals with the problem of designing regression models for evaluating the parameters of the operation of complex technological equipment–hydroturbine units. A promising approach to the construction of regression models based on nonparametric Nadaraya-Watson kernel estimates is considered. A known problem in applying this approach is to determine the effective values of kernel-smoothing coefficients. Kernel-smoothing factors significantly impact the accuracy of the regression model, especially under conditions of variability of noise and parameters of samples in the input space of models. This fully corresponds to the characteristics of the problem of estimating the parameters of hydraulic turbines. We propose to use the evolutionary genetic algorithm with an addition in the form of a local-search stage to adjust the smoothing coefficients. This ensures the local convergence of the tuning procedure, which is important given the high sensitivity of the quality criterion of the nonparametric model. On a set of test problems, the results were obtained showing a reduction in the modelling error by 20percent and 28percent for the methods of adjusting the coefficients by the standard and hybrid genetic algorithms, respectively, in comparison with the case of an arbitrary choice of the values of such coefficients. For the task of estimating the parameters of the operation of a hydroturbine unit, a number of promising approaches to constructing regression models based on artificial neural networks, multidimensional adaptive splines, and an evolutionary method of genetic programming were included in the research. The proposed nonparametric approach with a hybrid smoothing coefficient tuning scheme was found to be most effective with a reduction in modelling error of about 5percent compared with the best of the alternative approaches considered in the study, which, according to the results of numerical experiments, was the method of multivariate adaptive regression splines. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/computation9080083 %U https://www.mdpi.com/2079-3197/9/8/83 %U http://dx.doi.org/10.3390/computation9080083 %0 Journal Article %T A Study on a Probabilistic Method for Designing Artificial Neural Networks for the Formation of Intelligent Technology Assemblies with High Variability %A Bukhtoyarov, Vladimir V. %A Tynchenko, Vadim S. %A Nelyub, Vladimir A. %A Masich, Igor S. %A Borodulin, Aleksey S. %A Gantimurov, Andrei P. %J Electronics %D 2023 %V 12 %N 1 %@ 2079-9292 %F bukhtoyarov:2023:Electronics %X Currently, ensemble approaches based, among other things, on the use of non-network models are powerful tools for solving data analysis problems in various practical applications. An important problem in the formation of ensembles of models is ensuring the synergy of solutions by using the properties of a variety of basic individual solutions; therefore, the problem of developing an approach that ensures the maintenance of diversity in a preliminary pool of models for an ensemble is relevant for development and research. This article is devoted to the study of the possibility of using a method for the probabilistic formation of neural network structures developed by the authors. In order to form ensembles of neural networks, the influence of parameters of neural network structure generation on the quality of solving regression problems is considered. To improve the quality of the overall ensemble solution, using a flexible adjustment of the probabilistic procedure for choosing the type of activation function when filling in the layers of a neural network is proposed. In order to determine the effectiveness of this approach, a number of numerical studies on the effectiveness of using neural network ensembles on a set of generated test tasks and real datasets were conducted. The procedure of forming a common solution in ensembles of neural networks based on the application of an evolutionary method of genetic programming is also considered. This article presents the results of a numerical study that demonstrate a higher efficiency of the approach with a modified structure formation procedure compared to a basic approach of selecting the best individual neural networks from a preformed pool. These numerical studies were carried out on a set of test problems and several problems with real datasets that, in particular, describe the process of ore-thermal melting. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/electronics12010215 %U https://www.mdpi.com/2079-9292/12/1/215 %U http://dx.doi.org/10.3390/electronics12010215 %P ArticleNo.215 %0 Conference Proceedings %T Evolutionary Computing in Multi-Agent Environments: Speciation and Symbiogenesis %A Bull, Lawrence %A Fogarty, Terence C. %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 bull:1996:SandS %X In this paper we introduce two macro-level operators to enhance the use of population-based evolutionary computing techniques in multiagent environments: speciation and symbiogenesis. We describe their use in conjunction with the genetic algorithm to evolve Pittsburgh-style classifier systems, where each classifier system represents an agent in a cooperative multi-agent system. The reasons for implementing these kinds of operators are discussed and we then examine their performance in developing a controller for the gait of a wall-climbing quadrupedal robot, where each leg of the quadruped is controlled by a classifier system. We find that the use of such operators can give improved performance over static population/agent configurations. %K genetic algorithms %R 10.1007/3-540-61723-X_965 %U http://dx.doi.org/10.1007/3-540-61723-X_965 %P 12-21 %0 Conference Proceedings %T Evolutionary Computing in Multi-Agent Environments: Eusociality %A Bull, Larry %A Holland, Owen %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 Bull:1997:ecmaee %K Genetic Algorithms %P 347-352 %0 Conference Proceedings %T On using ZCS in a Simulated Continuous Double-Auction Market %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 bull:1999:OZSCDM %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-806.ps %P 83-90 %0 Conference Proceedings %T On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems %A Bull, Larry %A Preen, Richard %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 Bull:2009:eurogp %K genetic algorithms, genetic programming %R 10.1007/978-3-642-01181-8_4 %U http://dx.doi.org/10.1007/978-3-642-01181-8_4 %P 37-48 %0 Journal Article %T On dynamical genetic programming: simple Boolean networks in learning classifier systems %A Bull, Larry %J International Journal of Parallel, Emergent and Distributed Systems %D 2009 %8 oct %V 24 %N 5 %I Taylor & Francis %@ 1744-5760 %F Bull:2009:IJPEDS %X Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end. Significantly, Turing proposed a form of discrete dynamical system and yet dynamical representations remain almost unexplored within conventional genetic programming (GP). This paper presents results from an initial investigation into using simple dynamical GP representations within a learning classifier system. It is shown possible to evolve ensembles of dynamical Boolean function networks to solve versions of the well-known multiplexer problem. Both synchronous and asynchronous systems are considered. %K genetic algorithms, genetic programming, discrete, dynamical systems, evolution, multiplexer, unorganised machines %9 journal article %R 10.1080/17445760802660387 %U http://dx.doi.org/10.1080/17445760802660387 %P 421-442 %0 Book Section %T Chemical Computing Through Simulated Evolution %A Bull, Larry %A Toth, Rita %A Stone, Chris %A De Lacy Costello, Ben %A Adamatzky, Andrew %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 Bull:2017:miller %X Many forms of unconventional computing, i.e., massively parallel computers which exploit the non-linear material properties of their substrate, can be realised through simulated evolution. That is, the behaviour of non-linear media can be controlled automatically and the structural design of the media optimized through the nature-inspired machine learning approach. This chapter describes work using the Belousov-Zhabotinsky reaction as a non-linear chemical medium in which to realise computation. Firstly, aspects of the basic structure of an experimental chemical computer are evolved to implement two Boolean logic functions through a collision-based scheme. Secondly, a controller is evolved to dynamically affect the rich spatio-temporal chemical wave behaviour to implement three Boolean functions, in both simulation and experimentation. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-67997-6_13 %U http://dx.doi.org/10.1007/978-3-319-67997-6_13 %P 269-286 %0 Journal Article %T learning and the Stability of Cycles %A Bullard, James B. %A Duffy, John %J Macroeconomic Dynamics %D 1998 %8 mar %V 2 %N 1 %F bullard:1998:MD %X We investigate the extent to which agents can learn to coordinate on stationary perfect-foresight cycles in a general-equilibrium environment. Depending on the value of a preference parameter, the limiting backward (direction of time reversed) perfect-foresight dynamics are characterized by steady-state, periodic, or chaotic trajectories for real money balances. We relax the perfect-foresight assumption and examine how a population of artificial, heterogeneous adaptive agents might learn in such an environment. These artificial agents optimize given their forecasts of future prices, and they use forecast rules that are consistent with steady-state or periodic trajectories for prices. The agents’ forecast rules are updated by a genetic algorithm. We find that the population of artificial adaptive agents is able eventually to coordinate on steady state and low-order cycles, but not on the higher-order periodic equilibria that exist under the perfect-foresight assumption. %K genetic algorithms, Learning, Multiple Equilibria, Coordination %9 journal article %P 22-48 %0 Journal Article %T A model of learning and emulation with artificial adaptive agents %A Bullard, James %A Duffy, John %J Journal of Economic Dynamics and Control %D 1998 %8 feb %V 22 %N 2 %F bullard:1998:JEDC %X We study adaptive learning in a sequence of overlapping generations economies in which agents live for n periods. Agents initially have heterogeneous beliefs, and form multi-step-ahead forecasts using a forecast rule chosen from a vast set of candidate rules. Agents learn in every period by creating new forecast rules and by emulating the forecast rules of other agents. Computational experiments show that systems so defined can yield three qualitatively different types of long-run outcomes: (1) coordination on a low inflation, stationary perfect foresight equilibrium; (2) persistent currency collapse; and (3) coordination failure within the allotted time frame. %K genetic algorithms, Learning, Coordination, Overlapping generations %9 journal article %R 10.1016/S0165-1889(97)00072-9 %U http://dx.doi.org/10.1016/S0165-1889(97)00072-9 %P 179-207 %0 Conference Proceedings %T Building Cross-Sectional Trading Strategies via Geometric Semantic Genetic Programming %A Bunjerdtaweeporn, Kritpol %A Moraglio, Alberto %Y Garcia-Sanchez, Pablo %Y Hart, Emma %Y Thomson, Sarah L. %S 28th International Conference, EvoApplications 2025 %S LNCS %D 2025 %8 March 5 apr %V 15612 %I Springer %C Trieste, Italy %F Bunjerdtaweeporn:2025:evoapplications %K genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Portfolio Construction, Stock Returns Prediction, Stock Selection %R 10.1007/978-3-031-90062-4_2 %U http://dx.doi.org/10.1007/978-3-031-90062-4_2 %P 20-37 %0 Conference Proceedings %T Geometric Semantic Genetic Programming for Evolving Real-Valued Functions with Order Awareness %A Bunjerdtaweeporn, Kritpol %A Moraglio, Alberto %Y Ekart, Aniko %Y Pillay, Nelishia %S Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion %S GECCO ’25 Companion %D 2025 %8 14 18 jul %I Association for Computing Machinery %C Malaga, Spain %F bunjerdtaweeporn:2025:GECCOcomp %X In many applications, the relative order of the outputs is more crucial than their absolute numerical values. Geometric semantic genetic programming (GSGP) typically operates on function outputs directly but lacks an inherent behaviour to represent the order structure of a function, making it less suitable for problems where decisions are driven by comparison of outputs. In this study, we explore a novel perspective of semantics in the context of GSGP rooted in the order structure of real-valued functions referred to as order semantics. We show that existing geometric semantic operators for real-valued functions retain their geometric properties under order semantics when an alternative notion of semantic distance is considered instead of Euclidean distance. Consequently, the fitness landscape seen by these operators is unimodal with respect to this choice of distance in order semantic space. We validate our method through experiments comparing standard GSGP and a newly proposed GSGP based on order semantics on randomly generated functions. Our results demonstrate that the proposed GSGP improves ranking accuracy at the cost of numerical precision. %K genetic algorithms, genetic programming, symbolic regression, geometric semantic genetic programming, semantics: Poster %R 10.1145/3712255.3726777 %U https://doi.org/10.1145/3712255.3726777 %U http://dx.doi.org/10.1145/3712255.3726777 %P 595-598 %0 Journal Article %T Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data %A Buontempo, Frances V. %A Wang, Xue Zhong %A Mwense, Mulaisho %A Horan, Nigel %A Young, Anita %A Osborn, Daniel %J Journal of Chemical Information and Modeling %D 2005 %V 45 %F buontempo:2005:CIM %O ASAP article. Web Release Date: May 12, 2005 %X Automatic induction of decision trees and production rules from data to develop structure-activity models for toxicity prediction has recently received much attention, and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node. These approaches can be successful; however, the greedy search will necessarily miss regions of the search space. Recent literature has demonstrated the applicability of genetic programming to decision tree induction to overcome this problem. This paper presents a variant of this novel approach, using fewer mutation options and a simpler fitness function, demonstrating its utility in inducing decision trees for ecotoxicity data, via a case study of two data sets giving improved accuracy and generalization ability over a popular decision tree inducer. %K genetic algorithms, genetic programming, decision trees, model ecotoxicity, EPTree, C5.0 See5, recursive partitioning, S-Plus, SIMCA-P 8.0, QSAR %9 journal article %R 10.1021/ci049652n %U http://dx.doi.org/10.1021/ci049652n %P 904-912 %0 Journal Article %T Solving variational and Cauchy problems with self-configuring genetic programming algorithm %A Burakov, Sergei V. %A Semenkin, Eugene S. %J International Journal of Innovative Computing and Applications %D 2013 %V 5 %N 3 %@ 1751-648X %F Burakov:2013:IJICA %O Special Issue on: BIOMA 2012 Advances in Bio-inspired Computing. Guest Editors: Assistant Professor Jurij Silc and Associate Professor Bogdan Filipic %X It is suggested to use genetic programming techniques for solving Cauchy problem and variational problem that allows getting the exact analytical solution if it does exist and an approximate analytical expression otherwise. Features of solving process with this approach are considered. Results of numerical experiments are given. The approach improvement is fulfilled by adopting the self-configuring genetic programming algorithm that does not require extra efforts for choosing its effective settings but demonstrates the competitive performance %K genetic algorithms, genetic programming, Ordinary differential equations %K Cauchy problem %K variational problem %K numeric methods %K genetic programming algorithm %K self-configuration. %9 journal article %R 10.1504/IJICA.2013.055931 %U http://dx.doi.org/10.1504/IJICA.2013.055931 %P 152-162 %0 Conference Proceedings %T A Contribution to the Foundations of AI: Genetic Programming and Support Vector Machines %A Burbidge, Robert %Y McGinnity, T. M. %S Workshop and Summer School on Evolutionary Computing Lecture Series by Pioneers %D 2008 %8 18 20 aug %C University of Ulster %F wssec-rb-final %X The aim of genetic programming is to automatically find computer programs that solve problems; using an algorithm inspired by biological evolution. The aim of the support vector machine is to model empirical data; using an algorithm based on statistical optimality. Fundamentally, both these techniques, and all artificial intelligence disciplines, use search; with differing representations, search operators and objective functions. We formally compare these two techniques as a contribution toward the foundations of artificial intelligence, and less grandiosely, in order to encourage transfer of knowledge between the two disciplines. %K genetic algorithms, genetic programming, SVM %U http://users.aber.ac.uk/rvb/wssec-rb-final.pdf %0 Conference Proceedings %T A Grammar for Evolution of a Robot Controller %A Burbidge, Robert %A Walker, Joanne H. %A Wilson, Myra S. %Y Kyriacou, Theocharis %Y Nehmzow, Ulrich %Y Melhuish, Chris %Y Witkowski, Mark %S TAROS 2009 Towards Autonomous Robotic Systems %S Intelligent Systems Research Centre Technical Report Series %D 2009 %8 aug 31 sep 2 %C University of Ulster, Londonderry, United Kingdom %F Burbidge:2009:TAROS %X An autonomous mobile robot requires an onboard controller that allows it to perform its tasks for long periods in isolation. One possibility is for the robot to adapt to its environment using some form of artificial intelligence. Evolutionary techniques such as genetic programming (GP) offer the possibility of automatically programming the controller based on the robot’s experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been successfully applied to various problems, particularly those for which GP has been successful. We present a method for applying GE to autonomous robot control and evaluate it in simulation for the Khepera robot. %K genetic algorithms, genetic programming, grammatical evolution, robot control %U http://isrc.ulster.ac.uk/images/stories/publications/report-series/TAROS_2009.pdf %P 182-189 %0 Conference Proceedings %T Grammatical evolution of a robot controller %A Burbidge, Robert %A Walker, Joanne H. %A Wilson, Myra S. %S IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 %D 2009 %8 November 15 oct %C St. Louis, USA %F Burbidge:2009:IROS %X An autonomous mobile robot requires an on board controller that allows it to perform its tasks for long periods in isolation. One possibility is for the robot to adapt to its environment using some form of artificial intelligence. Evolutionary techniques such as genetic programming (GP) offer the possibility of automatically programming the controller based on the robot’s experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been successfully applied to various problems, particularly those for which GP has been successful. We present a method for applying GE to autonomous robot control and evaluate it in simulation for the Khepera robot. %K genetic algorithms, genetic programming, grammatical evolution, Khepera robot, artificial intelligence, autonomous mobile robot, evolutionary algorithm, evolutionary technique, onboard controller, robot controller, grammars, mobile robots %R 10.1109/IROS.2009.5354411 %U http://dx.doi.org/10.1109/IROS.2009.5354411 %P 357-362 %0 Journal Article %T Vector-valued function estimation by grammatical evolution for autonomous robot control %A Burbidge, Robert %A Wilson, Myra S. %J Information Sciences %D 2014 %V 258 %@ 0020-0255 %F Burbidge:2014:IS %X An autonomous mobile robot requires a robust onboard controller that makes intelligent responses in dynamic environments. Current solutions tend to lead to unnecessarily complex solutions that only work in niche environments. Evolutionary techniques such as genetic programming (GP) can successfully be used to automatically program the controller, minimising the limitations arising from explicit or implicit human design criteria, based on the robot’s experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been applied to various problems, particularly those for which GP has performed. We formulate robot control as vector-valued function estimation and present a novel generative grammar for vector valued functions. A consideration of the crossover operator leads us to propose a design criterion for the application of GE to vector-valued function estimation, along with a second novel generative grammar which meets this criterion. The suitability of these grammars for vectorvalued function estimation is assessed empirically on a simulated task for the Khepera robot %K genetic algorithms, genetic programming, Grammatical evolution, Evolutionary robotics, Vector-valued function, Ripple crossover, Schema %9 journal article %R 10.1016/j.ins.2013.09.044 %U http://www.sciencedirect.com/science/article/pii/S0020025513006920 %U http://dx.doi.org/10.1016/j.ins.2013.09.044 %P 182-199 %0 Conference Proceedings %T Does Chomsky complexity affect genetic programming computational requirements? %A Burger, Clayton %A Du Plessis, Mathys C. %S Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment %D 2011 %I ACM %C Cape Town, South Africa %F Burger:2011:SAICSIT %X This paper presents an exploration into the relationship between Chomsky problem complexity, as defined by Theory of Computation, and the computational requirements to evolve solutions to these problems. Genetic programming is used to explore these computational requirements by evolving Turing machines that accept the languages posed. Quantifiable results are obtained by applying various metrics to the evolutionary success of these evolved Turing machines. The languages posed are samples out of three language classes from the Chomsky hierarchy, with each class having increasing levels of complexity based on the hierarchy. These languages are evolved on a two-tape Turing machine representation by making use of genetic operators found to be effective in the literature. By exploring the effects of the genetic programming algorithm population sizes and coupled genetic operator rates, it was found that the evolutionary success rates of the classes of Regular and Context-Sensitive problems have no statistical difference in computational requirements, while the Context-Free class was found to be more difficult than the other two Chomsky problem classes through the statistical significance discovered when compared to the other classes. %K genetic algorithms, genetic programming, theory of computation, Turing machines %R 10.1145/2072221.2072226 %U http://dx.doi.org/10.1145/2072221.2072226 %P 31-39 %0 Journal Article %T Can genetic programming improve software effort estimation? A comparative evaluation %A Burgess, Colin J. %A Lefley, Martin %J Information and Software Technology %D 2001 %8 15 dec %V 43 %N 14 %F Burgess:2001:IST %X Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently, attention has turned to a variety of machine learning (ML) methods. This paper attempts to evaluate critically the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches, in terms of accuracy and ease of use. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. The input variables are restricted to those available from the specification stage and significant effort is put into the GP and all of the other solution strategies to offer a realistic and fair comparison. There is evidence that GP can offer significant improvements in accuracy but this depends on the measure and interpretation of accuracy used. GP has the potential to be a valid additional tool for software effort estimation but set up and running effort is high and interpretation difficult, as it is for any complex meta-heuristic technique. %K genetic algorithms, genetic programming, Case-based reasoning, Machine learning, Neural networks, Software effort estimation %9 journal article %R 10.1016/S0950-5849(01)00192-6 %U http://www.sciencedirect.com/science/article/B6V0B-44D4196-7/1/20f45986fc0a4827ad09169178379d73 %U http://dx.doi.org/10.1016/S0950-5849(01)00192-6 %P 863-873 %0 Book Section %T Can Genetic Programming improve Software Effort Estimation? A Comparative Evaluation %A Burgess, C. J. %A Lefley, M. %E Zhang, Du %E Tsai, Jeffrey J. P. %B Machine Learning Applications In Software Engineering: Series on Software Engineering and Knowledge Engineering %D 2005 %8 may %V 16 %I World Scientific Publishing Co. %@ 981-256-094-7 %F 2000240 %X Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently attention has turned to a variety of machine learning methods. This paper attempts to critically evaluate the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. It shows that GP can offer some significant improvements in accuracy and has the potential to be a valid additional tool for software effort estimation. %K genetic algorithms, genetic programming, Artificial Intelligence, Machine Learning, SBSE %P 95-105 %0 Report %T Finding Approximate Analytic Solutions To Differential Equations Using Genetic Programming %A Burgess, Glenn %D 1999 %8 Feb %N DSTO-TR-0838 %I Surveillance Systems Division, Defence Science and Technology Organisation, Australia %C Salisbury, SA, 5108, Australia %F burgess:1999:faasdeGP %X The computational optimisation technique, genetic programming, is applied to the analytic solution of general differential equations. The approach generates a mathematical expression that is an approximate or exact solution to the particular equation under consideration. The technique is applied to a number of differential equations of increasing complexity in one and two dimensions. Comparative results are given for varying several parameters of the algorithm such as the size of the calculation stack and the variety of available mathematical operators. Several novel approaches gave negative results. Angeline’s module acquisition (MA) and Koza’s automatically defined functions (ADF) are considered and the results of some modifications are presented. One result of significant theoretical interest is that the syntax-preserving crossover used in Genetic Programming may be generalised to allow the exchange of n-argument functions without adverse effects. The results show that Genetic Programming is an effective technique that can give reasonable results, given plenty of computing resources. The technique used here can be applied to higher dimensions; although in practice the algorithmic complexity may be too high. %K genetic algorithms, genetic programming, differential equations %U http://www.dsto.defence.gov.au/corporate/reports/DSTO-TR-0838.pdf %0 Conference Proceedings %T Bounded and periodic evolutionary machines %A Burgin, Mark %A Eberbach, Eugene %S IEEE Congress on Evolutionary Computation (CEC 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Burgin:2010:cec %X The aim of this paper is the development of foundations for evolutionary computation. We introduce and study two classes of evolutionary automata: bounded and periodic evolutionary machines. %K genetic algorithms, genetic programming %R 10.1109/CEC.2010.5586271 %U http://dx.doi.org/10.1109/CEC.2010.5586271 %0 Conference Proceedings %T Reduction of fitness calculations in Cartesian Genetic Programming %A Burian, Petr %S International Conference on Applied Electronics (AE 2013) %D 2013 %8 October 12 sep %C Pilsen %F Burian:2013:AE %X This paper deals with the valuation issue in Cartesian Genetic Programming. It explores the possibilities of the reduction of candidate solutions which are needed to be evaluated. This reduction may accelerate the process of the evolution - evolutionary design. The paper presents the approach that detects changes in the phenotype and, based on that, the algorithm can omit the valuation of a candidate solution. The author shows this approach on the evolutionary design of multipliers. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, Multiplier, Evolutionary design, Evolutionary Algorithm, Multiplier %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6636478 %0 Conference Proceedings %T Fast detection of active genes in Cartesian Genetic Programming %A Burian, P. %S International Conference on Signals and Electronic Systems (ICSES 2014) %D 2014 %8 sep %F Burian:2014:ICSES %X This paper deals with the implementation of fast active genes detector by an FPGA device. The author introduces the modular structure that determines active genes in genotypes of Cartesian Genetic Programming. The use of the detector is suitable if the l-back parameter takes value of 1 or 2. The paper also discusses timing performance. The introduced active genes detector can be used for the reduction of fitness calculations in Cartesian Genetic Programming. %K genetic algorithms, genetic programming, Cartesian Genetic Programming %R 10.1109/ICSES.2014.6948721 %U http://dx.doi.org/10.1109/ICSES.2014.6948721 %0 Conference Proceedings %T Compact version of Cartesian Genetic Programming %A Burian, P. %S International Conference on Applied Electronics (AE 2014) %D 2014 %8 sep %F Burian:2014:AE %X This paper deals with the design of the compact version of Cartesian Genetic Programming. The focus is given to the search algorithm of type (1+1). The paper presents the approach that detects changes in the phenotype and, based on that, the algorithm can omit the evaluation of a candidate solution. The author uses the evolutionary design of multipliers as benchmark to present the efficiency of the algorithm. %K genetic algorithms, genetic programming, Cartesian Genetic Programming %R 10.1109/AE.2014.7011669 %U http://dx.doi.org/10.1109/AE.2014.7011669 %P 63-66 %0 Book Section %T Genetic Algorithms Go to Grade School %A Burjorjee, Keki M. %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 burjorjee:1999:GAGGS %K genetic algorithms, genetic programming %P 31-40 %0 Journal Article %T Putting More Genetics into Genetic Algorithms %A Burke, Donald S. %A De Jong, Kenneth A. %A Grefenstette, John J. %A Ramsey, Connie Loggia %A Wu, Annie S. %J Evolutionary Computation %D 1998 %8 Winter %V 6 %N 4 %F burk:1998:pmgGA %X The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). The VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems. %K genetic algorithms, Models of viral evolution, variable-length representation, length penalty functions, genome length adaptation, noncoding regions, duplicative genes %9 journal article %R 10.1162/evco.1998.6.4.387 %U http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.387 %U http://dx.doi.org/10.1162/evco.1998.6.4.387 %P 387-410 %0 Generic %T Putting More Genetics into Genetic Algorithms %A Burke, Donald S. %A De Jong, Kenneth A. %A Grefenstette, John J. %A Ramsey, Connie Loggia %A Wu, Annie S. %D 1998 %8 19 oct %I preprint of \citeburk:1998:pmgGA %F burk:1998:pmgGAx %K genetic algorithms, Models of viral evolution, variable-length representation, length penalty functions, genome length adaptation, noncoding regions, duplicative genes %U http://www.ib3.gmu.edu/gref/papers/burke-ec98.ps %0 Conference Proceedings %T A Survey And Analysis Of Diversity Measures In Genetic Programming %A Burke, Edmund %A Gustafson, Steven %A Kendall, Graham %Y Langdon, W. B. %Y Cantú-Paz, E. %Y Mathias, K. %Y Roy, R. %Y Davis, D. %Y Poli, R. %Y Balakrishnan, K. %Y Honavar, V. %Y Rudolph, G. %Y Wegener, J. %Y Bull, L. %Y Potter, M. A. %Y Schultz, A. C. %Y Miller, J. F. %Y Burke, E. %Y Jonoska, N. %S GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %D 2002 %8 September 13 jul %I Morgan Kaufmann Publishers %C New York %@ 1-55860-878-8 %F burke:2002:gecco %K genetic algorithms, genetic programming, diversity, population diversity, population dynamics %U http://www.cs.nott.ac.uk/~smg/research/publications/gecco-diversity-2002.ps %P 716-723 %0 Conference Proceedings %T Advanced Population Diversity Measures in Genetic Programming %A Burke, Edmund %A Gustafson, Steven %A Kendall, Graham %A Krasnogor, Natalio %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 burke:ppsn2002:pp341 %X This paper presents a survey and comparison of significant diversity measures in the genetic programming literature. This study builds on previous work by the authors to gain a deeper understanding of the conditions under which genetic programming evolution is successful. Three benchmark problems (Artificial Ant, Symbolic Regression and Even-5-Parity) are used to illustrate different diversity measures and to analyse their correlation with performance. Results show that measures of population diversity based on edit distances and phenotypic diversity suggest that successful evolution occurs when populations converge to a similar structure but with high fitness diversity. %K genetic algorithms, genetic programming, Theory of EC, Evolution dynamics %R 10.1007/3-540-45712-7_33 %U http://www.gustafsonresearch.com/research/publications/ppsn-2002.pdf %U http://dx.doi.org/10.1007/3-540-45712-7_33 %P 341-350 %0 Conference Proceedings %T Ramped Half-n-Half Initialisation Bias in GP %A Burke, Edmund %A Gustafson, Steven %A Kendall, Graham %Y Cantú-Paz, E. %Y Foster, J. A. %Y Deb, K. %Y Davis, D. %Y Roy, R. %Y O’Reilly, U.-M. %Y Beyer, H.-G. %Y Standish, R. %Y Kendall, G. %Y Wilson, S. %Y Harman, M. %Y Wegener, J. %Y Dasgupta, D. %Y Potter, M. A. %Y Schultz, A. C. %Y Dowsland, K. %Y Jonoska, N. %Y Miller, J. %S Genetic and Evolutionary Computation – GECCO-2003 %S LNCS %D 2003 %8 December 16 jul %V 2724 %I Springer-Verlag %C Chicago %@ 3-540-40603-4 %F burke:2003:gecco %X Tree initialisation techniques for genetic programming (GP) are examined in [4,3], highlighting a bias in the standard implementation of the initialisation method Ramped Half-n-Half (RHH) [1]. GP trees typically evolve to random shapes, even when populations were initially full or minimal trees [2]. In canonical GP, unbalanced and sparse trees increase the probability that bigger subtrees are selected for recombination, ensuring code growth occurs faster and that subtree crossover will have more difficultly in producing trees within specified depth limits. The ability to evolve tree shapes which allow more legal crossover operations, by providing more possible crossover points (by being bushier), and control code growth is critical. The GP community often uses RHH [4]. The standard implementation of the RHH method selects either the grow or full method with 0.5 probability to produce a tree. If the tree is already in the initial population it is discarded and another is created by grow or full. As duplicates are typically not allowed, this standard implementation of RHH favours full over grow and possibly biases the evolutionary process. %K genetic algorithms, genetic programming, poster %R 10.1007/3-540-45110-2_71 %U http://www.cs.nott.ac.uk/~smg/research/publications/gecco-poster-2003.ps %U http://dx.doi.org/10.1007/3-540-45110-2_71 %P 1800-1801 %0 Conference Proceedings %T Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one %A Burke, Edmund K. %A Hyde, Matthew R. %A Kendall, Graham %A Woodward, John %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 1277273 %X It is possible to argue that online bin packing heuristics should be evaluated by using metrics based on their performance over the set of all bin packing problems, such as the worst case or average case performance. However, this method of assessing a heuristic would only be relevant to a user who employs the heuristic over a set of problems which is actually representative of the set of all possible bin packing problems. On the other hand, a real world user will often only deal with packing problems that are representative of a particular sub-set. Their piece sizes will all belong to a particular distribution. The contribution of this paper is to show that a Genetic Programming system can automate the process of heuristic generation and produce heuristics that are human-competitive over a range of sets of problems, or which excel on a particular sub-set. We also show that the choice of training instances is vital in the area of automatic heuristic generation, due to the trade-off between the performance and generality of the heuristics generated and their applicability to new problems. %K genetic algorithms, genetic programming, bin packing, heuristics, hyper heuristic, reliability %R 10.1145/1276958.1277273 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1559.pdf %U http://dx.doi.org/10.1145/1276958.1277273 %P 1559-1565 %0 Conference Proceedings %T The Scalability of Evolved on Line Bin Packing Heuristics %A Burke, E. K. %A Hyde, M. R. %A Kendall, G. %A Woodward, J. R. %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 Burke:2007:cec %X The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed ’bestfit’ algorithm. Here we examine the performance of the evolved heuristics on larger instances of the problem, which contain many more pieces than the problem instances used in training. In previous work, we concluded that we could confidently apply our heuristics to new instances of the same class of problem. Now we can make the additional claim that we can confidently apply our heuristics to problems of much larger size, not only without deterioration of solution quality, but also within a constant factor of the performance obtained by ’best fit’. Interestingly, our evolved heuristics respond to the number of pieces in a problem instance although they have no explicit access to that information. We also comment on the important point that, when solutions are explicitly constructed for single problem instances, the size of the search space explodes. How- ever, when working in the space of algorithmic heuristics, the distribution of functions represented in the search space reaches some limiting distribution and therefore the combinatorial explosion can be controlled. %K genetic algorithms, genetic programming %R 10.1109/CEC.2007.4424789 %U 1668.pdf %U http://dx.doi.org/10.1109/CEC.2007.4424789 %P 2530-2537 %0 Journal Article %T Hyper-heuristics: a survey of the state of the art %A Burke, Edmund K. %A Gendreau, Michel %A Hyde, Matthew %A Kendall, Graham %A Ochoa, Gabriela %A Ozcan, Ender %A Qu, Rong %J Journal of the Operational Research Society %D 2013 %8 dec %V 64 %N 12 %I Palgrave Macmillan %@ 0160-5682 %F Burke2013 %X Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new, it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed. %K genetic algorithms, genetic programming, Hyper-heuristics, evolutionary computation, metaheuristics, machine learning, combinatorial optimisation, scheduling %9 journal article %R 10.1057/jors.2013.71 %U http://www.cs.nott.ac.uk/~rxq/files/HHSurveyJORS2013.pdf %U http://dx.doi.org/10.1057/jors.2013.71 %P 1695-1724 %0 Conference Proceedings %T Genetic Programming of Crops to Sustain or Increase Yields under Reduced Irrigation %A Burke, John J. %Y Walton, Raymond %S World Water and Environmental Resources Congress 2005 %D 2005 %8 may 15 19 %C Anchorage, Alaska, USA %F Burke:2005:WWERC %X Crop productivity is determined by the plant’s capacity to convert energy, nutrients, and water into harvestable yield of high quality and high value. The challenge is to sustain or enhance the outputs with a declining land base, reduced water supplies, and a changing global environment. The process of crop adaptation to the environment is restricted by the genetic potential of the plant. Improving the capacity of crops to overcome or adapt to factors that limit growth would increase yield and quality, while reducing demand for irrigation. Research identifying the molecular and biochemical factors underlying crop productivity, adaptation to stressful environments, and production of high-value end products is providing new insights into strategies for germplasm improvement. Characterisation of existing genetic diversity within U.S germplasm collections for water-deficit and temperature stress resistance; and the use of biotechnology to enhance yield stabilisation in water limited environments will ensure farming sustainability in the future. %R 10.1061/40792(173)532 %U http://dx.doi.org/10.1061/40792(173)532 %0 Conference Proceedings %T Evolving Bin Packing Heuristics with Genetic Programming %A Burke, E. K. %A Hyde, M. R. %A Kendall, G. %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 Burke:PPSN:2006 %X The bin-packing problem is a well known NP-Hard optimisation problem, and, over the years, many heuristics have been developed to generate good quality solutions. This paper outlines a genetic programming system which evolves a heuristic that decides whether to put a piece in a bin when presented with the sum of the pieces already in the bin and the size of the piece that is about to be packed. This heuristic operates in a fixed framework that iterates through the open bins, applying the heuristic to each one, before deciding which bin to use. The best evolved programs emulate the functionality of the human designed first-fit heuristic. Thus, the contribution of this paper is to demonstrate that genetic programming can be employed to automatically evolve bin packing heuristics which are the same as high quality heuristics which have been designed by humans. %K genetic algorithms, genetic programming %R 10.1007/11844297_87 %U http://www.cs.nott.ac.uk/~mvh/ppsn2006.pdf %U http://dx.doi.org/10.1007/11844297_87 %P 860-869 %0 Book Section %T A Classification of Hyper-heuristics Approaches %A Burke, Edmund K. %A Hyde, Matthew %A Kendall, Graham %A Ochoa, Gabriela %A Ozcan, Ender %A Woodward, John R. %E Gendreau, Michel %E Potvin, Jean-Yves %B Handbook of Metaheuristics %S International Series in Operations Research & Management Science %D 2010 %V 57 %7 2nd %I Springer %F Burke:2010:HBMH %X The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research. %K genetic algorithms, genetic programming %R 10.1007/978-1-4419-1665-5_15 %U http://www.cs.nott.ac.uk/~gxo/papers/ChapterClassHH.pdf %U http://dx.doi.org/10.1007/978-1-4419-1665-5_15 %P 449-468 %0 Journal Article %T Grammatical Evolution of Local Search Heuristics %A Burke, Edmund K. %A Hyde, Matthew R. %A Kendall, Graham %J IEEE Transactions on Evolutionary Computation %D 2012 %8 jun %V 16 %N 3 %@ 1089-778X %F Burke:2011:ieeeTEC %X Genetic programming approaches have been employed in the literature to automatically design constructive heuristics for cutting and packing problems. These heuristics obtain results superior to human-created constructive heuristics, but they do not generally obtain results of the same quality as local search heuristics, which start from an initial solution and iteratively improve it. If local search heuristics can be successfully designed through evolution, in addition to a constructive heuristic which initialises the solution, then the quality of results which can be obtained by automatically generated algorithms can be significantly improved. This paper presents a grammatical evolution methodology which automatically designs good quality local search heuristics that maintain their performance on new problem instances. %K genetic algorithms, genetic programming, Grammatical Evolution, Grammar, Heuristic algorithms, Production, Search problems, Bin packing, heuristics, local search, stock cutting %9 journal article %R 10.1109/TEVC.2011.2160401 %U http://dx.doi.org/10.1109/TEVC.2011.2160401 %P 406-417 %0 Book Section %T Optimization via Gene Expression Algorithms %A Burkowski, Forbes %E Olariu, Stephan %E Zomaya, Albert Y. %B Handbook of Bioinspired Algorithms and Applications %S Computer & Information Science Series %D 2005 %I Chapman and Hall/CRC %F Burkowski:2005:HBBAA %K SVM %R 10.1201/9781420035063.ch8 %U http://dx.doi.org/10.1201/9781420035063.ch8 %P Pages8-121–8–134 %0 Conference Proceedings %T An Efficient Structural Diversity Technique for Genetic Programming %A Burks, Armand R. %A Punch, William F. %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 Burks:2015:GECCO %X Genetic diversity plays an important role in avoiding premature convergence, which is a phenomenon that stifles the search effectiveness of evolutionary algorithms. However, approaches that avoid premature convergence by maintaining genetic diversity can do so at the cost of efficiency, requiring more fitness evaluations to find high quality solutions. We introduce a simple and efficient genetic diversity technique that is capable of avoiding premature convergence while maintaining a high level of search quality in tree-based genetic programming. Our method finds solutions to a set of benchmark problems in significantly fewer fitness evaluations than the algorithms that we compared against. %K genetic algorithms, genetic programming %R 10.1145/2739480.2754649 %U http://doi.acm.org/10.1145/2739480.2754649 %U http://dx.doi.org/10.1145/2739480.2754649 %P 991-998 %0 Conference Proceedings %T An Investigation of Hybrid Structural and Behavioral Diversity Methods in Genetic Programming %A Burks, Armand R. %A Punch, William F. %Y Riolo, Rick %Y Worzel, Bill %Y Goldman, Brian %Y Tozier, Bill %S Genetic Programming Theory and Practice XIV %D 2016 %8 19 21 may %I Springer %C Ann Arbor, USA %F Burks:2016:GPTP %X Premature convergence is a serious problem that plagues genetic programming, stifling its search performance. Several genetic diversity maintenance techniques have been proposed for combating premature convergence and improving search efficiency in genetic programming. Recent research has shown that while genetic diversity is important, focusing directly on sustaining behavioural diversity may be more beneficial. These two related areas have received a lot of attention, yet they have often been developed independently. We investigated the feasibility of hybrid genetic and behavioral diversity techniques on a suite of problems. %K genetic algorithms, genetic programming, premature convergence, genetic diversity, structural diversity, behavioural diversity, semantics %R 10.1007/978-3-319-97088-2_2 %U https://www.springer.com/us/book/9783319970875 %U http://dx.doi.org/10.1007/978-3-319-97088-2_2 %P 19-34 %0 Journal Article %T An analysis of the genetic marker diversity algorithm for genetic programming %A Burks, Armand R. %A Punch, William F. %J Genetic Programming and Evolvable Machines %D 2017 %8 jun %V 18 %N 2 %@ 1389-2576 %F Burks:2016:GPEM %X Many diversity techniques have been developed for addressing premature convergence, which is a serious problem that stifles the search effectiveness of evolutionary algorithms. However, approaches that aim to avoid premature convergence can often take longer to discover a solution. The Genetic Marker Diversity algorithm is a new technique that has been shown to find solutions significantly faster than other approaches while maintaining diversity in genetic programming. This study provides a more in-depth analysis of the search behaviour of this technique compared to other state-of-the-art methods, as well as a comparison of the performance of these techniques on a larger and more modern set of test problems. %K genetic algorithms, genetic programming, Genotypic diversity, Structural diversity, Premature convergence %9 journal article %R 10.1007/s10710-016-9281-9 %U http://dx.doi.org/10.1007/s10710-016-9281-9 %P 213-245 %0 Thesis %T Hybrid Structural and Behavioral Diversity Techniques for Effective Genetic Programming %A Burks, Armand Rashad %D 2017 %C USA %C Computer Science, Michigan State University %F Burks:thesis %X Sustaining the diversity of evolving populations is a fundamental issue in genetic programming. We describe a novel measure of structural diversity for tree-based genetic programming, and we demonstrate its utility compared to other diversity techniques. We demonstrate our technique on the real-world application of tuberculosis screening from X-ray images. We then introduce a new paradigm of genetic programming that involves simultaneously maintaining structural and behavioural diversity in order to further improve the efficiency of genetic programming. Our results show that simultaneously promoting structural and behavioural diversity improves genetic programming by leveraging the benefits of both aspects of diversity while overcoming the shortcomings of either technique in isolation. The hybridization increases the behavioral diversity of our structural diversity technique, and increases the structural diversity of the behavioural diversity techniques. This increased diversity leads to performance gains compared to either technique in isolation. We found that in many cases, our structural diversity technique provides significant performance improvement compared to other state-of-the-art techniques. Our results from the experiments comparing the hybrid techniques indicate that the largest performance gain was typically attributed to our structural diversity technique. The incorporation of the behavioural diversity techniques provide additional improvement in many cases. %K genetic algorithms, genetic programming, GMD-GP, Tuberculosis Diagnosis %9 Ph.D. thesis %U https://search.proquest.com/docview/1952843117 %0 Conference Proceedings %T Genetic programming for tuberculosis screening from raw X-ray images %A Burks, Armand R. %A Punch, William F. %Y Aguirre, Hernan %Y Takadama, Keiki %Y Handa, Hisashi %Y Liefooghe, Arnaud %Y Yoshikawa, Tomohiro %Y Sutton, Andrew M. %Y Ono, Satoshi %Y Chicano, Francisco %Y Shirakawa, Shinichi %Y Vasicek, Zdenek %Y Gross, Roderich %Y Engelbrecht, Andries %Y Hart, Emma %Y Risi, Sebastian %Y Aniko, Ekart %Y Togelius, Julian %Y Verel, Sebastien %Y Blum, Christian %Y Browne, Will %Y Nojima, Yusuke %Y Tusar, Tea %Y Zhang, Qingfu %Y Hansen, Nikolaus %Y Lozano, Jose Antonio %Y Thierens, Dirk %Y Yu, Tian-Li %Y Branke, Juergen %Y Jin, Yaochu %Y Silva, Sara %Y Iba, Hitoshi %Y Esparcia-Alcazar, Anna I. %Y Bartz-Beielstein, Thomas %Y Sarro, Federica %Y Antoniol, Giuliano %Y Auger, Anne %Y Lehre, Per Kristian %S GECCO ’18: Proceedings of the Genetic and Evolutionary Computation Conference %D 2018 %8 15 19 jul %I ACM %C Kyoto, Japan %F Burks:2018:GECCO %X Genetic programming has been successfully applied to several real-world problem domains. One such application area is image classification, wherein genetic programming has been used for a variety of problems such as breast cancer detection, face detection, and pedestrian detection, to name a few. We present the use of genetic programming for detecting active tuberculosis in raw X-ray images. Our results demonstrate that genetic programming evolves classifiers that achieve promising accuracy results compared to that of traditional image classification techniques. Our classifiers do not require pre-processing, segmentation, or feature extraction beforehand. Furthermore, our evolved classifiers process a raw X-ray image and return a classification orders of magnitude faster than the reported times for traditional techniques. %K genetic algorithms, genetic programming %R 10.1145/3205455.3205461 %U http://dx.doi.org/10.1145/3205455.3205461 %P 1214-1221 %0 Conference Proceedings %T Evolution Tracking in Genetic Programming %A Burlacu, Bogdan %A Affenzeller, Michael %A Kommenda, Michael %A Winkler, Stephan M. %A Kronberger, Gabriel %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 Burlacu:2012:EMSS %X Much effort has been put into understanding the artificial evolutionary dynamics within genetic programming (GP). However, the details are yet unclear so far, as to which elements make GP so powerful. This paper presents an attempt to study the evolution of a population of computer programs using HeuristicLab. A newly developed methodology for recording heredity information, based on a general conceptual framework of evolution, is employed for the analysis of algorithm behaviour on a symbolic regression benchmark problem. In our example, we find the complex interplay between selection and crossover to be the cause for size increase in the population, as the average amount of genetic information transmitted from parents to offspring remains constant and independent of run constraints (i.e., tree size and depth limits). Empirical results reveal many interesting details and confirm the validity and generality of our approach, as a tool for understanding the complex aspects of GP. %K genetic algorithms, genetic programming, tree fragments, evolutionary dynamics, schema theory, population diversity, bloat, introns %U http://research.fh-ooe.at/en/publication/3444 %0 Conference Proceedings %T On the Evolutionary Behavior of Genetic Programming with Constants Optimization %A Burlacu, Bogdan %A Affenzeller, Michael %A Kommenda, Michael %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 %F conf/eurocast/BurlacuAK13 %O 14th International Conference, Revised Selected Papers %X Evolutionary systems are characterised by two seemingly contradictory properties: robustness and evolvability. Robustness is generally defined as an organism’s ability to withstand genetic perturbation while maintaining its phenotype. Evolvability, as an organism’s ability to produce useful variation. In genetic programming, the relationship between the two, mediated by selection and variation-producing operators (recombination and mutation), makes it difficult to understand the behaviour and evolutionary dynamics of the search process. In this paper, we show that a local gradient-based constants optimisation step can improve the overall population evolvability by inducing a beneficial structure-preserving bias on selection, which in the long term helps the process maintain diversity and produce better solutions. %K genetic algorithms, genetic programming, evolutionary behaviour, constant optimisation, symbolic regression, algorithm analysis %R 10.1007/978-3-642-53856-8_36 %U http://dx.doi.org/10.1007/978-3-642-53856-8_36 %P 284-291 %0 Conference Proceedings %T Visualization of genetic lineages and inheritance information in genetic programming %A Burlacu, Bogdan %A Affenzeller, Michael %A Kommenda, Michael %A Winkler, Stephan %A Kronberger, Gabriel %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 Burlacu:2013:GECCOcomp %X Many studies emphasise the importance of genetic diversity and the need for an appropriate tuning of selection pressure in genetic programming. Additional important aspects are the performance and effects of the genetic operators (crossover and mutation) on the transfer and stabilisation of inherited information blocks during the run of the algorithm. In this context, different ideas about the usage of lineage and genealogical information for improving genetic programming have taken shape in the last decade. Our work builds on those ideas by introducing an evolution tracking framework for assembling genealogical and inheritance graphs of populations. The proposed approach allows detailed investigation of phenomena related to building blocks, size evolution, ancestry and diversity. We introduce the notion of genetic fragments to represent sub-trees that are affected by reproductive operators (mutation and crossover) and present a methodology for tracking such fragments using flexible similarity measures. A fragment matching algorithm was designed to work on both structural and semantic levels, allowing us to gain insight into the exploratory and exploitative behaviour of the evolutionary process. The visualisation part which is the subject of this paper integrates with the framework and provides an easy way of exploring the population history. The paper focuses on a case study in which we investigate the evolution of a solution to a symbolic regression benchmark problem. %K genetic algorithms, genetic programming %R 10.1145/2464576.2482714 %U http://dx.doi.org/10.1145/2464576.2482714 %P 1351-1358 %0 Conference Proceedings %T Building Blocks Identification Based on Subtree Sample Counts for Genetic Programming %A Burlacu, Bogdan %A Kommenda, Michael %A Affenzeller, Michael %S 2015 Asia-Pacific Conference on Computer Aided System Engineering (APCASE) %D 2015 %8 jul %F Burlacu:2015:APCASE %X Often, the performance of genetic programming (GP) is explained in terms of building blocks – high-quality solution elements that get gradually assembled into larger and more complex patterns by the evolutionary process. However, the weak theoretical foundations of GP building blocks causes their role in GP evolutionary dynamics to remain still somewhat of a mystery. This paper presents a methodology for identifying GP building blocks based on their respective sample counts in the population (defined as the number of times they were sampled by the recombination operators and included in surviving offspring). Our approach represents a problem-independent way to identify important solution elements based on their influence on the evolutionary process. %K genetic algorithms, genetic programming %R 10.1109/APCASE.2015.34 %U http://dx.doi.org/10.1109/APCASE.2015.34 %P 152-157 %0 Conference Proceedings %T On the Effectiveness of Genetic Operations in Symbolic Regression %A Burlacu, Bogdan %A Affenzeller, Michael %A Kommenda, Michael %Y Moreno-Diaz, Roberto %Y Pichler, Franz %Y Quesada-Arencibia, Alexis %S 15th International Conference Computer Aided Systems Theory, EUROCAST 2015 %S Lecture Notes in Computer Science %D 2015 %8 feb 8 13 %V 9520 %I Springer %C Las Palmas de Gran Canaria, Spain %F DBLP:conf/eurocast/BurlacuAK15 %O Revised Selected Papers %X This paper describes a methodology for analysing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree tracing approach for identifying the origins of genes in the structure of individuals, and we show that only a small fraction of ancestor individuals are responsible for the evolution of the best solutions in the population. %K genetic algorithms, genetic programming, Evolutionary dynamics, Algorithm analysis, Symbolic regression %R 10.1007/978-3-319-27340-2_46 %U https://doi.org/10.1007/978-3-319-27340-2_46 %U http://dx.doi.org/10.1007/978-3-319-27340-2_46 %P 367-374 %0 Book Section %T Methods for Genealogy and Building Block Analysis in Genetic Programming %A Burlacu, Bogdan %A Affenzeller, Michael %A Winkler, Stephan M. %A Kommenda, Michael %A Kronberger, Gabriel %E Borowik, Grzegorz %E Chaczko, Zenon %E Jacak, Witold %E Luba, Tadeusz %B Computational Intelligence and Efficiency in Engineering Systems %S Studies in Computational Intelligence %D 2015 %V 595 %I Springer %F series/sci/BurlacuAWKK15 %X Genetic programming gradually assembles high-level structures from low-level entities or building blocks. This chapter describes methods for investigating emergent phenomena in genetic programming by looking at a population’s collective behaviour. It details how these methods can be used to trace genotypic changes across lineages and genealogies. Part of the methodology, we present an algorithm for decomposing arbitrary subtrees from the population to their inherited parts, picking up the changes performed by either crossover or mutation across ancestries. This powerful tool creates new possibilities for future theoretical investigations on evolutionary algorithm behavior concerning building blocks and fitness landscape analysis. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-15720-7_5 %U http://dx.doi.org/10.1007/978-3-319-15720-7 %U http://dx.doi.org/10.1007/978-3-319-15720-7_5 %P 61-74 %0 Conference Proceedings %T Analysis of Schema Frequencies in Genetic Programming %A Burlacu, Bogdan %A Affenzeller, Michael %A Kommenda, Michael %A Kronberger, Gabriel %A Winkler, Stephan M. %Y Moreno-Diaz, Roberto %Y Pichler, Franz %Y Quesada-Arencibia, Alexis %S 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, Part I %S Lecture Notes in Computer Science %D 2017 %8 feb 19 24 %V 10671 %I Springer %C Las Palmas de Gran Canaria, Spain %F DBLP:conf/eurocast/BurlacuAKKW17 %O Revised Selected Papers %X Genetic Programming (GP) schemas are structural templates equivalent to hyperplanes in the search space. Schema theories provide information about the properties of subsets of the population and the behaviour of genetic operators. In this paper we propose a practical methodology to identify relevant schemas and measure their frequency in the population. We demonstrate our approach on an artificial symbolic regression benchmark where the parts of the formula are already known. Experimental results reveal how solutions are assembled within GP and explain diversity loss in GP populations through the proliferation of repeated patterns. %K genetic algorithms, genetic programming, building blocks, Schema analysis, Symbolic regression, Tree pattern matching, Evolutionary dynamics, Loss of diversity %R 10.1007/978-3-319-74718-7_52 %U https://doi.org/10.1007/978-3-319-74718-7_52 %U http://dx.doi.org/10.1007/978-3-319-74718-7_52 %P 432-438 %0 Conference Proceedings %T Schema Analysis in Tree-Based Genetic Programming %A Burlacu, Bogdan %A Affenzeller, Michael %A Kommenda, Michael %A Kronberger, Gabriel %A Winkler, Stephan %Y Banzhaf, Wolfgang %Y Olson, Randal S. %Y Tozier, William %Y Riolo, Rick %S Genetic Programming Theory and Practice XV %D 2017 %8 may 18–20 %I Springer %C University of Michigan in Ann Arbor, USA %F burlacu2018schema %X In this chapter we adopt the concept of schemata from schema theory and use it to analyse population dynamics in genetic programming for symbolic regression. We define schemata as tree-based wild card patterns and we empirically measure their frequencies in the population at each generation. Our methodology consists of two steps: in the first step we generate schemata based on genealogical information about crossover parents and their offspring, according to several possible schema definitions inspired from existing literature. In the second step, we calculate the matching individuals for each schema using a tree pattern matching algorithm. We test our approach on different problem instances and algorithmic flavours and we investigate the effects of different selection mechanisms on the identified schemata and their frequencies. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-90512-9_2 %U https://link.springer.com/chapter/10.1007/978-3-319-90512-9_2 %U http://dx.doi.org/10.1007/978-3-319-90512-9_2 %P 17-37 %0 Thesis %T Tracing of Evolutionary Search Trajectories in Complex Hypothesis Spaces %A Burlacu, Bogdan %D 2017 %C Austria %C University Linz %F Burlacu:thesis %X Understanding the internal functioning of evolutionary algorithms is an essential requirement for improving their performance and reliability. Increased computational resources available in current mainstream computers make it possible for new previously infeasible research directions to be explored. Therefore, a comprehensive theoretical analysis of their mechanisms and dynamics using modern tools becomes possible. Recent algorithmic achievements like offspring selection in combination with linear scaling have enabled genetic programming (GP) to achieve high quality results in system identification in less than 50 generations using populations of only several hundred individuals. Therefore, the active gene pool of evolutionary search remains manageable and may be subjected to new theoretical investigations closely related to genetic programming schema theories, building block hypotheses and bloat theories. Genetic algorithms emulate emergent systems in which complex patterns are formed from an initially simple and random pool of elementary structures. In GP, complexity emerges under the influence of stabilizing selection which preserves the useful genetic variation created by recombination and mutation. The mapping between the structures used for solution representation and the ones used for the evaluation of fitness has a major influence on algorithm behavior. Population-wide effects concerning building blocks, genetic diversity and bloat can be conceptually seen as results of the complex interaction between phenotypic operators (selection) and genotypic operators (mutation and recombination). This coupling known as the variation-selection loop is the main engine for GP emergent behavior and constitutes the main topic of this research. This thesis aims to provide a unified theoretical framework which can explain GP evolution. To this end, it explores the way in which the genotype-phenotype map, in relation with the evolutionary operators (selection, recombination, mutation) determines algorithmic behaviour. As the title suggests, the main contribution consists of a novel tracing framework that makes it possible to determine the exact patterns of building block and gene propagation through the generations and the way smaller elements are gradually assembled into more complex structures by the evolutionary algorithm. %K genetic algorithms, genetic programming, evolutionary computation, evolutionary tracing, evolutionary dynamics, genotype-phenotype map, system identification, genetische Programmierung, evolutionare Algorithmus, evolutionaere Verfolgung, evolutionaere Dynamik, Genotyp-Phaenotyp Karte, Systemidentifikation %9 Ph.D. thesis %U https://epub.jku.at/obvulihs/content/titleinfo/2246376 %0 Conference Proceedings %T Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression %A Burlacu, Bogdan %A Kammerer, Lukas %A Affenzeller, Michael %A Kronberger, Gabriel %Y Moreno-Diaz, Roberto %Y Pichler, Franz %Y Quesada-Arencibia, Alexis %S International Conference on Computer Aided Systems Theory, EUROCAST 2019 %S Lecture Notes in Computer Science %D 2019 %8 17 22 feb %V 12013 %I Springer %C Las Palmas de Gran Canaria, Spain %F Burlacu:2019:EUROCAST %X We introduce in this paper a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees, with two important applications in genetic programming for symbolic regression: fast, online calculation of population diversity and algebraic simplification of symbolic expression trees. Based on this hashing approach, we propose a simple diversity-preservation mechanism with promising results on a collection of symbolic regression benchmark problems. %K genetic algorithms, genetic programming %R 10.1007/978-3-030-45093-9_44 %U http://dx.doi.org/10.1007/978-3-030-45093-9_44 %P 361-369 %0 Conference Proceedings %T Schema-based diversification in genetic programming %A Burlacu, Bogdan %A Affenzeller, Michael %Y Aguirre, Hernan %Y Takadama, Keiki %Y Handa, Hisashi %Y Liefooghe, Arnaud %Y Yoshikawa, Tomohiro %Y Sutton, Andrew M. %Y Ono, Satoshi %Y Chicano, Francisco %Y Shirakawa, Shinichi %Y Vasicek, Zdenek %Y Gross, Roderich %Y Engelbrecht, Andries %Y Hart, Emma %Y Risi, Sebastian %Y Aniko, Ekart %Y Togelius, Julian %Y Verel, Sebastien %Y Blum, Christian %Y Browne, Will %Y Nojima, Yusuke %Y Tusar, Tea %Y Zhang, Qingfu %Y Hansen, Nikolaus %Y Lozano, Jose Antonio %Y Thierens, Dirk %Y Yu, Tian-Li %Y Branke, Juergen %Y Jin, Yaochu %Y Silva, Sara %Y Iba, Hitoshi %Y Esparcia-Alcazar, Anna I. %Y Bartz-Beielstein, Thomas %Y Sarro, Federica %Y Antoniol, Giuliano %Y Auger, Anne %Y Lehre, Per Kristian %S GECCO ’18: Proceedings of the Genetic and Evolutionary Computation Conference %D 2018 %8 15 19 jul %I ACM %C Kyoto, Japan %F Burlacu:2018:GECCO %X In genetic programming (GP), population diversity represents a key aspect of evolutionary search and a major factor in algorithm performance. In this paper we propose a new schema-based approach for observing and steering the loss of diversity in GP populations. We employ a well-known hyperschema definition from the literature to generate tree structural templates from the population’s genealogy, and use them to guide the search via localized mutation within groups of individuals matching the same schema. The approach depends only on genealogy information and is easily integrated with existing GP variants. We demonstrate its potential in combination with Offspring Selection GP (OSGP) on a series of symbolic regression benchmark problems where our algorithmic variant called OSGP-S obtains superior results. %K genetic algorithms, genetic programming %R 10.1145/3205455.3205594 %U http://dx.doi.org/10.1145/3205455.3205594 %P 1111-1118 %0 Conference Proceedings %T Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure %A Burlacu, B. %A Affenzeller, M. %A Kronberger, G. %A Kommenda, M. %S 2019 IEEE Congress on Evolutionary Computation (CEC) %D 2019 %8 jun %F Burlacu:2019:CEC %X Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires expensive tree distance measures which have a negative impact on the algorithm’s runtime performance. In this work we introduce a fast, hash-based tree distance measure to massively speed-up the calculation of population diversity during the algorithmic run. We combine this measure with the standard GA and the NSGA-II genetic algorithms to steer the search towards higher diversity. We validate the approach on a collection of benchmark problems for symbolic regression where our method consistently outperforms the standard GA as well as NSGA-II configurations with different secondary objectives. %K genetic algorithms, genetic programming %R 10.1109/CEC.2019.8790162 %U http://dx.doi.org/10.1109/CEC.2019.8790162 %P 2175-2182 %0 Conference Proceedings %T Parsimony measures in multi-objective genetic programming for symbolic regression %A Burlacu, Bogdan %A Kronberger, Gabriel %A Kommenda, Michael %A Affenzeller, Michael %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 Burlacu:2019:GECCOcomp %K genetic algorithms, genetic programming %R 10.1145/3319619.3322087 %U http://dx.doi.org/10.1145/3319619.3322087 %P 338-339 %0 Conference Proceedings %T Operon C++: An Efficient Genetic Programming Framework for Symbolic Regression %A Burlacu, Bogdan %A Kronberger, Gabriel %A Kommenda, Michael %Y Allmendinger, Richard %Y Marin, Hugo Terashima %Y Montes, Efren Mezura %Y Bartz-Beielstein, Thomas %Y Filipic, Bogdan %Y Tang, Ke %Y Howard, David %Y Hart, Emma %Y Eiben, Gusz %Y Eftimov, Tome %Y La Cava, William %Y Naujoks, Boris %Y Oliveto, Pietro %Y Volz, Vanessa %Y Weise, Thomas %Y Derbel, Bilel %Y Li, Ke %Y Li, Xiaodong %Y Zapotecas, Saul %Y Zhang, Qingfu %Y Wang, Rui %Y Cheng, Ran %Y Wu, Guohua %Y Li, Miqing %Y Ishibuchi, Hisao %Y Fieldsend, Jonathan %Y Akman, Ozgur %Y Alyahya, Khulood %Y Branke, Juergen %Y Woodward, John R. %Y Tauritz, Daniel R. %Y Baioletti, Marco %Y Uribe, Josu Ceberio %Y McCall, John %Y Milani, Alfredo %Y Wagner, Stefan %Y Affenzeller, Michael %Y Alexander, Bradley %Y Brownlee, Alexander (Sandy) %Y Haraldsson, Saemundur O. %Y Wagner, Markus %Y Sanchez-Pi, Nayat %Y Marti, Luis %Y Fernandez Alzueta, Silvino %Y Valledor Pellicer, Pablo %Y Stuetzle, Thomas %Y Johns, Matthew %Y Ross, Nick %Y Keedwell, Ed %Y Mahmoud, Herman %Y Walker, David %Y Stein, Anthony %Y Nakata, Masaya %Y Paetzel, David %Y Vaughan, Neil %Y Smith, Stephen %Y Cagnoni, Stefano %Y Patton, Robert M. %Y De Falco, Ivanoe %Y Della Cioppa, Antonio %Y Scafuri, Umberto %Y Tarantino, Ernesto %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 Hemberg, Erik %Y Alshammari, Riyad %Y Makanju, Tokunbo %Y Zelinka, Ivan %Y Das, Swagatam %Y Nagaratnam, Ponnuthurai %Y Senkerik, Roman %E Fuijimino-shi %S Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion %S GECCO ’20 %D 2020 %8 jul 8 12 %I Association for Computing Machinery %C internet %F Burlacu:2020:GECCOcomp %X Genetic Programming (GP) is a dynamic field of research where empirical testing plays an important role in validating new ideas and algorithms. The ability to easily prototype new algorithms by reusing key components and quickly obtain results is therefore important for the researcher. In this paper we introduce Operon, a C++ GP framework focused on performance, modularity and usability, featuring an efficient linear tree encoding and a scalable concurrency model where each logical thread is responsible for generating a new individual. We model the GP evolutionary process around the concept of an offspring generator, a streaming operator that defines how new individuals are obtained. The approach allows different algorithmic variants to be expressed using high-level constructs within the same generational basic loop. The evaluation routine supports both scalar and dual numbers, making it possible to calculate model derivatives via automatic differentiation. This functionality allows seamless integration with gradient-based local search approaches. We discuss the design choices behind the proposed framework and compare against two other popular GP frameworks, DEAP and HeuristicLab. We empirically show that Operon is competitive in terms of solution quality, while being able to generate results significantly faster. %K genetic algorithms, genetic programming, C++, symbolic regression %R 10.1145/3377929.3398099 %U https://doi.org/10.1145/3377929.3398099 %U http://dx.doi.org/10.1145/3377929.3398099 %P 1562-1570 %0 Journal Article %T Population diversity and inheritance in genetic programming for symbolic regression %A Burlacu, Bogdan %A Yang, Kaifeng %A Affenzeller, Michael %J Natural Computing %D 2024 %8 sep %V 23 %N 3 %F burlacu:NC %O Special Issue: Selected papers from the 27th International Conference on DNA Computing and Molecular Programming %X we aim to empirically characterize two important dynamical aspects of GP search: the evolution of diversity and the propagation of inheritance patterns. Diversity is calculated at the genotypic and phenotypic levels using efficient similarity metrics. Inheritance information is obtained via a full genealogical record of evolution as a directed acyclic graph and a set of methods for extracting relevant patterns. Advances in processing power enable our approach to handle previously infeasible graph sizes of millions of arcs and vertices. To enable a more comprehensive analysis we employ three closely-related but different evolutionary models: canonical GP, offspring selection and age-layered population structure. Our analysis reveals that a relatively small number of ancestors are responsible for producing the majority of descendants in later generations, leading to diversity loss. We show empirically across a selection of five benchmark problems that each configuration is characterised by different rates of diversity loss and different inheritance patterns, in support of the idea that each new problem may require a unique approach to solve optimally. %K genetic algorithms, genetic programming, XAI, Symbolic regression, Supervised learning, Explainable AI, Optimization algorithm, Evolutionary algorithm %9 journal article %R 10.1007/s11047-022-09934-x %U https://rdcu.be/c7n0f %U http://dx.doi.org/10.1007/s11047-022-09934-x %P 531-566 %0 Conference Proceedings %T Revisiting Gradient-Based Local Search in Symbolic Regression %A Burlacu, Bogdan %A Winkler, Stephan M. %A Affenzeller, Michael %Y Winkler, Stephan M. %Y Banzhaf, Wolfgang %Y Hu, Ting %Y Lalejini, Alexander %S Genetic Programming Theory and Practice XXI %S Genetic and Evolutionary Computation %D 2024 %8 jun 6 8 %I Springer %C University of Michigan, USA %F Burlacu:2024:GPTP %K genetic algorithms, genetic programming %R 10.1007/978-981-96-0077-9_13 %U https://heal.heuristiclab.com/news/post/gptp-xxi %U http://dx.doi.org/10.1007/978-981-96-0077-9_13 %P 259-273 %0 Conference Proceedings %T Backend-agnostic Tree Evaluation for Genetic Programming %A Burlacu, Bogdan %Y Wagner, Stefan %Y Affenzeller, Michael %S Open Source Software for Evolutionary Computation %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F burlacu:2024:GECCOcomp %X The explicit vectorization of the mathematical operations required for fitness calculation can dramatically increase the efficiency of tree-based genetic programming for symbolic regression. In this paper, we introduce a modern software design for the seamless integration of vectorized math libraries with tree evaluation, and we benchmark each library in terms of runtime, solution quality and energy efficiency. The latter, in particular, is an aspect of increasing concern given the growing carbon footprint of AI. With this in mind, we introduce metrics for measuring the energy usage and power draw of the evolutionary algorithm. Our results show that an optimized math backend can decrease energy usage by as much as 35% (with a proportional decrease in runtime) without any negative effects in the quality of solutions. %K genetic algorithms, genetic programming, energy efficiency, symbolic regression %R 10.1145/3638530.3664161 %U http://dx.doi.org/10.1145/3638530.3664161 %P 1649-1657 %0 Conference Proceedings %T Embedded Dynamic Improvement %A Burles, Nathan %A Swan, Jerry %A Bowles, Edward %A Brownlee, Alexander E. I. %A Kocsis, Zoltan A. %A Veerapen, Nadarajen %Y Langdon, William B. %Y Petke, Justyna %Y White, David R. %S Genetic Improvement 2015 Workshop %D 2015 %8 November 15 jul %I ACM %C Madrid %F Swan:2015:gi %X We discuss the useful role that can be played by a subtype of improvement programming, which we term Embedded Dynamic Improvement. In this approach, developer-specified variation points define the scope of improvement. A search framework is embedded at these variation points, facilitating the creation of adaptive software that can respond online to changes in its execution environment. %K genetic algorithms, genetic programming, Genetic Improvement %R 10.1145/2739482.2768423 %U http://gpbib.cs.ucl.ac.uk/gi2015/embedded_dynamic_improvement.pdf %U http://dx.doi.org/10.1145/2739482.2768423 %P 831-832 %0 Conference Proceedings %T Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava %A Burles, Nathan %A Bowles, Edward %A Brownlee, Alexander E. I. %A Kocsis, Zoltan A. %A Swan, Jerry %A Veerapen, Nadarajen %Y Labiche, Yvan %Y Barros, Marcio %S SSBSE %S LNCS %D 2015 %8 sep 5 7 %V 9275 %I Springer %C Bergamo, Italy %F Burles:2015:SSBSE %X In this work we use metaheuristic search to improve Google’s Guava library, finding a semantically equivalent version of com.google.common.collect.ImmutableMultimap with reduced energy consumption. Semantics-preserving transformations are found in the source code, using the principle of subtype polymorphism. We introduce a new tool, Opacitor, to deterministically measure the energy consumption, and find that a statistically significant reduction to Guava’s energy consumption is possible. We corroborate these results using Jalen, and evaluate the performance of the metaheuristic search compared to an exhaustive search-finding that the same result is achieved while requiring almost 200 times fewer fitness evaluations. Finally, we compare the metaheuristic search to an independent exhaustive search at each variation point, finding that the metaheuristic has superior performance. %K genetic algorithms, genetic programming, Genetic Improvement, SBSE, Object-oriented programming, Subclass substitution, Liskov Substitution Principle, Energy profiling %R 10.1007/978-3-319-22183-0_20 %U https://dspace.stir.ac.uk/bitstream/1893/22227/1/SSBSE15-oogiiecgg.pdf %U http://dx.doi.org/10.1007/978-3-319-22183-0_20 %P 255-261 %0 Conference Proceedings %T Specialising Guava’s Cache to Reduce Energy Consumption %A Burles, Nathan %A Bowles, Edward %A Bruce, Bobby R. %A Srivisut, Komsan %Y Labiche, Yvan %Y Barros, Marcio %S SSBSE %S LNCS %D 2015 %8 sep 5 7 %V 9275 %I Springer %C Bergamo, Italy %F Burles:2015:SSBSEa %X In this article we use a Genetic Algorithm to perform parameter tuning on Google Guava Cache library, specialising it to OpenTripPlanner. A new tool, Opacitor, is used to deterministically measure the energy consumed, and we find that the energy consumption of OpenTripPlanner may be significantly reduced by tuning the default parameters of Guava’s Cache library. Finally we use Jalen, which uses time and CPU load as a proxy to calculate energy consumption, to corroborate these results. %K genetic algorithms, genetic programming, genetic improvement, SBSE, Parameter tuning, Library specialisation, Energy profiling, Reduced power consumption %R 10.1007/978-3-319-22183-0_23 %U http://www.cs.ucl.ac.uk/staff/R.Bruce/Burles2015Specialising.pdf %U http://dx.doi.org/10.1007/978-3-319-22183-0_23 %P 276-281 %0 Conference Proceedings %T Evolutionary Algorithms for Classification of Mammographic Densities using Local Binary Patterns and Statistical Features %A Burling-Claridge, Francine %A Iqbal, Muhammad %A Zhang, Mengjie %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 Burling-Claridge:2016:CEC %X Millions of women are suffering from breast cancer, which can be treated effectively if it is detected early. Breast density is one of the many factors that lead to an increased risk of breast cancer for women. However, it is difficult for radiologists to provide both accurate and uniform evaluations of different density levels in a large number of mammographic images generated in the screening process. Various computer aided diagnosis systems for digital mammograms have been reported in literature, but very few of them thoroughly investigate mammographic densities. This study presents a thorough analysis of classifying mammographic densities using different local binary patterns and statistical features of digital mammograms in two evolutionary algorithms, i.e., genetic programming and learning classifier systems; and four conventional classification methods, i.e., naive Bayes, decision trees, K-nearest neighbour, and support vector machines. The obtained results show that evolutionary algorithms have potential to solve these challenging real-world tasks. It is found that statistical features produced better results than local binary patterns for the experiments conducted in this study. Further, in genetic programming, the reuse of extracted knowledge from one feature set to another shows statistically significant improvement over the standard approach. %K genetic algorithms, genetic programming %R 10.1109/CEC.2016.7744277 %U http://dx.doi.org/10.1109/CEC.2016.7744277 %P 3847-3854 %0 Conference Proceedings %T Exploring the landscape of the space of heuristics for local search in SAT %A Burnett, Andrew W. %A Parkes, Andrew J. %Y Lozano, Jose A. %S 2017 IEEE Congress on Evolutionary Computation (CEC) %D 2017 %8 May 8 jun %I IEEE %C Donostia, San Sebastian, Spain %F burnett:2017:CEC %X Local search is a powerful technique on many combinatorial optimisation problems. However, the effectiveness of local search methods will often depend strongly on the details of the heuristics used within them. There are many potential heuristics, and so finding good ones is in itself a challenging search problem. A natural method to search for effective heuristics is to represent the heuristic as a small program and then apply evolutionary methods, such as genetic programming. However, the search within the space of heuristics is not well understood, and in particular little is known of the associated search landscapes. In this paper, we consider the domain of propositional satisfiability (SAT), and a generic class of local search methods called ‘WalkSAT’. We give a language for generating the heuristics; using this we generated over three million heuristics, in a systematic manner, and evaluated their associated fitness values. We then use this data set as the basis for an initial analysis of the landscape of the space of heuristics. We give evidence that the heuristic landscape exhibits clustering. We also consider local search on the space of heuristics and show that it can perform quite well, and could complement genetic programming methods on that space. %K genetic algorithms, genetic programming, computability, search problems, WalkSAT, clustering, combinatorial optimisation problems, evolutionary methods, local search, propositional satisfiability, search space landscapes, Computer science, Measurement, Reactive power, Systematics %R 10.1109/CEC.2017.7969611 %U http://dx.doi.org/10.1109/CEC.2017.7969611 %P 2518-2525 %0 Thesis %T Automated Heuristic Generation By Intelligent Search %A Burnett, Andrew W. %D 2021 %8 mar %C UK %C School of Computer Science, University of Nottingham %F Burnett:thesis %X This thesis presents research that examines the effectiveness of several different program synthesis techniques when used to automate the creation of heuristics for a local search-based Boolean Satisfiability solver. Previous research focused on the automated creation of heuristics has almost exclusively relied on evolutionary computation techniques such as genetic programming to achieve its goal. In wider program synthesis research, there are many other techniques which can automate the creation of programs. However, little effort has been expended on using these alternate techniques in automated heuristic creation. In this thesis we analyse how three different program synthesis techniques perform when used to automatically create heuristics for our problem domain. These are genetic programming, exhaustive enumeration and a new technique called local search program synthesis. We show how genetic programming can create effective heuristics for our domain. By generating millions of heuristics, we demonstrate how exhaustive enumeration can create small, easily understandable and effective heuristics. Through an analysis of the memoized results from the exhaustive enumeration experiments, we then describe local search program synthesis, a program synthesis technique based on the minimum tree edit distance metric. Using the memoized results, we simulate local search program synthesis on our domain, and present evidence that suggests it is a viable technique for automatically creating heuristics. We then define the necessary algorithms required to use local search program synthesis without any reliance on memoised data. Through experimentation, we show how local search program synthesis can be used to create effective heuristics for our domain. We then identify examples of heuristics created that are of higher quality than those produced from other program synthesis methods. At certain points in this thesis, we perform a more detailed analysis on some of the heuristics created. Through this analysis, we show that, on certain problem instances, several of the heuristics have better performance than some state-of-the-art, hand-crafted heuristics. %K genetic algorithms, genetic programming, SAT, GSAT, WalkSAT, LS-SAT, MTED, TPPTs, Language EX-1, heuristics, computational intelligence, algorithms, Science, Mathematics, Electronic computers, Computer science, Technology %9 Ph.D. thesis %U https://eprints.nottingham.ac.uk/id/eprint/74496 %0 Thesis %T Hybridising evolution and temporal difference learning %A Burrow, Peter Richard %D 2011 %C UK %C University of Essex %F Burrow:thesis %X This work investigates combinations of two different nature-inspired machine learning algorithms - Evolutionary Algorithms and Temporal Difference Learning. Both algorithms are introduced along with a survey of previous work in the field. A variety of ways of hybridising the two algorithms are considered, falling into two main categories - those where both algorithms operate on the same set of parameters, and those where evolution searches for beneficial parameters to aid Temporal Difference Learning. These potential approaches to hybridisation are explored by applying them to three different problem domains, all loosely linked by the theme of games. The Mountain Car task is a common reinforcement learning benchmark that has been shown to be potentially problematic for neural networks. Ms. Pac-Man is a classic arcade game with a complex virtual environment, and Othello is a popular two-player zero sum board game. Results show that simple hybridisation approaches often do not improve performance, which can be dependent on many factors of the individual algorithms. However, results have also shown that these factors can be successfully tuned by evolution. The main contributions of this thesis are an analysis of the factors that can affect individual algorithm performance, and demonstration of some novel approaches to hybridisation. These consist of use of Evolution Strategies to tune Temporal Difference Learning parameters on multiple problem domains, and evolution of n-tuple configurations for Othello board evaluation. In the latter case, a level of performance was achieved that was competitive with the state of the art. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.572783 %0 Conference Proceedings %T Automatic Generation of Control Programs for Walking Robots Using Genetic Programming %A Busch, Jens %A Ziegler, Jens %A Banzhaf, Wolfgang %A Ross, Andree %A Sawitzki, Daniel %A Aue, Christian %Y Foster, James A. %Y Lutton, Evelyne %Y Miller, Julian %Y Ryan, Conor %Y Tettamanzi, Andrea G. B. %S Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002 %S LNCS %D 2002 %8 March 5 apr %V 2278 %I Springer-Verlag %C Kinsale, Ireland %@ 3-540-43378-3 %F busch:2002:EuroGP %X We present the system SIGEL that combines the simulation and visualization of robots with a Genetic Programming system for the automated evolution of walking. It is designed to automatically generate control programs for arbitrary robots without depending on detailed analytical information of the robots’ kinematic structure. Different fitness functions as well as a variety of parameters allow the easy and interactive configuration and adaptation of the evolution process and the simulations. %K genetic algorithms, genetic programming %R 10.1007/3-540-45984-7_25 %U http://ls2-www.cs.uni-dortmund.de/~sawitzki/AGoCPfWRUGP_Proc.pdf %U http://dx.doi.org/10.1007/3-540-45984-7_25 %P 258-267 %0 Generic %T Genetically Induced Communication Network Fault Tolerance %A Bush, Stephen F. %A Kulkarni, Amit B. %D 2002 %I Invited Paper: SFI Workshop: Resilient and Adaptive Defence of Computing Networks 2002 %G en %F oai:CiteSeerPSU:572931 %X This paper presents the architecture and initial feasibility results of a proto-type communication network that uses genetic programming to evolve services and protocols as part of network operation. The network evolves responses to environmental conditions in a manner that could not be pre-programmed within legacy network nodes apriori. Apriori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The transfer and execution of code in support of the evolution of network protocols and services would not be possible without the active network environment. Rapid generation of network service code occurs via a genetic programming paradigm. Complexity and Algorithmic Information Theory play a key role in understanding and guiding code evolution within the network. %K genetic algorithms, genetic programming %U http://www.crd.ge.com/~bushsf/ftn/GE-SFI-AdaptiveSecurity.pdf %0 Journal Article %T Genetically induced communication network fault tolerance %A Bush, Stephen F. %J Complexity %D 2003 %V 9 %N 2 %I John Wiley & Sons, Inc. %@ 1076-2787 %F 1005412 %X This article presents the architecture and initial feasibility results of a proto-type communication network that uses genetic programming to evolve services and protocols as part of network operation. The network evolves responses to environmental conditions in a manner that could not be pre-programmed within legacy network nodes a priori. A priori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The transfer and execution of code in support of the evolution of network protocols and services would not be possible without the active network environment. Rapid generation of network service code occurs via a genetic programming paradigm. Complexity and algorithmic information theory play a key role in understanding and guiding code evolution within the network. %K genetic algorithms, genetic programming, active networks, algorithmic information theory, Kolmogorov complexity, complexity theory, self-healing networks %9 journal article %R 10.1002/cplx.20002 %U http://www.crd.ge.com/~bushsf/pdfpapers/ComplexityJournal.pdf %U http://dx.doi.org/10.1002/cplx.20002 %P 19-33 %0 Conference Proceedings %T Can neural network constraints in GP provide power to detect genes associated with human disease? %A Bush, William S. %A Motsinger, Alison A. %A Dudek, Scott M. %A Ritchie, Marylyn D. %Y Rothlauf, Franz %Y Branke, Juergen %Y Cagnoni, Stefano %Y Corne, David W. %Y Drechsler, Rolf %Y Jin, Yaochu %Y Machado, Penousal %Y Marchiori, Elena %Y Romero, Juan %Y Smith, George D. %Y Squillero, Giovanni %S Applications of Evolutionary Computing, EvoWorkshops2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC %S LNCS %D 2005 %8 30 mar 1 apr %V 3449 %I Springer Verlag %C Lausanne, Switzerland %@ 3-540-25396-3 %F bush:evows05 %X A major goal of human genetics is the identification of susceptibility genes associated with common, complex diseases. Identifying gene-gene and gene-environment interactions which comprise the genetic architecture for a majority of common diseases is a difficult challenge. To this end, novel computational approaches have been applied to studies of human disease. Previously, a GP neural network (GPNN) approach was employed. Although the GPNN method has been quite successful, a clear comparison of GPNN and GP alone to detect genetic effects has not been made. In this paper, we demonstrate that using NN evolved by GP can be more powerful than GP alone. This is most likely due to the confined search space of the GPNN approach, in comparison to a free form GP. This study demonstrates the benefits of using GP to evolve NN in studies of the genetics of common, complex human disease. %K genetic algorithms, genetic programming, evolutionary computation, ANN %R 10.1007/978-3-540-32003-6_5 %U https://rdcu.be/dEt3y %U http://dx.doi.org/10.1007/978-3-540-32003-6_5 %P 44-53 %0 Report %T EDDIE Beats the Bookies %A Butler, James M. %A Tsang, Edward P. K. %D 1995 %8 15 dec %N CSM-259 %I Computer Science, University of Essex %C Colchester CO4 3SQ, UK %F butler:1995:eddie %X Betting on a horse race is, in many ways, like investing in a financial market. You invest your money on the horse that you believe is going to win the race, in the hope of a return on your investment. Like some financial investments, horse race betting is a high risk investment, in that you can lose all of your money. As with making the right financial decision, the return on your investment, if you bet on the winning horse, can be considerable. In this paper, we present EDDIE, a genetic... %K genetic algorithms, genetic programming %U http://cswww.essex.ac.uk/CSP/papers/CSM-259.ps.Z %0 Conference Proceedings %T Optimizing a Pseudo Financial Factor Model with Support Vector Machines and Genetic Programming %A Butler, Matthew %A Keselj, Vlado %Y Gao, Yong %Y Japkowicz, Nathalie %S 22nd Canadian Conference on Artificial Intelligence, Canadian AI 2009 %S Lecture Notes in Computer Science %D 2009 %8 may 25 27 %V 5549 %I Springer %C Kelowna, Canada %F conf/ai/ButlerK09a %X We compare the effectiveness of Support Vector Machines (SVM) and Tree-based Genetic Programming (GP) to make accurate predictions on the movement of the Dow Jones Industrial Average (DJIA). The approach is facilitated though a novel representation of the data as a pseudo financial factor model, based on a linear factor model for representing correlations between the returns in different assets. To demonstrate the effectiveness of the data representation the results are compared to models developed using only the monthly returns of the inputs. Principal Component Analysis (PCA) is initially used to translate the data into PC space to remove excess noise that is inherent in financial data. The results show that the algorithms were able to achieve superior investment returns and higher classification accuracy with the aid of the pseudo financial factor model. As well, both models outperformed the market benchmark, but ultimately the SVM methodology was superior in terms of accuracy and investment returns. %K genetic algorithms, genetic programming, support vector machines, financial forecasting, principle component analysis %R 10.1007/978-3-642-01818-3_21 %U http://dx.doi.org/10.1007/978-3-642-01818-3_21 %P 191-194 %0 Journal Article %T Artificial intelligence for fashion, Leanne Luce, Apress 2019, ISBN 978-1-4842-3930-8 how AI is revolutionizing the fashion industry %A Buttler, Grace %J Genetic Programming and Evolvable Machines %D 2022 %8 mar %V 23 %N 1 %@ 1389-2576 %F Buttler:GPEM %O Book Review %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-021-09422-8 %U https://rdcu.be/cAfT5 %U http://dx.doi.org/10.1007/s10710-021-09422-8 %P 159-160 %0 Journal Article %T Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection %A Butz, Martin V. %A Sastry, Kumara %A Goldberg, David E. %J Genetic Programming and Evolvable Machines %D 2005 %8 mar %V 6 %N 1 %@ 1389-2576 %F butz:2005:GPEM %X Recent analysis of the XCS classifier system have shown that successful genetic learning strongly depends on the amount of fitness pressure towards accurate classifiers. Since the traditionally used proportionate selection is dependent on fitness scaling and fitness distribution, the resulting evolutionary fitness pressure may be neither stable nor sufficiently strong. Thus, we apply tournament selection to XCS. In particular, we exhibit the weakness of proportionate selection and suggest tournament selection as a more reliable alternative. We show that tournament selection results in a learning classifier system that is more parameter independent, noise independent, and more efficient in exploiting fitness guidance in single-step problems as well as multistep problems. The evolving population is more focused on promising subregions of the problem space and thus finds the desired accurate, maximally general representation faster and more reliably. %K genetic algorithms, classifier systems, LCS, learning classifier systems, XCS, tournament selection, genetics based machine learning %9 journal article %R 10.1007/s10710-005-7619-9 %U http://dx.doi.org/10.1007/s10710-005-7619-9 %P 53-77 %0 Journal Article %T Problem solution sustenance in XCS: Markov chain analysis of niche support distributions and the impact on computational complexity %A Butz, Martin V. %A Goldberg, David E. %A Lanzi, Pier Luca %A Sastry, Kumara %J Genetic Programming and Evolvable Machines %D 2007 %8 mar %V 8 %N 1 %@ 1389-2576 %F Butz:2006:GPEM %X Michigan-style learning classifier systems iteratively evolve a distributed solution to a problem in the form of potentially overlapping subsolutions. Each problem niche is covered by subsolutions that are represented by a set of predictive rules, termed classifiers. The genetic algorithm is designed to evolve classifier structures that together cover the whole problem space and represent a complete problem solution. An obvious challenge for such an online evolving, distributed knowledge representation is to continuously sustain all problem subsolutions covering all problem niches, that is, to ensure niche support. Effective niche support depends both on the probability of reproduction and on the probability of deletion of classifiers in a niche. In XCS, reproduction is occurrence-based whereas deletion is support-based. In combination, niche support is assured effectively. we present a Markov chain analysis of the niche support in XCS, which we validate experimentally. Evaluations in diverse Boolean function settings, which require non-overlapping and overlapping solution structures, support the theoretical derivations. We also consider the effects of mutation and crossover on niche support. With respect to computational complexity, the paper shows that XCS is able to maintain (partially overlapping) niches with a computational effort that is linear in the inverse of the niche occurrence frequency. %K genetic algorithms, classifier systems, Learning classifier systems, LCS, XCS, Niching, Markov chain analysis, Solution sustenance, Mutation %9 journal article %R 10.1007/s10710-006-9012-8 %U http://dx.doi.org/10.1007/s10710-006-9012-8 %P 5-37 %0 Journal Article %T Data Fusion by Intelligent Classifier Combination %A Buxton, B. F. %A Langdon, W. B. %A Barrett, S. J. %J Measurement and Control %D 2001 %8 oct %V 34 %N 8 %@ 0020-2940 %F buxton:2001:MC %X The use of hybrid intelligent systems in industrial and commercial applications is briefly reviewed. The potential for application of such systems, in particular those that combine results from several constituent classifiers, to problems in drug design is discussed. It is shown that, although there are no general rules as to how a number of classifiers should best be combined, effective combinations can automatically be generated by genetic programming (GP). A robust performance measure based on the area under classifier receiver-operating-characteristic (ROC) curves is used as a fitness measure in order to facilitate evolution of multi-classifier systems that outperform their constituent individual classifiers. The approach is illustrated by application to publicly available Landsat data and to pharmaceutical data of the kind used in one stage of the drug design process. %K genetic algorithms, genetic programming %9 journal article %R 10.1177/002029400103400802 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/mc/ %U http://dx.doi.org/10.1177/002029400103400802 %P 229-234 %0 Generic %T Intelligent Data Analysis and Fusion Techniques in Pharmaceuticals, Bioprocessing and Process Control %A Buxton, B. F. %A Holden, S. B. %A Treleaven, P. C. %D 2002 %8 oct %F buxton:2002:rocket %K genetic algorithms, genetic programming, boosting, support vector machines %U http://www.cs.ucl.ac.uk/staff/W.Langdon/rocket/EPSRC-final-report.htm %0 Conference Proceedings %T Generation of tests for programming challenge tasks using evolution algorithms %A Buzdalov, Maxim %Y Nicolau, Miguel %S GECCO 2011 Graduate students workshop %D 2011 %8 December 16 jul %I ACM %C Dublin, Ireland %F Buzdalov:2011:GECCOcomp %X In this paper, an automated method for generation of tests in order to detect inefficient (slow) solutions for programming challenge tasks is proposed. The method is based on genetic algorithms. The proposed method was applied to a task from the Internet problem archive - the Timus Online Judge. For this problem, none of the existed solutions passed the generated set of tests. %K genetic algorithms, genetic programming, SBSE %R 10.1145/2001858.2002086 %U http://dx.doi.org/10.1145/2001858.2002086 %P 763-766 %0 Conference Proceedings %T Evolving EFSMs solving a path-planning problem by genetic programming %A Buzdalov, Maxim %A Sokolov, Andrey %Y Motsinger-Reif, Alison %S GECCO 2012 Graduate Students Workshop %D 2012 %8 July 11 jul %I ACM %C Philadelphia, Pennsylvania, USA %F Buzdalov:2012:GECCOcomp %X In this paper, we present an approach to evolving of an algorithm encoded as an extended finite-state machine that solves a simple path-planning problem - finding a path in an unknown area filled with obstacles using a constant amount of memory - by means of genetic programming. Experiments show that in 100percent of cases a reasonably correct EFSM with behavior similar to one of the BUG algorithms is evolved. %K genetic algorithms, genetic programming %R 10.1145/2330784.2330880 %U http://dx.doi.org/10.1145/2330784.2330880 %P 591-594 %0 Conference Proceedings %T Evaluating the community partition quality of a network with a genetic programming approach %A Buzzanca, Marco %A Carchiolo, Vincenza %A Longheu, Alessandro %A Malgeri, Michele %A Mangioni, Giuseppe %Y Cherifi, Hocine %Y Gaito, Sabrina %Y Quattrociocchi, Walter %Y Sala, Alessandra %S International Conference on Complex Networks and their Applications %S Studies in Computational Intelligence %D 2016 %8 30 nov 2 dec %V 693 %I Springer %C Milan %F Buzzanca:2016:CNA %X Although the problem of partition quality evaluation is well-known in literature, most of the traditional approaches involve the application of a model built upon a theoretical foundation and then applied to real data. Conversely, this work presents a novel approach: it extracts a model from a network which partition in ground-truth communities is known, so that it can be used in other contexts. The extracted model takes the form of a validation function, which is a function that assigns a score to a specific partition of a network: the closer the partition is to the optimal, the better the score. In order to obtain a suitable validation function, we make use of genetic programming, an application of genetic algorithms where the individuals of a population are computer programs. In this paper we present a computationally feasible methodology to set up the genetic programming run, and show our design choices for the terminal set, function set, fitness function and control parameters. %K genetic algorithms, genetic programming, Community Detection, Normalize Mutual Information, Validation Function, Partition Quality %R 10.1007/978-3-319-50901-3_24 %U http://dx.doi.org/10.1007/978-3-319-50901-3_24 %P 299-308 %0 Conference Proceedings %T Digital enzymes: agents of reaction inside robotic controllers for the foraging problem %A Byers, Chad M. %A Cheng, Betty H. C. %A McKinley, Philip K. %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 Byers:2011:GECCO %X Over billions of years, natural selection has continued to select for a framework based on (1) parallelism and (2) cooperation across various levels of organisation within organisms to drive their behaviours and responses. We present a design for a bottom-up, reactive controller where the agent’s response emerges from many parallelled, enzymatic interactions (bottom-up) within the biologically-inspired process of signal transduction (reactive). We use enzymes to explore the potential for evolving simulated robot controllers for the central-place foraging problem. The properties of the robot and stimuli present in its environment are encoded in a digital format (molecule) capable of being manipulated and altered through self-contained computational programs (enzymes) executing in parallel inside each controller to produce the robot’s foraging behaviour. Evaluation of this design in unbounded worlds reveals evolved strategies employing one or more of the following complex behaviors: (1) swarming, (2) coordinated movement, (3) communication of concepts using a primitive language based on sound and colour, (4) cooperation, and (5) division of labour. %K genetic algorithms, genetic programming, Artificial life/robotics/evolvable hardware %R 10.1145/2001576.2001610 %U http://dx.doi.org/10.1145/2001576.2001610 %P 243-250 %0 Conference Proceedings %T Cooperative Robot Swarm Locomotion Using Genetic Algorithms %A Byington, M. D. %A Bishop, B. E. %S System Theory, 2008. SSST 2008. 40th Southeastern Symposium on %D 2008 %8 mar %F 4480232 %K genetic algorithms, cooperative robot swarm locomotion, decentralized controller design, locomotion controllers, robotic agents, control system synthesis, decentralised control, mobile robots, motion control, multi-robot systems %R 10.1109/SSST.2008.4480232 %U http://dx.doi.org/10.1109/SSST.2008.4480232 %P 252-256 %0 Conference Proceedings %T Analysis of Constant Creation Techniques on the Binomial-3 Problem with Grammatical Evolution %A Byrne, Jonathan %A O’Neill, Michael %A Hemberg, Erik %A Brabazon, Anthony %Y Tyrrell, Andy %S 2009 IEEE Congress on Evolutionary Computation %D 2009 %8 18 21 may %I IEEE Press %C Trondheim, Norway %F Byrne:2009:cec %X This paper studies the difference between Persistent Random Constants (PRC) and Digit Concatenation as methods for generating constants. It has been shown that certain problems have different fitness landscapes depending on how they are represented, independent of changes to the combinatorial search space, thus changing problem difficulty. In this case we show that the method for generating the constants can also influence how hard the problem is for Genetic Programming. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1109/CEC.2009.4982996 %U P522.pdf %U http://dx.doi.org/10.1109/CEC.2009.4982996 %P 568-573 %0 Conference Proceedings %T Structural and nodal mutation in grammatical evolution %A Byrne, Jonathan %A O’Neill, Michael %A Brabazon, Anthony %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/ByrneOB09 %X This study focuses on mutation in Grammatical Evolution and divides mutation events into those that are structural in nature and those that are nodal. A structural event being one that alters the length of the phenotype. A nodal event simply alters the value at any node of a derivation tree. We analyse and compare the effect of integer, nodal and structural mutations on fitness for randomly generated individuals before continuing this analysis to their relative problem-solving performance over full runs. The study highlights the importance of understanding how the search operators of an evolutionary algorithm behave. The result in this case being a form of mutation for Grammatical Evolution, node mutation, with a better property of locality than standard integer-based mutation, which does not discriminate between structural and nodal contexts. %K genetic algorithms, genetic programming, grammatical evolution, Poster %R 10.1145/1569901.1570215 %U http://dx.doi.org/10.1145/1569901.1570215 %P 1881-1882 %0 Conference Proceedings %T An Analysis of the Behaviour of Mutation in Grammatical Evolution %A Byrne, Jonathan %A McDermott, James %A O’Neill, Michael %A Brabazon, Anthony %Y Esparcia-Alcazar, Anna Isabel %Y Ekart, Aniko %Y Silva, Sara %Y Dignum, Stephen %Y Uyar, A. Sima %S Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010 %S LNCS %D 2010 %8 July 9 apr %V 6021 %I Springer %C Istanbul %F Byrne:2010:EuroGP %X This study attempts to decompose the behaviour of mutation in Grammatical Evolution (GE). Standard GE mutation can be divided into two types of events, those that are structural in nature and those that are nodal. A structural event can alter the length of the phenotype whereas a nodal event simply alters the value at any terminal (leaf or internal node) of a derivation tree. We analyse the behaviour of standard mutation and compare it to the behaviour of its nodal and structural components. These results are then compared with standard GP operators to see how they differ. This study increases our understanding of how the search operators of an evolutionary algorithm behave. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-12148-7_2 %U http://dx.doi.org/10.1007/978-3-642-12148-7_2 %P 14-25 %0 Conference Proceedings %T Optimising Offensive Moves in Toribash %A Byrne, J. %A O’Neill, M. %A Brabazon, A. %Y Matousek, R. %S Proceedings of Mendel 2010 16th International Conference on Soft Computing %D 2010 %8 23 25 jun %I Brno University of Technology %C Brno, Czech Republic %F byrne_oneill_brabazon:mendel2010 %P 78-85 %0 Conference Proceedings %T Implementing an Intuitive Mutation Operator for Interactive Evolutionary 3D Design %A Byrne, Jonathan %A McDermott, James %A Galvan-Lopez, Edgar %A O’Neill, Michael %S 2010 IEEE World Congress on Computational Intelligence %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F byrne_etal:cec2010 %X Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been described as a key element in Evolutionary Computation. Grammatical Evolution (GE) is a generative system as it uses grammar rules to derive a program from an integer encoded genome. The genome, upon which the evolutionary process is carried out, goes through several transformations before it produces an output. The aim of this paper is to investigate the impact of locality during the generative process using both qualitative and quantitative techniques. To explore this, we examine the effects of standard GE mutation using distance metrics and conduct a survey of the output designs. There are two different kinds of event that occur during standard GE Mutation. We investigate how each event type affects the locality on different phenotypic stages when applied to the problem of interactive design generation. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1109/CEC.2010.5586485 %U http://dx.doi.org/10.1109/CEC.2010.5586485 %P 2919-2925 %0 Conference Proceedings %T Combining Structural Analysis and Multi-Objective Criteria for Evolutionary Architectural Design %A Byrne, Jonathan %A Fenton, Michael %A Hemberg, Erik %A McDermott, James %A O’Neill, Michael %A Shotton, Elizabeth %A Nally, Ciaran %Y Di Chio, Cecilia %Y Brabazon, Anthony %Y Di Caro, Gianni %Y Drechsler, Rolf %Y Ebner, Marc %Y Farooq, Muddassar %Y Grahl, Joern %Y Greenfield, Gary %Y Prins, Christian %Y Romero, Juan %Y Squillero, Giovanni %Y Tarantino, Ernesto %Y Tettamanzi, Andrea G. B. %Y Urquhart, Neil %Y Uyar, A. Sima %S Applications of Evolutionary Computing, EvoApplications 2011: EvoCOMNET, EvoFIN, EvoHOT, EvoMUSART, EvoSTIM, EvoTRANSLOG %S LNCS %D 2011 %8 27 29 apr %V 6625 %I Springer Verlag %C Turin, Italy %F byrne:evoapps11 %X This study evolves and categorises a population of conceptual designs by their ability to handle physical constraints. The design process involves a trade-off between form and function. The aesthetic considerations of the designer are constrained by physical considerations and material cost. In previous work, we developed a design grammar capable of evolving aesthetically pleasing designs through the use of an interactive evolutionary algorithm. This work implements a fitness function capable of applying engineering objectives to automatically evaluate designs and, in turn, reduce the search space that is presented to the user. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1007/978-3-642-20520-0_21 %U http://dx.doi.org/10.1007/978-3-642-20520-0_21 %P 204-213 %0 Report %T Interactive Operators for Evolutionary Architectural Design %A Byrne, Jonathan %A Hemberg, Erik %A O’Neill, Michael %D 2011 %8 apr 12 %N UCD-CSI-2011-05 %I Natural Computing Research & Applications Group, School of Computer Science and Informatics, University College, Dublin %C Dublin, Ireland %F ByrneHembergONeill:TechReport052011 %X In this paper we explore different techniques that allow the user to direct interactive evolutionary search. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space. This paper examines whether additional feedback from the user can be a benefit to the problem of evolutionary design. We find that the interface between the user and the search space plays a vital role in this process. %K genetic algorithms, genetic programming %U http://www.csi.ucd.ie/files/UCD-CSI-2011-05.pdf %0 Conference Proceedings %T Interactive operators for evolutionary architectural design %A Byrne, Jonathan %A Hemberg, Erik %A O’Neill, Michael %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 Byrne:2011:GECCOcomp %X In this paper we explore different techniques that allow the user to direct interactive evolutionary search. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space. This paper examines whether additional feedback from the user can be a benefit to the problem of evolutionary design. We find that the interface between the user and the search space plays a vital role in this process. %K genetic algorithms, genetic programming, grammatical evolution, Digital entertainment technologies and arts: Poster %R 10.1145/2001858.2001884 %U http://dx.doi.org/10.1145/2001858.2001884 %P 43-44 %0 Conference Proceedings %T A Local Search Interface for Interactive Evolutionary Architectural Design %A Byrne, Jonathan %A Hemberg, Erik %A Brabazon, Anthony %A O’Neill, Michael %Y Machado, Penousal %Y Romero, Juan %Y Carballal, Adrian %S Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012 %S LNCS %D 2012 %8 November 13 apr %V 7247 %I Springer Verlag %C Malaga, Spain %F Byrne:2012:EvoMUSART %X A designer should be able to express their intentions with a design tool. This paper describes an evolutionary design tool that enables the architect to directly interact with the encoding of designs they find aesthetically pleasing. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space. We conduct user trials on an interface for making localised changes to an individual and evaluate if it is capable of directing search. Examination of the locality of changes made by the users provides an insight into how they explore the search space. %K genetic algorithms, genetic programming, grammatical evolution, architectural design %R 10.1007/978-3-642-29142-5_3 %U http://dx.doi.org/10.1007/978-3-642-29142-5_3 %P 23-34 %0 Thesis %T Approaches to Evolutionary Architectural Design Exploration Using Grammatical Evolution %A Byrne, Jonathan %D 2012 %8 aug 6 %C Ireland %C School of Computer Science and Informatics, University College Dublin %F JonathanByrneThesis %X The architectural design process is both subjective and objective in nature. The designer and end user judge a design not only by objective functionality but also by subjective form. Despite the ability of evolutionary algorithms to produce creative and novel designs, they have primarily been used to aid the design process by optimising the functionality of a design, once it has been instantiated. Designers should be able to express their subjective and objective intentions with a design tool. Grammatical evolution (GE) is a form of genetic programming that allows evolutionary techniques to be applied to systems that can be represented as a grammar. This thesis examines approaches that allow grammatical evolution to be used in the exploration phase of the architectural design process as well as optimising the design to maximise functionality. The primary focus of this thesis is to increase the amount of direct and indirect interaction available to the designer for evolutionary design exploration. The research gaps which this thesis investigates are the use of novel GE operators for active user intervention, the development of interfaces suitable for directing evolutionary search and the application of functional constraints for guiding aesthetic evolution. The contributions made by this thesis are the development of two component mutation operators, a novel animated interface for user-directed evolution and the implementation of a multi-objective finite element analysis fitness function in GE for the first time. An examination of fitness functions, operators and representations is carried out so that the designer’s input to the evolutionary algorithm can be enhanced. An extensive review of computer-generated architecture, interactive evolution and grammatical evolution is conducted. Initial investigations explore whether the constraints placed on architectural designs can be expressed as a multi-objective fitness function. The application of this technique, as a means of reducing the search space presented to the architect, is then evaluated. Broadening interaction beyond evaluation increases the amount of feedback and bias a user can apply to the search. A study is conducted to examine how integer mutation in GE explores the search space. Two novel and distinct behavioural components in GE mutation are shown to exist, nodal and structural mutation. The locality of the operations is examined at different levels of the derivation process. It is shown that nodal and structural mutation cause different magnitudes of change at the phenotypic level. An interface is designed that enables the architect to directly mutate design encodings that they find aesthetically pleasing. User trials are then conducted on an interface for making localised changes to an individual and evaluate whether it is capable of directing search. The results show that users initially apply structural mutations to explore the search space and then apply smaller nodal mutations to fine tune a solution. The novel interface is shown to enable directed evolutionary search. %K genetic algorithms, genetic programming, Grammatical Evolution %9 Ph.D. thesis %U https://rms.ucd.ie/ufrs/!W_VA_PUB_BOOK.EDIT?POPUP=TRUE&object_id=368144095 %0 Journal Article %T A methodology for user directed search in evolutionary design %A Byrne, Jonathan %A Hemberg, Erik %A O’Neill, Michael %A Brabazon, Anthony %J Genetic Programming and Evolvable Machines %D 2013 %8 287–314 %V 24 %N 3 %@ 1389-2576 %F Byrne:2013:GPEM %X A designer should be able to express their intentions with a design tool. This work describes a methodology that enables the architect to directly interact with the encoding of designs they find aesthetically pleasing. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space. We conduct trials on two interfaces for making localised changes to a design in order to evaluate if the user is capable of directing search. In addition, an examination of the locality of changes made by the users provides an insight into how they explore the search space. The results show that a suitably designed interface is capable of directing search and that the participants used different magnitudes of change during directed search. %K genetic algorithms, genetic programming, Grammatical evolution, Interactivity, Interrupt intervene and resume %9 journal article %R 10.1007/s10710-013-9189-6 %U http://dx.doi.org/10.1007/s10710-013-9189-6 %P September %0 Conference Proceedings %T Evolving an Aircraft Using a Parametric Design System %A Byrne, Jonathan %A Cardiff, Phillip %A Brabazon, Anthony %A O’Neill, Michael %Y Romero, Juan %Y McDermott, James %Y Correia, Joao %S 16th European Conference on the Applications of Evolutionary Computation (EvoMusArt 2014) %S Lecture Notes in Computer Science %D 2014 %8 apr %V 8601 %I Springer %C Granada, Spain %F byrne:eaauapds:2014 %X Traditional CAD tools generate a static solution to a design problem. Parametric systems allow the user to explore many variations on that design theme. Such systems make the computer a generative design tool and are already used extensively as a rapid prototyping technique in architecture and aeronautics. Combining a design generation tool with an evolutionary algorithm provides a methodology for optimising designs. This works uses NASA’s parametric aircraft design tool (OpenVSP) and an evolutionary algorithm to evolve a range of aircraft that maximise lift and reduce drag while remaining within the framework of the original design. Our approach allows the designer to automatically optimise their chosen design and to generate models with improved aerodynamic efficiency. %K genetic algorithms, genetic programming %R 10.1007/978-3-662-44335-4_11 %U http://dx.doi.org/10.1007/978-3-662-44335-4_11 %P 119-130 %0 Conference Proceedings %T An Examination of Synchronisation in Artificial Gene Regulatory Networks %A Byrne, Jonathan %A Nicolau, Miguel %A Brabazon, Anthony %A O’Neill, Michael %Y Coello Coello, Carlos A. %S Proceedings of the 2014 IEEE Congress on Evolutionary Computation %D 2014 %8 June 11 jul %I IEEE Press %C Beijing, China %@ 0-7803-8515-2 %F byrne:aeosiagrn:cec2014 %X An Artificial Genetic Regulatory Network (GRN) is a model of the gene expression regulation mechanism in biological organisms. It is a dynamical system that is capable of mimicking non-linear time series. The GRN was adapted to allow for input and output so that the system’s rich dynamics could be used for dynamic problem solving. In order for the GRN to be embedded in the environment, the time scale of the physical system has to be mapped to that of the GRN and so a synchronisation process was introduced. This work examines the impact of different synchronisation intervals and how they effect the overall performance of the GRN. A variable synchronisation step that stops once the system has stabilised is also explored as a mechanism for automatically choosing the interval size. %K genetic algorithms, genetic programming, Evolutionary Developmental Systems, Complex Networks and Evolutionary Computation %R 10.1109/CEC.2014.6900385 %U http://dx.doi.org/10.1109/CEC.2014.6900385 %P 2764-2769 %0 Journal Article %T Optimising complex pylon structures with grammatical evolution %A Byrne, Jonathan %A Fenton, Michael %A Hemberg, Erik %A McDermott, James %A O’Neill, Michael %J Information Sciences %D 2014 %8 sep %V 316 %@ 0020-0255 %F Byrne:2014:IS %X Evolutionary algorithms have proved their ability to optimise architectural designs but are limited by their representation, i.e., the structures that the algorithm is capable of generating. The representation is normally constrained to small structures, or parts of a larger structure, to prevent a preponderance of invalid designs. This work uses a grammar based representation to generate large scale pylon designs. It removes invalid designs from the search space, but still allows complex and large scale constructions. In order to show the suitability of this method to real world design problems, we apply it to the Royal Institute of British Architects pylon design competition. This work shows that a combination of a grammar representation with real world constraints is capable of exploring different design configurations while evolving viable and optimised designs. %K genetic algorithms, genetic programming, grammatical evolution, Structural optimisation, Architecture, Grammar %9 journal article %R 10.1016/j.ins.2014.03.010 %U http://www.sciencedirect.com/science/article/pii/S0020025514002904 %U http://dx.doi.org/10.1016/j.ins.2014.03.010 %P 582-597 %0 Journal Article %T Evolving Parametric Aircraft Models for Design Exploration and Optimisation %A Byrne, Jonathan %A Cardiff, Phillip %A Brabazon, Anthony %A O’Neill, Michael %J Neurocomputing %D 2014 %8 oct %V 142 %F byrne:epamfdeao:2014 %X Traditional CAD tools generate a static solution to a design problem. Parametric systems allow the user to explore many variations on that design theme. Such systems make the computer a generative design tool and are already used extensively as a rapid prototyping technique in architecture and aeronautics. Combining a design generation tool with an analysis software and an evolutionary algorithm provides a methodology for optimising designs. This work combines NASA’s parametric aircraft design tool (OpenVSP) with a fluid dynamics solver (OpenFOAM) to create and analyse aircraft. An evolutionary algorithm is then used to generate a range of aircraft that maximise lift and reduce drag while remaining within the framework of the original design. Our approach allows the designer to automatically optimise their chosen design and to generate models with improved aerodynamic efficiency. Different components on three aircraft models are varied to highlight the ease and effectiveness of the parametric model optimisation. %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.neucom.2014.04.004 %U http://www.sciencedirect.com/science/article/pii/S092523121400530X %U http://dx.doi.org/10.1016/j.neucom.2014.04.004 %P 39-47 %0 Journal Article %T Constitutive modeling of Leighton Buzzard Sands using genetic programming %A Cabalar, Ali Firat %A Cevik, Abdulkadir %A Guzelbey, Ibrahim H. %J Neural Computing and Applications %D 2010 %V 19 %N 5 %I Springer London %@ 0941-0643 %F Cabalar:2010:NCA %X This paper investigates the results of laboratory experiments and numerical simulations conducted to examine the behaviour of mixtures composed of coarse (i.e. Leighton Buzzard Sand fraction B) and fine (i.e. Leighton Buzzard Sand fraction E) sand particles. Emphasis was placed on assessing the role of fines content in mixture and strain level on the deviatoric stress and pore water pressure generation using experimental (i.e. Triaxial testing) and numerical approaches (i.e. genetic programming, GP). The experimental database used for GP modelling is based on a laboratory study of the properties of saturated coarse and fine sand mixtures with various mix ratios under a 100 kPa effective stresses in a 100 mm diameter conventional triaxial testing apparatus. Experimental results show that coarse-fine sand mixtures exhibit clay-like behavior due to particle-particle effects with the increase in fines content. It is shown that GP modeling of coarse-fine sand mixtures is observed to be quite satisfactory. The results have implications in the design of compressible particulate systems and in the development of prediction tools for the field performance coarse-fine sands. %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s00521-009-0317-4 %U http://dx.doi.org/10.1007/s00521-009-0317-4 %P 657-665 %0 Journal Article %T Genetic programming-based attenuation relationship: An application of recent earthquakes in Turkey %A Cabalar, Ali Firat %A Cevik, Abdulkadir %J Computers & Geosciences %D 2009 %V 35 %N 9 %@ 0098-3004 %F Cabalar20091884 %X This study investigates an application of genetic programming (GP) for the prediction of peak ground acceleration (PGA) using strong-ground-motion data from Turkey. The input variables in the developed GP model are the average shear-wave velocity, earthquake source to site distance and earthquake magnitude, and the output is the PGA values. The proposed GP model is based on the most reliable database compiled for earthquakes in Turkey. The results show that the consistency between the observed PGA values and the predicted ones by the GP model yields relatively high correlation coefficients (R2=0.75). The proposed model is also compared with an existing attenuation relationship and found to be more accurate. %K genetic algorithms, genetic programming, Attenuation relationship %9 journal article %R 10.1016/j.cageo.2008.10.015 %U http://www.sciencedirect.com/science/article/B6V7D-4W99W08-1/2/aa19b6639659945b1d4e78c6209fe435 %U http://dx.doi.org/10.1016/j.cageo.2008.10.015 %P 1884-1896 %0 Journal Article %T Triaxial behavior of sand-mica mixtures using genetic programming %A Cabalar, Ali Firat %A Cevik, Abdulkadir %J Expert Systems with Applications %D 2011 %V 38 %N 8 %@ 0957-4174 %F Cabalar201110358 %X This study investigates an application of genetic programming (GP) for modelling of coarse rotund sand-mica mixtures. An empirical model equation is developed by means of GP technique. The experimental database used for GP modeling is based on a laboratory study of the properties of saturated coarse rotund sand and mica mixtures with various mix ratios under a 100 kPa effective stresses, because of its unusual behaviour. In the tests, deviatoric stress, and pore pressure generation, and strain have been measured in a 100 mm diameter conventional triaxial testing apparatus. The input variables in the developed GP models are the mica content, and strain, and the outputs are deviatoric stress, pore water pressure generation. The performance of accuracies of proposed GP based equations is observed to be quite satisfactory. %K genetic algorithms, genetic programming, Leighton Buzzard Sand, Mica, Triaxial testing, Modelling %9 journal article %R 10.1016/j.eswa.2011.02.051 %U http://www.sciencedirect.com/science/article/B6V03-524FSB9-M/2/eb83d6182c4d3c0b1271b301c5a04e15 %U http://dx.doi.org/10.1016/j.eswa.2011.02.051 %P 10358-10367 %0 Journal Article %T Burned area estimations derived from Landsat ETM+ and OLI data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees %A Cabral, Ana I. R. %A Silva, Sara %A Silva, Pedro C. %A Vanneschi, Leonardo %A Vasconcelos, Maria J. %J ISPRS Journal of Photogrammetry and Remote Sensing %D 2018 %V 142 %@ 0924-2716 %F CABRAL:2018:IJPRS %X Every year, large areas of savannas and woodlands burn due to natural conditions and land management practices. Given the relevant level of greenhouse gas emissions produced by biomass burning in tropical regions, it is becoming even more important to clearly define historic fire regimes so that prospective emission reduction management strategies can be well informed, and their results Measured, Reported, and Verified (MRV). Thus, developing tools for accurately, and periodically mapping burned areas, based on cost advantageous, expedite, and repeatable rigorous approaches, is important. The main objective of this study is to investigate the potential of novel Genetic Programming (GP) methodologies for classifying burned areas in satellite imagery over savannas and tropical woodlands and to assess if they can improve over the popular and currently applied methods of Maximum Likelihood classification and Classification and Regression Tree analysis. The tests are performed using three Landsat images from Brazil (South America), Guinea-Bissau (West Africa) and the Democratic Republic of Congo (Central Africa). Burned areas were digitized on-screen to produce mapped information serving as surrogate ground-truth. Validation results show that all methods provide an overestimation of burned area, but GP achieves higher accuracy values in two of the three cases. GP is the most versatile machine learning method available today, but still largely underused in remote sensing. This study shows that standard GP can produce better results than two classical methods, and illustrates its versatility and potential in becoming a mainstream method for more difficult tasks involving the large amounts of newly available data %K genetic algorithms, genetic programming, Burned area mapping, Savana woodlands, Classification and Regression Trees, Maximum Likelihood, Landsat ETM+/OLI %9 journal article %R 10.1016/j.isprsjprs.2018.05.007 %U http://www.sciencedirect.com/science/article/pii/S0924271618301400 %U http://dx.doi.org/10.1016/j.isprsjprs.2018.05.007 %P 94-105 %0 Conference Proceedings %T Design of B-spline Neural Networks using a Bacterial Programming Approach %A Cabrita, C. %A Botzheim, J. %A Ruano, A. E. %A Koczy, L. T. %S Proceedings of the International Joint Conference on Neural Networks, IJCNN 2004 %D 2004 %8 jul %C Budapest, Hungary %F CabritaBotzheimRuanoKoczy04 %X The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives. %K genetic algorithms, genetic programming, Evolution (biology), Evolutionary computation, Informatics, Intelligent networks, Intelligent systems, Microorganisms, Neural networks, Spline, Telecommunication network topology, evolution (biological), microorganisms, neural nets, splines (mathematics), B-spline neural network design, bacterial programming method, heuristics method, microbial evolution phenomenon %R 10.1109/IJCNN.2004.1380987 %U http://dx.doi.org/10.1109/IJCNN.2004.1380987 %P 2313-2318 %0 Conference Proceedings %T Multiplicity computing: a vision of software engineering for next-generation computing platform applications %A Cadar, Cristian %A Pietzuch, Peter %A Wolf, Alexander L. %Y Sullivan, Kevin %S Proceedings of the FSE/SDP workshop on Future of software engineering research %S FoSER ’10 %D 2010 %8 July 11 nov %I ACM %C Santa Fe, New Mexico, USA %F Cadar:2010:FoSER %X New technologies have recently emerged to challenge the very nature of computing: multicore processors, virtualised operating systems and networks, and data-centre clouds. One can view these technologies as forming levels within a new, global computing platform. We aim to open a new area of research, called multiplicity computing, that takes a radically different approach to the engineering of applications for this platform. Unlike other efforts, which are largely focused on innovations within specific levels, multiplicity computing embraces the platform as a virtually unlimited space of essentially redundant resources. This space is formed as a whole from the cross product of resources available at each level in the platform, offering a multiplicity of end-to-end resources. We seek to discover fundamentally new ways of exploiting the combinatorial multiplicity of computational, communication, and storage resources to obtain scalable applications exhibiting improved quality, dependability, and security that are both predictable and measurable. %K genetic improvement, cloud computing, data centers, multicore, virtualization, Design, Experimentation, Measurement, Performance, Reliability, Security %R 10.1145/1882362.1882380 %U http://www.doc.ic.ac.uk/~cristic/papers/multicomp-foser-10.pdf %U http://dx.doi.org/10.1145/1882362.1882380 %P 81-86 %0 Conference Proceedings %T Genetic Programming Algorithm Creating and Assembling Subtrees for Making Analytical Functions %A Cadrik, Tomas %A Mach, Marian %Y Matousek, Radek %S Proceedings of the 22nd International Conference on Soft Computing (MENDEL 2016) %S AISC %D 2016 %8 jun 8 10 %V 576 %I Springer %C Brno, Czech Republic %F Cadrik:2016:MENDEL %X There are many optimization algorithms which can be used for solving different tasks. One of those is the genetic programming method, which can build an analytical function which can describe data. The function is coded in a tree structure. The problem is that when we decide to use lower maximal depth of the tree, the genetic programming is not able to compose a function which is good enough. This paper describes the way how to solve this problem. The approach is based on creating partial solutions represented by subtrees and composing them together to create the last tree. This approach was tested for finding a function which can correctly calculate the output according to the given inputs. The experiments showed that even when using a small maximal depth, the genetic programming using our approach can create functions with good results. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-58088-3_6 %U http://dx.doi.org/10.1007/978-3-319-58088-3_6 %P 55-63 %0 Conference Proceedings %T Symbolic Regression Trees as Embedded Representations %A Caetano, Victor %A Candido Teixeira, Matheus %A Pappa, Gisele Lobo %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F caetano:2023:GECCO %X Representation learning is an area responsible for learning data representations that makes it easier for machine learning algorithms to extract useful information from them. Deep learning currently has the most effective methods for this task and can learn distributed representations - also known as embeddings - able to represent different properties of the data and their relationship. In this direction, this paper introduces a new way to look at tree-like GP individuals for symbolic regression. Given a set of predefined operators and a sufficiently large number of solutions sampled from the space, we train a transformer to learn an encoding/decoding function. By transforming a tree representation into a distributed representation, we are able to measure distances between trees in a much more efficient way and, more importantly, generate the potential for these representations to capture semantics. We show the distance accounting for embedding presents results very similar to those of a tree-edition, which reflects their syntactic similarity. Although the model as it stands is not able to capture semantics yet, we show its potential by using the generated tree-representation model in a simple task: measuring distances between trees in a fitness-sharing scenario. %K genetic algorithms, genetic programming, embedded representations, semantics, transformers %R 10.1145/3583131.3590423 %U http://dx.doi.org/10.1145/3583131.3590423 %P 411-419 %0 Journal Article %T A simple formulation for effective flexural stiffness of circular reinforced concrete columns %A Caglar, Naci %A Demir, Aydin %A Ozturk, Hakan %A Akkaya, Abdulhalim %J Engineering Applications of Artificial Intelligence %D 2015 %V 38 %@ 0952-1976 %F Caglar:2015:EAAI %X Concrete cracking reduces flexural and shear stiffness of reinforced concrete (RC) members. Therefore analysing RC structures without considering the cracking effect may not represent actual behaviour. Effective flexural stiffness resulting from concrete cracking depends on some important parameters such as confinement, axial load level, section dimensions and material properties of concrete and reinforcing steel. In this study, a simple formula as a securer, quicker and more robust is proposed to determine the effective flexural stiffness of cracked sections of circular RC columns. This formula is generated by genetic programming (GP). The generalisation capabilities of the explicit formulations are compared by cross sectional analysis results and confirmed on a 3-D building model. Moreover the results from GP based formulation are compared with EC-8 and TEC-2007. It is demonstrated that the GP based model is highly successful to determine the effective flexural stiffness of circular RC columns. %K genetic algorithms, genetic programming, Effective flexural stiffness, Moment-curvature, Reinforced concrete, Eurocode-8, TEC-2007 %9 journal article %R 10.1016/j.engappai.2014.10.011 %U http://www.sciencedirect.com/science/article/pii/S0952197614002516 %U http://dx.doi.org/10.1016/j.engappai.2014.10.011 %P 79-87 %0 Conference Proceedings %T GECCO2004 Workshop Proceedings: Preface %A Cagnoni, S. %Y Poli, R. %Y Cagnoni, S. %Y Keijzer, M. %Y Costa, E. %Y Pereira, F. %Y Raidl, G. %Y Upton, S. C. %Y Goldberg, D. %Y Lipson, H. %Y de Jong, E. %Y Koza, J. %Y Suzuki, H. %Y Sawai, H. %Y Parmee, I. %Y Pelikan, M. %Y Sastry, K. %Y Thierens, D. %Y Stolzmann, W. %Y Lanzi, P. L. %Y Wilson, S. W. %Y O’Neill, M. %Y Ryan, C. %Y Yu, T. %Y Miller, J. F. %Y Garibay, I. %Y Holifield, G. %Y Wu, A. S. %Y Riopka, T. %Y Meysenburg, M. M. %Y Wright, A. W. %Y Richter, N. %Y Moore, J. H. %Y Ritchie, M. D. %Y Davis, L. %Y Roy, R. %Y Jakiela, M. %S GECCO 2004 Workshop Proceedings %D 2004 %8 26 30 jun %C Seattle, Washington, USA %F cagnoni:2004:pre:preproc %K genetic algorithms, genetic programming, grammatical evolution %U http://gpbib.cs.ucl.ac.uk/gecco2004/ %0 Journal Article %T Evolving Binary Classifiers Through Parallel Computation of Multiple Fitness Cases %A Cagnoni, Stefano %A Bergenti, Federico %A Mordonini, Monica %A Adorni, Giovanni %J IEEE Transactions on Systems, Man and Cybernetics - Part B %D 2005 %8 jun %V 35 %N 3 %@ 1083-4419 %F cagnoni:2005:SMC %X We describe two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimised by allowing multiple solutions to be computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly, taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared to a reference classifier. %K genetic algorithms, genetic programming, cellular programming, sub-machine code genetic programming, multiple classifiers, pattern recognition %9 journal article %R 10.1109/TSMCB.2005.846671 %U http://dx.doi.org/10.1109/TSMCB.2005.846671 %P 548-555 %0 Journal Article %T Genetic and evolutionary Computation %A Cagnoni, Stefano %A Poli, Riccardo %J Intelligenza Artificiale %D 2006 %8 Marzo Giugno %V 3 %N 1/2 %@ 1724-8035 %F Cagnoni:2006:IA %X In this paper, we start by providing a gentle introduction to the field of genetic and evolutionary computation, particularly focusing on genetic algorithms, but also touching upon other areas. We then move on to briefly analyse the geographic distribution of research excellence in this field, focusing our attention specifically on Italian researchers. We then present our own interpretation of where and how genetic and evolutionary computation fits in the broader landscape of artificial intelligence research. We conclude by making a prediction of the future impact of this technology in the short term. %K genetic algorithms, genetic programming, gec, gas, es, gsice, italian GEC, human-competitive %9 journal article %U http://cswww.essex.ac.uk/staff/poli/papers/ai50-2006.pdf %P 94-101 %0 Journal Article %T Editorial Introduction to the Special Issue on Evolutionary Computer Vision %A Cagnoni, S. %A Lutton, E. %A Olague, G. %J Evolutionary Computation %D 2008 %8 Winter %V 16 %N 4 %@ 1063-6560 %F Cagnoni:2008:EC %K genetic algorithms, genetic programming %9 journal article %R 10.1162/evco.2008.16.4.437 %U http://dx.doi.org/10.1162/evco.2008.16.4.437 %P 437-438 %0 Conference Proceedings %T Evolutionary Computer Vision and Image Processing: some FAQs, Current Challenges and Future Perspectives %A Cagnoni, Stefano %A Zhang, Mengjie %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 Cagnoni:2016:CEC %X Applications to image/signal processing and analysis have been studied since the very early years in the history of Evolutionary Computation up to a degree of popularity which has allowed terms like Evolutionary Computer Vision (ECV) and Evolutionary Image Processing (EIP) to become common among researchers. Within these fields, the role of EC has gone well beyond basic optimization of the parameters of traditional Computer Vision (CV) or Image Processing (IP) algorithms or mere use within those algorithms which comprise an optimization stage anyway. This paper, far from having the pretence of making an exhaustive review, tries to sketch the motivations behind the success of ECV/EIP, the present status of research in such a field, and a personal view of its possible developments in the near future, based on the authors’ more than 20-year long direct experience. %K genetic algorithms, genetic programming, Evolutionary Computer Vision, Evolutionary Image Processing, GPU %R 10.1109/CEC.2016.7743933 %U http://dx.doi.org/10.1109/CEC.2016.7743933 %P 1267-1271 %0 Journal Article %T ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics %A Cahon, S. %A Melab, N. %A Talbi, E. G. %J Journal of Heuristics %D 2004 %8 may %V 10 %N 3 %@ 1381-1231 %F Cahon:2004:JoH %O Special Issue: New Advances on Parallel Meta-Heuristics for Complex Problems %X We present the ParadisEO white-box object-oriented framework dedicated to the reusable design of parallel and distributed metaheuristics (PDM). ParadisEO provides a broad range of features including evolutionary algorithms (EA), local searches (LS), the most common parallel and distributed models and hybridization mechanisms, etc. This high content and utility encourages its use at European level. ParadisEO is based on a clear conceptual separation of the solution methods from the problems they are intended to solve. This separation confers to the user a maximum code and design reuse. Furthermore, the fine-grained nature of the classes provided by the framework allow a higher flexibility compared to other frameworks. ParadisEO is of the rare frameworks that provide the most common parallel and distributed models. Their implementation is portable on distributed-memory machines as well as on shared-memory multiprocessors, as it uses standard libraries such as MPI, PVM and PThreads. The models can be exploited in a transparent way, one has just to instantiate their associated provided classes. Their experimentation on the radio network design real-world application demonstrate their efficiency. %K genetic algorithms, genetic programming, metaheuristics, design and code reuse, parallel and distributed models, object-oriented frameworks, performance and robustness %9 journal article %R 10.1023/B:HEUR.0000026900.92269.ec %U https://rdcu.be/cMLxW %U http://dx.doi.org/10.1023/B:HEUR.0000026900.92269.ec %P 357-380 %0 Conference Proceedings %T Genetic-Programming-Based Symbolic Regression for Heat Transfer Correlations of a Compact Heat Exchanger %A Cai, Weihua %A Sen, Mihir %A Yang, K. T. %A Pacheco-Vega, Arturo %S ASME Summer Heat Transfer Conference (HT2005) %D 2005 %8 jul 17 22 %V 4 %I ASME %C San Francisco, California, USA %@ 0-7918-4734-9 %F Cai:2005:HT %X We describe a symbolic regression methodology based on genetic programming to find correlations that can be used to estimate the performance of compact heat exchangers. Genetic programming is an evolutionary search technique in which functions represented as parse trees evolve as the search proceeds. An advantage of this approach is that functional forms of the correlation need not be assumed. The algorithm performs symbolic regression by seeking both the functional structure of the correlation and the coefficients therein that enable the closest fit to experimental data. This search is conducted within a functional domain constructed from sets of operators and terminals that are used to build tree-structures representing functions. A penalty function is used to prevent large correlations. The methodology is tested using first artificial data from a one-dimensional function and later a set of published heat exchanger experiments. Comparison with published results from the same data show that symbolic-regression correlations are as good or better. The effect of the penalty parameters on the best function is also analysed. %K genetic algorithms, genetic programming %R 10.1115/HT2005-72293 %U http://dx.doi.org/10.1115/HT2005-72293 %P 367-374 %0 Journal Article %T Heat transfer correlations by symbolic regression %A Cai, Weihua %A Pacheco-Vega, Arturo %A Sen, Mihir %A Yang, K. T. %J International Journal of Heat and Mass Transfer %D 2006 %8 nov %V 49 %N 23-24 %F Cai:2006:IJHMT %X We describe a methodology that uses symbolic regression to extract correlations from heat transfer measurements by searching for both the form of the correlation equation and the constants in it that enable the closest fit to experimental data. For this purpose we use genetic programming modified by a penalty procedure to prevent large correlation functions. The advantage of using this technique is that no initial assumption on the form of the correlation is needed. The procedure is tested using two sets of published experimental data, one for a compact heat exchanger and the other for liquid flow in a circular pipe. In both situations, predictive errors from correlations found from symbolic regression are smaller than their published counterparts. A parametric analysis of the penalty function is also carried out. %K genetic algorithms, genetic programming, Heat transfer, Correlations, Symbolic regression, Heat exchanger %9 journal article %R 10.1016/j.ijheatmasstransfer.2006.04.029 %U http://dx.doi.org/10.1016/j.ijheatmasstransfer.2006.04.029 %P 4352-4359 %0 Book Section %T Chapter 16 - Soil temperature prediction in ordinary and extremely hot weather using genetic programming %A Cai, Xiatong %A Mohammadian, Abdolmajid %A Hiedra Cobo, Juan %A Shirkhani, Hamidreza %A Imanian, Hanifeh %A Payeur, Pierre %E Bonakdari, Hossein %E Gumiere, Silvio Jose %B Intelligence Systems for Earth, Environmental and Planetary Sciences %D 2024 %I Elsevier %F Cai:2024:ISEEPS %X The prediction of soil temperature under climate change plays an important role in understanding hydrological processes. Genetic programming can create a mathematical equation that can be used for predictions with very high efficiency due to its explicit analytical form. However, it is rarely used in soil temperature prediction, especially in extremely hot weather conditions. The fitness of multigene genetic programming (MGGP) in ordinary weather was found to be R2=0.97, and R2=0.83 in extremely hot weather. We compared the performance of single-gene genetic programming (SGGP) and multigene genetic programming (MGGP) with benchmark linear and AI models. Results show that the MGGP algorithm outperforms linear models and is comparable with some distance-based and tree-based benchmark AI models in both ordinary and extremely hot weather. MGGP underperformed the artificial neural network. Using only a polynomial equation rather than executing a complicated model with a large input dataset, MGGP shows good simplification in soil temperature prediction %K genetic algorithms, genetic programming, Soil temperature, Symbolic regression, Extreme weather, Climate prediction, Artificial intelligence, ANN %R 10.1016/B978-0-443-13293-3.00019-1 %U https://www.sciencedirect.com/science/article/pii/B9780443132933000191 %U http://dx.doi.org/10.1016/B978-0-443-13293-3.00019-1 %P 441-464 %0 Conference Proceedings %T Benefits of Employing an Implicit Context Representation on Hardware Geometry of CGP %A Cai, Xinye %A Smith, Stephen L. %A Tyrrell, Andrew M. %Y Moreno, Juan Manuel %Y Madrenas, Jordi %Y Cosp, Jordi %S Evolvable Systems: From Biology to Hardware, 6th International Conference, ICES 2005, Proceedings %S Lecture Notes in Computer Science %D 2005 %8 sep 12 14 %V 3637 %I Springer %C Sitges, Spain %@ 3-540-28736-1 %F conf/ices/CaiST05 %X Cartesian Genetic Programming (CGP) has successfully been applied to the evolution of simple image processing filters and implemented in intrinsic evolvable hardware by the authors. However, conventional CGP exhibits the undesirable characteristic of positional dependence in which the specific location of genes within the chromosome has a direct or indirect influence on the phenotype. An implicit context representation of CGP (IRCGP) has been implemented by the authors which is positionally independent and outperforms conventional CGP in this application. This paper describes the additional benefits of IRCGP when considering alternative geometries for the hardware components. Results presented show that smaller hardware arrays under IRCGP are more robust and outperform equivalent arrays implemented in conventional CGP. %K genetic algorithms, genetic programming, Cartesian Genetic Programming %R 10.1007/11549703_14 %U http://dx.doi.org/10.1007/11549703_14 %P 143-154 %0 Conference Proceedings %T Positional Independence and Recombination in Cartesian Genetic Programming %A Cai, Xinye %A Smith, Stephen L. %A Tyrrell, Andy 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:CaiSmothTyrrell %X Previously, recombination (or crossover) has proved to be unbeneficial in Cartesian Genetic Programming (CGP). This paper describes the implementation of an implicit context representation for CGP in which the specific location of genes within the chromosome has no direct or indirect influence on the phenotype. Consequently, recombination has a beneficial effect and is shown to outperform conventional CGP in the even-3 parity problem. %K genetic algorithms, genetic programming, Cartesian Genetic Programming %R 10.1007/11729976_32 %U http://dx.doi.org/10.1007/11729976_32 %P 351-360 %0 Conference Proceedings %T Discovering structures in gene regulatory networks using genetic programming and particle swarms %A Cai, Xinye %A Welch, Stephen M. %A Koduru, Praveen %A Das, Sanjoy %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 1277300 %X GP + PSO for gene network discovery %K genetic algorithms, genetic programming: Poster, bioinformatics, gene regulatory network, Particle Swarm Optimisation %R 10.1145/1276958.1277300 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1750.pdf %U http://dx.doi.org/10.1145/1276958.1277300 %P 1750-1750 %0 Journal Article %T Simultaneous structure discovery and parameter estimation in gene networks using a multi-objective GP-PSO hybrid approach %A Cai, Xinye %A Koduru, Praveen %A Das, Sanjoy %A Welch, Stephen M. %J International Journal of Bioinformatics Research and Applications %D 2009 %8 November %V 5 %N 3 %I Inderscience Publishers %@ 1744-5493 %F Cai:2009:IJBRA %X This paper presents a hybrid algorithm based on Genetic Programming (GP) and Particle Swarm Optimisation (PSO) for the automated recovery of gene network structure. It uses gene expression time series data as well as phenotypic data pertaining to plant flowering time as its input data. The algorithm then attempts to discover simple structures to approximate the plant gene regulatory networks that produce model gene expressions and flowering times that closely resemble the input data. To show the efficacy of the proposed approach, simulation results applied to flowering time control in Arabidopsis thaliana are demonstrated and discussed. %K genetic algorithms, genetic programming, gene regulatory networks, PSO, particle swarm optimisation, multi-objective optimisation, Bioinformatics, structure discovery, parameter estimation, gene networks, plant genes, plant flowering times, gene expressions %9 journal article %R 10.1504/IJBRA.2009.026418 %U http://www.inderscience.com/link.php?id=26418 %U http://dx.doi.org/10.1504/IJBRA.2009.026418 %P 254-268 %0 Thesis %T A multi-objective GP-PSO hybrid algorithm for gene regulatory network modeling %A Cai, Xinye %D 2009 %8 may %C Manhattan, Kansas, USA %C Department of Electrical and Computer Engineering, Kansas State University %F Xinye_Cai:thesis %X Stochastic algorithms are widely used in various modelling and optimization problems. Evolutionary algorithms are one class of population-based stochastic approaches that are inspired from Darwinian evolutionary theory. A population of candidate solutions is initialized at the first generation of the algorithm. Two variation operators, crossover and mutation, that mimic the real world evolutionary process, are applied on the population to produce new solutions from old ones. Selection based on the concept of survival of the fittest is used to preserve parent solutions for next generation. Examples of such algorithms include genetic algorithm (GA) and genetic programming (GP). Nevertheless, other stochastic algorithms may be inspired from animals behaviour such as particle swarm optimization (PSO), which imitates the cooperation of a flock of birds. In addition, stochastic algorithms are able to address multi-objective optimization problems by using the concept of dominance. Accordingly, a set of solutions that do not dominate each other will be obtained, instead of just one best solution. This thesis proposes a multi-objective GP-PSO hybrid algorithm to recover gene regulatory network models that take environmental data as stimulus input. The algorithm infers a model based on both phenotypic and gene expression data. The proposed approach is able to simultaneously infer network structures and estimate their associated parameters, instead of doing one or the other iteratively as other algorithms need to. In addition, a non-dominated sorting approach and an adaptive histogram method based on the hypergrid strategy are adopted to address convergence and diversity issues in multi-objective optimization. Gene network models obtained from the proposed algorithm are compared to a synthetic network, which mimics key features of Arabidopsis flowering control system, visually and numerically. Data predicted by the model are compared to synthetic data, to verify that they are able to closely approximate the available phenotypic and gene expression data. At the end of this thesis, a novel breeding strategy, termed network assisted selection, is proposed as an extension of our hybrid approach and application of obtained models for plant breeding. Breeding simulations based on network assisted selection are compared to one common breeding strategy, marker assisted selection. The results show that NAS is better both in terms of breeding speed and final phenotypic level %K genetic algorithms, genetic programming, Cartesian Genetic Programming, multi-objective optimization, particle swarm optimization, gene regulatory network modelling, plant breeding simulation, Arabidopsis, NK fitness landscape models %9 Ph.D. thesis %U http://hdl.handle.net/2097/1492 %0 Journal Article %T Genetic programming for prediction of earthquake sequence type %A Cai, Yu-Dong %J Acta Seismologica Sinica %D 1996 %8 feb %V 9 %N 1 %I Seismological Society of China %@ 1000-9116 %F Cai:1996:ASS %X The genetic programming for the prediction of earthquake sequence type was studied, and the reliability for a group of samples was tested. The results show that the performance of the genetic programming is good, and therefore it might be referred as an effective technique for the prediction of earthquake sequence type. %K genetic algorithms, genetic programming, earthquake sequence, prediction %9 journal article %R 10.1007/BF02650623 %U http://dx.doi.org/10.1007/BF02650623 %P 53-57 %0 Journal Article %T Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach %A Cakiroglu, Celal %A Aydin, Yaren %A Bekdas, Gebrail %A Isikdag, Umit %A Sadeghifam, Aidin Nobahar %A Abualigah, Laith %J Energy and Buildings %D 2024 %V 313 %@ 0378-7788 %F Cakiroglu:2024:enbuild %X Since the cooling systems used in buildings in hot climates account for a significant portion of the energy consumption, it is very important for both economy and environment to accurately predict the cooling load and consider it in building designs. This study aimed to maximize energy efficiency by appropriately selecting the features of a building that affect its cooling load. To this end, data-driven, accurate, and accessible tools were developed that enable the prediction of the cooling load of a building by practitioners. The study involves simulating the energy consumption of a mid-rise, double-story terrace house in Malaysia using building information modelling (BIM) and estimating the cooling load using ensemble machine learning models and genetic programming. Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) models have been developed and made available as an online interactive graphical user interface on the Streamlit platform. Furthermore, the symbolic regression technique has been used to obtain a closed-form equation that predicts the cooling load. The dataset used for training the predictive models comprised 94,310 data points with 10 input variables and the cooling load as the output variable. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used to measure the predictive model performances. The results of the machine learning models indicated successful prediction, with the CatBoost model achieving the highest score (R2 = 0.9990) among the four ensemble models and the predictive equation. The SHAP analysis determined the aspect ratio of the building as the most impactful feature of the building %K genetic algorithms, genetic programming, Cooling load, BIM, Energy efficiency, Predictive modeling %9 journal article %R 10.1016/j.enbuild.2024.114254 %U https://www.sciencedirect.com/science/article/pii/S0378778824003700 %U http://dx.doi.org/10.1016/j.enbuild.2024.114254 %P 114254 %0 Journal Article %T Corrigendum to “Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach” [Energy Build. 313 (2024) 114254] %A Cakiroglu, Celal %A Aydin, Yaren %A Bekdas, Gebrail %A Isikdag, Umit %A Sadeghifam, Aidin Nobahar %A Abualigah, Laith %J Energy and Buildings %D 2024 %V 315 %@ 0378-7788 %F Cakiroglu:2024:enbuild_Corrigendum %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.enbuild.2024.114329 %U https://www.sciencedirect.com/science/article/pii/S0378778824004456 %U http://dx.doi.org/10.1016/j.enbuild.2024.114329 %P 114329 %0 Journal Article %T Genetic Programming-Based Feature Construction for System Setting Recognition and Component-Level Prognostics %A Calabrese, Francesca %A Regattieri, Alberto %A Piscitelli, Raffaele %A Bortolini, Marco %A Galizia, Francesco Gabriele %J Applied Sciences %D 2022 %V 12 %N 9 %@ 2076-3417 %F calabrese:2022:AS %X Extracting representative feature sets from raw signals is crucial in Prognostics and Health Management (PHM) for components’ behaviour understanding. The literature proposes various methods, including signal processing in the time, frequency, and time–frequency domains, feature selection, and unsupervised feature learning. An emerging task in data science is Feature Construction (FC), which has the advantages of both feature selection and feature learning. In particular, the constructed features address a specific objective function without requiring a label during the construction process. Genetic Programming (GP) is a powerful tool to perform FC in the PHM context, as it allows to obtain distinct feature sets depending on the analysis goal, i.e., diagnostics and prognostics. This paper adopts GP to extract system-level features for machinery setting recognition and component-level features for prognostics. Three distinct fitness functions are considered for the GP training, which requires a set of statistical time-domain features as input. The methodology is applied to vibration signals extracted from a test rig during run-to-failure tests under different settings. The performances of constructed features are evaluated through the classification accuracy and the Remaining Useful Life (RUL) prediction error. Results demonstrate that GP-based features classify known and novel machinery operating conditions better than feature selection and learning methods. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/app12094749 %U https://www.mdpi.com/2076-3417/12/9/4749 %U http://dx.doi.org/10.3390/app12094749 %P ArticleNo.4749 %0 Conference Proceedings %T Interpretability analysis of Symbolic Regression models for dynamical systems %A Calapristi, Marco %A Patane, Luca %A Sapuppo, Francesca %A Xibilia, Maria Gabriella %S 2024 International Conference on Control, Automation and Diagnosis (ICCAD) %D 2024 %8 may %F Calapristi:2024:ICCAD %X Symbolic Regression (SR) is a machine learning paradigm that focuses on the automatic discovery of mathematical equations that best describe the relationship between input and output features in experimental datasets. Such an approach leads to interpretable models that make it easy to incorporate the knowledge available in the system. This article addresses the application of SR as an interpretable modelling approach for industrial applications involving nonlinear dynamical systems. Interpretability in model identification is crucial for understanding system processes. While SR models are inherently interpretable, the inclusion of explainability techniques such as SHAP is being explored to improve model validation while facilitating model interpretation by process technologists. In this paper, the Narendra-Li system, which is commonly used as a testbed for dynamical system identification, is considered. The performance of the proposed solutions is evaluated using appropriate interpretability metrics that provide insight into their efficiency in capturing the underlying dynamics of the system. %K genetic algorithms, genetic programming, Measurement, Visualization, Accuracy, System dynamics, Machine learning, Predictive models %R 10.1109/ICCAD60883.2024.10553801 %U http://dx.doi.org/10.1109/ICCAD60883.2024.10553801 %0 Conference Proceedings %T Symbolic Regression for Industrial Applications: An NN-Based Approach %A Calapristi, Marco %A Patane, Luca %A Sapuppo, Francesca %A Caponetto, Riccardo %A Xibilia, Maria Gabriella %S 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) %D 2024 %8 oct %F Calapristi:2024:MetroXRAINE %X Symbolic Regression (SR) is a machine learning approach developed for the automated identification of mathematical equations that accurately capture the relationships between input and output features within the experimental dataset. This method is capable of creating interpretable models while incorporating existing knowledge into the system. This paper addresses a problem in the development of interpretable Soft Sensors (SS) for industrial applications using SR. The challenge arises from the need to increase the dimensionality of the problem in order to capture the system dynamics, which often leads to a significant degradation in SR performance. Existing literature has highlighted this problem and proposed some solutions, such as employing Recurrent Neural Networks (RNN) instead of Genetic Programming (GP) in the SR procedure or applying Deep Learning (DL) techniques to reduce the input space. In this paper, we present a novel approach to develop interpretable SSs for industrial processes that involve the use of DL to encode the system dynamics. This effectively reduces the input space and supports the SR process without compromising the interpretability of the final solution. %K genetic algorithms, genetic programming, Deep learning, Training, Recurrent neural networks, Accuracy, System dynamics, Soft sensors, Artificial neural networks, Mathematical models, Encoding, system identification, dynamical nonlinear systems, neural networks, ANN, model interpretability %R 10.1109/MetroXRAINE62247.2024.10797142 %U http://dx.doi.org/10.1109/MetroXRAINE62247.2024.10797142 %P 618-623 %0 Conference Proceedings %T Genetic Programming For Automatic Design Of Self-Adaptive Robots %A Calderoni, Stephane %A Marcenac, Pierre %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 calderoni:1998:GPadsar %X The general framework tackled in this paper is the automatic generation of intelligent collective behaviors using genetic programming and reinforcement learning. We define a behavior-based system relying on automatic design process using artificial evolution to synthesize high level behaviors for autonomous agents. Behavioral strategies are described by tree-based structures, and manipulated by genetic evolving processes. Each strategy is dynamically evaluated during simulation, and weighted by an adaptative value. This value is a quality factor that reflects the relevance of a strategy as a good solution for the learning task. It is computed using heterogeneous reinforcement techniques associating immediate and delayed reinforcements as dynamic progress estimators. This work has been tested upon a canonical experimentation framework: the foraging robots problem. Simulations have been conducted and have produced some promising results. %K genetic algorithms, genetic programming %R 10.1007/BFb0055936 %U http://citeseer.ist.psu.edu/cache/papers/cs/13194/http:zSzzSzwww.univ-reunion.frzSz~caldezSzpublicationszSzpaperszSzlncs1391.pdf/calderoni98genetic.pdf %U http://dx.doi.org/10.1007/BFb0055936 %P 163-177 %0 Conference Proceedings %T Genetic Encoding of Agent Behavioral Strategy %A Calderoni, Stephane %A Marcenac, Pierre %A Courdier, Remy %S Proceedings of the 3rd International Conference on Multi Agent Systems %D 1998 %I IEEE Computer Society %@ 0-8186-8500-X %G en %F oai:CiteSeerPSU:185735 %X The general framework tackled in this paper is the automatic generation of intelligent collective behaviors using genetic programming and reinforcement learning. We define a behavior-based system relying on automatic design process using artificial evolution to synthesize high level behaviors for autonomous agents. Behavioral strategies are described by tree-based structures, and manipulated by genetic evolving processes. Each strategy is dynamically evaluated during simulation, and is weighted by an adaptation function as a quality factor that reflects its relevance as good solution for the learning task. It is computed using heterogeneous reinforcement techniques associating immediate reinforcements and delayed reinforcements as dynamic progress estimators. %K genetic algorithms, genetic programming %U http://portal.acm.org/citation.cfm?id=852213&jmp=cit&dl=portal&dl=ACM %P 403 %0 Conference Proceedings %T Behavior-Based Control System in MultiAgent Domain %A Calderoni, Stephane %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 calderoni:1999:BCSMD %K genetic algorithms, genetic programming, artificial life, adaptive behavior and agents, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/AA-048.pdf %P 1439 %0 Conference Proceedings %T Generic Control Ssystem in MultiAgent Domain %A Calderoni, Stephane %S World Multiconference on Systemics, Cybernetics and Informatics SCI-99 %D 1999 %V 7 %G en %F oai:CiteSeerPSU:247844 %X This paper reports on-going works dealing with collective learning in autonomous agents context. We propose a methodology to design robust and flexible adaptive behavior with both genetic and reinforcement learning techniques.The originality of this contribution relies on the ability of the agents to manage themselves their learning task. Indeed, rather than coming from the environment, as it is implemented in many programs, we consider that the reinforcement must be intrinsically deduced by the agent itself, from satisfaction and disapointment indicators. We show that in such a way, the agents are capable of robustness facing with unexpected situations. A collective regulation problem is presented to help in clarify the different issues tackled in this paper. A software toolkit has been developped as a support for these works. %K genetic algorithms, genetic programming, Multiagent Systems, Control Systems, Reinforcement Learning %U http://citeseer.ist.psu.edu/247844.html %0 Conference Proceedings %T Optimising SQL Queries Using Genetic Improvement %A Callan, James %A Petke, Justyna %Y Petke, Justyna %Y Bruce, Bobby R. %Y Huang, Yu %Y Blot, Aymeric %Y Weimer, Westley %Y Langdon, W. B. %S GI @ ICSE 2021 %D 2021 %8 30 may %I IEEE %C internet %F Callan:2021:GI %X Structured Query Language (SQL) queries are ubiquitous in modern software engineering. These queries can be costly when run on large databases with many entries and tables to consider. We propose using Genetic Improvement (GI) to explore patches for these queries, with the aim of optimising their execution time, whilst maintaining the functionality of the program in which they are used. Specifically, we propose three ways in which SQL JOIN statements can be mutated in order to improve performance. We also discuss the requirements of software being improved in this manner and the potential challenges of our approach. %K genetic algorithms, genetic programming, genetic improvement, SQL, query optimisation %R 10.1109/GI52543.2021.00010 %U https://geneticimprovementofsoftware.com/paper_pdfs/gi2021icse/callan_gi-icse_2021.pdf %U http://dx.doi.org/10.1109/GI52543.2021.00010 %P 9-10 %0 Conference Proceedings %T Improving Android App Responsiveness through Search-Based Frame Rate Reduction %A Callan, James %A Petke, Justyna %Y O’Reilly, Una-May %Y Devroey, Xavier %S SSBSE 2021 %S LNCS %D 2021 %8 November 12 oct %V 12914 %I Springer %C Bari %F Callan:2021:SSBSE %X Responsiveness is one of the most important properties of Android applications to both developers and users. Recent survey on automated improvement of non-functional properties of Android applications shows there is a gap in application of search-based techniques to improve responsiveness. Therefore, we explore the use of genetic improvement (GI) to achieve this task. We extend Gin, an open source GI framework, to work with Android applications. Next, we apply GI to four open source Android applications, measuring frame rate as proxy for responsiveness. We find that while there are improvements to be found in UI-implementing code (up to 43percent), often applications test suites are not strong enough to safely perform GI, leading to generation of many invalid patches. We also apply GI to areas of code which have highest test-suite coverage, but find no patches leading to consistent frame rate reductions. This shows that although GI could be successful in improvement of Android apps responsiveness, any such test-based technique is currently hindered by availability of test suites covering UI elements. %K genetic algorithms, genetic programming, genetic improvement, SBSE, Search-based software engineering, Responsiveness, Android, Mobile applications, User Interface, UI, Gin, ANR %R 10.1007/978-3-030-88106-1_10 %U https://conf.researchr.org/details/ssbse-2021/ssbse-2021-rene---replications-and-negative-results/2/Improving-Android-App-Responsiveness-through-Search-Based-Frame-Rate-Reduction %U http://dx.doi.org/10.1007/978-3-030-88106-1_10 %P 136-150 %0 Journal Article %T How Do Android Developers Improve Non-Functional Properties of Software? %A Callan, James %A Krauss, Oliver %A Petke, Justyna %A Sarro, Federica %J Empirical Software Engineering %D 2022 %V 27 %I Springer %@ 1382-3256 %F callan2022 %O Topical Collection:Software Performance %X Nowadays there is an increased pressure on mobile app developers to take non-functional properties into account. An app that is too slow or uses much bandwidth will decrease user satisfaction, and thus can lead to users simply abandoning the app. Although automated software improvement techniques exist for traditional software, these are not as prevalent in the mobile domain. Moreover, it is yet unknown if the same software changes would be as effective. With that in mind, we mined overall 100 Android repositories to find out how developers improve execution time, memory consumption, bandwidth usage and frame rate of mobile apps. We categorised non-functional property (NFP) improving commits related to performance to see how existing automated software improvement techniques can be improved. Our results show that although NFP improving commits related to performance are rare, such improvements appear throughout the development life-cycle. We found altogether 560 NFP commits out of a total of 74408 commits analysed. Memory consumption is sacrificed most often when improving execution time or bandwidth usage, although similar types of changes can improve multiple non-functional properties at once. Code deletion is the most frequently used strategy except for frame rate, where increase in concurrency is the dominant strategy. We find that automated software improvement techniques for mobile domain can benefit from addition of SQL query improvement, caching and asset manipulation. Moreover, we provide a classifier which can drastically reduce manual effort to analyse NFP improving commits. %K genetic algorithms, genetic programming, genetic improvement, Non-Functional property optimisation, Android optimisation, Mining android, Execution time, Bandwidth, Frame rate, Memory, NFP %9 journal article %R 10.1007/s10664-022-10137-2 %U https://discovery.ucl.ac.uk/id/eprint/10145101/ %U http://dx.doi.org/10.1007/s10664-022-10137-2 %P Article113 %0 Conference Proceedings %T Multi-objective Genetic Improvement: A Case Study with EvoSuite %A Callan, James %A Petke, Justyna %Y Papadakis, Mike %Y Vergilio, Silvia Regina %S 14th International Symposium on Search Based Software Engineering SSBSE 2020 %S LNCS %D 2022 %8 17 18 nov %V 13711 %I Springer %C Singapore %F Callan:2022:SSBSE %X Automated multi-objective software optimisation offers an attractive solution to software developers wanting to balance often conflicting objectives, such as memory consumption and execution time. Work on using multi-objective search-based approaches to optimise for such non-functional software behaviour has so far been scarce, with tooling unavailable for use. To fill this gap we extended an existing generalist, open source, genetic improvement tool, Gin, with a multi-objective search strategy, NSGA-II. We ran our implementation on a mature, large software to show its use. In particular, we chose EvoSuite, a tool for automatic test case generation for Java. We use our multi-objective extension of Gin to improve both the execution time and memory usage of EvoSuite. We find improvements to execution time of up to 77.8percent and improvements to memory of up to 9.2percent on our test set. We also release our code, providing the first open source multi-objective genetic improvement tooling for improvement of memory and runtime for Java. %K genetic algorithms, genetic programming, Genetic Improvement, GIN, NSGA2 %R 10.1007/978-3-031-21251-2_8 %U http://dx.doi.org/10.1007/978-3-031-21251-2_8 %P 111-117 %0 Journal Article %T How do Android developers improve non-functional properties of software? %A Callan, James %A Krauss, Oliver %A Petke, Justyna %A Sarro, Federica %J Empirical Software Engineering %D 2022 %V 27 %N 5 %F DBLP:journals/ese/CallanKPS22 %K genetic algorithms, genetic programming, genetic improvement, Non-Functional property optimisation, Android optimisation, Miningandroid, Execution time, Bandwidth, Framerate, Memory %9 journal article %R 10.1007/S10664-022-10137-2 %U https://doi.org/10.1007/s10664-022-10137-2 %U http://dx.doi.org/10.1007/S10664-022-10137-2 %P 113 %0 Generic %T Multi-Objective Improvement of Android Applications %A Callan, James %A Petke, Justyna %D 2023 %8 22 aug %I arXiv %F callan2023multiobjective %O arxiv, 2308.11387 %X .. we use Genetic improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to runtime, memory, and bandwidth use. Our technique automatically re-discovers 64percent of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35percent, and memory use by up to 33percent in these apps. %K genetic algorithms, genetic programming, genetic improvement, multi-objective optimization, Android apps, search-based software engineering, SBSE, mobile computing, GIDroid, Java %U https://arxiv.org/abs/2308.11387 %0 Conference Proceedings %T On Reducing Network Usage with Genetic Improvement %A Callan, James %A Langdon, William B. %A Petke, Justyna %S "13th International Workshop on Genetic Improvement %F callan:2024:GI %0 Journal Article %D 2024 %8 16 apr %I ACM %C Lisbon %F 2024"d %X Mobile applications can be very network-intensive. Mobile phone users are often on limited data plans, while network infrastructure has limited capacity. There is little work on optimising network usage of mobile applications. The most popular approach has been prefetching and caching assets. However, past work has shown that developers can improve the network usage of Android applications by making changes to Java source code. We built upon this insight and investigated the effectiveness of automated, heuristic application of software patches, a technique known as Genetic Improvement (GI), to improve network usage. Genetic improvement has already shown effective at reducing the execution time and memory usage of Android applications. We thus adapt our existing GIdroid framework with a new mutation operator and develop a new profiler to identify network-intensive methods to target. Unfortunately, our approach is unable to find improvements. We conjecture this is due to the fact source code changes affecting network might be rare in the large patch search space. We thus advocate use of more intelligent search strategies in future work. %K genetic algorithms, genetic programming, Genetic Improvement, Java, Source coding, Prefetching, Memory management, Genetics, Search problems, Software, SBSE, HTTP, GIDroid, Robolectric, FDroid, bandwidth %9 journal article %R 10.1145/3643692.3648262 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/callan_2024_GI.pdf %U http://dx.doi.org/10.1145/3643692.3648262 %P 23-30 %0 Thesis %T Improving the Non-Functional Properties of Android Applications with Genetic Improvement %A Callan, James %D 2024 %8 19 mar %C London, UK %C Computer Science, University College, London %F callan:thesis %X There are 3.5 billion Android applications on the Google Play Store. However, surprisingly little work exists on automatically improving the source code of Android apps, especially when compared to traditional software. Genetic Improvement is a technique for automatically improving software which has proven successful in the past. However, its applicability in the Android domain is yet to be explored. In this thesis, we explore how GI can be used to improve Android apps. The first contribution of this thesis is an investigation into applying GI to Android with minimal changes from the standard technique, however, we achieved limited success. Next, we mined git repositories to try to find the changes that real developers make to improve the non-functional properties of applications. With what we learned in this study, we modified and successfully applied GI to improve the responsiveness of Android applications. We then moved on to multi-objective improvement, improving execution time and memory usage, but failing to improve network usage. We also provide a benchmark of tested applications that can be used to evaluate future automated improvement tools. We developed a profiler to find the most network-intensive methods to target and a novel mutation operator. Our final contribution is an adapted version of GI framework for network usage optimisation. We found that Genetic Improvement is an effective tool for improving multiple non-functional properties of Android apps. We found that by using simulation-based testing, rather than testing variants on devices, we could make GI faster and more practical. We found that there were many opportunities for GI to more closely mimic the types of changes made by developers and that caching in particular is an effective change type. We recommend future work further explores this direction. %K genetic algorithms, genetic programming, genetic improvement %9 Ph.D. thesis %U https://discovery.ucl.ac.uk/id/eprint/10189386/ %0 Journal Article %T Multi-objective improvement of Android applications %A Callan, James %A Petke, Justyna %J Automated Software Engineering %D 2025 %V 32 %@ 0928-8910 %F Callan:2025:ASE %O Accepted 04 Nov 2024 %X Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. We propose a practical approach and the first open-source tool, GIDroid for multi-objective automated improvement of Android apps. In particular, we use Genetic Improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic Improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to run-time, memory, and bandwidth use. Our technique automatically re-discovers 64percent of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35percent, and memory use by up to 33percent in these apps. %K genetic algorithms, genetic programming, genetic improvement, Android apps, Multi-objective optimization, Search-based software engineering %9 journal article %R 10.1007/s10515-024-00472-7 %U https://rdcu.be/d0UCc %U http://dx.doi.org/10.1007/s10515-024-00472-7 %P articlenumber2 %0 Conference Proceedings %T Diversity-driven learning for multimodal image retrieval with relevance feedback %A Calumby, R. T. %A da Silva Torres, R. %A Goncalves, M. A. %S IEEE International Conference on Image Processing (ICIP 2014) %D 2014 %8 oct %F Calumby:2014:ICIP %X We introduce a new genetic programming approach for enhancing the user search experience based on relevance feedback over results produced by a multimodal image retrieval technique with explicit diversity promotion. We have studied maximal marginal relevance re-ranking methods for result diversification and their impacts on the overall retrieval effectiveness. We show that the learning process using diverse results may improve user experience in terms of both the number of relevant items retrieved and subtopic coverage. %K genetic algorithms, genetic programming %R 10.1109/ICIP.2014.7025445 %U http://dx.doi.org/10.1109/ICIP.2014.7025445 %P 2197-2201 %0 Journal Article %T Multimodal retrieval with relevance feedback based on genetic programming %A Calumby, Rodrigo Tripodi %A da Silva Torres, Ricardo %A Goncalves, Marcos Andre %J Multimedia Tools Appl %D 2014 %V 69 %N 3 %F journals/mta/CalumbyTG14 %K genetic algorithms, genetic programming %9 journal article %U http://dx.doi.org/10.1007/s11042-012-1152-7 %P 991-1019 %0 Conference Proceedings %T Multi-objective semantic mutation for genetic programming %A Calvo Fracasso, Joao V. %A Von Zuben, Fernando J. %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 Calvo-Fracasso:2018:CEC %X Genetic Programming is a branch of Evolutionary Computation devoted to the evolution of programs. Several genetic operators have been proposed to increase the power of the search, given that the space of admissible programs is very challenging to be properly explored toward high quality solutions. Semantically-driven genetic operators are gaining more attention lately, given that the behaviour of the search operators are more predictable, possibly leading to a more efficient evolution. Nonetheless, Semantic Genetic Programming may undergo the bloat phenomenon, characterized by an uncontrolled increase in the program size along the generations. Some attempts have been made in the literature to refrain code bloat, and here we are proposing three novel semantic-driven mutation operators for the tree structure codification. A multi-objective perspective is adopted, where the mutated subtrees correspond to nondominated instances in a previously defined library of candidate subtrees. Several conflicting objectives may be incorporated into the decision making process, such as accuracy, semantic distance to a reference behaviour, and size of the subtree. Experimental results reveal that our proposed operators are effective in restraining bloating, without a negative impact on the other performance metrics, and are competitive with other relevant approaches available in the literature. %K genetic algorithms, genetic programming %R 10.1109/CEC.2018.8477675 %U http://dx.doi.org/10.1109/CEC.2018.8477675 %0 Book Section %T Comparing Grammatical Evolution’s Mapping Processes on Feature Generation for Pattern Recognition Problems %A Calzada-Ledesma, Valentin %A Soberanes, Hector Jose Puga %A Dominguez, Alfonso Rojas %A Ornelas-Rodriguez, Manuel %A Valadez, Juan Martin Carpio %A Santillan, Claudia Guadalupe Gomez %E Melin, Patricia %E Castillo, Oscar %E Kacprzyk, Janusz %B Nature-Inspired Design of Hybrid Intelligent Systems %S Studies in Computational Intelligence %D 2017 %V 667 %I Springer %F series/sci/Calzada-LedesmaSDOVS17 %X Grammatical Evolution (GE) is a grammar-based form of Genetic Programming. In GE, a Mapping Process (MP) and a Backus-Naur Form grammar (defined in the problem context) are used to transform each individual’s genotype into its phenotype form (functional representation). There are several MPs proposed in the state-of-the-art, each of them defines how the individuals genes are used to build its phenotype form. This paper compares two MPs: the Depth-First standard map and the Position Independent Grammatical Evolution (pi GE). The comparison was performed using as use case the problem of the selection and generation of features for pattern recognition problems. A Wilcoxon Rank-Sum test was used to compare and validate the results of the different approaches. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1007/978-3-319-47054-2_52 %U http://dx.doi.org/10.1007/978-3-319-47054-2_52 %P 775-785 %0 Journal Article %T Evolutionary Design of Problem-Adapted Image Descriptors for Texture Classification %A Calzada-Ledesma, Valentin %A Puga-Soberanes, Hector J. %A Ornelas-Rodriguez, Manuel %A Rojas-Dominguez, Alfonso %A Carpio-Valadez, Juan Martin %A Espinal, Andris %A Soria-Alcaraz, Jorge A. %A Sotelo-Figueroa, Marco A. %J IEEE Access %D 2018 %V 6 %@ 2169-3536 %F Calzada-Ledesma:2018:IEEEAccess %X Effective texture classification requires image descriptors capable of efficiently detecting, extracting, and describing the most relevant information in the images, so that, for instance, different texture classes can be distinguished despite image distortions such as varying illuminations, viewpoints, scales, and rotations. Designing such an image descriptor is a challenging task that typically involves the intervention of human experts. In this paper, a general method to automatically design effective image descriptors is proposed. Our method is based on grammatical evolution and, using a set of example images from a texture classification problem and a classification algorithm as inputs, generates problem-adapted image descriptors that achieve very competitive classification results. Our method is tested on five well-known texture data sets with different number of classes and image distortions to prove its effectiveness and robustness. Our classification results are statistically compared against those obtained by means of six popular hand-crafted texture descriptors in the state of the art. This statistical analysis shows that our evolutionarily designed descriptors outperform most of those designed by human experts. %K genetic algorithms, genetic programming, grammatical evolution %9 journal article %R 10.1109/ACCESS.2018.2858660 %U http://dx.doi.org/10.1109/ACCESS.2018.2858660 %P 40450-40462 %0 Conference Proceedings %T Intrinsic evolvable hardware for combinatorial synthesis based on SoC+FPGA and GPU platforms %A Camargo Bareno, Carlos Ivan %A Pedraza Bonilla, Cesar Augusto %A Nino, Luis Fernado %A Martinez Torre, Jose Ignacio %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 Camargo-Bareno:2011:GECCOcomp %X This paper presents a novel a parallel genetic programming (PGP) Boolean synthesis implementation on a low cost cluster of an embedded open platform called SIE. Some tasks of the PGP have been accelerated through a hardware coprocessor called FCU, that allows to evaluate individuals onchip as intrinsic evolution. Results have been compared with GPU and HPC implementations, resulting in speedup values up to approximately 2 and 180 respectively. %K genetic algorithms, genetic programming, GPU: Poster %R 10.1145/2001858.2001964 %U http://dx.doi.org/10.1145/2001858.2001964 %P 189-190 %0 Thesis %T Mining Software Artifacts for use in Automated Machine Learning %A Cambronero Sanchez, Jose Pablo %D 2021 %8 may 13 2021 %C USA %C Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology %F Cambronero:thesis %X Successfully implementing classical supervised machine learning pipelines requires that users have software engineering, machine learning, and domain experience. Machine learning libraries have helped along the first two dimensions by providing modular implementations of popular algorithms. However, implementing a pipeline remains an iterative, tedious, and data-dependent task as users have to experiment with different pipeline designs. To make the pipeline development process accessible to non-experts and more efficient for experts, automated techniques can be used to efficiently search for high performing pipelines with little user intervention. The collection of techniques and systems that automate this task are commonly termed automated machine learning (AutoML). Inspired by the success of software mining in areas such as code search, program synthesis, and program repair, we investigate the hypothesis that information mined from software artifacts can be used to build, improve interactions with, and address missing use cases of AutoML. In particular, I will present three systems – AL, AMS, and Janus – that make use of software artifacts. AL mines dynamic execution traces of a collection of programs that implement machine learning pipelines and uses these mined traces to learn to produce new pipelines. AMS mines documentation and program examples to automatically generate a search space for an AutoML tool by starting from a user-chosen set of API components. And Janus mines pipeline transformations from a collection of machine learning pipelines, which can be used to improve an input pipeline while producing a nearby variant. Jointly, these systems and their experimental results show that mining software artifacts can simplify AutoML systems, make their customization easier, and apply them to novel use cases. %K genetic algorithms, genetic programming, SBSE, TPOT %9 Ph.D. thesis %U https://dspace.mit.edu/handle/1721.1/139465 %0 Book Section %T Evaluation of Genetic Programming for Determining Reservoir Operating Rules %A Campbell, Elliott %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 campbell:2000:EGPDROR %K genetic algorithms, genetic programming %P 54-59 %0 Journal Article %T Reviews %A Campbell, Paul J. %J Mathematics Magazine %D 1993 %V 66 %N 2 %@ 0025570x %F campbell:1993:MM %K genetic algorithms, genetic programming %9 journal article %U http://links.jstor.org/sici?sici=0025-570X%28199304%2966%3A2%3C136%3AR%3E2.0.CO%3B2-4 %P 136-137 %0 Journal Article %T Neuro-genetic programming for multigenre classification of music content %A Campobello, Giuseppe %A Dell’Aquila, Daniele %A Russo, Marco %A Segreto, Antonino %J Applied Soft Computing %D 2020 %8 sep %V 94 %@ 1568-4946 %F CAMPOBELLO2020106488 %X A machine learning approach based on hybridization of genetic programming and neural networks is used to derive mathematical models for music genre classification. We design three multi-label classifiers with different trade-offs between complexity and accuracy, which are able to identify the degree of belonging of music content to ten different music genres. Our approach is innovative as it entirely relies on simple analytical functions and a reduced number of features. Resulting classifiers have an extremely low computational complexity and are suitable to be easily integrated in low-cost embedded systems for real-time applications. The GTZAN dataset is used for model training and to evaluate the accuracy of the proposed classifiers. Despite of the reduced number of features used in our approach, the accuracy of our models is found to be similar to that of more complex music genre classification tools previously published in the literature. %K genetic algorithms, genetic programming, Artificial neural networks, ANN, Music genre recognition, Multi-label classifiers, Fuzzy classification %9 journal article %R 10.1016/j.asoc.2020.106488 %U http://www.sciencedirect.com/science/article/pii/S1568494620304270 %U http://dx.doi.org/10.1016/j.asoc.2020.106488 %P 106488 %0 Journal Article %T Generating networks of genetic processors %A Campos, Marcelino %A Sempere, Jose M. %J Genetic Programming and Evolvable Machines %D 2022 %8 mar %V 23 %N 1 %@ 1389-2576 %F Campos:GPEM %X The Networks of Genetic Processors (NGPs) are non-conventional models of computation based on genetic operations over strings, namely mutation and crossover operations as it was established in genetic algorithms. Initially, they have been proposed as acceptor machines which are decision problem solvers. In that case, it has been shown that they are universal computing models equivalent to Turing machines. we propose NGPs as enumeration devices and we analyze their computational power. First, we define the model and we propose its definition as parallel genetic algorithms. Once the correspondence between the two formalisms has been established, we carry out a study of the generation capacity of the NGPs under the research framework of the theory of formal languages. We investigate the relationships between the number of processors of the model and its generative power. Our results show that the number of processors is important to increase the generative capability of the model up to an upper bound, and that NGPs are universal models of computation if they are formulated as generation devices. This allows us to affirm that parallel genetic algorithms working under certain restrictions can be considered equivalent to Turing machines and, therefore, they are universal models of computation. %K genetic algorithms, genetic programming, Natural computing, Networks of bio-inspired processors, Parallel genetic algorithms, Formal languages, Descriptive complexity %9 journal article %R 10.1007/s10710-021-09423-7 %U https://rdcu.be/czY20 %U http://dx.doi.org/10.1007/s10710-021-09423-7 %P 133-155 %0 Conference Proceedings %T Origami: (un)folding the Abstraction of Recursion Schemes for Program Synthesis %A Campos Fernandes, Matheus %A de Franca, Fabricio Olivetti %A Francesquini, Emilio %Y Winkler, Stephan %Y Trujillo, Leonardo %Y Ofria, Charles %Y Hu, Ting %S Genetic Programming Theory and Practice XX %S Genetic and Evolutionary Computation %D 2023 %8 jun 1 3 %I Springer %C Michigan State University, USA %F deFranca:2023:GPTP %X Program synthesis with Genetic Programming searches for a correct program that satisfies the input specification, which is usually provided as input-output examples. One particular challenge is how to effectively handle loops and recursion avoiding programs that never terminate. A helpful abstraction that can alleviate this problem is the employment of Recursion Schemes that generalize the combination of data production and consumption. Recursion Schemes are very powerful as they allow the construction of programs that can summarize data, create sequences, and perform advanced calculations. The main advantage of writing a program using Recursion Schemes is that the programs are composed of well-defined templates with only a few parts that need to be synthesized. In this paper, we make an initial study of the benefits of using program synthesis with fold and unfold templates and outline some preliminary experimental results. To highlight the advantages and disadvantages of this approach, we manually solved the entire GPSB benchmark using recursion schemes, highlighting the parts that should be evolved compared to alternative implementations. We noticed that, once the choice of which recursion scheme is made, the synthesis process can be simplified as each of the missing parts of the template are reduced to simpler functions, which are further constrained by their own input and output types. %K genetic algorithms, genetic programming %R 10.1007/978-981-99-8413-8_14 %U http://dx.doi.org/10.1007/978-981-99-8413-8_14 %P 263-281 %0 Conference Proceedings %T HOTGP - Higher-Order Typed Genetic Programming %A Campos Fernandes, Matheus %A Olivetti De Franca, Fabricio %A Francesquini, Emilio %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F fernandes:2023:GECCO %X Program synthesis is the process of generating a computer program following a set of specifications, which can be a high-level description of the problem and/or a set of input-output examples. The synthesis can be modeled as a search problem in which the search space is the set of all the programs valid under a grammar. As the search space is vast, brute force is usually not viable and search heuristics, such as genetic programming, also have difficulty navigating it without any guidance. In this paper we present HOTGP, a new genetic programming algorithm that synthesizes pure, typed, and functional programs. HOTGP leverages the knowledge provided by the rich data-types associated with the specification and the built-in grammar to constrain the search space and improve the performance of the synthesis. The grammar is based on Haskell’s standard base library (the synthesized code can be directly compiled using any standard Haskell compiler) and includes support for higher-order functions, Λ-functions, and parametric polymorphism. Experimental results show that, when compared to 6 state-of-the-art algorithms using a standard set of benchmarks, HOTGP is competitive and capable of synthesizing the correct programs more frequently than any other of the evaluated algorithms. %K genetic algorithms, genetic programming, functional programming, inductive program synthesis %R 10.1145/3583131.3590464 %U http://dx.doi.org/10.1145/3583131.3590464 %P 1091-1099 %0 Conference Proceedings %T Going Bananas! - Unfolding Program Synthesis with Origami %A Campos Fernandes, Matheus %A Olivetti de Franca, Fabricio %A Francesquini, Emilio %Y Paes, Aline %Y Verri, Filipe A. N. %S Brazilian Conference on Intelligent Systems %S LNAI %D 2025 %V 15413 %I Springer %F Campos:BRACIS %X Automatically creating a computer program using input-output examples can be a challenging task, especially when trying to synthesize computer programs that require loops or recursion. Even though the use of recursion can make the algorithmic description more succinct and declarative, this concept creates additional barriers to program synthesis algorithms such as the creation and the attempt to evaluate non-terminating programs. One reason is that the recursive function must define how to traverse (or generate) the data structure and, at the same time, how to process it. In functional programming, the concept of Recursion Schemes decouples these two tasks by putting a major focus on the data processing. This can also help to avoid some of the pitfalls of recursive functions during program synthesis, as argued in the paper that introduced the Origami technique. The authors showed how this technique can be effective in finding solutions for programs that require folding a list. In this work, we incorporate other Recursion Schemes into Origami, such as accumulated folding, unfolding, and the combination of unfolding and folding. We evaluated it on the 29 problems of the standard General Program Synthesis Benchmark Suite 1, obtaining favorable results against other well-known algorithms. Overall, Origami achieves the best results in 25 percent more problems than its predecessor (HOTGP) and an even higher increase when compared to other approaches. Not only that, but it can also consistently find a solution to problems for which other concurrent algorithms report a low success rate. %K genetic algorithms, genetic programming, Program Synthesis, Recursion Schemes %R 10.1007/978-3-031-79032-4_1 %U https://arxiv.org/abs/2406.01500 %U http://dx.doi.org/10.1007/978-3-031-79032-4_1 %P 3-18 %0 Conference Proceedings %T Sequential metamodelling with genetic programming and particle swarms %A Can, Birkan %A Heavey, Cathal %S Proceedings of the 2009 Winter Simulation Conference (WSC) %D 2009 %8 13 16 dec %F Can:2010:WSC %X This article presents an application of two main component methodologies of evolutionary algorithms in simulation-based metamodelling. We present an evolutionary framework for constructing analytical metamodels and apply it to simulations of manufacturing lines with buffer allocation problem. In this framework, a particle swarm algorithm is integrated to genetic programming to perform symbolic regression of the problem. The sampling data is sequentially generated by the particle swarm algorithm, while genetic programming evolves symbolic functions of the domain. The results are promising in terms of efficiency in design of experiments and accuracy in global metamodelling. %K genetic algorithms, genetic programming, PSO, buffer allocation, design of experiment, discrete event simulation, evolutionary algorithm, global metamodelling, manufacturing lines, particle swarm algorithm, sampling data, sequential metamodelling, simulation-based metamodelling, symbolic function, symbolic regression, design of experiments, discrete event simulation, manufacturing systems, particle swarm optimisation, regression analysis, sampling methods %R 10.1109/WSC.2009.5429276 %U http://dx.doi.org/10.1109/WSC.2009.5429276 %P 3150-3157 %0 Thesis %T Evolutionary Modelling of Industrial Systems with Genetic Programming %A Can, Birkan %D 2011 %8 oct %C Ireland %C University of Limerick %F Can:thesis %X Knowledge, experience, and intuition are integral parts of decision making. However, these alone are not sufficient to manage today’s industrial systems. Often predictive models are required to weigh options and determine potential changes which provide the best outcome for a system. In this respect, the dissertation develops approximate models, metamodels, of industrial systems to facilitate a means to quantify system performance when the trade-off between approximation error and efficiency (time and effort spent on model development, validation, maintenance and execution) is appropriate. Discrete-event simulation (DES) is widely used to assist decision makers in the management of systems. DES facilitates analysis with high fidelity models as a consequence of its flexibility. However, this descriptiveness introduces an overhead to model building and maintenance. Furthermore, due to stochastic elements and the size of the systems modelled, model execution times can be computationally demanding. Hence, its use in operational tasks such as design, sensitivity analysis and optimisation can be significantly undermined when efficiency is a concern. In this thesis, these shortcomings are addressed through research into the use of genetic programming for metamodelling. Genetic programming is a branch of evolutionary algorithms which emulate the natural evolution of species. It can evolve programs of a domain via symbolic regression. These programs can be interpreted as logic instructions, analytical functions etc. Furthermore, genetic programming develops the models without prior assumptions about the underlying function of the training data. This can provide significant advantage for modelling of complex systems with non-linear and multimodal response characteristics. Exploiting these properties, the dissertation presents research towards developing metamodels of manufacturing systems (or their DES models) via genetic programming in the context of symbolic regression. In particular, it contributes to; (i) exploration of an appropriate experimental design method suitable to use with genetic programming, (ii) to a comparison of the performance of genetic programming with neural networks, using three different stochastic industrial problems to identify its robustness; (iii) research into an improved genetic programming and dynamic flow time estimation. %K genetic algorithms, genetic programming %9 Doctorate of Philosophy in Engineering %9 Ph.D. thesis %U http://hdl.handle.net/10344/1693 %0 Journal Article %T Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems %A Can, Birkan %A Heavey, Cathal %J Computer & Industrial Engineering %D 2011 %8 oct %V 61 %N 3 %@ 0360-8352 %F Can2011 %X In this article, an empirical analysis of experimental design approaches in simulation-based metamodelling of manufacturing systems with genetic programming (GP) is presented. An advantage of using GP is that prior assumptions on the structure of the metamodels are not required. On the other hand, having an unknown structure necessitates an analysis of the experimental design techniques used to sample the problem domain and capture its characteristics. Therefore, the study presents an empirical analysis of experimental design methods while developing GP metamodels to predict throughput rates in a common industrial system, serial production lines. The objective is to identify a robust sampling approach suitable for GP in simulation-based meta-modelling. Experiments on different sizes of production lines are presented to demonstrate the effects of the experimental designs on the complexity and quality of approximations as well as their variance. The analysis showed that GP delivered system-wide meta-models with good predictive characteristics even with the limited sample data. %K genetic algorithms, genetic programming, Meta-modelling, Design of experiments, Discrete-event simulation, Decision support %9 journal article %R 10.1016/j.cie.2011.03.012 %U http://dx.doi.org/10.1016/j.cie.2011.03.012 %P 447-462 %0 Journal Article %T A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models %A Can, Birkan %A Heavey, Cathal %J Computer & Operations Research %D 2012 %8 feb %V 39 %N 2 %@ 0305-0548 %F Can2012424 %X Genetic programming (GP) and artificial neural networks (ANNs) can be used in the development of surrogate models of complex systems. The purpose of this paper is to provide a comparative analysis of GP and ANNs for metamodelling of discrete-event simulation (DES) models. Three stochastic industrial systems are empirically studied: an automated material handling system (AMHS) in semiconductor manufacturing, an (s,S) inventory model and a serial production line. The results of the study show that GP provides greater accuracy in validation tests, demonstrating a better generalisation capability than ANN. However, GP when compared to ANN requires more computation in metamodel development. Even given this increased computational requirement, the results presented indicate that GP is very competitive in metamodelling of DES models. %K genetic algorithms, genetic programming, Simulation metamodel, Symbolic regression, ANN, Neural networks, Design of experiments, Decision support tool %9 journal article %R 10.1016/j.cor.2011.05.004 %U http://www.sciencedirect.com/science/article/pii/S0305054811001286 %U http://dx.doi.org/10.1016/j.cor.2011.05.004 %P 424-436 %0 Conference Proceedings %T A demonstration of machine learning for explicit functions for cycle time prediction using MES data %A Can, Birkan %A Heavey, Cathal %S 2016 Winter Simulation Conference (WSC) %D 2016 %8 dec %F Can:2016:WSC %X Cycle time prediction represents a challenging problem in complex manufacturing scenarios. This paper demonstrates an approach that uses genetic programming (GP) and effective process time (EPT) to predict cycle time using a discrete event simulation model of a production line, an approach that could be used in complex manufacturing systems, such as a semiconductor fab. These predictive models could be used to support control and planning of manufacturing systems. GP results in a more explicit function for cycle time prediction. The results of the proposed approach show a difference between 1-6percent on the demonstrated production line. %K genetic algorithms, genetic programming %R 10.1109/WSC.2016.7822289 %U http://dx.doi.org/10.1109/WSC.2016.7822289 %P 2500-2511 %0 Journal Article %T Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming %A Canakci, Hanifi %A Baykasoglu, Adil %A Gullu, Hamza %J Neural Computing and Applications %D 2009 %V 18 %N 8 %F journals/nca/CanakciBG09 %X In this paper, two soft computing approaches, which are known as artificial neural networks and Gene Expression Programming (GEP) are used in strength prediction of basalts which are collected from Gaziantep region in Turkey. The collected basalts samples are tested in the geotechnical engineering laboratory of the University of Gaziantep. The parameters, ultrasound pulse velocity, water absorption, dry density, saturated density, and bulk density which are experimentally determined based on the procedures given in ISRM (Rock characterisation testing and monitoring. Pergamon Press, Oxford, 1981) are used to predict uniaxial compressive strength and tensile strength of Gaziantep basalts. It is found out that neural networks are quite effective in comparison to GEP and classical regression analyses in predicting the strength of the basalts. The results obtained are also useful in characterizing the Gaziantep basalts for practical applications. %K genetic algorithms, genetic programming, gene expression programming, Artificial neural networks, Basalt, Tensile and compressive strength %9 journal article %R 10.1007/s00521-008-0208-0 %U http://dx.doi.org/10.1007/s00521-008-0208-0 %P 1031-1041 %0 Conference Proceedings %T Heterochrony and Adaptation in Developing Neural Networks %A Cangelosi, Angelo %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 cangelosi:1999:HADNN %K artificial life, adaptive behavior and agents %U http://gpbib.cs.ucl.ac.uk/gecco1999/AA-008.pdf %P 1241-1248 %0 Journal Article %T A Hardware Artificial Immune System and Embryonic Array for Fault Tolerant Systems %A Canham, Richard O. %A Tyrrell, Andy M. %J Genetic Programming and Evolvable Machines %D 2003 %8 dec %V 4 %N 4 %@ 1389-2576 %F canham:2003:GPEM %X Nature demonstrates amazing levels of fault tolerance; animals can survive injury, damage, wear and tear, and are under continual attack from infectious pathogens. This paper details inspiration from biology to provide fault tolerant electronic circuits. An artificial immune system (AIS) is used to detect faults and an embryonic array to quickly reconfigure around them. The AIS makes use of a negative selection algorithm to detect abnormal behaviour. The embryonic array takes its inspiration from the development of multi-cellular organisms; each cell contains all the information necessary to describe the complete individual. Should an electronic cell fail, its neighbours have the configuration data to take over the failed cell’s functionality. Two demonstration robot control systems have been implemented to provide a Khepera robot with fault tolerance. The first is very simple and is implemented on an embryonic array within a Virtex FPGA. An AIS is also implemented within the array which learns normal behaviour. Injected stuck-at faults were detected and accommodated. The second system uses fuzzy rules (implemented in software) to provide a more graceful functionality. A small AIS has been implemented to provide fault detection; it detected all faults that produced an error greater than 15% (or 23% off straight). %K artificial immune systems, embryonic array, fault tolerance %9 journal article %R 10.1023/A:1026143128448 %U http://dx.doi.org/10.1023/A:1026143128448 %P 359-382 %0 Conference Proceedings %T Detect Wi-Fi Network Attacks Using Parallel Genetic Programming %A Canh Vu, Van %A Hoang, Tuan-Hao %S 2018 10th International Conference on Knowledge and Systems Engineering (KSE) %D 2018 %8 nov %F CanhVu:2018:KSE %X Wi-Fi network have been widely used nowadays. However, Intrusion Detection System (IDS) researches on Wi-Fi network were few and difficult since there was no common dataset between researchers on this area. Recently, Kolias et al. [2] published a comprehensive Wi-Fi network dataset extracting from real Wi-Fi traces, which is called the AWID dataset. Gene programming has proven effective in detecting network attacks, but the processing time is quite slow. Today, the development of GPU technology for high-speed parallel processing, the study of parallel programming solutions is essential. In this paper, we examined the Parallel Genetic Programming (Karoo GP) [13] in wireless attack detection to improve detection rates and processing time. The experiments showed that the processing time of Karoo GP was significantly improved compared to standard GP. %K genetic algorithms, genetic programming %R 10.1109/KSE.2018.8573378 %U http://dx.doi.org/10.1109/KSE.2018.8573378 %P 370-375 %0 Report %T Towards Automated Malware Creation: Code Generation and Code Integration %A Cani, A. %A Gaudesi, M. %A Sanchez, E. %A Squillero, G. %A Tonda, A. %D 2013 %8 dec 3 %I Electronic CAD and Reliability Group, Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino %C Corso Duca degli Abruzzi, 24, 10129 Turin, Italy %F Cani:2014:SACtr %X The analogies between computer malware and biological viruses are more than obvious. The very idea of an artificial ecosystem where malicious software can evolve and autonomously find new, more effective ways of attacking legitimate programs and damaging sensitive information is both terrifying and fascinating. The paper proposes two different ways for exploiting an evolutionary algorithm to devise malware: the former targeting heuristic-based anti-virus scanner; the latter optimising a Trojan attack. Testing the stability of a system against attacks, or checking the reliability of the heuristic scan of anti-virus software could be interesting for the research community and advantageous to the IT industry. Experimental results shows the feasibility of the proposed approaches on simple real-world test cases. %K genetic algorithms, genetic programming, MicroGP %U http://www.cad.polito.it/downloads/White_papers/Towards%20Automated%20Malware%20Creation%20-%20Code%20Generation%20&%20Code%20Integration.pdf %0 Report %T Towards Automated Malware Creation: Code Generation and Code Integration %A Cani, A. %A Gaudesi, M. %A Sanchez, E. %A Squillero, G. %A Tonda, A. %D 2014 %8 25 jan %I Electronic CAD and Reliability Group, Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino %C Corso Duca degli Abruzzi, 24, 10129 Turin, Italy %F Cani:2014:SACtr2 %X The analogies between computer malware and biological viruses are more than obvious. The very idea of an artificial ecosystem where malicious software can evolve and autonomously find new, more effective ways of attacking legitimate programs and damaging sensitive information is both terrifying and fascinating. The paper proposes two different ways for exploiting an evolutionary algorithm to devise malware: the former targeting heuristic-based anti-virus scanner; the latter optimizing a Trojan attack. Testing the stability of a system against attacks, or checking the reliability of the heuristic scan of anti-virus software could be interesting for the research community and advantageous to the IT industry. Experimental results shows the feasibility of the proposed approaches on simple real-world test cases. A short paper on the same subject appeared at the 29th Symposium On Applied Computing (SAC’14). %K genetic algorithms, genetic programming, MicroGP %9 Internal Report %U http://www.cad.polito.it/2014/Cani_2014_SACtr2.pdf %0 Conference Proceedings %T Towards automated malware creation: code generation and code integration %A Cani, Andrea %A Gaudesi, Marco %A Sanchez, Ernesto %A Squillero, Giovanni %A Tonda, Alberto %S SAC ’14: Proceedings of the 29th Annual ACM Symposium on Applied Computing %D 2014 %8 24 28 mar %C Gyeongju, Korea %F Cani:2014:SAC %X This short paper proposes two different ways for exploiting an evolutionary algorithm to devise malware: the former targeting heuristic-based anti-virus scanner; the latter optimising a Trojan attack. An extended internal technical report on the same the subject can be downloaded from http://www.cad.polito.it/downloads/ \citeCani:2014:SACtr2 %K genetic algorithms, genetic programming, Evolutionary Computation, Malware, virus, evolutionary algorithms, security, artificial Intelligence, Software, Automatic Programming %R 10.1145/2554850.2555157 %U https://doi.org/10.1145/2554850.2555157 %U http://dx.doi.org/10.1145/2554850.2555157 %P 157-160 %0 Journal Article %T CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology %A Cankorur-Cetinkaya, Ayca %A Dias, Joao M. L. %A Kludas, Jana %A Slater, Nigel K. H. %A Rousu, Juho %A Oliver, Stephen G. %A Dikicioglu, Duygu %J Microbiology %D 2017 %8 January %V 163 %F Cankorur-Cetinkaya:2017:MB %X Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available online %K genetic algorithms, genetic programming, Pichia pastoris, experimental design tool, recombinant protein production, evolutionary algorithms, symbolic regression %9 journal article %R 10.1099/mic.0.000477 %U http://dx.doi.org/10.1099/mic.0.000477 %P 829-839 %0 Conference Proceedings %T Solving Classification Problems Using Genetic Programming Algorithms on GPUs %A Cano, Alberto %A Zafra, Amelia %A Ventura, Sebastian %Y Corchado, Emilio %Y Grana Romay, Manuel %Y Manhaes Savio, Alexandre %S Hybrid Artificial Intelligence Systems %S Lecture Notes in Computer Science %D 2010 %8 jun 23 25 %V 6077 %I Springer %C San Sebastian, Spain %F Cano:2010:HAIS %X Genetic Programming is very efficient in problem solving compared to other proposals but its performance is very slow when the size of the data increases. This paper proposes a model for multi-threaded Genetic Programming classification evaluation using a NVIDIA CUDA GPUs programming model to parallelise the evaluation phase and reduce computational time. Three different well-known Genetic Programming classification algorithms are evaluated using the parallel evaluation model proposed. Experimental results using UCI Machine Learning data sets compare the performance of the three classification algorithms in single and multithreaded Java, C and CUDA GPU code. Results show that our proposal is much more efficient. %K genetic algorithms, genetic programming, gpu, gpgpu, gpgpgpu %R 10.1007/978-3-642-13803-4_3 %U http://dx.doi.org/10.1007/978-3-642-13803-4_3 %P 17-26 %0 Journal Article %T Speeding up the evaluation phase of GP classification algorithms on GPUs %A Cano, Alberto %A Zafra, Amelia %A Ventura, Sebastian %J Soft Computing - A Fusion of Foundations, Methodologies and Applications %D 2012 %8 feb %V 16 %N 2 %I Springer Berlin / Heidelberg %@ 1432-7643 %F Cano:2011:SC %X The efficiency of evolutionary algorithms has become a studied problem since it is one of the major weaknesses in these algorithms. Specifically, when these algorithms are employed for the classification task, the computational time required by them grows excessively as the problem complexity increases. This paper proposes an efficient scalable and massively parallel evaluation model using the NVIDIA CUDA GPU programming model to speed up the fitness calculation phase and greatly reduce the computational time. Experimental results show that our model significantly reduces the computational time compared to the sequential approach, reaching a speedup of up to 820 times. Moreover, the model is able to scale to multiple GPU devices and can be easily extended to any evolutionary algorithm. %K genetic algorithms, genetic programming, GPU, Computer Science %9 journal article %R 10.1007/s00500-011-0713-4 %U http://dx.doi.org/10.1007/s00500-011-0713-4 %P 187-202 %0 Conference Proceedings %T A Parallel Genetic Programming Algorithm for Classification %A Cano, Alberto %A Zafra, Amelia %A Ventura, Sebastian %Y Corchado, Emilio %Y Kurzynski, Marek %Y Wozniak, Michal %S Proceedings of the 6th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2011) Part I %S Lecture Notes in Computer Science %D 2011 %8 may 23 25 %V 6678 %I Springer %C Wroclaw, Poland %F conf/hais/CanoZV11 %X In this paper a Grammar Guided Genetic Programming-based method for the learning of rule-based classification systems is proposed. The method learns disjunctive normal form rules generated by means of a context-free grammar. The individual constitutes a rule based decision list that represents the full classifier. To overcome the problem of computational time of this system, it parallelises the evaluation phase reducing significantly the computation time. Moreover, different operator genetics are designed to maintain the diversity of the population and get a compact set of rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability. %K genetic algorithms, genetic programming, KEEL, JCLEC %R 10.1007/978-3-642-21219-2_23 %U http://dx.doi.org/10.1007/978-3-642-21219-2_23 %P 172-181 %0 Conference Proceedings %T A Grammar-Guided Genetic Programming Algorithm for Multi-Label Classification %A Cano, Alberto %A Zafra, Amelia %A Gibaja, Eva L. %A Ventura, Sebastian %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 cano:2013:EuroGP %X Multi-label classification is a challenging problem which demands new knowledge discovery methods. This paper presents a Grammar-Guided Genetic Programming algorithm for solving multi-label classification problems using IF-THEN classification rules. This algorithm, called G3P-ML, is evaluated and compared to other multi-label classification techniques in different application domains. Computational experiments show that G3P-ML often obtains better results than other algorithms while achieving a lower number of rules than the other methods. %K genetic algorithms, genetic programming, grammar-guided genetic programming, Multi-label classification, rule learning %R 10.1007/978-3-642-37207-0_19 %U http://dx.doi.org/10.1007/978-3-642-37207-0_19 %P 217-228 %0 Journal Article %T Parallel multi-objective Ant Programming for classification using GPUs %A Cano, Alberto %A Olmo, Juan Luis %A Ventura, Sebastian %J Journal of Parallel and Distributed Computing %D 2013 %8 jun %V 73 %N 6 %@ 0743-7315 %F Cano:2013:JPDC %X Classification using Ant Programming is a challenging data mining task which demands a great deal of computational resources when handling data sets of high dimensionality. This paper presents a new parallelisation approach of an existing multi-objective Ant Programming model for classification, using GPUs and the nVidia CUDA programming model. The computational costs of the different steps of the algorithm are evaluated and it is discussed how best to parallelise them. The features of both the CPU parallel and GPU versions of the algorithm are presented. An experimental study is carried out to evaluate the performance and efficiency of the interpreter of the rules, and reports the execution times and speedups regarding variable population size, complexity of the rules mined and dimensionality of the data sets. Experiments measure the original single-threaded and the new multi-threaded CPU and GPU times with different number of GPU devices. The results are reported in terms of the number of Giga GP operations per second of the interpreter (up to 10 billion GPops/s) and the speedup achieved (up to 834 times vs CPU, 212 times vs 4-threaded CPU). The proposed GPU model is demonstrated to scale efficiently to larger datasets and to multiple GPU devices, which allows the expansion of its applicability to significantly more complicated data sets, previously unmanageable by the original algorithm in reasonable time. %K genetic algorithms, genetic programming, GPU, Reverse Polish RPN, grammar based, Ant programming (AP), Ant colony optimisation (ACO), Parallel computing, Classification %9 journal article %R 10.1016/j.jpdc.2013.01.017 %U http://dx.doi.org/10.1016/j.jpdc.2013.01.017 %P 713-728 %0 Journal Article %T An Interpretable Classification Rule Mining Algorithm %A Cano, Alberto %A Zafra, Amelia %A Ventura, Sebastian %J Information Sciences %D 2013 %V 240 %@ 0020-0255 %F Cano:2013:INS %X Obtaining comprehensible classifiers may be as important as achieving high accuracy in many real-life applications such as knowledge discovery tools and decision support systems. This paper introduces an efficient Evolutionary Programming algorithm for solving classification problems by means of very interpretable and comprehensible IF-THEN classification rules. This algorithm, called the Interpretable Classification Rule Mining (ICRM) algorithm, is designed to Maximo the comprehensibility of the classifier by minims the number of rules and the number of conditions. The evolutionary process is conducted to construct classification rules using only relevant attributes, avoiding noisy and redundant data information. The algorithm is evaluated and compared to 9 other well-known classification techniques in 35 varied application domains. Experimental results are validated using several non-parametric statistical tests applied on multiple classification and interpretability metrics. The experiments show that the proposal obtains good results, improving significantly the interpretability measures over the rest of the algorithms, while achieving competitive accuracy. This is a significant advantage over other algorithms as it allows to obtain an accurate and very comprehensible classifier quickly. %K genetic algorithms, genetic programming, Classification, Evolutionary Programming, Interpretability, Rule Mining %9 journal article %R 10.1016/j.ins.2013.03.038 %U http://www.sciencedirect.com/science/article/pii/S0020025513002430 %U http://dx.doi.org/10.1016/j.ins.2013.03.038 %P 1-20 %0 Journal Article %T High performance evaluation of evolutionary-mined association rules on GPUs %A Cano, Alberto %A Luna, Jose Maria %A Ventura, Sebastian %J The Journal of Supercomputing %D 2013 %8 dec %V 66 %N 3 %I Springer %@ 0920-8542 %G English %F Cano:2013:JSUP %X Association rule mining is a well-known data mining task, but it requires much computational time and memory when mining large scale data sets of high dimensionality. This is mainly due to the evaluation process, where the antecedent and consequent in each rule mined are evaluated for each record. This paper presents a novel methodology for evaluating association rules on graphics processing units (GPUs). The evaluation model may be applied to any association rule mining algorithm. The use of GPUs and the compute unified device architecture (CUDA) programming model enables the rules mined to be evaluated in a massively parallel way, thus reducing the computational time required. This proposal takes advantage of concurrent kernels execution and asynchronous data transfers, which improves the efficiency of the model. In an experimental study, we evaluate interpreter performance and compare the execution time of the proposed model with regard to single-threaded, multi-threaded, and graphics processing unit implementation. The results obtained show an interpreter performance above 67 billion giga operations per second, and speed-up by a factor of up to 454 over the single-threaded CPU model, when using two NVIDIA 480 GTX GPUs. The evaluation model demonstrates its efficiency and scalability according to the problem complexity, number of instances, rules, and GPU devices. %K genetic algorithms, genetic programming, Performance evaluation, Association rules, Parallel computing, GPU %9 journal article %R 10.1007/s11227-013-0937-4 %U http://link.springer.com/article/10.1007/s11227-013-0937-4/fulltext.html %U http://dx.doi.org/10.1007/s11227-013-0937-4 %P 1438-1461 %0 Thesis %T New Classification Models through Evolutionary Algorithms %A Cano Rojas, Alberto %D 2014 %8 jan %C Spain %C University of Granada %F Thesis_Alberto_Cano %X The objective of this thesis is the development of classification models using evolutionary algorithms, focusing on the aspects of scalability, interpretability and accuracy in complex datasets and high dimensionality. This Ph.D. thesis presents new computational models on data classification which address new open problems and challenges in data classification by means of evolutionary algorithms. Specifically, we pursue to improve the performance, scalability, interpretability and accuracy of classification models on challenging data. The performance and scalability of evolutionary-based classification models were improved through parallel computation on GPUs, which demonstrated to achieve high efficiency on speeding up classification algorithms. The conflicting problem of the interpretability and accuracy of the classification models was addressed through a highly interpretable classification algorithm which produced very comprehensible classifiers by means of classification rules. Performance on challenging data such as the imbalanced classification was improved by means of a data gravitation classification algorithm which demonstrated to achieve better classification performance both on balanced and imbalanced data. All the methods proposed in this thesis were evaluated in a proper experimental framework, by using a large number of data sets with diverse dimensionality and by comparing their performance against other state-of-the-art and recently published methods of proved quality. The experimental results obtained have been verified by applying non-parametric statistical tests which support the better performance of the methods proposed. %K genetic algorithms, genetic programming, classification, evolutionary algorithms, graphic processing units, GPU, Nvidia CUDA, UCI, ARFF, KEEL, JCLEC, G3P-MI, DGC+ %9 Ph.D. thesis %U https://www.people.vcu.edu/~acano/pdf/Thesis%20Alberto%20Cano.pdf %0 Journal Article %T Parallel evaluation of Pittsburgh rule-based classifiers on GPUs %A Cano, Alberto %A Zafra, Amelia %A Ventura, Sebastian %J Neurocomputing %D 2014 %8 27 feb %V 126 %@ 0925-2312 %F Cano:2014:Neurocomputing %O Recent trends in Intelligent Data Analysis Selected papers of the The 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2011) Online Data Processing Including a selection of papers from the International Conference on Adaptive and Intelligent Systems 2011 (ICAIS 2011) %X Individuals from Pittsburgh rule-based classifiers represent a complete solution to the classification problem and each individual is a variable-length set of rules. Therefore, these systems usually demand a high level of computational resources and run-time, which increases as the complexity and the size of the data sets. It is known that this computational cost is mainly due to the recurring evaluation process of the rules and the individuals as rule sets. In this paper we propose a parallel evaluation model of rules and rule sets on GPUs based on the NVIDIA CUDA programming model which significantly allows reducing the run-time and speeding up the algorithm. The results obtained from the experimental study support the great efficiency and high performance of the GPU model, which is scalable to multiple GPU devices. The GPU model achieves a rule interpreter performance of up to 64 billion operations per second and the evaluation of the individuals is speed up of up to 3461 fold when compared to the CPU model. This provides a significant advantage of the GPU model, especially addressing large and complex problems within reasonable time, where the CPU run-time is not acceptable %K genetic algorithms, Pittsburgh, Classification, Rule sets, Parallel computing, GPU, GPGPU %9 journal article %R 10.1016/j.neucom.2013.01.049 %U http://www.sciencedirect.com/science/article/pii/S0925231213006875 %U http://dx.doi.org/10.1016/j.neucom.2013.01.049 %P 45-57 %0 Conference Proceedings %T GPU-parallel subtree interpreter for genetic programming %A Cano, Alberto %A Ventura, Sebastian %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 Cano:2014:GECCO %X Genetic Programming (GP) is a computationally intensive technique but its nature is embarrassingly parallel. Graphic Processing Units (GPUs) are many-core architectures which have been widely employed to speed up the evaluation of GP. In recent years, many works have shown the high performance and efficiency of GPUs on evaluating both the individuals and the fitness cases in parallel. These approaches are known as population parallel and data parallel. This paper presents a parallel GP interpreter which extends these approaches and adds a new parallelisation level based on the concurrent evaluation of the individual’s subtrees. A GP individual defined by a tree structure with nodes and branches comprises different depth levels in which there are independent subtrees which can be evaluated concurrently. Threads can cooperate to evaluate different subtrees and share the results via GPU’s shared memory. The experimental results show the better performance of the proposal in terms of the GP operations per second (GPops/s) that the GP interpreter is capable of processing, achieving up to 21 billion GPops/s using a NVIDIA 480 GPU. However, some issues raised due to limitations of currently available hardware are to be overcome by the dynamic parallelisation capabilities of the next generation of GPUs. %K genetic algorithms, genetic programming, GPU %R 10.1145/2576768.2598272 %U http://doi.acm.org/10.1145/2576768.2598272 %U http://dx.doi.org/10.1145/2576768.2598272 %P 887-894 %0 Journal Article %T Speeding up multiple instance learning classification rules on GPUs %A Cano, Alberto %A Zafra, Amelia %A Ventura, Sebastian %J Knowledge and Information Systems %D 2015 %8 jul %V 44 %N 1 %@ 0219-1377 %F 2014-KAIS-Cano %X Multiple instance learning is a challenging task in supervised learning and data mining. However, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing units (GPUs) are being used for reducing computing time of algorithms. This paper presents an implementation of the G3P-MI algorithm on GPUs for solving multiple instance problems using classification rules. The GPU model proposed is distributable to multiple GPUs, seeking for its scalability across large-scale and high-dimensional data sets. The proposal is compared to the multi-threaded CPU algorithm with streaming SIMD extensions parallelism over a series of data sets. Experimental results report that the computation time can be significantly reduced and its scalability improved. Specifically, an speedup of up to 149 times can be achieved over the multi-threaded CPU algorithm when using four GPUs, and the rules interpreter achieves great efficiency and runs over 108 billion genetic programming operations per second. %K genetic algorithms, genetic programming, Multi-instance learning, Classification, Parallel computing, GPU %9 journal article %R 10.1007/s10115-014-0752-0 %U http://dx.doi.org/10.1007/s10115-014-0752-0 %P 127-145 %0 Journal Article %T A Classification Module for Genetic Programming Algorithms in JCLEC %A Cano, Alberto %A Luna, Jose Maria %A Zafra, Amelia %A Ventura, Sebastian %J Journal of Machine Learning Research %D 2015 %8 mar %V 16 %I Microtome Publishing %@ 1533-7928 (electronic); 1532-4435 (paper) %F Cano:2015:JMLR %X JCLEC-Classification is a usable and extensible open source library for genetic programming classification algorithms. It houses implementations of rule-based methods for classification based on genetic programming, supporting multiple model representations and providing to users the tools to implement any classifier easily. The software is written in Java and it is available from http://jclec.sourceforge.net/classification under the GPL license. %K genetic algorithms, genetic programming %9 journal article %U http://www.jmlr.org/ %P 491-494 %0 Journal Article %T Multi-objective genetic programming for feature extraction and data visualization %A Cano, Alberto %A Ventura, Sebastian %A Cios, Krzysztof J. %J Soft Computing %D 2017 %8 apr %V 21 %N 8 %@ 1433-7479 %F Cano:2016:SC %X Feature extraction transforms high-dimensional data into a new subspace of lower dimensionality while keeping the classification accuracy. Traditional algorithms do not consider the multi-objective nature of this task. Data transformations should improve the classification performance on the new subspace, as well as to facilitate data visualization, which has attracted increasing attention in recent years. Moreover, new challenges arising in data mining, such as the need to deal with imbalanced data sets call for new algorithms capable of handling this type of data. This paper presents a Pareto-based multi-objective genetic programming algorithm for feature extraction and data visualization. The algorithm is designed to obtain data transformations that optimize the classification and visualization performance both on balanced and imbalanced data. Six classification and visualization measures are identified as objectives to be optimized by the multi-objective algorithm. The algorithm is evaluated and compared to 11 well-known feature extraction methods, and to the performance on the original high-dimensional data. Experimental results on 22 balanced and 20 imbalanced data sets show that it performs very well on both types of data, which is its significant advantage over existing feature extraction algorithms. %K genetic algorithms, genetic programming, Classification, Feature extraction, Visualization %9 journal article %R 10.1007/s00500-015-1907-y %U http://dx.doi.org/10.1007/s00500-015-1907-y %P 2069-2089 %0 Journal Article %T Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams %A Cano, Alberto %A Krawczyk, Bartosz %J Pattern Recognition %D 2019 %V 87 %@ 0031-3203 %F CANO:2019:PR %X Designing efficient algorithms for mining massive high-speed data streams has become one of the contemporary challenges for the machine learning community. Such models must display highest possible accuracy and ability to swiftly adapt to any kind of changes, while at the same time being characterized by low time and memory complexities. However, little attention has been paid to designing learning systems that will allow us to gain a better understanding of incoming data. There are few proposals on how to design interpretable classifiers for drifting data streams, yet most of them are characterized by a significant trade-off between accuracy and interpretability. In this paper, we show that it is possible to have all of these desirable properties in one model. We introduce ERulesD2S: evolving rule-based classifier for drifting data Streams. By using grammar-guided genetic programming, we are able to obtain accurate sets of rules per class that are able to adapt to changes in the stream without a need for an explicit drift detector. Additionally, we augment our learning model with new proposals for rule propagation and data stream sampling, in order to maintain a balance between learning and forgetting of concepts. To improve efficiency of mining massive and non-stationary data, we implement ERulesD2S parallelized on GPUs. A thorough experimental study on 30 datasets proves that ERulesD2S is able to efficiently adapt to any type of concept drift and outperform state-of-the-art rule-based classifiers, while using small number of rules. At the same time ERulesD2S is highly competitive to other single and ensemble learners in terms of accuracy and computational complexity, while offering fully interpretable classification rules. Additionally, we show that ERulesD2S can scale-up efficiently to high-dimensional data streams, while offering very fast update and classification times. Finally, we present the learning capabilities of ERulesD2S for sparsely labeled data streams %K genetic algorithms, genetic programming, Machine learning, Data streams, Concept drift, Rule-based classification, GPU, High-performance data mining %9 journal article %R 10.1016/j.patcog.2018.10.024 %U http://www.sciencedirect.com/science/article/pii/S0031320318303765 %U http://dx.doi.org/10.1016/j.patcog.2018.10.024 %P 248-268 %0 Journal Article %T Interpretable Multiview Early Warning System Adapted to Underrepresented Student Populations %A Cano, Alberto %A Leonard, John D. %J IEEE Transactions on Learning Technologies %D 2019 %8 apr %V 12 %N 2 %@ 1939-1382 %F Cano:2019:LT %X Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decision-making reasoning to motivate the reasons behind the predictions, and they are generally biased toward the general student body, ignoring the idiosyncrasies of underrepresented student populations (determined by socio-demographic factors such as race, gender, residency, or status as a freshmen, transfer, adult, or first-generation students) that traditionally have greater difficulties and performance gaps. This paper presents a multiview early warning system built with comprehensible Genetic Programming classification rules adapted to specifically target underrepresented and underperforming student populations. The system integrates many student information repositories using multiview learning to improve the accuracy and timing of the predictions. Three interfaces have been developed to provide personalized and aggregated comprehensible feedback to students, instructors, and staff to facilitate early intervention and student support. Experimental results, validated with statistical analysis, indicate that this multiview learning approach outperforms traditional classifiers. Learning outcomes will help instructors and policy-makers to deploy strategies to increase retention and improve academics. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TLT.2019.2911079 %U http://dx.doi.org/10.1109/TLT.2019.2911079 %P 198-211 %0 Conference Proceedings %T Hardware Design of a Model Generator Based on Grammars and Cartesian Genetic Programming for Blood Glucose Prediction %A Cano, Jorge %A Hidalgo, J. Ignacio %A Garnica, Oscar %A Lanchares, Juan %Y Petke, Justyna %Y Ekart, Aniko %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F cano:2023:GECCOcomp %X People with diabetes need to control their blood glucose levels to avoid dangerous situations such as getting into hypoglycemia or hyperglycemia, which can lead to long-term and short-term complications. One of the most important daily tasks of people with diabetes is to estimate or predict the glucose in a near future as a consequence of medication, eating, or insulin administration events. We present a parameterized hardware implementation of a blood glucose level predictor generator. The design was implemented over a Field Programmable Gate Array and uses as input variables a set of data from the person (blood glucose levels, carbohydrates, and insulin units). Our implementation produces personal devices the patient can use whenever new readings of the variable are available. Moreover, it could be combined with insulin pumps and continuous glucose monitoring systems to develop an artificial pancreas. For the model generation, we designed a novel technique based on grammars, cartesian genetic programming with an evolutionary strategy (1+lambda) and a fitness function based on the Clarke Error Grid Analysis. Preliminary results show that our hardware implementation achieved higher speeds and lower power consumption than its software counterparts while preserving or even improving the accuracy of the predictions. %K genetic algorithms, genetic programming, cartesian genetic programming %R 10.1145/3583133.3596427 %U http://dx.doi.org/10.1145/3583133.3596427 %P 55-56 %0 Journal Article %T Hardware real-time individualised blood glucose predictor generator based on grammars and cartesian genetic programming %A Cano, Jorge %A Hidalgo, J. Ignacio %A Garnica, Oscar %J Genetic Programming and Evolvable Machines %D 2025 %V 26 %@ 1389-2576 %F Cano:2025:GPEM %O Online first %K genetic algorithms, genetic programming, cartesian genetic programming %9 journal article %R 10.1007/s10710-024-09500-7 %U http://dx.doi.org/10.1007/s10710-024-09500-7 %P Articleno3 %0 Journal Article %T Empirically Based Simulation: The Case of Twin Peaks in National Income %A Cantner, Uwe %A Ebersberger, Bernd %A Hanusch, Horst %A Kruger, Jens J. %A Pyka, Andreas %J The Journal of Artificial Societies and Social Simulation %D 2001 %8 30 jun %@ 1460-7425 %F cantner:2001:JASSS %X Only recently a new stylised fact of economic growth has been introduced, the bimodal shape of the distribution of per capita income or the twin-peaked nature of that distribution. Drawing on the Summers/Hestons Penn World Table 5.6 (1991) we determine kernel density distributions which are able to detect the aforementioned twin peaked structure and show that the world income distribution starting with an unimodal structure in 1960 evolves subsequently to a bimodal or twin-peak structure. This empirical results can be explained theoretically by a synergetic model based on the master equation approach as in Pyka/Kruger/Cantner (1999). This paper attempts to extend this discussion by taking the reverse procedure, that is to find empirical evidence for the working mechanism of the theoretical model. We determine empirically the transition rates used in the synergetic approach by applying alternatively NLS to chosen functional forms and genetic programming in order to determine the functional forms and the parameters simultaneously. Using the so determined transition rates in the synergetic model leads in both cases to the emergence of the bimodal distribution, which, however, is only in the latter case a persistent phenomenon. %K genetic algorithms, genetic programming, bimodal productivity structure, master equation approach %9 journal article %U http://jasss.soc.surrey.ac.uk/4/3/9.html %0 Conference Proceedings %T Modeling Idealized Bounding Cases of Parallel Genetic Algorithms %A Cantu-Paz, Erick %A Goldberg, David E. %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 Cantu-Paz:1997:mibcpGA %K Genetic Algorithms %P 353-361 %0 Conference Proceedings %T Designing Efficient Master-Slave Parallel Genetic Algorithms %A Cantu-Paz, Erick %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 %F cantu:1998:demsPGA %K genetic algorithms %P 455 %0 Conference Proceedings %T Using Markov Chains to Analyze a Bounding Case of Parallel Genetic Algorithms %A Cantu-Paz, Erick %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 %F cantu:1998:mcabcPGA %K genetic algorithms %P 456-462 %0 Conference Proceedings %T Migration Policies and Takeover Times in Genetic Algorithms %A Cantu-Paz, Erick %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 cantu-paz:1999:MPTTGA %K genetic algorithms and classifier systems, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco99-migpolicy.pdf %P 775 %0 Conference Proceedings %T Topologies, Migration Rates, and Multi-Population Parallel Genetic Algorithms %A Cantu-Paz, Erick %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 cantu-paz:1999:TMRMPGA %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco99-topologies.pdf %P 91-98 %0 Conference Proceedings %T Migration policies, selection pressure, and parallel evolutionary algorithms %A Cantu-Paz, Erick %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 cantu-paz:1999:M %P 65-73 %0 Conference Proceedings %T Late Breaking papers at the Genetic and Evolutionary Computation Conference (GECCO-2002) %E Cantú-Paz, Erick %D 2002 %8 jul %I AAAI %C New York, NY %F cantu-paz:2002:gecco:lbp %K genetic algorithms, genetic programming, Evolvable Network Architecture , Dynamic Neural Net, Pattern Recognition , Evolutionary Computation, Automated Sensor, Multiagent Systems, Optimisation, Evolvable Hardware , Genetic Multi-Agent Planning, Evolutionary Testing, Evolving Neural Network Architectures, Evolving Software, Airline Fleet Assignment, Ant Colony Algorithm, Artificial Immune System , Artificial Life, Evolving Cellular Automata %U http://gpbib.cs.ucl.ac.uk/gecco2002lb.bib %0 Conference Proceedings %T Genetic and Evolutionary Computation – GECCO 2003, Part I %E Cantú-Paz, Erick %E Foster, James A. %E Deb, Kalyanmoy %E Davis, Lawrence %E Roy, Rajkumar %E O’Reilly, Una-May %E Beyer, Hans-Georg %E Standish, Russell K. %E Kendall, Graham %E Wilson, Stewart W. %E Harman, Mark %E Wegener, Joachim %E Dasgupta, Dipankar %E Potter, Mitchell A. %E Schultz, Alan C. %E Dowsland, Kathryn A. %E Jonoska, Natasha %E Miller, Julian F. %S Lecture Notes in Computer Science %D 2003 %8 December 16 jul %V 2723 %I Springer %C Chicago, IL, USA %@ 3-540-40602-6 %F GECCO2003-PartI %K genetic algorithms, genetic programming, A-Life, Adaptive Behaviour, Agents, Artificial Immune Systems, Coevolution, DNA computing, Evolution Strategies, Evolutionary Programming, Evolutionary Robotics, Evolutionary Scheduling Routing, Evolvable Hardware, Learning Classifier Systems, Molecular computing, Quantum Computing, Real World Applications, Search Based Software Engineering, Ant Colony Optimization, grammatical evolution %R 10.1007/3-540-45105-6 %U http://dx.doi.org/10.1007/3-540-45105-6 %0 Conference Proceedings %T Genetic and Evolutionary Computation – GECCO 2003, Part II %E Cantú-Paz, Erick %E Foster, James A. %E Deb, Kalyanmoy %E Davis, Lawrence %E Roy, Rajkumar %E O’Reilly, Una-May %E Beyer, Hans-Georg %E Standish, Russell K. %E Kendall, Graham %E Wilson, Stewart W. %E Harman, Mark %E Wegener, Joachim %E Dasgupta, Dipankar %E Potter, Mitchell A. %E Schultz, Alan C. %E Dowsland, Kathryn A. %E Jonoska, Natasha %E Miller, Julian F. %S Lecture Notes in Computer Science %D 2003 %8 December 16 jul %V 2724 %I Springer %@ 3-540-40603-4 %F GECCO2003-PartII %K genetic algorithms, genetic programming, A-Life, Adaptive Behavior, Agents, Artificial Immune Systems, Coevolution, DNA computing, Evolution Strategies, Evolutionary Programming, Evolutionary Robotics, Evolutionary Scheduling Routing, Evolvable Hardware, Learning Classifier Systems, Molecular computing, Quantum Computing, Real World Applications, Search Based Software Engineering, Ant Colony Optimization, grammatical evolution %R 10.1007/3-540-45110-2 %U http://dx.doi.org/10.1007/3-540-45110-2 %0 Journal Article %T Comprehensive-Forecast Multiobjective Genetic Programming for Neural Architecture Search %A Cao, Bin %A Luo2, Xiao %A Liu, Xin %A Li, Yun %J IEEE Transactions on Evolutionary Computation %@ 1089-778X %F Bin_Cao:ieeeTEC %O Early Access %K genetic algorithms, genetic programming, Computer architecture, Training, Microprocessors, Search problems, Network architecture, Accuracy, Predictive models, Genetic programming, Costs, Adaptation models, neural architecture search, ANN, evaluation strategies, multiobjective optimization %9 journal article %R 10.1109/TEVC.2025.3570195 %U http://dx.doi.org/10.1109/TEVC.2025.3570195 %0 Conference Proceedings %T Increasing Diversity and Controlling Bloat in Linear Genetic Programming %A Cao, Bo %A Jiang, Zongli %S 2016 3rd International Conference on Information Science and Control Engineering (ICISCE) %D 2016 %8 jul %F Cao:2016:ICISCE %X The objective of this paper is to use the age-layered population structure model to increase diversity and control bloat of the population in linear genetic programming. Firstly, we use two level tournament selection to increase the sub-population diversity in each layer, and then apply the new model to linear genetic programming to increase the sub-population and the entire population diversity. The age-layered population structure model segregates individuals into different age-layers by their age, so it limits the quantity of the old age and long length individuals. Besides, the model regularly introduces new randomly generated short individuals into the youngest layer to reduce the average length of the population. The experimental results show that the age-layered population structure model can increase diversity and control bloat of the population effectively. %K genetic algorithms, genetic programming %R 10.1109/ICISCE.2016.97 %U http://dx.doi.org/10.1109/ICISCE.2016.97 %P 414-419 %0 Conference Proceedings %T A Survey on Automatic Bug Fixing %A Cao, Heling %A Meng, YangXia %A Shi, Jianshu %A Li, Lei %A Liao, Tiaoli %A Zhao, Chenyang %S 2020 6th International Symposium on System and Software Reliability (ISSSR) %D 2020 %8 oct %F Cao:2020:ISSSR %X To reduce the cost of software debugging, Automatic Bug Fixing (ABF) techniques have been proposed for efficiently fixing and maintaining software, aiming to rapidly correct bugs in software. In this paper, we conduct a survey, analysing the capabilities of existing ABF techniques based on the test case set. We organise knowledge in this area by surveying 133 high-quality papers from 1990 to June 2020 and supplement 57 latest high-quality papers from 2017 to June 2020. This paper shows that existing ABF approaches can be divided into three main strategies: search-based, semantic-based, and template-based. Search-based ABF considers using search strategies, such as genetic programming, context similarity, to change the programs into the correct one. Semantic-based ABF involves symbolic execution and constraint solving, such as satisfiability modulo theories solver, contracts, to fix bugs. Different from the two kinds of theories above, template-based ABF is mainly based on fixing templates, such as other programs, bug reports, to fix bugs. Besides, we provide a summary of the commonly used defect benchmarks and all the available tools that are frequently used in the field of ABF. We also discuss the empirical foundations and argumentation in the area and prospect the trend of future study. %K genetic algorithms, genetic programming, genetic improvement, APR %R 10.1109/ISSSR51244.2020.00029 %U http://dx.doi.org/10.1109/ISSSR51244.2020.00029 %P 122-131 %0 Conference Proceedings %T Automated Repair of Java Programs with Random Search via Code Similarity %A Cao, Heling %A Liu, Fangzheng %A Shi, Jianshu %A Chu, Yonghe %A Deng, Miaolei %S 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C) %D 2021 %8 dec %F Cao:2021:QRS-C %X Automatic program repair is a cutting-edge research direction in software engineering in recent years. The existing program repair techniques based on genetic programming suffer from requiring verification of a large number of candidate patches, which consume a lot of computational resources. We instead propose Random search via Code Similarity based automate program Repair (RCSRepair). First, we use test filtering and test case prioritization techniques in fault localization to reduce and restructure test cases. Second, a combination of random search and code similarity is used to generate patches. Finally, overfitting detection is performed on the patches that pass the test cases to improve the quality of the patch. The experimental results show that our approach can successfully fix 54 bugs of 224 real-world bugs in Defects4J and has outperform the compared approaches. %K genetic algorithms, genetic programming, genetic improvement, APR, program repair, random search, test case prioritisation, patch overfitted %R 10.1109/QRS-C55045.2021.00075 %U http://dx.doi.org/10.1109/QRS-C55045.2021.00075 %P 470-477 %0 Journal Article %T A Hybrid Evolutionary Modeling Algorithm for System of Ordinary Differential Equations %A Cao, Hongqing %A Kang, Lishan %A Michalewicz, Zbigniew %A Chen, Yuping %J Neural, Parallel & Scientific Computations %D 1998 %8 jun %V 6 %N 2 %I Dynamic Publishers %C Atlanta, USA %@ 1061-5369 %F cao:1998:NPSC %K genetic algorithms, genetic programming %9 journal article %U http://www.dynamicpublishers.com/Neural/neuralv6.htm %P 171-188 %0 Conference Proceedings %T A Two-level Evolutionary Algorithm for Modeling System of Ordinary Differential Equations %A Cao, Hongqing %A Kang, Lishan %A Michalewicz, Zbigniew %A Chen, Yuping %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 cao:1998:2eaode %K genetic algorithms, genetic programming, ODE, HEMA %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/cao_1998_2eaode.pdf %P 17-22 %0 Journal Article %T The Kinetic Evolutionary Modeling of Complex Systems of Chemical Reactions %A Cao, Hongqing %A Yu, Jingxian %A Kang, Lishan %A Chen, Yuping %A Chen, Yongyan %J Computers & Chemistry %D 1999 %8 30 mar %V 23 %N 2 %F cao:1999:CC %X To overcome the drawbacks of most available methods for kinetic analysis, this paper proposes a hybrid evolutionary modelling algorithm called HEMA to build kinetic models of systems of ordinary differential equations (ODEs) automatically for complex systems of chemical reactions. The main idea of the algorithm is to embed a genetic algorithm (GA) into genetic programming (GP) where GP is employed to optimise the structure of a model, while a GA is employed to optimize its parameters. The experimental results of two chemical reaction systems show that by running the HEMA, the computer can discover the kinetic models automatically which are appropriate for describing the kinetic characteristics of the reacting systems. Those models can not only fit the kinetic data very well, but also give good predictions. %K genetic algorithms, genetic programming, kinetic analysis, Complex systems of chemical reactions, Evolutionary modeling %9 journal article %R 10.1016/S0097-8485(99)00005-4 %U http://dx.doi.org/10.1016/S0097-8485(99)00005-4 %P 143-152 %0 Conference Proceedings %T Evolutionary Modeling of Ordinary Differential Equations for Dynamic Systems %A Cao, Hongqing %A Kang, Lishan %A Chen, Yuping %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 cao:1999:EMODEDS %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco1999/GP-401.pdf %P 959-965 %0 Journal Article %T Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming %A Cao, Hongqing %A Kang, Lishan %A Chen, Yuping %A Yu, Jingxian %J Genetic Programming and Evolvable Machines %D 2000 %8 oct %V 1 %N 4 %@ 1389-2576 %F cao:2000:odeGP %X This paper describes an approach to the evolutionary modeling problem of ordinary differential equations including systems of ordinary differential equations and higher-order differential equations. Hybrid evolutionary modeling algorithms are presented to implement the automatic modeling of one- and multi-dimensional dynamic systems respectively. The main idea of the method is to embed a genetic algorithm in genetic programming where the latter is employed to discover and optimize the structure of a model, while the former is employed to optimize its parameters. A number of practical examples are used to demonstrate the effectiveness of the approach. Experimental results show that the algorithm has some advantages over most available modeling methods. %K genetic algorithms, genetic programming, evolutionary modeling, system of ordinary differential equations, higher-order ordinary differential equation %9 journal article %R 10.1023/A:1010013106294 %U http://www.ees.adelaide.edu.au/people/enviro/cao/2000-05.pdf %U http://dx.doi.org/10.1023/A:1010013106294 %P 309-337 %0 Journal Article %T A two-level hybrid evolutionary algorithm for modeling one-dimensional dynamic systems by higher-order ODE models %A Cao, Hong-Qing %A Kang, Li-Shan %A Guo, Tao %A Chen, Yu-Ping %A de Garis, Hugo %J IEEE Transactions on Systems, Man and Cybernetics – Part B: Cybernetics %D 2000 %8 apr %V 40 %N 2 %@ 1083-4419 %F cao:2000:ode2GP %X This paper presents a new algorithm for modeling one-dimensional (1-D) dynamic systems by higher-order ordinary differential equation (HODE) models instead of the ARMA models as used in traditional time series analysis. A two-level hybrid evolutionary modeling algorithm (THEMA) is used to approach the modeling problem of HODE’s for dynamic systems. The main idea of this modeling algorithm is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure of a model (the upper level), while a GA is employed to optimize the parameters of the model (the lower level). In the GA, we use a novel crossover operator based on a nonconvex linear combination of multiple parents which works efficiently and quickly in parameter optimization tasks. Two practical examples of time series are used to demonstrate the THEMA’s effectiveness and advantages. %K genetic algorithms, genetic programming, evolutionary computation, evolutionary algorithm, ODE models, one-dimensional dynamic systems, ordinary differential equation, two-level hybrid evolutionary modeling algorithm, THEMA, crossover operator %9 journal article %U http://ieeexplore.ieee.org/iel5/3477/18067/00836383.pdf %P 351-357 %0 Journal Article %T Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms %A Cao, Hongqing %A Yu, Jingxian %A Kang, Lishan %A Yang, Hanxi %A Ai, Xinping %J Computers & Chemistry %D 2001 %8 may %V 25 %N 3 %@ 0097-8485 %F cao:2001:CC %X A hybrid evolutionary modeling algorithm (HEMA) is proposed to build the discharge lifetime models with multiple impact factors for battery systems as well as make predictions. The main idea of the HEMA is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimise the structure of a model, while a GA is employed to optimize its parameters. The experimental results on lithium-ion batteries show that the HEMA works effectively, automatically and quickly in modelling the discharge lifetime of battery systems. The algorithm has some advantages compared with most existing modelling methods and can be applied widely to solving the automatic modelling problems in many fields. %K genetic algorithms, genetic programming, Discharge lifetime of battery systems, Lithium-ion battery, Hybrid evolutionary modelling %9 journal article %R 10.1016/S0097-8485(00)00099-1 %U http://dx.doi.org/10.1016/S0097-8485(00)00099-1 %P 251-259 %0 Journal Article %T Parallel Implementations of Modeling Dynamical Systems by Using System of Ordinary Differential Equations %A Cao, Hongqing %A Kang, Lishan %A Yu, Jingxian %J Wuhan University Journal of Natural Sciences %D 2003 %V 8 %N IB %I Wuhan University %@ 1007-1202 %F cao:2003:WUJNS %X First, an asynchronous distributed parallel evolutionary modelling algorithm (PEMA) for building the model of system of ordinary differential equations for dynamical systems is proposed in this paper. Then a series of parallel experiments have been conducted to systematically test the influence of some important parallel control parameters on the performance of the algorithm. A lot of experimental results are obtained and we make some analysis and explanations to them. %K genetic algorithms, genetic programming, parallel genetic programming, evolutionary modeling, system of ordinary differential equations %9 journal article %R 10.1007/BF02899484 %U http://dx.doi.org/10.1007/BF02899484 %P 229-233 %0 Journal Article %T An Experimental Study of Some Control Parameters in Parallel Genetic Programming %A Cao, Hongqing %A Yu, Jingxian %A Kang, Lishan %A McKay, R. I. Bob %J Neural, Parallel and Scientific Computation %D 2003 %V 11 %N 4 %F cao:2003:NPSC %X Using the evolutionary modeling of system of ordinary differential equations (ODEs) as the test problem, this paper primarily investigates the influences of some important parallel control parameters within parallel genetic programming (GP), including the degree of connectivity between demes, the migration rate, the migration generation interval, and the migration policy, on the performance of the parallel evolutionary modelling algorithm (PEMA), which is measured from two perspectives: the solution quality and the parallel speedup. We compare the results with previous theoretical and experimental work in parallel genetic algorithms (GAs), and try to give some plausible analysis and explanations. The results may help to offer some useful design guidelines for researchers using parallel GP. %K genetic algorithms, genetic programming, parallel genetic programming, parallel control parameters, evolutionary modelling, system of ordinary differential equations %9 journal article %U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.6377.pdf %P 377-393 %0 Journal Article %T The Dynamic Evolutionary Modeling of HODEs for Time Series Prediction %A Cao, Hongqing %A Kang, Lishan %A Chen, Yuping %A Guo, Tao %J Computers & Mathematics with Applications %D 2003 %V 46 %N 8-9 %F cao:2003:CMA %X The prediction of future values of a time series generated by a chaotic dynamic system is an extremely challenging task. Besides some methods used in traditional time series analysis, a number of nonlinear prediction methods have been developed for time series prediction, especially the evolutionary algorithms. Many researchers have built various models by using different evolutionary techniques. Different from those available models, this paper presents a new idea for modelling time series using higher-order ordinary differential equations (HODEs) models. Accordingly, a dynamic hybrid evolutionary modeling algorithm called DHEMA is proposed to approach this task. Its main idea is to embed a genetic algorithm (GA) into genetic programming (GP) where GP is employed to optimise the structure of a model, while a GA is employed to optimize its parameters. By running the DHEMA, the modeling and predicting processes can be carried on successively and dynamically with the renewing of observed data. Two practical examples are used to examine the effectiveness of the algorithm in performing the prediction task of time series whose experimental results are compared with those of standard GP. %K genetic algorithms, genetic programming, Time series, Differential equation %9 journal article %R 10.1016/S0898-1221(03)90228-8 %U http://www.sciencedirect.com/science/article/B6TYJ-4BRR761-P/2/4d226ed6e682798de2e1d83d01cebd95 %U http://dx.doi.org/10.1016/S0898-1221(03)90228-8 %P 1397-1411 %0 Conference Proceedings %T An evolutionary approach for modeling the equivalent circuit for electrochemical impedance spectroscopy %A Cao, Hongqing %A Yu, Jingxian %A Kang, Lishan %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 Cao:2003:Aeafmtecfeis %X This paper proposes an evolutionary approach to build the equivalent circuit model for electrochemical impedance spectroscopy. It works by using a hybrid evolutionary modelling algorithm (HEMA) whose main idea is to embed a genetic algorithm (GA) in gene expression programming (GEP) where GEP is employed to discover and optimise the structure of a circuit, while the GA is employed to optimize the parameters of all the electric components contained in the circuit. By running the HEMA, the computer can automatically find suitable circuit structures as well as optimise the component parameters simultaneously. Compared with most available methods, it has the advantages of automation of modeling process, great diversity of model structures, high stability and efficiency of parameter optimisation. %K genetic algorithms, genetic programming, Gene Expression Programming, GEP, HEMA, Chemistry, Electrochemical impedance spectroscopy, Equivalent circuits, Gene expression, Laboratories, Linear programming, Programming profession, Software engineering, electrochemical impedance spectroscopy, equivalent circuits, component parameters, electrochemical impedance spectroscopy, equivalent circuit, hybrid evolutionary modelling, parameter optimisation %R 10.1109/CEC.2003.1299893 %U http://www.ees.adelaide.edu.au/people/enviro/cao/2003-05.pdf %U http://dx.doi.org/10.1109/CEC.2003.1299893 %P 1819-1825 %0 Journal Article %T Discovery of Predictive Rule Sets for Chlorophyll-a Dynamics in the Nakdong River (Korea) by Means of the Hybrid Evolutionary Algorithm HEA %A Cao, Hongqing %A Recknagel, Friedrich %A Joo, Gea-Jae %A Kim, Dong-Kyun %J Ecological Informatics %D 2006 %8 jan %V 1 %N 1 %@ 1574-9541 %F Cao:2006:EI %X We present a hybrid evolutionary algorithm (HEA) to discover complex rule sets predicting the concentration of chlorophyll-a (Chl.a) based on the measured meteorological, hydrological and limnological variables in the hypertrophic Nakdong River. The HEA is designed: (1) to evolve the structure of rule sets by using genetic programming and (2) to optimise the random parameters in the rule sets by means of a genetic algorithm. Time-series of input-output data from 1995 to 1998 without and with time lags up to 7 days were used for training HEA. Independent input output data for 1994 were used for testing HEA. HEA successfully discovered rule sets for multiple nonlinear relationships between physical, chemical variables and Chl.a, which proved to be predictive for unseen data as well as explanatory. The comparison of results by HEA and previously applied recurrent artificial neural networks to the same data with input–output time lags of 3 days revealed similar good performances of both methods. The sensitivity analysis for the best performing predictive rule set revealed relationships between seasons, specific input variables and Chl.a which to some degree correspond with known properties of the Nakdong River. The statistics of numerous random runs of the HEA also allowed determining most relevant input variables without a priori knowledge. %K genetic algorithms, genetic programming, Hybrid evolutionary algorithm, Rule sets, Chl.a, Sensitivity analysis, Nakdong River %9 journal article %R 10.1016/j.ecoinf.2005.08.001 %U http://dx.doi.org/10.1016/j.ecoinf.2005.08.001 %P 43-53 %0 Book Section %T Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication %A Cao, Hongqing %A Recknagel, Friedrich %A Kim, Bomchul %A Takamura, Noriko %E Recknagel, Friedrich %B Ecological Informatics: Scope, Techniques and Applications %D 2006 %7 2nd %I Springer-Verlag %C Berlin, Heidelberg, New York %@ 3-540-28383-8 %F Cao:2006:2lakes %X A hybrid evolutionary algorithm (HEA) has been developed to discover predictive rule sets in complex ecological data. It has been designed to evolve the structure of rule sets by using genetic programming and to optimise the random parameters in the rule sets by means of a genetic algorithm. HEA was successfully applied to long-term monitoring data of the shallow, eutrophic Lake Kasumigaura (Japan) and the deep, mesotrophic Lake Soyang (Korea). The results have demonstrated that HEA is able to discover rule sets, which can forecast for 7-days-ahead seasonal abundances of blue-green algae and diatom populations in the two lakes with relatively high accuracy but are also explanatory for relationships between physical, chemical variables and the abundances of algal populations. The explanations and the sensitivity analysis for the best rule sets correspond well with theoretical hypotheses and experimental findings in previous studies. %K genetic algorithms, genetic programming %R 10.1007/3-540-28426-5_17 %U http://dx.doi.org/10.1007/3-540-28426-5_17 %P 347-367 %0 Journal Article %T Process-based simulation library SALMO-OO for lake ecosystems. Part 2: Multi-objective parameter optimization by evolutionary algorithms %A Cao, Hongqing %A Recknagel, Friedrich %A Cetin, Lydia %A Zhang, Byron %J Ecological Informatics %D 2008 %V 3 %N 2 %@ 1574-9541 %F Cao2008181 %X SALMO-OO represents an object-oriented simulation library for lake ecosystems that allows to determine generic model structures for certain lake categories. It is based on complex ordinary differential equations that can be assembled by alternative process equations for algal growth and grazing as well as zooplankton growth and mortality. It requires 128 constant parameters that are causally related to the metabolic, chemical and transport processes in lakes either estimated from laboratory and field experiments or adopted from the literature. An evolutionary algorithm (EA) was integrated into SALMO-OO in order to facilitate multi-objective optimization for selected parameters and to substitute them by optimum temperature and phosphate functions. The parameters were related to photosynthesis, respiration and grazing of the three algal groups diatoms, green algae and blue-green algae. The EA determined specific temperature and phosphate functions for same parameters for 3 lake categories that were validated by ecological data of six lakes from Germany and South Africa. The results of this study have demonstrated that: (1) the hybridization of ordinary differential equations by EA provide a sophisticated approach to fine-tune crucial parameters of complex ecological models, and (2) the multi-objective parameter optimization of SALMO-OO by EA has significantly improved the accuracy of simulation results for three different lake categories. %K genetic algorithms, genetic programming, Multi-objective parameter optimization, SALMO-OO, Lake categories, Evolutionary algorithms %9 journal article %R 10.1016/j.ecoinf.2008.02.001 %U http://www.sciencedirect.com/science/article/B7W63-4S69SG8-1/2/95e920ec339c554888f67696a93f2f37 %U http://dx.doi.org/10.1016/j.ecoinf.2008.02.001 %P 181-190 %0 Journal Article %T Parameter Optimization Algorithms for Evolving Rule Models Applied to Freshwater Ecosystems %A Cao, Hongqing %A Recknagel, Friedrich %A Orr, Philip T. %J IEEE Transactions on Evolutionary Computation %D 2014 %8 dec %V 18 %N 6 %@ 1089-778X %F Cao:2014:ieeeEC %X Predictive rule models for early warning of cyanobacterial blooms in freshwater ecosystems were developed using a hybrid evolutionary algorithm (HEA). The HEA has been designed to evolve IF-THEN-ELSE model structures using genetic programming and to optimise the stochastical constants contained in the model using population-based algorithms. This paper intensively investigated the performances of the following six alternative population-based algorithms for parameter optimisation (PO) of rule models within this hybrid methodology: (1) Hill Climbing (HC), (2) Simulated Annealing (SA), (3) Genetic Algorithm (GA), (4) Differential Evolution (DE), (5) Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and (6) Estimation of Distribution Algorithm (EDA). The comparative study was carried out by predictive modelling of chlorophyll-a concentrations and the potentially toxic cyanobacterium Cylindrospermopsis raciborskii cell concentrations based on water quality time-series data in Lake Wivenhoe in Queensland (Australia) from 1998 to 2009. The experimental results demonstrate that with these PO methods, the rule models discovered by the HEA proved to be both predictive and explanatory whose IF condition indicates threshold values for some crucial water quality parameters. When Comparing different PO algorithms, HC always performed best followed by DE, GA and EDA. Whilst CMA-ES performed worst and the performance of SA varied with different data sets. %K genetic algorithms, genetic programming, Evolutionary algorithm, cyanobacterial blooms, population-based algorithms %9 journal article %R 10.1109/TEVC.2013.2286404 %U http://dx.doi.org/10.1109/TEVC.2013.2286404 %P 793-806 %0 Journal Article %T Spatially-explicit forecasting of cyanobacteria assemblages in freshwater lakes by multi-objective hybrid evolutionary algorithms %A Cao, Hongqing %A Recknagel, Friedrich %A Bartkow, Michael %J Ecological Modelling %D 2016 %V 342 %@ 0304-3800 %F Cao:2016:EM %X This paper proposes a novel multi-objective hybrid evolutionary algorithm (MOHEA) that allows spatially-explicit modelling of local outbreaks and dispersal of population density. The MOHEA was tested for modelling at once two cyanobacteria populations at one lake site, same population in two different lakes and same population at three different sites of one lake. All experiments with MOHEA used water quality time-series and abundances of Anabaena and Cylindrospermopsis monitored in the sub-tropical Lakes Wivenhoe and Somerset in Queensland (Australia) from 1999 to 2010. Results have demonstrated the capacity of MOHEA to determine generic rules that: (1) reveal crucial thresholds for outbreaks of cyanobacteria blooms, and (2) perform spatially-explicit forecasting of timing and magnitudes 7-day-ahead of bloom events. %K genetic algorithms, genetic programming, Multi-objective optimization, Hill climbing, Multi-output rule models, Cyanobacteria blooms %9 journal article %R 10.1016/j.ecolmodel.2016.09.024 %U http://www.sciencedirect.com/science/article/pii/S0304380016304938 %U http://dx.doi.org/10.1016/j.ecolmodel.2016.09.024 %P 97-112 %0 Journal Article %T A novel elemental composition based prediction model for biochar aromaticity derived from machine learning %A Cao, Hongliang %A Milan, Yaime Jefferson %A Mood, Sohrab Haghighi %A Ayiania, Michael %A Zhang, Shu %A Gong, Xuzhong %A Lora, Electo Eduardo Silva %A Yuan, Qiaoxia %A Garcia-Perez, Manuel %J Artificial Intelligence in Agriculture %D 2021 %V 5 %@ 2589-7217 %F CAO:2021:AIA %X The measurement of aromaticity in biochars is generally conducted using solid state 13C nuclear magnetic resonance spectroscopy, which is expensive, time-consuming, and only accessible in a small number of research-intensive universities. Mathematical modelling could be a viable alternative to predict biochar aromaticity from other much easier accessible parameters (e.g. elemental composition). In this research, Genetic Programming (GP), an advanced machine learning method, is used to develop new prediction models. In order to identify and evaluate the performance of prediction models, an experimental data set with 98 biochar samples collected from the literature was used. Due to the benefits of the intelligence iteration and learning of GP algorithm, a kind of underlying exponential relationship between the elemental compositions and the aromaticity of biochars is disclosed clearly. The exponential relationship is clearer and simpler than the polynomial mapping relationships implicated by Maroto-Valer, Mazumdar, and Mazumdar-Wang models. In this case, a novel exponential model is proposed for the prediction of biochar aromaticity. The proposed exponential model appears better prediction accuracy and generalization ability than existing polynomial models during the statistical parameter evaluation %K genetic algorithms, genetic programming, Biochar, C aromaticity, Prediction model, Machine learning %9 journal article %R 10.1016/j.aiia.2021.06.002 %U https://www.sciencedirect.com/science/article/pii/S2589721721000210 %U http://dx.doi.org/10.1016/j.aiia.2021.06.002 %P 133-141 %0 Conference Proceedings %T Classification of the Market States Using Neural Network %A Cao, Lijuan %A (Francis), Tay Eng Hock %A Lawrence, Ma %A Yeong, Wai Cheong %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 cao:1999:CMSUNN %K genetic algorithms and classifier systems, poster papers %P 776 %0 Conference Proceedings %T Neuro-Genetic Based Method to the Classification of Acupuncture Needle: A Case Study %A Cao, Lijuan %A (Francis), Tay Eng Hock %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 cao:1999:NBMCANACS %K genetic algorithms and classifier systems %P 99-105 %0 Conference Proceedings %T Symbolic Regression for the Automated Physical Model Identification in Reaction Engineering %A Cao, Liwei %A Neumann, Pascal %A Russo, Danilo %A Vassiliadis, Vassilios S. %A Lapkin, Alexei A. %S 2019 AIChE Annual Meeting %D 2019 %8 13 nov %I American Institute of Chemical Engineers %C Orlando, FL, USA %F Cao:2019:AIChE %X Understanding of a complex reaction system at a fundamental level is crucial as it reduces the time and resources required for process development and implementation at scale. The two distinct paradigms in developing fundamental knowledge of a chemical system start from either experimental observations (data-driven modeling), or from mechanistic a priori knowledge (physical models). With the rise of automation and tremendous modern advancements in data science the two approaches are gradually merging, although model identification for multivariable complex systems remains challenging in practice. In this work, the identification of interpretable and generalisable physical models is targeted by means of automatable, data-driven methods without a priori knowledge. A revised mixed-integer nonlinear programming (MINLP) formulation is proposed for symbolic regression (SR) to identify physical models from noisy experimental data. The identification of interpretable and generalizable models was enabled by assessing model complexity and extrapolation capability. The method is demonstrated by successful application for the identification of a kinetic model of the 4-nitrophenyl acetate (PNPA) hydrolysis reaction. %K genetic algorithms, genetic programming, Process Design and Development, Chemical Reaction Engineering %U https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/443c-symbolic-regression-automated-physical-model-identification-reaction-engineering %P 443c %0 Thesis %T Combining artificial intelligence and robotic system in chemical product/process design %A Cao, Liwei %D 2021 %8 jul %C UK %C Department of Chemical Engineering and Biotechnology, University of Cambridge %F PhD_Thesis_Liwei_Cao_revised_version %X Product design for formulations is an active and challenging area of research. The new challenges of a fast-paced market, products of increasing complexity, and practical translation of sustainability paradigms require re-examination the existing theoretical frameworks to include the advantages from business and research digitalisation. This thesis is based on the hypotheses that (i) new products with desired properties can be discovered by using a robotic platform combined with an intelligent optimization algorithm, and (ii) we can the connect data-driven optimisation with physico-chemical knowledge generation, which will result in a suitable model for translation of product discovery to production, thus impacting on the process development steps towards industrial applications. This thesis focuses on two complex physico chemical systems as case studies, namely the oil-in-water shampoo system and sunscreen products. Firstly, I report the coupling of a machine-learning classification algorithm with the Thompson-Sampling Efficient Multi-Optimization (TSEMO) for the simultaneous optimisation of continuous and discrete outputs. The methodology was successfully applied to the design of a formulated liquid product of commercial interest for which no physical models are available. Experiments were carried out in a semi-automated fashion using robotic platforms triggered by the machine-learning algorithms. The proposed closed-loop optimisation frame-work allowed to find suitable recipes meeting the customer-defined criteria within 15 working days, out performing human intuition in the target performance of the formulations. The framework was then extended to co-optimization of both formulation and process conditions and ingredients selection. Secondly, I report the methods for the identification of new physical knowledge in a complex system where a prior knowledge is insufficient. The application of feature engineering methods in sun cream protection prediction was discussed. It was found that the concentration of UVA and UVB filters are key features, together with product viscosity,which match with the experts’ domain knowledge in sun cream product design. It was also found that through the combination of feature engineering and machine learning, high-fidelity model could be constructed. Furthermore, a modified mixed-integer nonlinear programming (MINLP) formulation for symbolic regression method was proposed for identification of physical models from noisy experimental data. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variables. The methodology was proven to be successful in identifying the correct physical models describing the relationship between shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of chemical reactions. The work of this thesis shows that machine learning methods, together with automated experimental system, can speed-up the R&D process of formulated product design as well as gain new physical knowledge of the complex systems. %K genetic algorithms, genetic programming, symbolic regression, machine learning, closed-loop optimization, artificial intelligence, formulated product design, automated experimental platform, physical model identification, feature engineering %9 Ph.D. thesis %R 10.17863/CAM.76857 %U https://www.repository.cam.ac.uk/handle/1810/329408 %U http://dx.doi.org/10.17863/CAM.76857 %0 Conference Proceedings %T One-class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming %A Cao, Van Loi %A Nicolau, Miguel %A McDermott, James %Y Heywood, Malcolm I. %Y McDermott, James %Y Castelli, Mauro %Y Costa, Ernesto %Y Sim, Kevin %S EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming %S LNCS %D 2016 %8 30 mar –1 apr %V 9594 %I Springer Verlag %C Porto, Portugal %F Cao:2016:EuroGP %X A novel approach is proposed for fast anomaly detection by one-class classification. Standard kernel density estimation is first used to obtain an estimate of the input probability density function, based on the one-class input data. This can be used for anomaly detection: query points are classed as anomalies if their density is below some threshold. The disadvantage is that kernel density estimation is lazy, that is the bulk of the computation is performed at query time. For large datasets it can be slow. Therefore it is proposed to approximate the density function using genetic programming symbolic regression, before imposing the threshold. The runtime of the resulting genetic programming trees does not depend on the size of the training data. The method is tested on datasets including in the domain of network security. Results show that the genetic programming approximation is generally very good, and hence classification accuracy approaches or equals that when using kernel density estimation to carry out one-class classification directly. Results are also generally superior to another standard approach, one-class support vector machines. %K genetic algorithms, genetic programming, Anomaly detection, One-class classification, Kernel Density Estimation %R 10.1007/978-3-319-30668-1_1 %U http://dx.doi.org/10.1007/978-3-319-30668-1_1 %P 3-18 %0 Conference Proceedings %T Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Data %A Cao, Van Loi %A Le-Khac, Nhien-An %A O’Neill, Michael %A Nicolau, Miguel %A McDermott, James %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/CaoLONM16 %X Credit card classification based on machine learning has attracted considerable interest from the research community. One of the most important tasks in this area is the ability of classifiers to handle the imbalance in credit card data. In this scenario, classifiers tend to yield poor accuracy on the minority class despite realizing high overall accuracy. This is due to the influence of the majority class on traditional training criteria. In this paper, we aim to apply genetic programming to address this issue by adapting existing fitness functions. We examine two fitness functions from previous studies and develop two new fitness functions to evolve GP classifiers with superior accuracy on the minority class and overall. Two UCI credit card datasets are used to evaluate the effectiveness of the proposed fitness functions. The results demonstrate that the proposed fitness functions augment GP classifiers, encouraging fitter solutions on both the minority and the majority classes. %K genetic algorithms, genetic programming, Class imbalance, Credit card data, Fitness functions %R 10.1007/978-3-319-31204-0_3 %U http://dx.doi.org/10.1007/978-3-319-31204-0_3 %P 35-45 %0 Generic %T Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Datasets %A Cao, Van Loi %A Le-Khac, Nhien-An %A Nicolau, Miguel %A O’Neill, Michael %A McDermott, James %D 2017 %F journals/corr/CaoLNOM17 %K genetic algorithms, genetic programming %U http://arxiv.org/abs/1704.03522 %0 Conference Proceedings %T Late-acceptance and Step-counting Hill-climbing GP for Anomaly Detection %A Cao, Van Loi %A Nicolau, Miguel %A McDermott, James %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 Cao:2017:GECCO %K genetic algorithms, genetic programming %R 10.1145/3067695.3076091 %U http://doi.acm.org/10.1145/3067695.3076091 %U http://dx.doi.org/10.1145/3067695.3076091 %P 221-222 %0 Journal Article %T The Use of Vicinal-Risk Minimization for Training Decision Trees %A Cao, Yilong %A Rockett, Peter I. %J Applied Soft Computing %D 2015 %@ 1568-4946 %F Cao:2015:ASC %X We propose the use of Vapnik’s vicinal risk minimisation (VRM) for training decision trees to approximately maximise decision margins. We implement VRM by propagating uncertainties in the input attributes into the labelling decisions. In this way, we perform a global regularisation over the decision tree structure. During a training phase, a decision tree is constructed to minimise the total probability of classifying the labelled training examples, a process which approximately maximises the margins of the resulting classifier. We perform the necessary minimisation using an appropriate meta-heuristic (genetic programming) and present results over a range of synthetic and benchmark real datasets. We demonstrate the statistical superiority of VRM training over conventional empirical risk minimisation (ERM) and the well-known C4.5 algorithm, for a range of synthetic and real datasets. We also conclude that there is no statistical difference between trees trained by ERM and using C4.5. Training with VRM is shown to be more stable and repeatable than by ERM. %K genetic algorithms, genetic programming, Decision trees, Vicinal-risk minimisation, Decision trees, Classification %9 journal article %R 10.1016/j.asoc.2015.02.043 %U http://www.sciencedirect.com/science/article/pii/S1568494615001507 %U http://dx.doi.org/10.1016/j.asoc.2015.02.043 %0 Journal Article %T Automatic Repair of Java Programs Weighted Fusion Similarity via Genetic Programming %A Cao, Heling %A He, Zhenghaohe %A Meng, Yangxia %A Chu, Yonghe %J Information Technology and Control %D 2022 %V 51 %N 4 %@ 1392-124X %F DBLP:journals/itc/CaoHMC22 %X Recently, automated program repair techniques have been proven to be useful in the process of software development. However, how to reduce the large search space and the random of ingredient selection is still a challenging problem. In this paper, we propose a repair approach for buggy program based on weighted fusion similarity and genetic programming. Firstly, the list of modification points is generated by selecting modification points from the suspicious statements. Secondly, the buggy repair ingredient is selected according to the value of the weighted fusion similarity, and the repair ingredient is applied to the corresponding modification points according to the selected operator. Finally, we use the test case execution information to prioritize the test cases to improve individual verification efficiency. We have implemented our approach as a tool called WSGRepair. We evaluate WSGRepair in Defects4J and compare with other program repair techniques. Experimental results show that our approach improve the success rate of buggy program repair by 28.6percent, 64percent, 29percent, 64percent and 112percent compared with the GenProg, CapGen, SimFix, jKali and jMutRepair. %K genetic algorithms, genetic programming, genetic improvement, APR %9 journal article %R 10.5755/j01.itc.51.4.30515 %U https://doi.org/10.5755/j01.itc.51.4.30515 %U http://dx.doi.org/10.5755/j01.itc.51.4.30515 %P 738-756 %0 Journal Article %T Code Similarity and Location-Awareness Automatic Program Repair %A Cao, Heling %A Han, Dong %A Liu, Fangzheng %A Liao, Tianli %A Zhao, Chenyang %A Shi, Jianshu %J Applied Sciences %D 2023 %V 13 %N 14 %@ 2076-3417 %F cao:2023:AS %X Automatic program repair has drawn more and more attention since software quality is facing increasing challenges. In existing approaches, the unlimited search space is considered to be the main limitation in finding the correct patch. So how to reduce the search space to improve the efficiency of automatic program repair remains a problem to be solved. In this work, we represent a similarity-based and location-awareness-based automatic program repair (SLARepair). SLARepair takes the similarity between codes as important search information. The search space is further subdivided by the location-awareness strategy to improve search efficiency. In addition, to better guide the search process, a new fitness function is designed for genetic programming, which brings notable improvements. Moreover, the patch verification time is further reduced by using the test case prioritization approach combined with test case filtering. Extensive experiments demonstrate that our SLARepair outperforms the state-of-the-art approaches on the Defects4J benchmark and achieves competitive performances. %K genetic algorithms, genetic programming, genetic improvement, APR %9 journal article %R 10.3390/app13148519 %U https://www.mdpi.com/2076-3417/13/14/8519 %U http://dx.doi.org/10.3390/app13148519 %P ArticleNo.8519 %0 Conference Proceedings %T Genetic Programming Symbolic Regression with Simplification-Pruning Operator for Solving Differential Equations %A Cao, Lulu %A Zheng, Zimo %A Ding, Chenwen %A Cai, Jinkai %A Jiang, Min %S International Conference on Neural Information Processing %D 2023 %8 nov 20 23 %I Springer %C Changsha, China %F cao:2024:ICONIP %K genetic algorithms, genetic programming %R 10.1007/978-981-99-8132-8_22 %U http://link.springer.com/chapter/10.1007/978-981-99-8132-8_22 %U http://dx.doi.org/10.1007/978-981-99-8132-8_22 %P 287-298 %0 Conference Proceedings %T NetGP: A Hybrid Framework Combining Genetic Programming and Deep Reinforcement Learning for PDE Solutions %A Cao, Lulu %A Feng, Yinglan %A Jiang, Min %A Tan, Kay Chen %Y Jin, Yaochu %Y Baeck, Thomas %S 2025 IEEE Congress on Evolutionary Computation (CEC) %D 2025 %8 August 12 jun %I IEEE %C Hangzhou, China %F DBLP:conf/cec/CaoF0T25 %X Partial differential equations (PDEs) are fundamental in various scientific and engineering fields. Methods based on symbolic regression to solve PDEs have gained attention due to their inherent interpretability. However, existing symbolic regression methods rely solely on genetic programming (GP) during the search process, which presents opportunities for improvement in both precision and stability. We introduce a novel framework, itemd NetGP, which enhances symbolic regression for PDEs in three key aspects. First, NetGP employs prefix notation arrays to represent symbolic expressions, simplifying the evaluation process. Second, to improve the stability of the evolutionary process, deep reinforcement learning is integrated to generate new individuals. Additionally, a novel operator is proposed to avoid the generation of invalid expressions during crossover and mutation of array-based individuals. Empirical evaluations across five types of PDEs demonstrate that NetGP achieves outstanding accuracy and stability in solving these PDEs. The code can be found at https://github.com/grassdeerdeer/NetGP. %K genetic algorithms, genetic programming, Accuracy, Codes, Partial differential equations, Diversity reception, Evolutionary computation, Deep reinforcement learning, Hybrid power systems, Generators, Partial Differential Equation, Symbolic Regression, Physics-informed Machine Learning %R 10.1109/CEC65147.2025.11042987 %U https://doi.org/10.1109/CEC65147.2025.11042987 %U http://dx.doi.org/10.1109/CEC65147.2025.11042987 %0 Conference Proceedings %T Symbolic Regression Using Genetic Programming with Chaotic Method-Based Probability Mappings %A Cao, Pu %A Pei, Yan %A Li, Jianqiang %S Frontier Computing on Industrial Applications Volume 4 %S LNEE %D 2023 %8 October 13 jul %V 1134 %I Springer %C Tokyo, Japan %F cao:2023:FC %X we propose a novel pre-learning approach for genetic programming (GP) that aims to investigate the effect of the probability of being selected for each operator. Furthermore, we present a technique that combines chaos theory and searches for a relatively good possibility mapping for each operator using one-dimensional chaotic mapping. We conducted several sets of comparative experiments on real-world data to test the viability of the proposal. These experiments included comparisons with conventional GP, examination of the impact of various chaotic mappings on the proposed algorithm, and implementation of different optimization strategies to find the relative optimal probability mapping. The experimental results demonstrate that the proposed method can achieve better results than conventional GP in the tested dataset, without considering the total quantitative calculation amount. Through statistical tests, it has been proven that the proposed method is significantly different from the conventional method. However, the discussion regarding the circumstances under which the proposed method can obtain better results when the total calculation amount is limited is not yet fully explored due to the small-scale nature of the experiments. Our future studies will focus on improving and fully discussing this idea. %K genetic algorithms, genetic programming %R 10.1007/978-981-99-9342-0_32 %U http://link.springer.com/chapter/10.1007/978-981-99-9342-0_32 %U http://dx.doi.org/10.1007/978-981-99-9342-0_32 %P 300-312 %0 Journal Article %T Estimation and validation for fatigue properties of steels by symbolic regression %A Cao, Weiwen %A Sun, Xingyue %A Chen, Xu %J International Journal of Fatigue %D 2024 %8 sep %V 186 %@ 0142-1123 %F Cao:2024:ijfatigue %X Estimating fatigue properties through basic mechanical properties is an expectation for achieving fatigue life prediction in engineering applications. This work aims to employ symbolic regression (SR) methods to explore the relationship among 82 types of steel. With the help of genetic programming and multi-population evolution algorithms, two SR models are established and compared with seven semi-empirical methods and four machine learning models. By comparison, the SR method offers the best balance in terms of prediction performance and interpretability for fatigue properties. Furthermore, based on fatigue properties obtained by SR methods, the predicted fatigue life of up to 91.1 percent of the samples can be located within the 2-factor band %K genetic algorithms, genetic programming, Fatigue properties, Symbolic regression, Fatigue life prediction, Machine learning %9 journal article %R 10.1016/j.ijfatigue.2024.108416 %U https://www.sciencedirect.com/science/article/pii/S0142112324002743 %U http://dx.doi.org/10.1016/j.ijfatigue.2024.108416 %P 108416 %0 Journal Article %T Pattern Recognition of Biological Signals %A Caparelli, Paulo S. %A Costa, Eduardo %A Soares, Alexsandro S. %A Barbosa, Hipolito %J International Science Index %D 2009 %V 3 %N 3 %I World Academy of Science, Engineering and Technology %@ 1307-6892 %G en %F Caparelli:2009:waset %X This paper presents an evolutionary method for designing electronic circuits and numerical methods associated with monitoring systems. The instruments described here have been used in studies of weather and climate changes due to global warming, and also in medical patient supervision. Genetic Programming systems have been used both for designing circuits and sensors, and also for determining sensor parameters. The authors advance the thesis that the software side of such a system should be written in computer languages with a strong mathematical and logic background in order to prevent software obsolescence, and achieve program correctness. %K genetic algorithms, genetic programming, pattern recognition, evolutionary computation, biological signal, functional programming %9 journal article %U http://waset.org/publications/15962 %P 824-832 %0 Journal Article %T An evolving ontogenetic cellular system for better adaptiveness %A Capcarrece, Mathieu S. %J Biosystems %D 2004 %V 76 %N 1-3 %F Capcarrece:2004:BS %X we present an original cellular system named Phuon. The main motivation behind this project is to go beyond classical cellular systems, such as cellular automata (CA). CA often lack adaptability and turn out to be very brittle in uncertain environment. The idea here is to add ontogeny to cellularity, growth and development being means of adaptation and thus robustness. However, we do not wish to develop yet another cellular system for the sake of it. What we are seeking in the long term is a developmental system for problem solving. This global aim enticed us into finding a way to map a desired global behaviour of the system to the local behaviour of a cell. Quite naturally a peculiar brand of genetic programming was used for that purpose. The results are still preliminary but in our view they already validate some of the hypotheses behind this work. %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.biosystems.2004.05.020 %U http://www.sciencedirect.com/science/article/B6T2K-4D1R6V6-2/2/ceb26b0139eed613393486f88bc2ac23 %U http://dx.doi.org/10.1016/j.biosystems.2004.05.020 %P 177-189 %0 Conference Proceedings %T 8th European Conference on Advances in Artificial Life, ECAL 2005 %E Capcarrere, Mathieu S. %E Freitas, Alex Alves %E Bentley, Peter J. %E Johnson, Colin G. %E Timmis, Jon %S Lecture Notes in Computer Science %D 2005 %8 sep 5 9 %V 3630 %I Springer %C Canterbury, UK %@ 3-540-28848-1 %F DBLP:conf/ecal/2005 %0 Book Section %T Lessons Learned Using Genetic Programming in a Stock Picking Context %A Caplan, Michael %A Becker, Ying %E O’Reilly, Una-May %E Yu, Tina %E Riolo, Rick L. %E Worzel, Bill %B Genetic Programming Theory and Practice II %D 2004 %8 13 15 may %I Springer %C Ann Arbor %@ 0-387-23253-2 %F caplan:2004:GPTP %X This is a narrative describing the implementation of a genetic programming technique for stock picking in a quantitatively driven, risk-controlled, US equity portfolio. It describes, in general, the problems that the authors faced in their portfolio context when using genetic programming techniques and in gaining acceptance of the technique by a skeptical audience. We discuss in some detail the construction of the fitness function, the genetic programming system’s parametrisation (including data selection and internal function choice), and the interpretation and modification of the generated programs for eventual implementation. %K genetic algorithms, genetic programming, stock selection, data mining, fitness functions, quantitative portfolio management %R 10.1007/0-387-23254-0_6 %U http://dx.doi.org/10.1007/0-387-23254-0_6 %P 87-102 %0 Conference Proceedings %T Determining inverse kinematics of a serial robotic manipulator through the use of genetic programming algorithm %A Car, Zlatan %A Baressi Segota, Sandi %A Andelic, Nikola %A Lorencin, Ivan %A Musulin, Jelena %A Stifanic, Daniel %A Mrzljak, Vedran %Y Filipovic, Nenad %Y Kojic, Milos %S 18th International Congress of Serbian Society of Mechanics %D 2021 %8 jun 28 30 %C Kragujevac, Serbia %F car:2021:SSM %X Inverse kinematics is one of the key parts of any industrial robotic manipulator modeling.Solving the inverse kinematics of a robotic manipulator in the classical analytical manner is fairly complex and error-prone. While previous research has shown the possibility of AI application for inverse kinematics solutions, such models have certain pitfalls which makes the probability of their use low. For this reason, the authors propose Genetic Programming (GP), which is an AI method that generates models in the shapes of equations. The application of the algorithm in question shows promise, with errors being lower than 0.5 degrees for all regressed joints when evaluated using Mean Absolute Error (MAE). This points towards the fact that GP could be used in such an approach but some details of the algorithm need to be addressed, such as the tendency to generate large and simplifiable equations, or lower precision when compared to previous AI-based solutions to the same problem. %K genetic algorithms, genetic programming, industrial robotics, inverse kinematics %U https://www.bib.irb.hr:8443/1133808/download/1133808.Contribution_template_2021.edited.pdf %0 Conference Proceedings %T Inverse Response Systems Identification using Genetic Programming %A Carabali, Carmen Alicia %A Tituana, Luis %A Aguilar, Jose %A Camacho, Oscar %A Chavez, Danilo %Y Gusikhin, Oleg %Y Madani, Kurosh %S Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2017, Madrid, Spain, July 26-28, 2017, Volume 1 %D 2017 %I SciTePress %F conf/icinco/CarabaliT0CC17 %X In this paper, we apply genetic programming as a tool for identifying an inverse response system. In previous works, the genetic programming has been used in the context of identification problems, where the goal is to obtain the descriptions of a given system. Identification problems have been studied much from control theory, due to their practical application in industry. In some cases, a description of a system in terms of mathematical equations is not possible, for these cases are necessary new heuristic approaches like the genetic programming. Here, we like to test the quality of the genetic programming to identify inverse response systems, which are systems where the initial response is in a direction opposite to the final outcome. The tool used to develop the model of identification is GPTIPS V2, we use our approach in two cases: in the first one, the equation that describes inverse response system is determined; and in the second case, the transfer function of the system in the frequency domain is found. %K genetic algorithms, genetic programming, inverse response, system identification %R 10.5220/0006421602380245 %U http://www.scitepress.org/DigitalLibrary/ProceedingsDetails.aspx?ID=Hxr/q2f7PZ4= %U http://dx.doi.org/10.5220/0006421602380245 %P 238-245 %0 Conference Proceedings %T Evolving Fuzzy Rule Based Classifiers with GA-P: A Grammatical Approach %A Garcia, Santiago %A Gonzalez, Fermin %A Sanchez, Luciano %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 garcia:1999:efrbcGAPga %X Genetic Programming can be used to evolve Fuzzy Rulebased classifiers [7]. Fuzzy GP depends on a grammar defining valid expressions of fuzzy classifiers, and guarantees that all individuals in the population are valid instances of it all along the evolution process. This is accomplished by restricting crossover and mutation so that they only take place at points of the derivation tree representing the same non-terminal, thus generating valid subtrees [13]. In Fuzzy GP, terminal symbols are fuzzy constants and variables that are chosen beforehand. In this work we propose a method for evolving both fuzzy membership functions of the variables and the Rule Base. Our method extends the GA-P hybrid method [6] by introducing a new grammar with two functional parts, one for the Fuzzy Rule Base (GP Part), and the other for the constants that define the shapes of the fuzzy sets involved in the Fuzzy Rule Base (GA Part). We have applied this method to some classical benchmarks taken from the collection of test data at the UCI Repository of Machine Learning Databases [9]. %K genetic algorithms, genetic programming: Poster %R 10.1007/3-540-48885-5_17 %U http://dx.doi.org/10.1007/3-540-48885-5_17 %P 203-210 %0 Journal Article %T Evolutive Introns: A Non-Costly Method of Using Introns in GP %A Garcia Carbajal, Santiago %A Martinez, Fermin Gonzalez %J Genetic Programming and Evolvable Machines %D 2001 %8 jun %V 2 %N 2 %@ 1389-2576 %F carbajal:2001:GPEM %X We proposed a new strategy to explicitly define introns that increases the probability of selecting good crossover points as evolution goes on. Our approach differs from existing methods in the procedure followed to adapt the probabilities of groups of code being protected. We also provide some experimental results in symbolic regression and classification that reinforced our belief in the usefulness of this procedure. Collateral effects of Evolutive Introns (EIs) are also studied to determine possible modifications in the behavior of a classical Genetic Programming (GP) system. %K genetic algorithms, genetic programming, bloating, introns, intertwined spirals %9 journal article %R 10.1023/A:1011548229751 %U http://dx.doi.org/10.1023/A:1011548229751 %P 111-122 %0 Thesis %T Identificacion automatica de objetivos parciales mediante logica borrosa y programacion genetica dirigida por gramatica %A Garcia Carbajal, Santiago %D 2002 %C Spain %C Faculty of Informatics. GIJON, Universidad de Oviedo %F GarciaCarbajal:thesis %X Automatic Defined Functions (ADFs) concept is expanded with the use of Grammar Directed Genetic Programming. The approach is applied to classical regression problems and control systems. %K genetic algorithms, genetic programming, algorithms, grammar directed GP %9 Ph.D. thesis %U https://dialnet.unirioja.es/servlet/tesis?codigo=8570 %0 Conference Proceedings %T Multi Niche Parallel GP with a Junk-code Migration Model %A Garcia, Santiago %A Levine, John %A Gonzalez, Fermin %Y Ryan, Conor %Y Soule, Terence %Y Keijzer, Maarten %Y Tsang, Edward %Y Poli, Riccardo %Y Costa, Ernesto %S Genetic Programming, Proceedings of EuroGP’2003 %S LNCS %D 2003 %8 14 16 apr %V 2610 %I Springer-Verlag %C Essex %@ 3-540-00971-X %F garcia03 %X We describe in this paper a parallel implementation of Multi Niche Genetic Programming that we use to test the performance of a modified migration model. Evolutive introns is a technique developed to accelerate the convergence of GP in classification and symbolic regression problems. Here, we will copy into a differentiated subpopulation the individuals that due to the evolution process contain longer Evolutive Introns. Additionally, the multi island model is parallelised in order to speed up convergence. These results are also analysed. Our results prove that the multi island model achieves faster convergence in the three different symbolic regression problems tested, and that the junk-coded subpopulation is not significantly worse than the others, which reinforces our belief in that the important thing is not only fitness but keeping good genetic diversity along all the evolution process. The overhead introduced in the process by the existence of various island, and the migration model is reduced using a multi-thread approach. %K genetic algorithms, genetic programming: Poster %R 10.1007/3-540-36599-0_30 %U http://www.aiai.ed.ac.uk/~johnl/papers/garcia-eurogp03.ps %U http://dx.doi.org/10.1007/3-540-36599-0_30 %P 327-334 %0 Conference Proceedings %T EvolGL: Life in a Pond %A Garcia Carbajal, Santiago %A Moran, Martin Bosque %A Martinez, Fermin Gonzales %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 Carbajal:2004:AL %X In this work we present the first version of Evolgl, an artificial environment for the development and study of 3D artificial lifeforms. In this first phase on the development of the project we have focused in setting up a virtual world governed by its own laws, whose state had direct influence upon the artificial beings that inhabit it. Starting from the definition of this virtual world, we have designed a basic type of creature (Evolworm), and the genetic coding of its main characteristics. Evolutionary techniques are then used to evolve the morphological features and behavioural aspects of Evolworms. They must learn to be unfolded inside the world, escape from their enemies, find couple, and obtain food. All of this in absence of an explicitly defined fitness function. In the future we are using this environment to study some classical techniques in the evolutionary computation field, like niche programming, and promotion of junk code (introns). GA-P techniques are used to code the external appearance of the individuals (the texture), to let evolution end up with individuals adapted to be invisible in some zones of the world. The artificial system of vision, and the implementation of the worms’ behavioral mechanisms so that their actions are provoked exclusively by the sensory information are still under development. At this moment, we have obtained distinct forms of evolworms, as well as different bosses of behaviour that we describe in this article. %K genetic algorithms, genetic programming, GA-P, artificial Life %R 10.7551/mitpress/1429.003.0014 %U http://mitpress.mit.edu/books/artificial-life-ix %U http://dx.doi.org/10.7551/mitpress/1429.003.0014 %P 75-80 %0 Journal Article %T Hierarchical Reinforcement Learning with Grammar-Directed GA-P %A Garcia Carbajal, Santiago %A Rizk, Nouhad J. %J International Journal of Soft Computing %D 2006 %8 mar %V 1 %N 1 %@ 1816-9503 %F Garcia:2006:IJSC %X This article proposes a grammatical approach to hierarchical reinforcement learning.It is based on the grammatical description of a problem,a complex task,or objective.The use of a grammar to control the learning process,constraining the structure of the solutions generated with standard GP, permits the inclusion of knowledge about the problem in a straightforward manner,if this knowledge exists.When the problem to be solved involves the use of fuzzy concepts,the membership functions can be evolved simultaneously within the learning process using the advantages of the GA-P paradigm. Additionally,the inclusion of penalty factors in the evaluation function allows us to try to bias the search toward solutions that are optimal in safety or economical terms,not only taking into account control matters.We tested this approach with a real problem,obtaining three different control policies as a consequence of the different fitness functions employed.So,we conclude that the manipulation of fitness function and the use of a grammar to introduce as much knowledge as possible into the search process are useful tools when applying evolutionary techniques in industrial environments.The modified fitness functions and genetic operators are discussed in the paper,too. %K genetic algorithms, genetic programming, reinforcement learning, grammar, knowledge %9 journal article %U http://medwelljournals.com/abstract/?doi=ijscomp.2006.52.60 %P 52-60 %0 Book Section %T Parallelizing Automatic Induction of Langton Parameter with Genetic Programming %A Carbajal, Santiago Garcia %A Corne, David W. %A Conty, Alejandro %E Erbacci, Giovanni %B Science and Supercomputing in Europe %D 2007 %V 2006 %I Cineca, Italy %F Carbajal:2007:SSCE %X Many classifications for Cellular Automata have been proposed during time. One of them is based on Langton Parameter. Depending on the probability of a cell of being active at one moment, Cellular Automata are divided into four types. Experimentally, interesting Cellular Automata have been shown to have Langton parameter values close to 0.3. It is said that near this value, Artificial Life is possible. We use a Genetic Programming technique to obtain transition rules with any desired value for lambda. Exploring an environment of the theoretical chaos limit, and measuring the entropy of the resulting automata, we search for Cellular Automata with interesting behavior. %K genetic algorithms, genetic programming, cellular automata, parallel programming %U http://www.hpc-europa.org/CD2006/contents/112-Math-Garcia.PDF %P 540-544 %0 Generic %T Time Series Prediction Using Grammar-directed Genetic Programming Methods %A Garcia Carbajal, Santi %D 2007 %I NN3 Artificial Neural Network & Computational Intelligence 2006/07 Forecasting Competition for Neural Networks & Computational Intelligence %G en %F 55-NN3-Carjabal %O ISF-2007, IJCNN 2007, DMIN 2007 %X We use a modified Genetic Programming System to predict the values of the reduced set proposed as benchmark for the 2007 Neural Forecasting Contest. Genetic Programming is a well known method used in symbolic regression, and classification, based in the evolution of arithmetic expressions according to a fitness function. We introduce here a grammar into the Genetic system, to let us use conditional expressions inside the syntactic trees representing the solutions to the problem. Additionally, we employ GA-P methods to automatically obtain constants inside the expressions. Our results proof the known power of Genetic Programming as a tool for solving Symbolic Regression problems, as the obtained expressions fit acceptably the proposed series. For the predicted values, some of them seem promising while others present too flat behaviours. %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.295.1670 %0 Journal Article %T Parallelizing Three Dimensional Cellular Automata With OpenMP %A Garcia Carbajal, Santiago %J Parallel Processing Letters %D 2007 %8 dec %V 17 %N 4 %@ 0129-6264 %F GarciaCarbajal:2007:PPL %X This paper describes our research on using Genetic Programming to obtain transition rules for Cellular Automata, which are one type of massively parallel computing system. Our purpose is to determine the existence of a limit of chaos for three dimensional Cellular Automata, empirically demonstrated for the two dimensional case. To do so, we must study statistical properties of 3D Cellular Automata over long simulation periods. When dealing with big three dimensional meshes, applying the transition rule to the whole structure can become a extremely slow task. In this work we decompose the Automata into pieces and use OpenMp to parallelise the process. Results show that using a decomposition procedure, and distributing the mesh between a set of processors, 3D Cellular Automata can be studied without having long execution times. %K genetic algorithms, genetic programming, cellular automata, Parallel Programming %9 journal article %R 10.1142/S0129626407003083 %U http://www.worldscinet.com/ppl/ppl.shtml %U http://dx.doi.org/10.1142/S0129626407003083 %P 349-361 %0 Journal Article %T Data-mining approach to investigate sedimentation features in combined sewer overflows %A Carbone, M. %A Berardi, L. %A Laucelli, D. %A Piro, P. %J Journal of Hydroinformatics %D 2012 %V 14 %N 3 %@ 1464-7141 %F Carbone:2012:JH %X Sedimentation is the most common and effectively practiced method of urban drainage control in terms of operating installations and duration of service. Assessing the percentage of suspended solids removed after a given detention time is essential for both design and management purposes. In previous experimental studies by some of the authors, the expression of iso-removal curves (i.e. representing the water depth where a given percentage of suspended solids is removed after a given detention time in a sedimentation column) has been demonstrated to depend on two parameters which describe particle settling velocity and flocculation factor. This study proposes an investigation of the influence of some hydrological and pollutant aggregate information of the sampled events on both parameters. The Multi-Objective (EPR-MOGA) and Multi-Case Strategy (MCS-EPR) variants of the Evolutionary Polynomial Regression (EPR) are originally used as data-mining strategies. Results are proved to be consistent with previous findings in the field and some indications are drawn for relevant practical applicability and future studies. %K genetic algorithms, genetic programming, CSOs (combined sewer overflows), data-mining techniques, Evolutionary Polynomial Regression, urban drainage, water pollutant %9 journal article %R 10.2166/hydro.2011.003 %U http://dx.doi.org/10.2166/hydro.2011.003 %P 613-627 %0 Generic %T Genetic Programming of Wavelet Networks for Time Series Prediction %A Card, Stuart %E O’Reilly, Una-May %D 1999 %8 13 jul %C Orlando, Florida, USA %F card:1999:GPWNTSP %X A hybrid genetic programming / neural network / wavelet technique for time series prediction is proposed. Iterative software development and experimentation are ongoing. %K genetic algorithms, genetic programming, neural-nets, wavelets, time, scale, frequency, prediction, stochastic, nonlinear %U http://www.borg.com/~stu/GECCO99.html %P 341-342 %0 Conference Proceedings %T Time Series Prediction by Genetic Programming with Relaxed Assumptions in Mathematica %A Card, Stuart W. %Y Poli, R. %Y Cagnoni, S. %Y Keijzer, M. %Y Costa, E. %Y Pereira, F. %Y Raidl, G. %Y Upton, S. C. %Y Goldberg, D. %Y Lipson, H. %Y de Jong, E. %Y Koza, J. %Y Suzuki, H. %Y Sawai, H. %Y Parmee, I. %Y Pelikan, M. %Y Sastry, K. %Y Thierens, D. %Y Stolzmann, W. %Y Lanzi, P. L. %Y Wilson, S. W. %Y O’Neill, M. %Y Ryan, C. %Y Yu, T. %Y Miller, J. F. %Y Garibay, I. %Y Holifield, G. %Y Wu, A. S. %Y Riopka, T. %Y Meysenburg, M. M. %Y Wright, A. W. %Y Richter, N. %Y Moore, J. H. %Y Ritchie, M. D. %Y Davis, L. %Y Roy, R. %Y Jakiela, M. %S GECCO 2004 Workshop Proceedings %D 2004 %8 26 30 jun %C Seattle, Washington, USA %F card:2004:gsw:swcar %X Time series produced by black box systems with both stochastic and nonlinear dynamical components have proven resistant to prediction. Also, prediction alone is unsatisfying: insight into the hidden dynamics is desired. Automatic induction of a system model would be ideal. A genetic programming (GP) / neural network (NN) / wavelet approach is motivated. An initial test problem selection is justified. Data preprocessing is described. The GP is shown to rely on weaker assumptions than those implicit in orthodox methods. An implementation in Mathematica is illustrated. GP discovery of equations, NN optimization of their parameters, and joint time-frequency representations, should provide highly parsimonious descriptions, capturing local and global characteristics of stochastic attractors, amenable to meaningful interpretation %K genetic algorithms, genetic programming, ANN %U http://gpbib.cs.ucl.ac.uk/gecco2004/WGSW002.pdf %0 Conference Proceedings %T Information Theoretic Indicators of Fitness, Relevant Diversity & Pairing Potential in Genetic Programming %A Card, Stuart W. %A Mohan, Chilukuri K. %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 card:2005:CEC %X Commonly used fitness measures, such as mean squared error, often fail to reward individuals whose presence in the population is necessary to explain substantial portions of the data variance. Diversity indicators are often arbitrary, may reflect diversity irrelevant to solving the problem, and are incommensurate with fitness measures. By contrast, information theoretic functionals are computable general indicators of fitness and diversity without these typical failings. We propose normalised mutual information, redundancy and synergy measures for genetic programming. We also propose selection for recombination and survival by ’pairing potential’ and ’pair potential’ estimation, and offer numerical examples as empirical support for theoretical claims. %K genetic algorithms, genetic programming %R 10.1109/CEC.2005.1555013 %U http://dx.doi.org/10.1109/CEC.2005.1555013 %P 2545-2552 %0 Conference Proceedings %T Ensemble selection for evolutionary learning using information theory and Price’s theorem %A Card, Stuart W. %A Mohan, Chilukuri K. %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 2 %I ACM Press %C Seattle, Washington, USA %@ 1-59593-186-4 %F 1144254 %X an information theoretic perspective on design and analysis of evolutionary algorithms is presented. Indicators of solution quality are developed and applied not only to individuals but also to ensembles, thereby ensuring information diversity. Prices Theorem is extended to show how joint indicators can drive reproductive sampling rate of potential parental pairings. Heritability of mutual information is identified as a key issue %K genetic algorithms, genetic programming, Learning Classifier Systems and other Genetics-Based Machine Learning: Poster, evolutionary computation, ensemble models, group selection, mate selection, measurement, Price’s equation, theory, machine learning %R 10.1145/1143997.1144254 %U http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1587.pdf %U http://dx.doi.org/10.1145/1143997.1144254 %P 1587-1588 %0 Book Section %T Towards an Information Theoretic Framework for Genetic programming %A Card, Stuart W. %A Mohan, Chilukuri K. %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 Card:2007:GPTP %X An information-theoretic framework is presented for the development and analysis of the ensemble learning approach of genetic programming. As evolution proceeds, this approach suggests that the mutual information between the target and models should: (i) not decrease in the population; (ii) concentrate in fewer individuals; and (iii) be distilled from the inputs, eliminating excess entropy. Normalised information theoretic indices are developed to measure fitness and diversity of ensembles, without a priori knowledge of how the multiple constituent models might be composed into a single model. With the use of these indexes for reproductive and survival selection, building blocks are less likely to be lost and more likely to be recombined. Price’s Theorem is generalised to pair selection, from which it follows that the heritability of information should be stronger than the heritability of error, improving evolvability. We support these arguments with simulations using a logic function benchmark and a time series application. For a chaotic time series prediction problem, for instance, the proposed approach avoids familiar difficulties (premature convergence, deception, poor scaling, and early loss of needed building blocks) with standard GP symbolic regression systems; information-based fitness functions showed strong intergenerational correlations as required by Price’s Theorem. %K genetic algorithms, genetic programming %R 10.1007/978-0-387-76308-8_6 %U http://dx.doi.org/10.1007/978-0-387-76308-8_6 %P 87-106 %0 Book Section %T An Application of Information Theoretic Selection to Evolution of Models with Continuous-valued Inputs %A Card, Stuart W. %A Mohan, Chilukuri K. %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 Card:2008:GPTP %X Information theoretic functionals have significant benefits as compared with traditional error based indicators of fitness and diversity. Mutual Information (MI), various normalizations of it, and similarity and distance metrics derived from it, can be used advantageously in all phases of Genetic Programming (GP), starting with input selection. However, these functionals are based on Shannon’s entropy, which is strictly defined only for discrete random variables, so their application to problems involving continuous valued data requires their generalization and development of robust and efficient algorithms for their calculation. This paper outlines such algorithms and illustrates their application to a noisy continuous valued data set synthesized to test GP symbolic regression systems (Korns, 2007). Information theoretic sufficiency outperforms linear correlation in ranking the relevance of available inputs in this data set. Similar results are obtained on inputs filtered by functions that ’fold’ the data, thereby destroying information; ranking these intermediate evolutionary forms, sufficiency again outperforms correlation. Sufficiency also exhibits a distinct threshold separating irrelevant terms from terms that are indeed relevant in regression of these test problems. As a less computationally costly alternative to rankings of entire populations, tournament selection is often used; on this data set, for pairwise tournament selection, sufficiency greatly outperforms correlation. Multi-objective ranking, considering also information theoretic necessity to prefer appropriately filtered inputs (over corresponding raw inputs with excess entropies), is foreshadowed. %K genetic algorithms, genetic programming, genetic programming, information theory, input selection, building blocks, ensemble models, diversity, fitness, entropy, mutual information, redundancy, synergy, similarity, information distance, evolvability, heritability, sufficiency, necessity, copula %R 10.1007/978-0-387-87623-8_3 %U http://dx.doi.org/10.1007/978-0-387-87623-8_3 %P 29-43 %0 Conference Proceedings %T Information distance based fitness and diversity metrics %A Card, Stuart W. %Y Card, Stuart William %Y Borenstein, Yossi %S GECCO 2010 Entropy, information and complexity %D 2010 %8 July 11 jul %I ACM %C Portland, Oregon, USA %F Card:2010:geccocomp %X Commensurate indicators of diversity and fitness with desirable metric properties are derived from information distances based on Shannon entropy and Kolmogorov complexity. These metrics measure various useful distances: from an information theoretic characterisation of the phenotypic behaviour of a candidate model in the population to that of an ideal model of the target system’s input-output relationship (fitness); from behavior of one candidate model to that of another (total information diversity); from the information about the target provided by one model to that provided by another (target relevant information diversity); from the code of one model to that of another (genotypic representation diversity); etc. Algorithms are cited for calculating the Shannon entropy based metrics from discrete data and estimating analogs thereof from heuristically binned continuous data; references are cited to methods for estimating the Kolmogorov complexity based metric. Not in the paper, but at the workshop, results will be shown of applying these algorithms to several synthetic and real world data sets: the simplest known deterministic chaotic flow; symbolic regression test functions; industrial process monitoring and control variables; and international political leadership data. Ongoing work is outlined. %K genetic algorithms, genetic programming, Measurement, Theory, evolutionary computation, machin e learning, fitness, diversity, metric, distance, mutual information, interaction information, algorithmic information, complexity, entropy, Kolmogorov Complexity, Kolmogorov Distance, Shannon Distance, Rajski Distance, Normalized Compression Distance, NCD, Multi-Objective Selection %R 10.1145/1830761.1830815 %U http://dx.doi.org/10.1145/1830761.1830815 %P 1851-1854 %0 Thesis %T Towards an Information Theoretic Framework for Evolutionary Learning %A Card, Stuart William %D 2011 %8 aug %C USA %C Electrical Engineering and Computer Science, Syracuse University %F Card:thesis %X The vital essence of evolutionary learning consists of information flows between the environment and the entities differentially surviving and reproducing therein. Gain or loss of information in individuals and populations due to evolutionary steps should be considered in evolutionary algorithm theory and practice. Information theory has rarely been applied to evolutionary computation, a lacuna that this dissertation addresses, with an emphasis on objectively and explicitly evaluating the ensemble models implicit in evolutionary learning. Information theoretic functionals can provide objective, justifiable, general, computable, commensurate measures of fitness and diversity. We identify information transmission channels implicit in evolutionary learning. We define information distance metrics and indices for ensembles. We extend Price’s Theorem to non-random mating, give it an effective fitness interpretation and decompose it to show the key factors influencing heritability and evolvability. We argue that heritability and evolvability of our information theoretic indicators are high. We illustrate use of our indices for reproductive and survival selection. We develop algorithms to estimate information theoretic quantities on mixed continuous and discrete data via the empirical copula and information dimension. We extend statistical resampling. We present experimental and real world application results: chaotic time series prediction; parity; complex continuous functions; industrial process control; and small sample social science data. We formalize conjectures regarding evolutionary learning and information geometry. %K genetic algorithms, genetic programming, diversity, ensemble model, evolvability, fitness, information distance, mutual information %9 Ph.D. thesis %U https://surface.syr.edu/eecs_etd/307 %0 Conference Proceedings %T Towards Information Theoretic GP of Causal Models %A Card, Stuart W. %Y Hu, Ting %Y Ofria, Charles %Y Trujillo, Leonardo %Y Winkler, Stephan %S Genetic Programming Theory and Practice XX %S Genetic and Evolutionary Computation %D 2023 %8 jun 1 3 %C Michigan State University, USA %F Card:2023:GPTP %K genetic algorithms, genetic programming %0 Conference Proceedings %T The Needs of the Few – Information Theory Aided Ensemble Selection for Robustly Generalizing Causal Models %A Card, Stuart %Y Banzhaf, Wolfgang %Y Burlacu, Bogdan %Y Kelly, Stephen %Y Lalejini, Alexander %Y Olivetti de Franca, Fabricio %S Genetic Programming Theory and Practice XXII %D 2025 %8 jun 5 7 %C Michigan State University, USA %F Card:2025:GPTP %K genetic algorithms, genetic programming %0 Conference Proceedings %T Dynamic Synthesis of Program Invariants using Genetic Programming %A Cardamone, Luigi %A Mocci, Andrea %A Ghezzi, Carlo %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 Cardamone:2011:DSoPIuGP %X In the field of software engineering, invariant detection techniques have been proposed to overcome the problem of software behaviour comprehension. If the code of a program is available, combining symbolic and concrete execution has been shown to provide an effective method to derive logic formulae that describe a program’s behavior. However, symbolic execution does not work very well with loops, and thus such methods are not able to derive useful descriptions of programs containing loops. we present a preliminary approach that aims to integrate genetic programming to synthesise a logic formula that describes the behaviour of a loop. Such formula could be integrated in a symbolic execution based approach for invariant detection to synthesize a complex program behaviour. We present a specific representation of formulae that works well with loops manipulating arrays. The technique has been validated with a set of relevant examples with increasing complexity. The preliminary results are promising and show the feasibility of our approach. %K genetic algorithms, genetic programming, SBSE, invariant formula, logic formulae, loop manipulating array, program comprehension, program in verification, statement execution, symbolic program manipulation, transformation rule, iterative methods, program verification, symbol manipulation %R 10.1109/CEC.2011.5949677 %U https://www.luigicardamone.it/bibtex.htm %U http://dx.doi.org/10.1109/CEC.2011.5949677 %P 617-624 %0 Thesis %T Evolutionary Learning and Search-Based Content Generation in Computer Games %A Cardamone, Luigi %D 2011 %C Piazza Leonardo da Vinci 32, Milan, I 20133, Italy %C Dipartimento di Elettronica e Informazione, Politecnico di Milano %F cardamone2012phd %X Modern computer games achieved an impressive level of realism ... %K genetic algorithms, search-based, content generation, evolutionary learning, on-line learning, transfer learning, neural networks, ANN, NEAT, TORCS %9 Ph.D. thesis %U https://www.luigicardamone.it/bibtex.htm %0 Journal Article %T M5GP: Parallel Multidimensional Genetic Programming with Multidimensional Populations for Symbolic Regression %A Cardenas Florido, Luis %A Trujillo, Leonardo %A Hernandez, Daniel E. %A Munoz Contreras, Jose Manuel %J Mathematical and Computational Applications %D 2024 %V 29 %N 2 %@ 2297-8747 %F cardenas-florido:2024:MaCA %X Machine learning and artificial intelligence are growing in popularity thanks to their ability to produce models that exhibit unprecedented performance in domains that include computer vision, natural language processing and code generation. However, such models tend to be very large and complex and impossible to understand using traditional analysis or human scrutiny. Conversely, Symbolic Regression methods attempt to produce models that are relatively small and (potentially) human-readable. In this domain, Genetic Programming (GP) has proven to be a powerful search strategy that achieves state-of-the-art performance. This paper presents a new GP-based feature transformation method called M5GP, which is hybridized with multiple linear regression to produce linear models, implemented to exploit parallel processing on graphical processing units for efficient computation. M5GP is the most recent variant from a family of feature transformation methods (M2GP, M3GP and M4GP) that have proven to be powerful tools for both classification and regression tasks applied to tabular data. The proposed method was evaluated on SRBench v2.0, the current standard benchmarking suite for Symbolic Regression. Results show that M5GP achieves performance that is competitive with the state-of-the-art, achieving a top-three rank on the most difficult subset of black-box problems. Moreover, it achieves the lowest computation time when compared to other GP-based methods that have similar accuracy scores. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/mca29020025 %U https://www.mdpi.com/2297-8747/29/2/25 %U http://dx.doi.org/10.3390/mca29020025 %P ArticleNo.25 %0 Conference Proceedings %T Local Search Approach to Genetic Programming for RF-PAs Modeling Implemented in FPGA %A Cardenas Valdez, J. R. %A Z-Flores, Emigdio %A Nunez Perez, Jose Cruz %A Trujillo, Leonardo %Y Schuetze, Oliver %Y Trujillo, Leonardo %Y Legrand, Pierrick %Y Maldonado, Yazmin %S NEO 2015: Results of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico %S Studies in Computational Intelligence %D 2015 %V 663 %I Springer %F Cardenas-Valdez:2015:NEO %X This paper presents a genetic programming (GP) approach enhanced with a local search heuristic (GP-LS) to emulate the Doherty 7 W @ 2.11 GHz Radio Frequency (RF) Power Amplifier (PA) conversion curves. GP has been shown to be a powerful modelling tool, but can be compromised by slow convergence and computational cost. The proposal is to combine the explorative search of standard GP, which build the syntax of the solution, with numerical methods that perform an exploitative and greedy local optimization of the evolved structures. The results are compared with traditional modeling techniques, particularly the memory polynomial model (MPM). The main contribution of the paper is the design, comparison and hardware emulation of GP-LS for FPGA real applications. The experimental results show that GP-LS can outperform standard MPM, and suggest a promising new direction of future work on digital pre-distortion (DPD) that requires complex behavioural models %K genetic algorithms, genetic programming, EHW, FPGA, Local search, MPM, Behavioral models, DPD %R 10.1007/978-3-319-44003-3_3 %U http://dx.doi.org/10.1007/978-3-319-44003-3_3 %P 67-88 %0 Conference Proceedings %T Evolving robust policies for community energy system management %A Cardoso, Rui P. %A Hart, Emma %A Pitt, Jeremy V. %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 Cardoso:2019:GECCO %X Community energy systems (CESs) are shared energy systems in which multiple communities generate and consume energy from renewable resources. At regular time intervals, each participating community decides whether to self-supply, store, trade, or sell their energy to others in the scheme or back to the grid according to a predefined policy which all participants abide by. The objective of the policy is to maximise average satisfaction across the entire CES while minimising the number of unsatisfied participants. We propose a multi-class, multi-tree genetic programming approach to evolve a set of specialist policies that are applicable to specific conditions, relating to abundance of energy, asymmetry of generation, and system volatility. Results show that the evolved policies significantly outperform a default hand crafted policy. Additionally, we evolve a generalist policy and compare its performance to specialist ones, finding that the best generalist policy can equal the performance of specialists in many scenarios. We claim that our approach can be generalised to any multi-agent system solving a common-pool resource allocation problem that requires the design of a suitable operating policy. %K genetic algorithms, genetic programming, Multi-agent system, Community energy system management %R 10.1145/3321707.3321763 %U http://dx.doi.org/10.1145/3321707.3321763 %P 1120-1128 %0 Journal Article %T Global sensitivity analysis of a generator-absorber heat exchange (GAX) system’s thermal performance with a hybrid energy source: An approach using artificial intelligence models %A Cardoso-Fernandez, V. %A Bassam, A. %A May Tzuc, O. %A Barrera Ch., M. A. %A de Jesus Chan-Gonzalez, Jorge %A Escalante Soberanis, M. A. %A Velazquez-Limon, N. %A Ricalde, Luis J. %J Applied Thermal Engineering %D 2023 %V 218 %@ 1359-4311 %F CARDOSOFERNANDEZ:2023:applthermaleng %X Generator-absorber heat exchange (GAX) systems represent a promising alternative to substitute environmentally harmful refrigeration devices based on conventional vapor compression, as long as a proper analysis of thermal performance and the complex interactions of heat transfer that occur into GAX cycle is taken in consideration. In this research, a cooling process based on a GAX system that uses ammonia-water working fluid and a hybrid source (natural gas-solar) is studied to analyze the variables that affect the system’s thermal performance. The work’s novelty is the hybridization between artificial intelligence (AI) modeling and the global sensitivity analysis (GSA) developed with the PAWN method. Experimental data was obtained from a system with a cooling capacity of 10.5 kW (3 Ton), designed to work at heat source temperatures of 200 degreeC. The measured variables were the temperatures at generator, heat at evaporator, and working fluid volumetric flow. Three AI techniques (artificial neural networks, genetic programming, and support vector machines) were evaluated for modeling the thermodynamic cycle. Results obtained from the PAWN method applied to the artificial neural network, since it was the best AI model, indicates that the operational parameters with a greater impact in the system’s performance are the inlet temperature at the generator (30.7 percent) and the heat measured at the evaporator for NH3 (27.4 percent), for the first output COPNH3. For the second output COPH2O, the inlet temperature at the generator (32.5 percent) and the and heat measured at the evaporator for H2O (26.7 percent), have a greater impact for such output. The proposed IA-GSA methodology contributes to the development of operational decision-making related to instrumentation, operation performance, and corrective and/or preventive maintenance actions of GAX systems. The developed thermal performance model has potential for implementation in embedded systems (smart sensors) as a critical element in control and optimization strategies to improve the performance of these cycles %K genetic algorithms, genetic programming, Generator-Absorber Heat Exchange (GAX), Solar refrigeration cycle, Hybrid renewable energy system, Data-driven models, PAWN method, Decision-making process, Absorption refrigeration %9 journal article %R 10.1016/j.applthermaleng.2022.119363 %U https://www.sciencedirect.com/science/article/pii/S1359431122012935 %U http://dx.doi.org/10.1016/j.applthermaleng.2022.119363 %P 119363 %0 Conference Proceedings %T Topological puzzles in biology: how geometry shapes evolution and applications to designing intelligent collectives %A Carja, Oana %Y Hu, Ting %Y Ofria, Charles %Y Trujillo, Leonardo %Y Winkler, Stephan %S Genetic Programming Theory and Practice XX %S Genetic and Evolutionary Computation %D 2023 %8 jun 1 3 %C Michigan State University, USA %F Carja:2023:GPTP %O Invited Keynote %K genetic algorithms, genetic programming %0 Conference Proceedings %T Evolutionary Algorithms-assisted Construction of Cryptographic Boolean Functions %A Carlet, Claude %A Jakobovic, Domagoj %A Picek, Stjepan %Y Chicano, Francisco %Y Tonda, Alberto %Y Krawiec, Krzysztof %Y Helbig, Marde %Y Cleghorn, Christopher W. %Y Wilson, Dennis G. %Y Yannakakis, Georgios %Y Paquete, Luis %Y Ochoa, Gabriela %Y Bacardit, Jaume %Y Gagne, Christian %Y Mostaghim, Sanaz %Y Jourdan, Laetitia %Y Schuetze, Oliver %Y Posik, Petr %Y Segura, Carlos %Y Tinos, Renato %Y Cotta, Carlos %Y Heywood, Malcolm %Y Zhang, Mengjie %Y Trujillo, Leonardo %Y Miikkulainen, Risto %Y Xue, Bing %Y Neumann, Aneta %Y Allmendinger, Richard %Y Ishikawa, Fuyuki %Y Medina-Bulo, Inmaculada %Y Neumann, Frank %Y Sutton, Andrew M. %S Proceedings of the 2021 Genetic and Evolutionary Computation Conference %S GECCO ’21 %D 2021 %8 jul 10 14 %I Association for Computing Machinery %C internet %F Carlet:2021:GECCO %X In the last few decades, evolutionary algorithms were successfully applied numerous times for creating Boolean functions with good cryptographic properties. Still, the applicability of such approaches was always limited as the cryptographic community knows how to construct suitable Boolean functions with deterministic algebraic constructions. Thus, evolutionary results so far helped to increase the confidence that evolutionary techniques have a role in cryptography, but at the same time, the results themselves were seldom used. We consider a novel problem using evolutionary algorithms to improve Boolean functions obtained through algebraic constructions. To this end, we consider a recent generalisation of Hidden Weight Boolean Function construction, and we show that evolutionary algorithms can significantly improve the cryptographic properties of the functions. Our results show that the genetic algorithm performs by far the best of all the considered algorithms and improves the non-linearity property in all Boolean function sizes. As there are no known algebraic techniques to reach the same goal, we consider this application a step forward in accepting evolutionary algorithms as a powerful tool in the cryptography domain. %K genetic algorithms, genetic programming, evolution strategies, Boolean function, Cryptography, Secondary Construction, Hidden Weight Boolean Function %R 10.1145/3449639.3459362 %U http://www.human-competitive.org/sites/default/files/picek_humies.txt %U http://dx.doi.org/10.1145/3449639.3459362 %P 565-573 %0 Conference Proceedings %T Evolving Constructions for Balanced, Highly Nonlinear Boolean Functions %A Carlet, Claude %A Djurasevic, Marko %A Jakobovic, Domagoj %A Mariot, Luca %A Picek, Stjepan %Y Rahat, Alma %Y Fieldsend, Jonathan %Y Wagner, Markus %Y Tari, Sara %Y Pillay, Nelishia %Y Moser, Irene %Y Aleti, Aldeida %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Hemberg, Erik %Y Cleghorn, Christopher %Y Sun, Chao-li %Y Yannakakis, Georgios %Y Bredeche, Nicolas %Y Ochoa, Gabriela %Y Derbel, Bilel %Y Pappa, Gisele L. %Y Risi, Sebastian %Y Jourdan, Laetitia %Y Sato, Hiroyuki %Y Posik, Petr %Y Shir, Ofer %Y Tinos, Renato %Y Woodward, John %Y Heywood, Malcolm %Y Wanner, Elizabeth %Y Trujillo, Leonardo %Y Jakobovic, Domagoj %Y Miikkulainen, Risto %Y Xue, Bing %Y Neumann, Aneta %Y Allmendinger, Richard %Y Medina-Bulo, Inmaculada %Y Bechikh, Slim %Y Sutton, Andrew M. %Y Oliveto, Pietro Simone %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F carlet:2022:GECCO %X Finding balanced, highly nonlinear Boolean functions is a difficult problem where it is not known what nonlinearity values are possible to be reached in general. At the same time, evolutionary computation is successfully used to evolve specific Boolean function instances, but the approach cannot easily scale for larger Boolean function sizes. Indeed, while evolving smaller Boolean functions is almost trivial, larger sizes become increasingly difficult, and evolutionary algorithms perform suboptimally.In this work, we ask whether genetic programming (GP) can evolve constructions resulting in balanced Boolean functions with high nonlinearity. This question is especially interesting as there are only a few known such constructions. Our results show that GP can find constructions that generalize well, i.e., result in the required functions for multiple tested sizes. Further, we show that GP evolves many equivalent constructions under different syntactic representations. Interestingly, the simplest solution found by GP is a particular case of the well-known indirect sum construction. %K genetic algorithms, genetic programming, Boolean functions, evolutionary algorithms, non-linearity, secondary constructions, balancedness %R 10.1145/3512290.3528871 %U https://doi.org/10.1145/3512290.3528871 %U http://dx.doi.org/10.1145/3512290.3528871 %P 1147-1155 %0 Conference Proceedings %T On Generalizing the Power Function Exponent Constructions with Genetic Programming %A Carlet, Claude %A Jakobovic, Domagoj %A Picek, Stjepan %Y Trautmann, Heike %Y Doerr, Carola %Y Moraglio, Alberto %Y Bartz-Beielstein, Thomas %Y Filipic, Bogdan %Y Gallagher, Marcus %Y Ong, Yew-Soon %Y Gupta, Abhishek %Y Kononova, Anna V. %Y Wang, Hao %Y Emmerich, Michael %Y Bosman, Peter A. N. %Y Zaharie, Daniela %Y Caraffini, Fabio %Y Dreo, Johann %Y Auger, Anne %Y Dietric, Konstantin %Y Dufosse, Paul %Y Glasmachers, Tobias %Y Hansen, Nikolaus %Y Mersmann, Olaf %Y Posik, Petr %Y Tusar, Tea %Y Brockhoff, Dimo %Y Eftimov, Tome %Y Kerschke, Pascal %Y Naujoks, Boris %Y Preuss, Mike %Y Volz, Vanessa %Y Derbel, Bilel %Y Li, Ke %Y Li, Xiaodong %Y Zapotecas, Saul %Y Zhang, Qingfu %Y Coletti, Mark %Y Schuman, Catherine (Katie) %Y Scott, Eric “Siggy” %Y Patton, Robert %Y Wiegand, Paul %Y Bassett, Jeffrey K. %Y Gunaratne, Chathika %Y Chugh, Tinkle %Y Allmendinger, Richard %Y Hakanen, Jussi %Y Tauritz, Daniel %Y Woodward, John %Y Lopez-Ibanez, Manuel %Y McCall, John %Y Bacardit, Jaume %Y Brownlee, Alexander %Y Cagnoni, Stefano %Y Iacca, Giovanni %Y Walker, David %Y Toutouh, Jamal %Y O’Reilly, UnaMay %Y Machado, Penousal %Y Correia, Joao %Y Nesmachnow, Sergio %Y Ceberio, Josu %Y Villanueva, Rafael %Y Hidalgo, Ignacio %Y Fernandez de Vega, Francisco %Y Paolo, Giuseppe %Y Coninx, Alex %Y Cully, Antoine %Y Gaier, Adam %Y Wagner, Stefan %Y Affenzeller, Michael %Y Bruce, Bobby R. %Y Nowack, Vesna %Y Blot, Aymeric %Y Winter, Emily %Y Langdon, William B. %Y Petke, Justyna %Y Fernandez Alzueta, Silvino %Y Valledor Pellicer, Pablo %Y Stuetzle, Thomas %Y Paetzel, David %Y Wagner, Alexander %Y Heider, Michael %Y Veerapen, Nadarajen %Y Malan, Katherine %Y Liefooghe, Arnaud %Y Verel, Sebastien %Y Ochoa, Gabriela %Y Omidvar, Mohammad Nabi %Y Sun, Yuan %Y Tarantino, Ernesto %Y Ivanoe, De Falco %Y Della Cioppa, Antonio %Y Umberto, Scafuri %Y Rieffel, John %Y Mouret, Jean-Baptiste %Y Doncieux, Stephane %Y Nikolaidis, Stefanos %Y Togelius, Julian %Y Fontaine, Matthew C. %Y Georgescu, Serban %Y Chicano, Francisco %Y Whitley, Darrell %Y Kyriienko, Oleksandr %Y Dahl, Denny %Y Shir, Ofer %Y Spector, Lee %Y Rahat, Alma %Y Everson, Richard %Y Fieldsend, Jonathan %Y Wang, Handing %Y Jin, Yaochu %Y Hemberg, Erik %Y Elsayed, Marwa A. %Y Kommenda, Michael %Y La Cava, William %Y Kronberger, Gabriel %Y Gustafson, Steven %S Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion %S GECCO ’22 %D 2022 %8 September 13 jul %I Association for Computing Machinery %C Boston, USA %F carlet:2022:GECCOcomp %X Many works are investigating Almost Perfect Nonlinear (APN) functions and, in particular, APN power functions. Such functions are of the form F(x) = xd, and they have practical relevance as they reach in characteristic 2 the best possible differential uniformity. This work investigates whether genetic programming (GP) can ’reinvent’ the known expressions used to obtain exponent values d resulting in APN functions. The ultimate goal is to find classes of exponents that would be ’transversal’ to the known infinite classes of APN exponents, and would contain new APN exponents (for values of n necessarily larger than those for which an exhaustive search could be made so far). This would be already a breakthrough, and our hope is to find this way new infinite classes of APN exponents. Our results show this is possible but difficult, and a careful trade-off between finding new values and skipping known values is required. %K genetic algorithms, genetic programming, evolutionary algorithms, APN, power function, s-box, almost perfect nonlinear %R 10.1145/3520304.3529081 %U http://dx.doi.org/10.1145/3520304.3529081 %P 691-694 %0 Conference Proceedings %T A New Angle: On Evolving Rotation Symmetric Boolean Functions %A Carlet, Claude %A Durasevic, Marko %A Gasperov, Bruno %A Jakobovic, Domagoj %A Mariot, Luca %A Picek, Stjepan %Y Smith, Stephen %Y Correia, Joao %Y Cintrano, Christian %S 27th International Conference, EvoApplications 2024 %S LNCS %D 2024 %8 March 5 apr %V 14634 %I Springer %C Aberystwyth %F Carlet:2024:evoapplications %X Rotation symmetric Boolean functions represent an interesting class of Boolean functions as they are relatively rare compared to general Boolean functions. At the same time, the functions in this class can have excellent cryptographic properties, making them interesting for various practical applications. The usage of metaheuristics to construct rotation symmetric Boolean functions is a direction that has been explored for almost twenty years. Despite that, there are very few results considering evolutionary computation methods. This paper uses several evolutionary algorithms to evolve rotation symmetric Boolean functions with different properties. Despite using generic metaheuristics, we obtain results that are competitive with prior work relying on customized heuristics. Surprisingly, we find that bitstring and floating point encodings work better than the tree encoding. Moreover, evolving highly nonlinear general Boolean functions is easier than rotation symmetric ones. %K genetic algorithms, genetic programming, rotation symmetry, Boolean functions, metaheuristics, nonlinearity %R 10.1007/978-3-031-56852-7_19 %U https://rdcu.be/dDZU2 %U http://dx.doi.org/10.1007/978-3-031-56852-7_19 %P 287-302 %0 Conference Proceedings %T Look into the Mirror: Evolving Self-dual Bent Boolean Functions %A Carlet, Claude %A Durasevic, Marko %A Jakobovic, Domagoj %A Mariot, Luca %A Picek, Stjepan %Y Giacobini, Mario %Y Xue, Bing %Y Manzoni, Luca %S EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming %S LNCS %D 2024 %8 March 5 apr %V 14631 %I Springer %C Aberystwyth %F Carlet:2024:EuroGP %X Bent Boolean functions are important objects in cryptography and coding theory, and there are several general approaches for constructing such functions. Metaheuristics proved to be a strong choice as they can provide many bent functions, even when the size of the Boolean function is large (e.g., more than 20 inputs). While bent Boolean functions represent only a small part of all Boolean functions, there are several subclasses of bent functions providing specific properties and challenges. One of the more interesting subclasses comprises (anti-)self-dual bent Boolean functions. %K genetic algorithms, genetic programming %R 10.1007/978-3-031-56957-9_10 %U http://dx.doi.org/10.1007/978-3-031-56957-9_10 %P 161-175 %0 Conference Proceedings %T A Systematic Evaluation of Evolving Highly Nonlinear Boolean Functions in Odd Sizes %A Carlet, Claude %A Durasevic, Marko %A Jakobovic, Domagoj %A Picek, Stjepan %A Mariot, Luca %Y Xue, Bing %Y Manzoni, Luca %Y Bakurov, Illya %S European Conference on Genetic Programming, EuroGP 2025 %S LNCS %D 2025 %8 23 25 apr %V 15609 %I Springer Nature %C Trieste %F carlet:2025:EuroGP %X Boolean functions are mathematical objects used in diverse applications. Different applications also have different requirements, making the research on Boolean functions very active. In the last 30 years, evolutionary algorithms have been shown to be a strong option for evolving Boolean functions in different sizes and with different properties. Still, most of those works consider similar settings and provide results that are mostly interesting from the evolutionary algorithm’s perspective. This work considers the problem of evolving highly nonlinear Boolean functions in odd sizes. While the formulation sounds simple, the problem is remarkably difficult, and the related work is extremely scarce. We consider three solutions encodings and four Boolean function sizes and run a detailed experimental analysis. Our results show that GP outperforms other EA in evolving highly nonlinear functions. Nevertheless, the problem is challenging, and finding optimal solutions is impossible except for the smallest tested size. However, once we added local search to the evolutionary algorithm, we managed to find a Boolean function in nine inputs with nonlinearity 241, which, to our knowledge, had never been accomplished before with evolutionary algorithms. %K genetic algorithms, genetic programming, Boolean functions, nonlinearity, evolutionary algorithms, odd dimension, encodings %R 10.1007/978-3-031-89991-1_2 %U https://www.human-competitive.org/sites/default/files/humies_entry_2025.txt %U http://dx.doi.org/10.1007/978-3-031-89991-1_2 %P 18-34 %0 Conference Proceedings %T The More the Merrier: On Evolving Five-Valued Spectra Boolean Functions %A Carlet, Claude %A Durasevic, Marko %A Jakobovic, Domagoj %A Mariot, Luca %A Picek, Stjepan %Y Garcia-Sanchez, Pablo %Y Hart, Emma %Y Thomson, Sarah L. %S 28th International Conference, EvoApplications 2025 %S LNCS %D 2025 %8 March 5 apr %V 15612 %I Springer %C Trieste, Italy %F Carlet:2025:evoapplications %K genetic algorithms, genetic programming, Boolean Functions, Plateaudness Algorithms, Five-valued Functions %R 10.1007/978-3-031-90062-4_4 %U http://dx.doi.org/10.1007/978-3-031-90062-4_4 %P 52-67 %0 Journal Article %T A systematic study on the design of odd-sized highly nonlinear boolean functions via evolutionary algorithms %A Carlet, Claude %A Durasevic, Marko %A Jakobovic, Domagoj %A Picek, Stjepan %A Mariot, Luca %J Genetic Programming and Evolvable Machines %D 2026 %V 27 %@ 1389-2576 %F Carlet:2026:GPEM %O Online first %X We evolve Boolean functions of odd sizes with high nonlinearity, a property of cryptographic relevance. Despite its simple formulation, this problem turns out to be remarkably difficult. We conduct a systematic evaluation by considering three solution encodings and four problem instances, analyzing how well different types of evolutionary algorithms behave in finding a maximally nonlinear Boolean function. Our results show that genetic programming generally outperforms other evolutionary algorithms, although it falls short of the best-known results achieved by ad-hoc heuristics. Interestingly, by adding local search and restricting the space to rotation symmetric Boolean functions, we show that a genetic algorithm with the bitstring encoding manages to evolve a 9-variable Boolean function with nonlinearity 241 %K genetic algorithms, genetic programming, Boolean functions, Nonlinearity, Evolutionary algorithms, Odd dimension, Primary construction, Secondary construction %9 journal article %R 10.1007/s10710-025-09526-5 %U http://dx.doi.org/10.1007/s10710-025-09526-5 %P Articleno1 %0 Journal Article %T Biomedical Classification Problems Automatically Solved by Computational Intelligence Methods %A Carlos Padierna, L. %A Villasenor-Mora, C. %A Lopez Juarez, S. A. %J IEEE Access %D 2020 %V 8 %@ 2169-3536 %F Carlos-Padierna:2020:ACC %X Biomedical classification problems are of great interest to both medical practitioners and computer scientists. Due to the harmful consequences of a wrong decision in this ambit, computational methods must be carefully designed to provide a reliable tool for helping physicians to obtain accurate predictions on unseen cases. Computational Intelligence (CI) provides robust models to perform optimization, classification and regression tasks. These models have been previously designed, mainly based on the expertise of computer scientists, to solve a vast number of biomedical problems. As the number of both CI algorithms and biomedical problems continues to grow, selecting the right method to solve a given problem becomes more challenging. To deal with this complexity, a systematic methodology for selecting a suitable model for a given classification problem is required. In this work, we review the more promising classification and optimization algorithms and reformulate them into a synergistic framework to automatically design and optimize pattern classifiers. Our proposal, including state-of-the-art evolutionary algorithms and support vector machines, is tested on a variety of biomedical problems. Experimental results on benchmark datasets allow us to conclude that the automatically designed classifiers reach higher or equal performance than those designed by computer specialists. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/ACCESS.2020.2998749 %U http://dx.doi.org/10.1109/ACCESS.2020.2998749 %P 101104-101117 %0 Conference Proceedings %T Interactive Evolution of Speech using VoiceXML Speaking to your GP System %A Carlsson, Jonas %A Paiz, Carlos %A Wolff, Krister %A Nordin, Peter %Y Callaos, Nagib %Y Pisarchik, Alexander %Y Ueda, Mitsuyoshi %S Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics %D 2002 %V VI %I IIIS %@ 980-07-8150-1 %F CarPai02 %X we describe and discuss experiments in which we try to evolve meaningful sentences in English using Genetic Programming with interactive evolution. We use VoiceXML as the user interface, through which the user hears each individual, acts as the fitness function and tells the system what individuals to select. This is the first GP-system that accepts voice as guidance for fitness calculations. We use context free grammars to define the individuals and the genetic operators make sure that the grammar is followed, avoiding destructive mutation and crossover. The results show that it is possible to evolve meaningful phrases with our approach but improvements to the system are required in order to fully achieve the goal. The wide availability of voice terminals, such as phones, enables powerful learning of, for example, natural language grammar with possible feedback even from the general public. The described work also constitutes the first GP-system written in JavaScript (ECMAScript) enabling easy distributed GP-run over the Web without any installation. %K genetic algorithms, genetic programming, voice XML %U http://citeseer.uark.edu:8080/citeseerx/showciting;jsessionid=3ACDA5B9DB9ACC6C5ECF27C2C8BEA296?cid=5226534 %P 58-62 %0 Conference Proceedings %T FuGePSD: Fuzzy Genetic Programming-based algorithm for Subgroup Discovery %A Carmona, Cristobal J. %A Gonzalez, Pedro %A del Jesus, Maria Jose %Y Alonso, Jose M. %Y Bustince, Humberto %Y Reformat, Marek %S 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15) %D 2015 %8 jun 30 3 jul %I Atlantis Press %C Gijon, Spain %F conf/eusflat/CarmonaGJ15 %X Evolutionary Fuzzy Systems (EFSs) are fuzzy systems augmented by a learning process based on evolutionary computation such as evolutionary algorithms (EAs). These systems contribute with several advantages in the development of algorithms, and specifically in the development of subgroup discovery (SD) approaches. SD is a descriptive data mining technique using supervised learning in order to describe data with respect to a property of interest. This paper present the main features of the FuGePSD algorithm, an EFS based on genetic programming and fuzzy logic. An experimental study with a wide number of datasets shows the quality of this algorithm with respect to the remaining EFSs for SD presented throughout the literature. %K genetic algorithms, genetic programming, subgroup discovery, evolutionary fuzzy system %R 10.2991/ifsa-eusflat-15.2015.65 %U http://www.atlantis-press.com/php/download_paper.php?id=23576 %U http://dx.doi.org/10.2991/ifsa-eusflat-15.2015.65 %P 448-454 %0 Journal Article %T A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans %A Carmona, C. J. %A Ruiz-Rodado, V. %A del Jesus, M. J. %A Weber, A. %A Grootveld, M. %A Gonzalez, P. %A Elizondo, D. %J Information Sciences %D 2015 %V 298 %@ 0020-0255 %F Carmona:2015:IS %X This paper proposes a novel algorithm for subgroup discovery task based on genetic programming and fuzzy logic called Fuzzy Genetic Programming-based for Subgroup Discovery (FuGePSD). The genetic programming allows to learn compact expressions with the main objective to obtain rules for describing simple, interesting and interpretable subgroups. This algorithm incorporates specific operators in the search process to promote the diversity between the individuals. The evolutionary scheme of FuGePSD is codified through the genetic cooperative-competitive approach promoting the competition and cooperation between the individuals of the population in order to find out the optimal solutions for the SD task. FuGePSD displays its potential with high-quality results in a wide experimental study performed with respect to others evolutionary algorithms for subgroup discovery. Moreover, the quality of this proposal is applied to a case study related to acute sore throat problems. %K genetic algorithms, genetic programming, Subgroup discovery, Evolutionary fuzzy system, Bioinformatics %9 journal article %R 10.1016/j.ins.2014.11.030 %U http://www.sciencedirect.com/science/article/pii/S0020025514011116 %U http://dx.doi.org/10.1016/j.ins.2014.11.030 %P 180-197 %0 Book Section %T Evolution of Game Playing Behavior: Using Genetic Programming to Create Players for Net Hack %A Carobus, Alexander P. %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 carobus:2000:EGPBUGPCPNH %K genetic algorithms, genetic programming %P 60-69 %0 Conference Proceedings %T Optical Random Neural Networks with Genetic Programming %A Carpinlioglu, Bora %A Danis, Bahrem Serhat %A Tegin, Ugur %S 2024 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR) %D 2024 %8 aug %F Carpinlioglu:2024:CLEO-PR %X We demonstrate a programmable optical random neural network by genetically optimising a scattering medium. Improvements in classification accuracy are shown for image datasets. Our results indicate the effectiveness of optical computing for future computing platforms. %K genetic algorithms, genetic programming, Accuracy, Neural networks, ANN, Scattering, Optical computing, Optical fiber networks, Optical imaging %R 10.1109/CLEO-PR60912.2024.10676723 %U http://dx.doi.org/10.1109/CLEO-PR60912.2024.10676723 %0 Conference Proceedings %T Evolution of Classification Rules for Comprehensible Knowledge Discovery %A Carreno, Emiliano %A Leguizamon, Guillermo %A Wagner, Neal %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 Carreno:2007:cec %X This article, which lies within the data mining framework, proposes a method to build classifiers based on the evolution of rules. The method, named REC (Rule Evolution for Classifiers), has three main features: it applies genetic programming to perform a search in the space of potential solutions; a procedure allows biasing the search towards regions of comprehensible hypothesis with high predictive quality and it includes a strategy for the selection of an optimum subset of rules (classifier) from the rules obtained as the result of the evolutionary process. A comparative study between this method and the rule induction algorithm C5.0 is carried out for two application problems (data sets). Experimental results show the advantages of using the method proposed. %K genetic algorithms, genetic programming %R 10.1109/CEC.2007.4424615 %U 1695.pdf %U http://dx.doi.org/10.1109/CEC.2007.4424615 %P 1261-1268 %0 Journal Article %T Long memory time series forecasting by using genetic programming %A Carreno Jara, Emiliano %J Genetic Programming and Evolvable Machines %D 2012 %8 dec %V 12 %N 4 %@ 1389-2576 %F CarrenoJara:2011:GPEM %X Real-world time series have certain properties, such as stationarity, seasonality, linearity, among others, which determine their underlying behaviour. There is a particular class of time series called long-memory processes, characterised by a persistent temporal dependence between distant observations, that is, the time series values depend not only on recent past values but also on observations of much prior time periods. The main purpose of this research is the development, application, and evaluation of a computational intelligence method specifically tailored for long memory time series forecasting, with emphasis on many-step-ahead prediction. The method proposed here is a hybrid combining genetic programming and the fractionally integrated (long-memory) component of autoregressive fractionally integrated moving average (ARFIMA) models. Another objective of this study is the discovery of useful comprehensible novel knowledge, represented as time series predictive models. In this respect, a new evolutionary multi-objective search method is proposed to limit complexity of evolved solutions and to improve predictive quality. Using these methods allows for obtaining lower complexity (and possibly more comprehensible) models with high predictive quality, keeping run time and memory requirements low, and avoiding bloat and over-fitting. The methods are assessed on five real-world long memory time series and their performance is compared to that of statistical models reported in the literature. Experimental results show the proposed methods’ advantages in long memory time series forecasting. %K genetic algorithms, genetic programming, Long memory, Time series forecasting, Multi-objective search, ARFIMA models %9 journal article %R 10.1007/s10710-011-9140-7 %U http://dx.doi.org/10.1007/s10710-011-9140-7 %P 429-456 %0 Conference Proceedings %T Self-evolving applications over opportunistic communication systems %A Carreras, Iacopo %A Linner, David %S 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops, 2010) %D 2010 %8 mar 29 apr 2 %F Carreras:2010:percomWKS %X In this work, we focus on an application scenario in which services running over users mobile devices exploit opportunistic communications in order to evolve over time, as the result of a distributed and collaborative process. We propose a framework which is based on genetic programming and supports an asynchronous and distributed evolution of composite services. We implement the framework over off-the-shelf components and evaluate it through field trials in the case of a gaming scenario. Results show the ability of the proposed system to evolve over time in order to adapt to varying contexts. %K genetic algorithms, genetic programming, BioNets, P2P, mobile devices, opportunistic communication systems, self-evolving applications, mobile radio %R 10.1109/PERCOMW.2010.5470677 %U http://dx.doi.org/10.1109/PERCOMW.2010.5470677 %P 153-158 %0 Journal Article %T Symbolic regression based prediction of anisotropic closure in deep tunnels %A Guayacan-Carrillo, Lina-Maria %A Sulem, Jean %J Computers and Geotechnics %D 2024 %V 171 %@ 0266-352X %F CarrilloGuayacan:2024:compgeo %X This work investigates the applicability of Genetic Programming with Symbolic Regression to analyse the closure evolution of tunnels during and after excavation. Special attention is given to anisotropic closure evolution, which depends on the anisotropy of the initial stress state and the intrinsic anisotropy of the rock mass formation. A methodology is proposed that takes into account the information recorded during excavation to train the algorithm and find a free-form simple mathematical expression that captures the closure evolution over time. The proposed methodology is applied to two case studies of deep tunnels with high anisotropic convergence evolution. The results obtained show that this approach performs well with the small dataset used in this work. The proposed approach is an interesting tool to improve the understanding and prediction of the ground response %K genetic algorithms, genetic programming, Tunnel excavation, Convergence measurements, Anisotropic deformation, Symbolic regression, Machine learning approach %9 journal article %R 10.1016/j.compgeo.2024.106355 %U https://www.sciencedirect.com/science/article/pii/S0266352X2400291X %U http://dx.doi.org/10.1016/j.compgeo.2024.106355 %P 106355 %0 Conference Proceedings %T A Framework for Evolving Fuzzy Classifier Systems Using Genetic Programming %A Carse, Brian %A Pipe, Anthony G. %Y Russell, Ingrid %Y Kolen, John F. %S Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference %D 2001 %8 may 21 23 %I AAAI Press %C Key West, Florida, USA %@ 1-57735-133-9 %F DBLP:conf/flairs/CarseP01 %X A fuzzy classifier system framework is proposed which employs a tree-based representation for fuzzy rule (classifier) antecedents and genetic programming for fuzzy rule discovery. Such a rule representation is employed because of the expressive power and generality it endows to individual rules. The framework proposes accuracy-based fitness for individual fuzzy classifiers and employs evolutionary competition between simultaneously matched classifiers. The evolutionary algorithm (GP) is therefore searching for compact fuzzy rule bases which are simultaneously general, accurate and co-adapted. Additional extensions to the proposed framework are suggested %K genetic algorithms, genetic programming %U http://www.aaai.org/Papers/FLAIRS/2001/FLAIRS01-089.pdf %P 465-469 %0 Generic %T Network surveillance and security system %A Carter, Ernst B. %A Zolotov, Vasily %D 2003 %8 mar 13 %I Google Patents %F carter2003network %O US Patent App. 09/766,560 %X A system that monitors and protects the security of computer networks uses artificial intelligence, including learning algorithms, neural networks and genetic programming, to learn from security events. The invention maintains a knowledge base of security events that updates autonomously in real time. The invention encrypts communications to exchange changes in its knowledge base with separate security systems protecting other computer networks. The invention autonomously alters its security policies in response to ongoing events. The invention tracks network communication traffic from inception at a well-known port throughout the duration of the communication including monitoring of any port the communication is switched to. The invention is able to track and UNIX processes for monitoring, threat detection, and threat response functions. The invention is able to subdivide the network communications into identifying tags for tracking and control of the communications without incurring lags in response times. %K genetic algorithms, genetic programming %U http://appft1.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=/netahtml/PTO/srchnum.html&r=1&f=G&l=50&s1=20030051026.PGNR. %0 Journal Article %T A genetic algorithm for discovering small disjunct rules in data mining %A Carvalho, D. R. %A Freitas, A. A. %J Applied Soft Computing %D 2002 %8 dec %V 2 %N 2 %F Carvalho:2002:ASC %X This paper addresses the well-known classification task of data mining, where the goal is to discover rules predicting the class of examples (records of a given dataset). In the context of data mining, small disjuncts are rules covering a small number of examples. Hence, these rules are usually error-prone, which contributes to a decrease in predictive accuracy. At first glance, this is not a serious problem, since the impact on predictive accuracy should be small. However, although each small-disjunct covers few examples, the set of all small disjuncts can cover a large number of examples. This paper presents evidence that this is the case in several datasets. This paper also addresses the problem of small disjuncts by using a hybrid decision-tree/genetic-algorithm approach. In essence, examples belonging to large disjuncts are classified by rules produced by a decision-tree algorithm (C4.5), while examples belonging to small disjuncts are classified by a genetic-algorithm specifically designed for discovering small-disjunct rules. We present results comparing the predictive accuracy of this hybrid system with the prediction accuracy of three versions of C4.5 alone in eight public domain datasets. Overall, the results show that our hybrid system achieves better predictive accuracy than all three versions of C4.5 alone. %K genetic algorithms, data mining, classification, Rule discovery, Small disjuncts %9 journal article %R 10.1016/S1568-4946(02)00031-5 %U http://www.sciencedirect.com/science/article/B6W86-477FN8B-1/2/2704d983f8282d055e302ebab5471fc1 %U http://dx.doi.org/10.1016/S1568-4946(02)00031-5 %P 75-88 %0 Conference Proceedings %T AutoLR: An Evolutionary Approach to Learning Rate Policies %A Carvalho, Pedro %A Lourenco, Nuno %A Assuncao, Filipe %A Machado, Penousal %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 Carvalho:2020:GECCO %X The choice of a proper learning rate is paramount for good Artificial Neural Network training and performance. In the past, one had to rely on experience and trial-and-error to find an adequate learning rate. Presently, a plethora of state of the art automatic methods exist that make the search for a good learning rate easier. While these techniques are effective and have yielded good results over the years, they are general solutions. This means the optimization of learning rate for specific network topologies remains largely unexplored. This work presents AutoLR, a framework that evolves Learning Rate Schedulers for a specific Neural Network Architecture using Structured Grammatical Evolution. The system was used to evolve learning rate policies that were compared with a commonly used baseline value for learning rate. Results show that training performed using certain evolved policies is more efficient than the established baseline and suggest that this approach is a viable means of improving a neural network’s performance. %K genetic algorithms, genetic programming, grammatical evolution, structured grammatical evolution, learning rate schedulers %R 10.1145/3377930.3390158 %U http://www.human-competitive.org/sites/default/files/carvalho-autolr.txt %U http://dx.doi.org/10.1145/3377930.3390158 %P 672-680 %0 Generic %T Evolving Learning Rate Optimizers for Deep Neural Networks %A Carvalho, Pedro %A Lourenco, Nuno %A Machado, Penousal %D 2021 %8 23 mar %I ArXiv %F DBLP:journals/corr/abs-2103-12623 %X Artificial Neural Networks (ANNs) became popular due to their successful application difficult problems such image and speech recognition. However, when practitioners want to design an ANN they need to undergo laborious process of selecting a set of parameters and topology. Currently, there are several state-of-the art methods that allow for the automatic selection of some of these aspects. Learning Rate optimisers are a set of such techniques that search for good values of learning rates. Whilst these techniques are effective and have yielded good results over the years, they are general solution i.e. they do not consider the characteristics of a specific network. We propose a framework called AutoLR to automatically design Learning Rate Optimizers. Two versions of the system are detailed. The first one, Dynamic AutoLR, evolves static and dynamic learning rate optimizers based on the current epoch and the previous learning rate. The second version, Adaptive AutoLR, evolves adaptive optimizers that can fine tune the learning rate for each network weight which makes them generally more effective. The results are competitive with the best state of the art methods, even outperforming them in some scenarios. Furthermore, the system evolved a classifier, ADES, that appears to be novel and innovative since, to the best of our knowledge, it has a structure that differs from state of the art methods. %K genetic algorithms, genetic programming, ANN %U http://www.human-competitive.org/sites/default/files/carvalho-autolr.txt %0 Conference Proceedings %T Evolving Adaptive Neural Network Optimizers for Image Classification %A Carvalho, Pedro %A Lourenco, Nuno %A Machado, Penousal %Y Medvet, Eric %Y Pappa, Gisele %Y Xue, Bing %S EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming %S LNCS %D 2022 %8 20 22 apr %V 13223 %I Springer Verlag %C Madrid, Spain %F Carvalho:2022:EuroGP %X The evolution of hardware has enabled Artificial Neural Networks to become a staple solution to many modern Artificial Intelligence problems such as natural language processing and computer vision. The neural network effectiveness is highly dependent on the optimizer used during training, which motivated significant research into the design of neural network optimizers. Current research focuses on creating optimizers that perform well across different topologies and network types. While there is evidence that it is desirable to fine-tune optimizer parameters for specific networks, the benefits of designing optimizers specialized for single networks remain mostly unexplored. we propose an evolutionary framework called Adaptive AutoLR (ALR) to evolve adaptive optimizers for specific neural networks in an image classification task. The evolved optimizers are then compared with state-of-the-art, human-made optimizers on two popular image classification problems. The results show that some evolved optimizers perform competitively in both tasks, even achieving the best average test accuracy in one dataset. An analysis of the best evolved optimizer also reveals that it functions differently from human-made approaches. The results suggest ALR can evolve novel, high-quality optimizers motivating further research and applications of the framework. %K genetic algorithms, genetic programming, Grammatical Evolution, ANN, Neuroevolution, Adaptive Optimizers, Structured Grammatical Evolution %R 10.1007/978-3-031-02056-8_1 %U https://www.human-competitive.org/sites/default/files/humiessubmission_2.txt %U http://dx.doi.org/10.1007/978-3-031-02056-8_1 %P 3-18 %0 Conference Proceedings %T Context Matters: Adaptive Mutation for Grammars %A Carvalho, Pedro %A Megane, Jessica %A Lourenco, Nuno %A Machado, Penousal %Y Pappa, Gisele %Y Giacobini, Mario %Y Vasicek, Zdenek %S EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming %S LNCS %D 2023 %8 December 14 apr %V 13986 %I Springer Verlag %C Brno, Czech Republic %F Carvalho:2023:EuroGP %X Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation. In SGE, the genotype of individuals contains a list for each non-terminal of the grammar that defines the search space. In our proposed mutation, each individual contains an array with a different, self-adaptive mutation rate for each non-terminal. We also propose Function Grouped Grammars, a grammar design procedure to enhance the benefits of the propose mutation. Experiments were conducted on three symbolic regression benchmarks using Probabilistic Structured Grammatical Evolution (PSGE), a variant of SGE. Results show our approach is similar or better when compared with the standard grammar and mutation. %K genetic algorithms, genetic programming, Adaptive Mutation, Grammar-design, Grammar-based Genetic Programming %R 10.1007/978-3-031-29573-7_8 %U https://rdcu.be/c8URQ %U http://dx.doi.org/10.1007/978-3-031-29573-7_8 %P 117-132 %0 Conference Proceedings %T Using Grammatical Evolution for Modelling Energy Consumption on a Computer Numerical Control Machine %A Carvalho, Samuel %A Sullivan, Joe %A Dias, Douglas %A Naredo, Enrique %A Ryan, Conor %Y Alzueta, Silvino Fernandez %Y Stuetzle, Thomas %Y Valledor, Pablo %S 6th Workshop on Industrial Applications of Metaheuristics %S GECCO ’21 %D 2021 %8 jul 10 14 %I Association for Computing Machinery %C internet %F Carvalho:2021:IAM %X Discrete manufacturing is known to be a high consumer of energy and much work has been done in continuous improvement and energy saving methods addressing this issue. Computer Numerical Control (CNC) machines, commonly used in the manufacturing of metal parts, are highly energy-demanding because of many required sub-systems, such as cooling, lubrication, logical interfaces and electric motors. For this reason, there is a large body of work focusing on modeling the energy needs of this class of machine. %K genetic algorithms, genetic programming, Grammatical Evolution, Real-World Applications, CNC Machines, Energy Consumption %R 10.1145/3449726.3463185 %U http://dx.doi.org/10.1145/3449726.3463185 %P 1557-1563 %0 Conference Proceedings %T A Multi-objective Approach for Symbolic Regression with Semantic Genetic Programming %A Casadei, Felipe %A Martins, Joao Francisco B. S. %A Pappa, Gisele L. %S 2019 8th Brazilian Conference on Intelligent Systems (BRACIS) %D 2019 %8 15 18 oct %C Salvador, Brazil %F Casadei:2019:BRACIS %X This paper proposes a multi-objective approach for solving symbolic regression problems using Geometric Semantic Genetic Programming (GSGP). The proposed method produces models specialized in smaller regions of the semantic search space, where the errors of the models into these different regions are the objectives being optimized. The method incorporates different ways of defining these sub-regions of the semantic space as well as a method to combine the models found intending to produce a unique prediction. Experimental results obtained over 10 real-world datasets show that the proposed method outperforms traditional GSGP in 7 out of 10 datasets. %K genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Symbolic Regression, Training, Semantic search, Sociology, Statistics, Predictive models, Multi-ojective Optimization %R 10.1109/BRACIS.2019.00021 %U http://dx.doi.org/10.1109/BRACIS.2019.00021 %P 66-71 %0 Conference Proceedings %T A Robust Method for Camera Calibration in Noisy Settings Based on Genetic Programming %A Casado, Ricardo S. %A Tronco, Mario L. %A Pedrino, Emerson C. %S 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC) %D 2024 %8 oct %F Casado:2024:SMC %X In this article, we introduce a novel camera cali-bration method using genetic programming, to calibrate cam-eras in noisy environments. Traditional calibration methods, such as those of Tsai and Zhang, extensively use the pinhole camera model, and are less accurate in the presence of noise. In this work, high precision calibration is achieved using pseudo linear genetic programming. Instead of the pinhole camera model, pseudo linear genetic programming generates mathematical functions which allow for far greater precision in the calibration process than classical methods, regardless of the environmental conditions. The method has several challenges such as identification of suitable calibration functions and creation of an extensive training database. However, the method provides advantages in terms of better results quality and practicality, as it eliminates the necessity of the intrinsic camera parameters. The results illustrate that this methodology is far superior in comparison to the current state-of-the-art technique, Zhang’s widely used method, with a 20x improvement in calibration accuracy. %K genetic algorithms, genetic programming, Training, Accuracy, Three-dimensional displays, Databases, Noise, Cameras, Mathematical models, Calibration, Noise measurement %R 10.1109/SMC54092.2024.10831486 %U http://dx.doi.org/10.1109/SMC54092.2024.10831486 %P 2461-2467 %0 Conference Proceedings %T Tradinnova-LCS: Dynamic stock portfolio decision-making assistance model with genetic based machine learning %A Casanova, Isidoro J. %S IEEE Congress on Evolutionary Computation (CEC 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Casanova:2010:cec %X This paper describes a decision system based on rules for the management of a stock portfolio using a mechanism of dynamic learning to select the stocks to be incorporated. This system simulates the intelligent behaviour of an investor, carrying out the buying and selling of stocks, such that during each day the best stocks will be selected to be incorporated in the portfolio by reinforcement learning with genetic programming. The system has been tested in 3 time periods (1 year, 3 years and 5 years), simulating the purchase/sale of stocks in the Spanish continuous market and the results have been compared with the revaluations obtained by the best investment funds operating in Spain. %K genetic algorithms, genetic programming %R 10.1109/CEC.2010.5586067 %U http://dx.doi.org/10.1109/CEC.2010.5586067 %0 Thesis %T Desarrollo de un modelo computacional para la especificacion de sistemas de analisis de sentimiento con comentarios de redes sociales en espanol %A Casasola Murillo, Edgar Enrique %D 2018 %C San Jose, Costa Rica %C Universidad de Costa Rica %F Edgar_Enrique_Casasola_Murillo %X El modelo propuesto en esta tesis permite llevar a cabo la especificacion de sistemas de analisis de sentimiento a partir de comentarios de texto en idioma espanol publicados en redes sociales. Se presenta un nuevo modelo llamado SAM que integra conceptos extraidos del analisis de sistemas existentes segun la teoria proveniente de campos como la linguistica, la inteligencia artificial, y la recuperacion de informacion. El modelo fue evaluado en terminos de su completitud, pertinencia y aplicabilidad. Se concluyo que el modelo permite formalizar conceptos comunes para la comunicacion entre grupos de investigacion, y ademas proporciona una base para descripcion de sistemas de clasificacion de opiniones. SAM cuenta con potencial para agilizar el desarrollo de sistemas al facilitar la deteccion de componentes utiles y nuevas posibles combinaciones. Asimismo, permite un analisis profundo para la comparacion de diferentes sistemas. El proceso de investigacion permitio llenar un vacio conceptual que existia en el campo y como aporte colateral permitio el desarrollo de recursos computacionales y linguisticos que incluyen: software para recoleccion y normalizacion de comentarios de texto en espanol obtenido desde redes sociales, nuevos corpus, diccionarios de terminos con polaridad y datos de prueba utilizados a nivel internacional. %K genetic algorithms, genetic programming, emociones, procesamiento del lenguaje natural (ciencias de la computacion), redes sociales en linea, mineria de datos %9 Ph.D. thesis %U https://catalogosiidca.csuca.org/Record/UCR.000604145 %0 Conference Proceedings %T Understanding Automatically-Generated Patches Through Symbolic Invariant Differences %A Cashin, Padraic %A Martinez, Carianne %A Weimer, Westley %A Forrest, Stephanie %S 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 %D 2019 %8 nov 11 15 %C San Diego, CA, USA %F DBLP:conf/kbse/CashinMWF19 %X Developer trust is a major barrier to the deployment of automatically-generated patches. Understanding the effect of a patch is a key element of that trust. We find that differences in sets of formal invariants characterize patch differences and that implication-based distances in invariant space characterize patch similarities. When one patch is similar to another it often contains the same changes as well as additional behaviour; this pattern is well-captured by logical implication. We can measure differences using a theorem prover to verify implications between invariants implied by separate programs. Although effective, theorem provers are computationally intensive; we find that string distance is an efficient heuristic for implication-based distance measurements. We propose to use distances between patches to construct a hierarchy highlighting patch similarities. We evaluated this approach on over 300 patches and found that it correctly categorises programs into semantically similar clusters. Clustering programs reduces human effort by reducing the number of semantically distinct patches that must be considered by over 50percent, thus reducing the time required to establish trust in automatically generated repairs. %K genetic algorithms, genetic programming, GenProg, daikon, APR %R 10.1109/ASE.2019.00046 %U https://doi.org/10.1109/ASE.2019.00046 %U http://dx.doi.org/10.1109/ASE.2019.00046 %P 411-414 %0 Thesis %T Adaption und Vergleich evolutionaerer mehrkriterieller Algorithmen mit Hilfe von Variablenwichtigkeitsmassen %A Casjens, Swaantje Wiarda %D 2013 %8 September %C Germany %C Der Fakultaet Statistik, der Technischen Universitaet Dortmund %F Casjens_Dissertation %X Bei der Herleitung eines Klassifikationsmodells ist neben der Vorhersageguete auch die Guete der Variablenauswahl ein wichtiges Kriterium. Bei Einflussvariablen mit unterschiedlichen Kosten ist eine kostensensitive Klassifikation erstrebenswert, bei der ein Kompromiss aus hoher Vorhersageguete und geringen Kosten getroffen werden kann. Werden konfliktaere Ziele, wie etwa hier die Vorhersageguete und die Kosten, gleichzeitig optimiert, entsteht ein mehrkriterielles Optimierungsproblem, fuer das keine einzelne sondern eine Menge unvergleichbarer Loesungen existieren. Fuer das Auffinden der unvergleichbaren Loesungen sind evolutionaere mehrkriterielle Optimierungsalgorithmen (EMOAs) gut geeignet, da sie unter anderem nach verschiedenen Loesungen parallel suchen koennen und unabhaengig von der zugrunde liegenden Datenverteilung sind. Haeufig werden EMOAs fuer die Loesung mehrkriterieller Klassifikationsprobleme in Form von Wrapper-Ansaetzen verwendet, wobei die EMOA-Individuen als binaere Zeichenketten (Bitstrings) codiert sind und jedes Bit die Verfuegbarkeit der entsprechenden Einflussvariable beschreibt. Basierend auf diesen Variablenteilmengen und gegebenen Daten erstellt der umhuellte (wrapped) Klassifikationsalgorithmus ein Klassifikationsmodell, mit dem Ziel die Vorhersageguete zu optimieren. Erst nach der Konstruktion des Klassifikationsmodells koennen weitere Zielkriterien, wie etwa die Kosten der selektierten Variablen, ausgewertet werden. Damit entsteht eine Hierarchie der zu optimierenden Zielkriterien mit Vorteil fuer die Vorhersageguete, sodass durch einen mehrkriteriellen Wrapper-Ansatz keine nicht-hierarchischen Loesungen gefunden werden koennen. Diese Hierarchie der Zielfunktionen wird erstmals in Rahmen dieser Arbeit beschrieben und untersucht. Als Alternative zum mehrkriteriellen Wrapper-Ansatz wird in dieser Arbeit ein nicht-hierarchischer evolutionaerer mehrkriterieller Optimierungsalgorithmus mit Baum-Repraesentation (NHEMOtree) entwickelt, um mehrkriterielle Optimierungsprobleme mit gleichberechtigten Optimierungszielen zu loesen. NHEMOtree basiert auf einem EMOA mit Baum-Repraesentation, der ohne internen Klassifikationsalgorithmus die Variablenselektion vollzieht und ohne Hierarchie in den Zielfunktionen mehrkriteriell optimierte binaere Entscheidungsbaeume erstellt. Des Weiteren werden ein auf mehrkriteriellen Variablenwichtigkeitsmassen (VIMs) basierter Rekombinationsoperator fuer NHEMOtree und eine NHEMOtree-Version mit lokaler Cutoff-Optimierung entwickelt. In dieser Arbeit werden erstmalig die Loesungen einer mehrkriteriellen Optimierung durch einen mehrkriteriellen Wrapper-Ansatz und durch einen EMOA mit Baum-Repraesentation (NHEMOtree) miteinander verglichen. Die Bewertung der Loesungen erfolgt dabei sowohl mittels der bekannten S-Metrik als auch durch den hier entwickelten Dominanzquotienten. Die Guete des VIM-basierten Rekombinationsoperators wird im Vergleich zum Standard-Rekombinationsoperator fuer EMOAs mit Baum-Repraesentation untersucht. Die mehrkriteriellen Optimierungsansaetze und Operatoren werden auf medizinische und simulierte Daten angewendet. Die Ergebnisse zeigen, dass NHEMOtree bessere Loesungen als der mehrkriterielle Wrapper-Ansatz findet. Die Verwendung des VIM-basierten Rekombinationsoperators fuehrt im Gegensatz zum Standard-Operator zu nochmals besseren Loesungen des mehrkriteriellen Optimierungsproblems und zu einer schnelleren Konvergenz des NHEMO trees. %K genetic algorithms, genetic programming, MOGP, Pareto, Baum-Repraesentation evolutionaere Algorithmen, kostensensitive Klassifikation, mehrkriterielle Optimierung, Variablenwichtigkeitsmasse %9 Ph.D. thesis %R 10.17877/DE290R-5588 %U https://eldorado.tu-dortmund.de/bitstream/2003/30431/1/Casjens_Dissertation.pdf %U http://dx.doi.org/10.17877/DE290R-5588 %0 Journal Article %T Automatic design of analog electronic circuits using grammatical evolution %A Castejon, Federico %A Carmona, Enrique J. %J Applied Soft Computing %D 2018 %8 jan %V 62 %@ 1568-4946 %F CASTEJON20181003 %X A new approach for automatic synthesis of analog electronic circuits based on grammatical evolution is presented. Grammatical evolution is an evolutionary algorithm based on grammar which can generate code in any programming language and uses variable length linear binary strings. The decoding of each chromosome determines which production rules in a Backus-Naur Form grammar definition are used in a genotype-to-phenotype mapping process. In our method, decoding focuses on obtaining circuit netlists. A new grammar for generating such netlists and a variant of the XOSites-based crossover operator are also presented. A post-processing stage is needed to adapt the decoded netlist prior its evaluation using the NGSpice simulator. Our approach was applied to several case studies, comprising a total of seven benchmark circuits. A comparison with previous works in the literature shows that our method produces competitive circuits in relation to the degree of compliance with the output specifications, the number of components and the number of evaluations used in the evolutionary process. %K genetic algorithms, genetic programming, Grammatical evolution, EHW, Automatic circuit design, Analog circuits, Evolutionary electronics, NGSpice %9 journal article %R 10.1016/j.asoc.2017.09.036 %U http://www.sciencedirect.com/science/article/pii/S1568494617305756 %U http://dx.doi.org/10.1016/j.asoc.2017.09.036 %P 1003-1018 %0 Journal Article %T Introducing Modularity and Homology in Grammatical Evolution to Address the Analog Electronic Circuit Design Problem %A Castejon, Federico %A Carmona, Enrique J. %J IEEE Access %D 2020 %V 8 %@ 2169-3536 %F Castejon:2020:ACC %X We present a new approach based on grammatical evolution (GE) aimed at addressing the analog electronic circuit design problem. In the new approach, called multi-grammatical evolution (MGE), a chromosome is a variable-length codon string that is divided into as many partitions as subproblems result from breaking down the original optimization problem: circuit topology and component sizing in our case. This leads to a modular approach where the solution of each subproblem is encoded and evolved in a partition of the chromosome. Additionally, each partition is decoded according to a specific grammar and the final solution to the original problem emerges as an aggregation result associated with the decoding process of the different partitions. Modularity facilitates the encoding and evolution of the solution in each subproblem. On the other way, homology helps to reduce the potentially destructive effect associated with standard crossover operators normally used in GE-based approaches. Seven analog circuit designs are addressed by an MGE-based method and the obtained results are compared to those obtained by different methods based on GE and other evolutionary paradigms. A simple parsimony mechanism was also introduced to ensure compliance with design specifications and reduce the number of components of the circuits obtained. We can conclude that our method obtains competitive results in the seven circuits analyzed. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/ACCESS.2020.3011641 %U http://dx.doi.org/10.1109/ACCESS.2020.3011641 %P 137275-137292 %0 Conference Proceedings %T Frenetic at the SBST 2021 Tool Competition %A Castellano, Ezequiel %A Cetinkaya, Ahmet %A Thanh, Cedric Ho %A Klikovits, Stefan %A Zhang, Xiaoyi %A Arcaini, Paolo %Y Zhang, Jie M. %Y Fredericks, Erik %S 2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST) %D 2021 %8 31 may %I IEEE %C internet %F Castellano:2021:SBST %X Frenetic is a genetic approach that leverages a curvature-based road representation. Given an autonomous driving agent, the goal of Frenetic is to generate roads where the agent fails to stay within its lane. In other words, Frenetic tries to minimise the out of bound distance, which is the distance between the car and either edge of the lane if the car is within the lane, and proceeds to negative values once the car drives off. This resembles classic aspects of genetic algorithms such as mutations and crossover, but introduces some nuances aiming at improving diversity of the generated roads. %K genetic algorithms, genetic programming %R 10.1109/SBST52555.2021.00016 %U https://raw.githubusercontent.com/ERATOMMSD/frenetic-sbst21/main/src/frenetic-sbst21-preprint.pdf %U http://dx.doi.org/10.1109/SBST52555.2021.00016 %P 36-37 %0 Conference Proceedings %T A Genetic Programming Approach Applied to Feature Selection from Medical Data %A Castellanos-Garzon, Jose A. %A Ramos, Juan %A Martin, Yeray Mezquita %A de Paz, Juan F. %A Costa, Ernesto %S Practical Applications of Computational Biology and Bioinformatics, 12th International Conference %D 2019 %I Springer %F castellanos-garzon:2019:PACBBIC %K genetic algorithms, genetic programming %R 10.1007/978-3-319-98702-6_24 %U http://link.springer.com/chapter/10.1007/978-3-319-98702-6_24 %U http://dx.doi.org/10.1007/978-3-319-98702-6_24 %0 Journal Article %T A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis %A Castellanos-Garzon, Jose A. %A Mezquita Martin, Yeray %A Jaimes Sanchez, Jose Luis %A Lopez Garcia, Santiago Manuel %A Costa, Ernesto %J Processes %D 2020 %V 8 %N 12 %@ 2227-9717 %F castellanos-garzon:2020:Processes %X This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analysed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/pr8121565 %U https://www.mdpi.com/2227-9717/8/12/1565 %U http://dx.doi.org/10.3390/pr8121565 %0 Journal Article %T An evolutionary framework for machine learning applied to medical data %A Castellanos-Garzon, Jose A. %A Costa, Ernesto %A Jaimes S., Jose Luis %A Corchado, Juan M. %J Knowledge-Based Systems %D 2019 %V 185 %@ 0950-7051 %F CASTELLANOSGARZON:2019:KS %X Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as a search method for classifiers, helping the applied machine learning technique. In this context, the knowledge representation in the form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction. Our proposal introduces genetic programming to build a search method for classification-rules (IF/THEN). From this approach, we deal with problems such as, maximum rule length and rule intersection. The experiments have been carried out on our domain of interest, medical data. The achieved results define a methodology to follow in the learning method evaluation for knowledge discovery from medical data. Moreover, the results compared to other methods have shown that our proposal can be very useful in data analysis and classification coming from the medical domain %K genetic algorithms, genetic programming, Machine learning, Logical rule induction, Data mining, Supervised learning, Evolutionary computation, Ensemble classifier, Medical data %9 journal article %R 10.1016/j.knosys.2019.104982 %U http://www.sciencedirect.com/science/article/pii/S0950705119304046 %U http://dx.doi.org/10.1016/j.knosys.2019.104982 %P 104982 %0 Journal Article %T Determining the maximum length of logical rules in a classifier and visual comparison of results %A Castellanos-Garzon, Jose A. %A Costa, Ernesto %A Jaimes, Jose Luis S. %A Corchado, Juan M. %J MethodsX %D 2020 %V 7 %@ 2215-0161 %F CASTELLANOSGARZON:2020:MethodsX %X Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as the classifier search method, helping the technique of applied machine learning. In this context, the knowledge representation in form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction and is based on the idea of sequential covering. We introduce genetic programming as the search method for classification-rules. From this approach, we have given results on subjects as maximum rule length, number of rules needed in a classifier and the rule intersection problem. The experiments developed on benchmark clinical data resulted in a methodology to follow in the learning method evaluation. Moreover, the results achieved compared to other methods have shown that our proposal can be v %K genetic algorithms, genetic programming, Machine learning, Logical rule induction, Data mining, Supervised learning, Evolutionary computation %9 journal article %R 10.1016/j.mex.2020.100846 %U http://www.sciencedirect.com/science/article/pii/S2215016120300650 %U http://dx.doi.org/10.1016/j.mex.2020.100846 %P 100846 %0 Conference Proceedings %T A comparison of the generalization ability of different genetic programming frameworks %A Castelli, Mauro %A Manzoni, Luca %A Silva, Sara %A Vanneschi, Leonardo %S IEEE Congress on Evolutionary Computation (CEC 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Castelli:2010:cec %X Generalisation is an important issue in machine learning. In fact, in several applications good results over training data are not as important as good results over unseen data. While this problem was deeply studied in other machine learning techniques, it has become an important issue for genetic programming only in the last few years. In this paper we compare the generalization ability of several different genetic programming frameworks, including some variants of multi-objective genetic programming and operator equalisation, a recently defined bloat free genetic programming system. The test problem used is a hard regression real-life application in the field of drug discovery and development, characterised by a high number of features and where the generalisation ability of the proposed solutions is a crucial issue. The results we obtained show that, at least for the considered problem, multi-optimization is effective in improving genetic programming generalization ability, outperforming all the other methods on test data. %K genetic algorithms, genetic programming %R 10.1109/CEC.2010.5585925 %U http://dx.doi.org/10.1109/CEC.2010.5585925 %0 Conference Proceedings %T A Quantitative Study of Learning and Generalization in Genetic Programming %A Castelli, Mauro %A Manzoni, Luca %A Silva, Sara %A Vanneschi, Leonardo %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 castelli:2011:EuroGP %X The relationship between generalisation and solutions functional complexity in genetic programming (GP) has been recently investigated. Three main contributions are contained in this paper: (1) a new measure of functional complexity for GP solutions, called Graph Based Complexity (GBC) is defined and we show that it has a higher correlation with GP performance on out-of-sample data than another complexity measure introduced in a recent publication. (2) A new measure is presented, called Graph Based Learning Ability (GBLA). It is inspired by the GBC and its goal is to quantify the ability of GP to learn difficult training points; we show that GBLA is negatively correlated with the performance of GP on out-of-sample data. (3) Finally, we use the ideas that have inspired the definition of GBC and GBLA to define a new fitness function, whose suitability is empirically demonstrated. The experimental results reported in this paper have been obtained using three real-life multidimensional regression problems. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-20407-4_3 %U http://dx.doi.org/10.1007/978-3-642-20407-4_3 %P 25-36 %0 Conference Proceedings %T Multi Objective Genetic Programming for Feature Construction in Classification Problems %A Castelli, Mauro %A Manzoni, Luca %A Vanneschi, Leonardo %Y Coello Coello, Carlos A. %S 5th International Conference Learning and Intelligent Optimization (LION 2011) %S Lecture Notes in Computer Science %D 2011 %8 jan 17 21 %V 6683 %C Rome, Italy %F conf/lion/CastelliMV11 %O Selected Papers %X This work introduces a new technique for features construction in classification problems by means of multi objective genetic programming (MOGP). The final goal is to improve the generalisation ability of the final classifier. MOGP can help in finding solutions with a better generalisation ability with respect to standard genetic programming as stated in [1]. The main issue is the choice of the criteria that must be optimised by MOGP. In this work the construction of new features is guided by two criteria: the first one is the entropy of the target classes as in [7] while the second is inspired by the concept of margin used in support vector machines. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-25566-3_39 %U http://dx.doi.org/10.1007/978-3-642-25566-3_39 %P 503-506 %0 Thesis %T Measures and Methods for Robust Genetic Programming %A Castelli, Mauro %D 2012 %C Milan, Italy %C University of Milano Bicocca %F Castelli:thesis %X Defended on February, 2012 Contents 1 Introduction 1 2 Genetic Programming: Introduction 14 3 Open Issues in Genetic Programming 35 4 Measures of Overfitting, Bloat and Functional Complexity 45 5 Multi Objective Optimisation in Genetic Programming 81 6 Generalisation 103 7 Semantic 135 8 Conclusions 172 9 Future works 188 %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://boa.unimib.it/bitstream/10281/28571/1/Phd_unimib_055904.pdf %0 Conference Proceedings %T Parameter tuning of evolutionary reactions systems %A Castelli, Mauro %A Manzoni, Luca %A Vanneschi, Leonardo %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 Castelli:2012:GECCO %X Reaction systems is a formalism inspired by chemical reactions introduced by Rozenberg and Ehrenfeucht. Recently, an evolutionary algorithm based on this formalism, called Evolutionary Reaction Systems, has been presented. This new algorithm proved to have comparable performances to other well-established machine learning methods, like genetic programming, neural networks and support vector machines on both artificial and real-life problems. Even if the results are encouraging, to make Evolutionary Reaction Systems an established evolutionary algorithm, an in depth analysis of the effect of its parameters on the search process is needed, with particular focus on those parameters that are typical of Evolutionary Reaction Systems and do not have a counterpart in traditional evolutionary algorithms. Here we address this problem for the first time. The results we present show that one particular parameter, between the ones tested, has a great influence on the performances of Evolutionary Reaction Systems, and thus its setting deserves practitioners’ particular attention: the number of symbols used to represent the reactions that compose the system. Furthermore, this work represents a first step towards the definition of a set of default parameter values for Evolutionary Reaction Systems, that should facilitate their use for beginners or inexpert practitioners. %K genetic algorithms, genetic programming %R 10.1145/2330163.2330265 %U http://dx.doi.org/10.1145/2330163.2330265 %P 727-734 %0 Generic %T An Efficient Genetic Programming System with Geometric Semantic Operators and its Application to Human Oral Bioavailability Prediction %A Castelli, Mauro %A Manzoni, Luca %A Vanneschi, Leonardo %D 2012 %8 December %I arXiv %F Castelli:2012:arXiv %X Very recently new genetic operators, called geometric semantic operators, have been defined for genetic programming. Contrarily to standard genetic operators, which are uniquely based on the syntax of the individuals, these new operators are based on their semantics, meaning with it the set of input-output pairs on training data. Furthermore, these operators present the interesting property of inducing a unimodal fitness landscape for every problem that consists in finding a match between given input and output data (for instance regression and classification). Nevertheless, the current definition of these operators has a serious limitation: they impose an exponential growth in the size of the individuals in the population, so their use is impossible in practice. This paper is intended to overcome this limitation, presenting a new genetic programming system that implements geometric semantic operators in an extremely efficient way. To demonstrate the power of the proposed system, we use it to solve a complex real-life application in the field of pharmacokinetic: the prediction of the human oral bioavailability of potential new drugs. Besides the excellent performances on training data, which were expected because the fitness landscape is unimodal, we also report an excellent generalisation ability of the proposed system, at least for the studied application. In fact, it outperforms standard genetic programming and a wide set of other well-known machine learning methods. %K genetic algorithms, genetic programming %U http://arxiv.org/abs/1208.2437 %0 Conference Proceedings %T Land Cover/Land Use Multiclass Classification Using GP with Geometric Semantic Operators %A Castelli, Mauro %A Silva, Sara %A Vanneschi, Leonardo %A Cabral, Ana %A Vasconcelos, Maria J. %A Catarino, Luis %A Carreiras, Joao M. B. %Y Esparcia-Alcazar, Anna I. %Y Cioppa, Antonio Della %Y De Falco, Ivanoe %Y Tarantino, Ernesto %Y Cotta, Carlos %Y Schaefer, Robert %Y Diwold, Konrad %Y Glette, Kyrre %Y Tettamanzi, Andrea %Y Agapitos, Alexandros %Y Burrelli, Paolo %Y Merelo, J. J. %Y Cagnoni, Stefano %Y Zhang, Mengjie %Y Urquhart, Neil %Y Sim, Kevin %Y Ekart, Aniko %Y Fernandez de Vega, Francisco %Y Silva, Sara %Y Haasdijk, Evert %Y Eiben, Gusz %Y Simoes, Anabela %Y Rohlfshagen, Philipp %S Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC %S LNCS %D 2013 %8 March 5 apr %V 7835 %I Springer Verlag %C Vienna %F Castelli:evoapps13 %X Multiclass classification is a common requirement of many land cover/land use applications, one of the pillars of land science studies. Even though genetic programming has been applied with success to a large number of applications, it is not particularly suited for multi-class classification, thus limiting its use on such studies. In this paper we take a step forward towards filling this gap, investigating the performance of recently defined geometric semantic operators on two land cover/land use multiclass classification problems and also on a benchmark problem. Our results clearly indicate that genetic programming using the new geometric semantic operators outperforms standard genetic programming for all the studied problems, both on training and test data. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-37192-9_34 %U http://dx.doi.org/10.1007/978-3-642-37192-9_34 %P 334-343 %0 Journal Article %T Semantic Search-Based Genetic Programming and the Effect of Intron Deletion %A Castelli, Mauro %A Vanneschi, Leonardo %A Silva, Sara %J IEEE Transactions on Cybernetics %D 2014 %V 44 %N 1 %@ 2168-2267 %F Castelli:2013:ieeeCybernetics %X The concept of semantics (in the sense of input–output behaviour of solutions on training data) has been the subject of a noteworthy interest in the genetic programming (GP) research community over the past few years. In this paper, we present a new GP system that uses the concept of semantics to improve search effectiveness. It maintains a distribution of different semantic behaviours and biases the search toward solutions that have similar semantics to the best solutions that have been found so far. We present experimental evidence of the fact that the new semantics-based GP system outperforms the standard GP and the well-known bacterial GP on a set of test functions, showing particularly interesting results for noncontinuous (i.e., generally harder to optimise) test functions. We also observe that the solutions generated by the proposed GP system often have a larger size than the ones returned by standard GP and bacterial GP and contain an elevated number of introns, i.e., parts of code that do not have any effect on the semantics. Nevertheless, we show that the deletion of introns during the evolution does not affect the performance of the proposed method. %K genetic algorithms, genetic programming, Generalisation, genetic programming (GP), introns, semantics %9 journal article %R 10.1109/TSMCC.2013.2247754 %U http://dx.doi.org/10.1109/TSMCC.2013.2247754 %P 103-113 %0 Conference Proceedings %T An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics %A Castelli, Mauro %A Castaldi, Davide %A Vanneschi, Leonardo %A Giordani, Ilaria %A Archetti, Francesco %A Maccagnola, Daniele %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 Castelli:2013:GECCOcomp %X In the last few years researchers have dedicated several efforts to the definition of Genetic Programming (GP) [?] systems based on the semantics of the solutions, where by semantics we generally intend the behaviour of a program once it is executed on a set of inputs, or more particularly the set of its output values on input training data (this definition has been used, among many others, for instance in [?, ?, ?, ?]). In particular, new genetic operators, called geometric semantic operators, have been proposed by Moraglio et al. [?]. They are defined s follows: %K genetic algorithms, genetic programming %R 10.1145/2464576.2464644 %U http://dx.doi.org/10.1145/2464576.2464644 %P 137-138 %0 Conference Proceedings %T An Efficient Implementation of Geometric Semantic Genetic Programming for Anticoagulation Level Prediction in Pharmacogenetics %A Castelli, Mauro %A Castaldi, Davide %A Giordani, Ilaria %A Silva, Sara %A Vanneschi, Leonardo %A Archetti, Francesco %A Maccagnola, Daniele %Y Correia, Luis %Y Reis, Luis Paulo %Y Cascalho, Jose %S Proceedings of the 16th Portuguese Conference on Artificial Intelligence, EPIA 2013 %S Lecture Notes in Computer Science %D 2013 %8 sep 9 12 %V 8154 %I Springer %C Angra do Heroismo, Azores, Portugal %F Castelli:2013:EPIA %X The purpose of this study is to develop an innovative system for Coumarin-derived drug dosing, suitable for elderly patients. Recent research highlights that the pharmacological response of the patient is often affected by many exogenous factors other than the dosage prescribed and these factors could form a very complex relationship with the drug dosage. For this reason, new powerful computational tools are needed for approaching this problem. The system we propose is called Geometric Semantic Genetic Programming, and it is based on the use of recently defined geometric semantic genetic operators. In this paper, we present a new implementation of this Genetic Programming system, that allow us to use it for real-life applications in an efficient way, something that was impossible using the original definition. Experimental results show the suitability of the proposed system for managing anticoagulation therapy. In particular, results obtained with Geometric Semantic Genetic Programming are significantly better than the ones produced by standard Genetic Programming both on training and on out-of-sample test data. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-40669-0_8 %U http://link.springer.com/chapter/10.1007/978-3-642-40669-0_8 %U http://dx.doi.org/10.1007/978-3-642-40669-0_8 %P 78-89 %0 Journal Article %T Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators %A Castelli, Mauro %A Vanneschi, Leonardo %A Silva, Sara %J Expert Systems with Applications %D 2013 %V 40 %N 17 %@ 0957-4174 %F Castelli:2013:ESA %X Concrete is a composite construction material made primarily with aggregate, cement, and water. In addition to the basic ingredients used in conventional concrete, high-performance concrete incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, high-performance concrete is a highly complex material and modelling its behaviour represents a difficult task. In this paper, we propose an intelligent system based on Genetic Programming for the prediction of high-performance concrete strength. The system we propose is called Geometric Semantic Genetic Programming, and it is based on recently defined geometric semantic genetic operators for Genetic Programming. Experimental results show the suitability of the proposed system for the prediction of concrete strength. In particular, the new method provides significantly better results than the ones produced by standard Genetic Programming and other machine learning methods, both on training and on out-of-sample data. %K genetic algorithms, genetic programming, High performance concrete, Strength prediction, Artificial intelligence, Geometric operators, Semantics, Weka, Linear regression, Square Regression, Isotonic Regression, Radial Basis Function Network, RBF, SVM, ANN %9 journal article %R 10.1016/j.eswa.2013.06.037 %U http://www.sciencedirect.com/science/article/pii/S0957417413004326 %U http://dx.doi.org/10.1016/j.eswa.2013.06.037 %P 6856-6862 %0 Journal Article %T Semantic Search-Based Genetic Programming and the Effect of Intron Deletion %A Castelli, Mauro %A Vanneschi, Leonardo %A Silva, Sara %J IEEE Transactions on Cybernetics %D 2014 %8 jan %V 44 %N 1 %@ 2168-2267 %F Castelli:2014:ieeeCybernetics %X The concept of semantics (in the sense of input-output behaviour of solutions on training data) has been the subject of a noteworthy interest in the genetic programming (GP) research community over the past few years. In this paper, we present a new GP system that uses the concept of semantics to improve search effectiveness. It maintains a distribution of different semantic behaviours and biases the search toward solutions that have similar semantics to the best solutions that have been found so far. We present experimental evidence of the fact that the new semantics-based GP system outperforms the standard GP and the well-known bacterial GP on a set of test functions, showing particularly interesting results for noncontinuous (i.e., generally harder to optimise) test functions. We also observe that the solutions generated by the proposed GP system often have a larger size than the ones returned by standard GP and bacterial GP and contain an elevated number of introns, i.e., parts of code that do not have any effect on the semantics. Nevertheless, we show that the deletion of introns during the evolution does not affect the performance of the proposed method. %K genetic algorithms, genetic programming, Generalisation, introns, semantics %9 journal article %R 10.1109/TSMCC.2013.2247754 %U http://dx.doi.org/10.1109/TSMCC.2013.2247754 %P 103-113 %0 Journal Article %T Prediction of the Unified Parkinson’s Disease Rating Scale assessment using a genetic programming system with geometric semantic genetic operators %A Castelli, Mauro %A Vanneschi, Leonardo %A Silva, Sara %J Expert Systems with Applications %D 2014 %V 41 %N 10 %@ 0957-4174 %F Castelli:2014:ESA %K genetic algorithms, genetic programming, Unified Parkinson’s Disease Rating Scale, Geometric operators, Semantics %9 journal article %R 10.1016/j.eswa.2014.01.018 %U http://www.sciencedirect.com/science/article/pii/S0957417414000396 %U http://dx.doi.org/10.1016/j.eswa.2014.01.018 %P 4608-4616 %0 Conference Proceedings %T The Influence of Population Size on Geometric Semantic GP %A Castelli, Mauro %A Manzoni, Luca %A Silva, Sara %A Vanneschi, Leonardo %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 Castelli:2014:SMGP %O Workshop at Parallel Problem Solving from Nature 2014 conference %X In this work we study the influence of the population size on the learning ability of Geometric Semantic Genetic Programming (GSGP) for the task of symbolic regression. The results show that having small populations results on a better training fitness with respect to the use of large populations after the same number of fitness evaluations. However, models obtained with large populations show a better performance on unseen data. %K genetic algorithms, genetic programming %U http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Castelli.pdf %0 Conference Proceedings %T Self-tuning Geometric Semantic GP %A Castelli, Mauro %A Manzoni, Luca %A Silva, Sara %A Vanneschi, Leonardo %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 Castelli:2014:SMGP2 %O Workshop at Parallel Problem Solving from Nature 2014 conference %X In Geometric Semantic GP (GSGP), similarly to normal GP, parameter tuning is necessary to attain good performances. Here we introduce a method for self-tuning GSGP that not only saves the user the tuning task, but it also outperforms traditional hand-tuned GSGP. %K genetic algorithms, genetic programming %U http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Castelli2.pdf %0 Journal Article %T A C++ framework for geometric semantic genetic programming %A Castelli, Mauro %A Silva, Sara %A Vanneschi, Leonardo %J Genetic Programming and Evolvable Machines %D 2015 %8 mar %V 16 %N 1 %@ 1389-2576 %F Castelli:2014:GPEM %X Geometric semantic operators are new and promising genetic operators for genetic programming. They have the property of inducing a unimodal error surface for any supervised learning problem, i.e., any problem consisting in finding the match between a set of input data and known target values (like regression and classification). Thanks to an efficient implementation of these operators, it was possible to apply them to a set of real-life problems, obtaining very encouraging results. We have now made this implementation publicly available as open source software, and here we describe how to use it. We also reveal details of the implementation and perform an investigation of its efficiency in terms of running time and memory occupation, both theoretically and experimentally. The source code and documentation are available for download at http://gsgp.sourceforge.net %K genetic algorithms, genetic programming, GSGP lib, Semantics, Geometric operators, C++ %9 journal article %R 10.1007/s10710-014-9218-0 %U http://dx.doi.org/10.1007/s10710-014-9218-0 %P 73-81 %0 Journal Article %T Corrections to “Semantic Search Based Genetic Programming and the Effect of Introns Deletion” [Jan 14 103-113] %A Castelli, M. %A Vanneschi, L. %A Silva, S. %J IEEE Transactions on Cybernetics %D 2014 %8 apr %V 44 %N 4 %@ 2168-2267 %F Castelli:2014:Cybernetics %X The paper above (ibid., vol. 44, no. 1, pp. 103-113, Jan. 2014), was printed with the incorrect author list as follows: M. Castelli, L. Vanneschi, S. Silva, A. Agapitos, and M. O’Neill. The correct author list is: M. Castelli, L. Vanneschi and S. Silva. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TCYB.2014.2303551 %U http://dx.doi.org/10.1109/TCYB.2014.2303551 %P 565 %0 Conference Proceedings %T How to Exploit Alignment in the Error Space: Two Different GP Models %A Castelli, Mauro %A Vanneschi, Leonardo %A Silva, Sara %A Ruberto, Stefano %Y Riolo, Rick %Y Worzel, William P. %Y Kotanchek, Mark %S Genetic Programming Theory and Practice XII %S Genetic and Evolutionary Computation %D 2014 %8 August 10 may %I Springer %C Ann Arbor, USA %F Castelli:2014:GPTP %X From a recent study, we know that if we are able to find two optimally aligned individuals, then we can reconstruct a globally optimal solution analytically for any regression problem. With this knowledge in mind, the objective of this chapter is to discuss two Genetic Programming (GP) models aimed at finding pairs of optimally aligned individuals. The first one of these models, already introduced in a previous publication, is SGP-1. The second model, discussed for the first time here, is called Pair Optimisation GP (POGO). The main difference between these two models is that, while SGP-1 represents solutions in a traditional way, as single expressions (as in standard GP), in POGO individuals are pairs of expressions, that evolution should push towards the optimal alignment. The results we report for both these models are extremely encouraging. In particular, ESAGP-1 outperforms standard GP and geometric semantic GP on two complex real-life applications. At the same time, a preliminary set of results obtained on a set of symbolic regression benchmarks indicate that POGP, although rather new and still in need of improvement, is a very promising model, that deserves future developments and investigation. %K genetic algorithms, genetic programming, Semantics, Error space, Geometry %R 10.1007/978-3-319-16030-6_8 %U http://dx.doi.org/10.1007/978-3-319-16030-6_8 %P 133-148 %0 Journal Article %T A geometric semantic genetic programming system for the electoral redistricting problem %A Castelli, Mauro %A Henriques, Roberto %A Vanneschi, Leonardo %J Neurocomputing %D 2015 %V 154 %@ 0925-2312 %F Castelli:2015:Neurocomputing %X Redistricting consists in dividing a geographic space or region of spatial units into smaller subregions or districts. In this paper, a Genetic Programming framework that addresses the electoral redistricting problem is proposed. The method uses new genetic operators, called geometric semantic genetic operators, that employ semantic information directly in the evolutionary search process with the objective of improving its optimisation ability. The system is compared to several different redistricting techniques, including evolutionary and non-evolutionary methods. The simulations were made on ten real data-sets and, even though the studied problem does not belong to the classes of problems for which geometric semantic operators induce a unimodal fitness landscape, the results we present demonstrate the effectiveness of the proposed technique. %K genetic algorithms, genetic programming, Electoral redistricting, Semantics, Search space %9 journal article %R 10.1016/j.neucom.2014.12.003 %U http://www.sciencedirect.com/science/article/pii/S0925231214016671 %U http://dx.doi.org/10.1016/j.neucom.2014.12.003 %P 200-207 %0 Journal Article %T Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case %A Castelli, Mauro %A Vanneschi, Leonardo %A De Felice, Matteo %J Energy Economics %D 2015 %V 47 %@ 0140-9883 %F Castelli:2015:EE %X Accurate and robust short-term load forecasting plays a significant role in electric power operations. This paper proposes a variant of genetic programming, improved by incorporating semantic awareness in algorithm, to address a short term load forecasting problem. The objective is to automatically generate models that could effectively and reliably predict energy consumption. The presented results, obtained considering a particularly interesting case of the South Italy area, show that the proposed approach outperforms state of the art methods. Hence, the proposed approach reveals appropriate for the problem of forecasting electricity consumption. This study, besides providing an important contribution to the energy load forecasting, confirms the suitability of genetic programming improved with semantic methods in addressing complex real-life applications. %K genetic algorithms, genetic programming, Forecasting, Electricity demand, Semantics %9 journal article %R 10.1016/j.eneco.2014.10.009 %U http://www.sciencedirect.com/science/article/pii/S0140988314002539 %U http://dx.doi.org/10.1016/j.eneco.2014.10.009 %P 37-41 %0 Journal Article %T Predicting Burned Areas of Forest Fires: an Artificial Intelligence Approach %A Castelli, Mauro %A Vanneschi, Leonardo %A Popovic, Ales %J Fire Ecology %D 2015 %V 11 %N 1 %I The Association for Fire Ecology %@ 1933-9747 %F Castelli:2015:FireEcology %X Forest fires importantly influence our environment and lives. The ability of accurately predicting the area that may be involved in a forest fire event may help in optimizing fire management efforts. Given the complexity of the task, powerful computational tools are needed for predicting the amount of area that will be burned during a forest fire. The purpose of this study was to develop an intelligent system based on genetic programming for the prediction of burned areas, using only data related to the forest under analysis and meteorological data. We used geometric semantic genetic programming based on recently defined geometric semantic genetic operators for genetic programming. Experimental results, achieved using a database of 517 forest fire events between 2000 and 2003, showed the appropriateness of the proposed system for the prediction of the burned areas. In particular, results obtained with geometric semantic genetic programming were significantly better than those produced by standard genetic programming and other state of the art machine learning methods on both training and out-of-sample data. This study suggests that deeper investigation of genetic programming in the field of forest fires prediction may be productive. %K genetic algorithms, genetic programming, geometric semantic genetic programming %9 journal article %R 10.4996/fireecology.1101106 %U http://dx.doi.org/10.4996/fireecology.1101106 %P 106-118 %0 Journal Article %T Energy Consumption Forecasting using Semantics Based Genetic Programming with Local Search Optimizer %A Castelli, Mauro %A Vanneschi, Leonardo %A Trujillo, Leonardo %J Computational Intelligence and Neuroscience %D 2015 %8 may %V 2015 %F Castelli:2015:CINS %X Energy consumption forecasting (ECF) is an important policy issue in today’s economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data. %K genetic algorithms, genetic programming %9 journal article %R 10.1155/2015/971908 %U http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464001/ %U http://dx.doi.org/10.1155/2015/971908 %0 Conference Proceedings %T Geometric Semantic Genetic Programming with Local Search %A Castelli, Mauro %A Trujillo, Leonardo %A Vanneschi, Leonardo %A Silva, Sara %A Z-Flores, Emigdio %A Legrand, Pierrick %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 Castelli:2015:GECCO %X Since its introduction, Geometric Semantic Genetic Programming (GSGP) has aroused the interest of numerous researchers and several studies have demonstrated that GSGP is able to effectively optimize training data by means of small variation steps, that also have the effect of limiting overfitting. In order to speed up the search process, in this paper we propose a system that integrates a local search strategy into GSGP (called GSGP-LS). Furthermore, we present a hybrid approach, that combines GSGP and GSGP-LS, aimed at exploiting both the optimization speed of GSGP-LS and the ability to limit overfitting of GSGP. The experimental results we present, performed on a set of complex real-life applications, show that GSGP-LS achieves the best training fitness while converging very quickly, but severely overfits. On the other hand, GSGP converges slowly relative to the other methods, but is basically not affected by overfitting. The best overall results were achieved with the hybrid approach, allowing the search to converge quickly, while also exhibiting a noteworthy ability to limit overfitting. These results are encouraging, and suggest that future GSGP algorithms should focus on finding the correct balance between the greedy optimization of a local search strategy and the more robust geometric semantic operators. %K genetic algorithms, genetic programming %R 10.1145/2739480.2754795 %U http://doi.acm.org/10.1145/2739480.2754795 %U http://dx.doi.org/10.1145/2739480.2754795 %P 999-1006 %0 Conference Proceedings %T Electricity Demand Modelling with Genetic Programming %A Castelli, Mauro %A De Felice, Matteo %A Manzoni, Luca %A Vanneschi, Leonardo %Y Pereira, Francisco C. %Y Machado, Penousal %Y Costa, Ernesto %Y Cardoso, Amilcar %S Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Coimbra, Portugal, September 8-11, 2015. Proceedings %S Lecture Notes in Computer Science %D 2015 %V 9273 %I Springer %F conf/epia/CastelliFMV15 %K genetic algorithms, genetic programming %U http://dx.doi.org/10.1007/978-3-319-23485-4 %P 213-225 %0 Journal Article %T Prediction of energy performance of residential buildings: a genetic programming approach %A Castelli, Mauro %A Trujillo, Leonardo %A Vanneschi, Leonardo %A Popovic, Ales %J Energy and Buildings %D 2015 %8 sep %V 102 %N 1 %@ 0378-7788 %F Castelli:2015:EB %X Energy consumption has long been emphasized as an important policy issue in today’s economies. In particular, the energy efficiency of residential buildings is considered a top priority of a country’s energy policy. The paper proposes a genetic programming-based framework for estimating the energy performance of residential buildings. The objective is to build a model able to predict the heating load and the cooling load of residential buildings. An accurate prediction of these parameters facilitates a better control of energy consumption and, moreover, it helps choosing the energy supplier that better fits the energy needs, which is considered an important issue in the deregulated energy market. The proposed framework blends a recently developed version of genetic programming with a local search method and linear scaling. The resulting system enables us to build a model that produces an accurate estimation of both considered parameters. Extensive simulations on 768 diverse residential buildings confirm the suitability of the proposed method in predicting heating load and cooling load. In particular, the proposed method is more accurate than the existing state-of-the-art techniques. %K genetic algorithms, genetic programming, Energy consumption, Heating load, Cooling load, Machine learning %9 journal article %R 10.1016/j.enbuild.2015.05.013 %U http://www.sciencedirect.com/science/article/pii/S0378778815003849 %U http://dx.doi.org/10.1016/j.enbuild.2015.05.013 %P 67-74 %0 Journal Article %T Self-tuning geometric semantic Genetic Programming %A Castelli, Mauro %A Manzoni, Luca %A Vanneschi, Leonardo %A Silva, Sara %A Popovic, Ales %J Genetic Programming and Evolvable Machines %D 2016 %8 mar %V 17 %N 1 %@ 1389-2576 %F Castelli:2016:GPEM %X The process of tuning the parameters that characterize evolutionary algorithms is difficult and can be time consuming. This paper presents a self-tuning algorithm for dynamically updating the crossover and mutation probabilities during a run of genetic programming. The genetic operators that are considered in this work are the geometric semantic genetic operators introduced by Moraglio et al. Differently from other existing self-tuning algorithms, the proposed one works by assigning a (different) crossover and mutation probability to each individual of the population. The experimental results we present show the appropriateness of the proposed self-tuning algorithm: on seven different test problems, the proposed algorithm finds solutions of a quality that is better than, or comparable to, the one achieved using the best known values for the geometric semantic crossover and mutation rates for the same problems. Also, we study how the mutation and crossover probabilities change during the execution of the proposed self-tuning algorithm, pointing out an interesting insight: mutation is basically the only operator used in the exploration phase, while crossover is used for exploitation, further improving good quality solutions. %K genetic algorithms, genetic programming, Semantics, Parameters Tuning %9 journal article %R 10.1007/s10710-015-9251-7 %U http://dx.doi.org/10.1007/s10710-015-9251-7 %P 55-74 %0 Journal Article %T Prediction of relative position of CT slices using a computational intelligence system %A Castelli, Mauro %A Trujillo, Leonardo %A Vanneschi, Leonardo %A Popovic, Ales %J Applied Soft Computing %D 2016 %V 46 %@ 1568-4946 %F Castelli:2015:ASC %X One of the most common techniques in radiology is the computerized tomography (CT) scan. Automatically determining the relative position of a single CT slice within the human body can be very useful. It can allow for an efficient retrieval of slices from the same body region taken in other volume scans and provide useful information to the non-expert user. This work addresses the problem of determining which portion of the body is shown by a stack of axial CT image slices. To tackle this problem, this work proposes a computational intelligence system that combines semantics-based operators for Genetic Programming with a local search algorithm, coupling the exploration ability of the former with the exploitation ability of the latter. This allows the search process to quickly converge towards (near-)optimal solutions. Experimental results, using a large database of CT images, have confirmed the suitability of the proposed system for the prediction of the relative position of a CT slice. In particular, the new method achieves a median localization error of 3.4 cm on unseen data, outperforming standard Genetic Programming and other techniques that have been applied to the same dataset. In summary, this paper makes two contributions: (i) in the radiology domain, the proposed system outperforms current state-of-the-art techniques; (ii) from the computational intelligence perspective, the results show that including a local searcher in Geometric Semantic Genetic Programming can speed up convergence without degrading test performance. %K genetic algorithms, genetic programming, Computerized tomography, Radiology, Semantics, Local search %9 journal article %R 10.1016/j.asoc.2015.09.021 %U http://www.sciencedirect.com/science/article/pii/S1568494615005931 %U http://dx.doi.org/10.1016/j.asoc.2015.09.021 %P 537-542 %0 Journal Article %T Semantic genetic programming for fast and accurate data knowledge discovery %A Castelli, Mauro %A Vanneschi, Leonardo %A Manzoni, Luca %A Popovic, Ales %J Swarm and Evolutionary Computation %D 2016 %V 26 %@ 2210-6502 %F Castelli:2016:SEC %X Big data knowledge discovery emerged as an important factor contributing to advancements in society at large. Still, researchers continuously seek to advance existing methods and provide novel ones for analysing vast data sets to make sense of the data, extract useful information, and build knowledge to inform decision making. In the last few years, a very promising variant of genetic programming was proposed: geometric semantic genetic programming. Its difference with the standard version of genetic programming consists in the fact that it uses new genetic operators, called geometric semantic operators, that, acting directly on the semantics of the candidate solutions, induce by definition a unimodal error surface on any supervised learning problem, independently from the complexity and size of the underlying data set. This property should improve the evolvability of genetic programming in presence of big data and thus makes geometric semantic genetic programming an extremely promising method for mining vast amounts of data. Nevertheless, to the best of our knowledge, no contribution has appeared so far to employ this new technology to big data problems. This paper intends to fill this gap. For the first time, in fact, we show the effectiveness of geometric semantic genetic programming on several complex real-life problems, characterized by vast amounts of data, coming from several different application domains. %K genetic algorithms, genetic programming, Semantics, Knowledge discovery %9 journal article %R 10.1016/j.swevo.2015.07.001 %U http://www.sciencedirect.com/science/article/pii/S2210650215000516 %U http://dx.doi.org/10.1016/j.swevo.2015.07.001 %0 Journal Article %T Parameter evaluation of geometric semantic genetic programming in pharmacokinetics %A Castelli, Mauro %A Vanneschi, Leonardo %A Popovic, Ales %J Int. J. of Bio-Inspired Computation %D 2016 %8 feb 10 %V 8 %N 1 %I Inderscience Publishers %@ 1758-0374 %G eng %F Castelli:2016:IJBIC %X The role of crossover and mutation in genetic programming has been the subject of much debate since the emergence of the field. Recently new genetic operators, called geometric semantic operators, have been introduced. Contrary to standard genetic operators, these operators present the interesting property of inducing a unimodal fitness landscape for every problem that consists in finding a match between inputs and targets. As the definition of these operators is quite recent, their effect on the evolutionary dynamics is still in many senses unknown and deserves to be studied. This paper intends to fill this gap, with a specific focus on applications in the field of pharmacokinetic. Results show that a mixture of semantic crossover and mutation is always beneficial compared to the use of only one of these operators. Furthermore, we show that the best results are obtained using values of the semantic mutation rate which are considerably higher than the ones that are typically used when traditional subtree mutation is employed. %K genetic algorithms, genetic programming, semantics, geometric semantic operators, regression, parameter evaluation, pharmacokinetics, semantic crossover, semantic mutation, drug discovery %9 journal article %R 10.1504/IJBIC.2016.074634 %U http://www.inderscience.com/link.php?id=74634 %U http://dx.doi.org/10.1504/IJBIC.2016.074634 %P 42-50 %0 Journal Article %T Controlling Individuals Growth in Semantic Genetic Programming through Elitist Replacement %A Castelli, Mauro %A Vanneschi, Leonardo %A Popovic, Ales %J Computational Intelligence and Neuroscience %D 2016 %I Hindawi Publishing Corporation %G en %F Castelli:2016:CIN %X In 2012, Moraglio and coauthors introduced new genetic operators for Genetic Programming, called geometric semantic genetic operators. They have the very interesting advantage of inducing a unimodal error surface for any supervised learning problem. At the same time, they have the important drawback of generating very large data models that are usually very hard to understand and interpret. The objective of this work is to alleviate this drawback, still maintaining the advantage. More in particular, we propose an elitist version of geometric semantic operators, in which offspring are accepted in the new population only if they have better fitness than their parents. We present experimental evidence, on five complex real-life test problems, that this simple idea allows us to obtain results of a comparable quality (in terms of fitness), but with much smaller data models, compared to the standard geometric semantic operators. In the final part of the paper, we also explain the reason why we consider this a significant improvement, showing that the proposed elitist operators generate manageable models, while the models generated by the standard operators are so large in size that they can be considered unmanageable. %K genetic algorithms, genetic programming %9 journal article %U http://dx.doi.org/10.1155/2016/8326760 %P ArticleID8326760 %0 Conference Proceedings %T An Analysis of Geometric Semantic Crossover: A Computational Geometry Approach %A Castelli, Mauro %A Manzoni, Luca %A Goncalves, Ivo %A Vanneschi, Leonardo %A Trujillo, Leonardo %A Silva, Sara %S Proceedings of the 8th International Joint Conference on Computational Intelligence, IJCCI (ECTA) 2016 %D 2016 %I Scitepress %F castelli2016analysis %X Geometric semantic operators have recently shown their ability to outperform standard genetic operators on different complex real world problems. Nonetheless, they are affected by drawbacks. In this paper, we focus on one of these drawbacks, i.e. the fact that geometric semantic crossover has often a poor impact on the evolution. Geometric semantic crossover creates an offspring whose semantics stands in the segment joining the parents (in the semantic space). So, it is intuitive that it is not able to find, nor reasonably approximate, a globally optimal solution, unless the semantics of the individuals in the population contains the target. In this paper, we introduce the concept of convex hull of a genetic programming population and we present a method to calculate the distance from the target point to the convex hull. Then, we give experimental evidence of the fact that, in four different real-life test cases, the target is always outside the convex hull. As a consequence, we show that geometric semantic crossover is not helpful in those cases, and it is not even able to approximate the population to the target. Finally, in the last part of the paper, we propose ideas for future work on how to improve geometric semantic crossover. %K genetic algorithms, genetic programming, Semantics, Convex Hull %R 10.5220/0006056402010208 %U http://dx.doi.org/10.5220/0006056402010208 %P 201-208 %0 Journal Article %T Predicting per capita violent crimes in urban areas: an artificial intelligence approach %A Castelli, Mauro %A Sormani, Raul %A Trujillo, Leonardo %A Popovic, Ales %J Journal of Ambient Intelligence and Humanized Computing %D 2017 %8 feb %V 8 %N 1 %@ 1868-5145 %F castelli:2017:jaihc %X A major challenge facing all law-enforcement organizations is to accurately and efficiently analyse the growing volumes of crime data in order to extract useful knowledge for decision makers. This is an increasingly important task, considering the fast growth of urban populations in most countries. In particular, to reconcile urban growth with the need for security, a fundamental goal is to optimize the allocation of law enforcement resources. Moreover, optimal allocation can only be achieved if we can predict the incidence of crime within different urban areas. To answer this call, in this paper we propose an artificial intelligence system for predicting per capita violent crimes in urban areas starting from socio-economic data, law-enforcement data and other crime-related data obtained from different sources. The proposed framework blends a recently developed version of genetic programming that uses the concept of semantics during the search process with a local search method. To analyze the appropriateness of the proposed computational method for crime prediction, different urban areas of the United States have been considered. Experimental results confirm the suitability of the proposed method for addressing the problem at hand. In particular, the proposed method produces a lower error with respect to the existing state-of-the art techniques and it is particularly suitable for analysing large amounts of data. This is an extremely important feature in a world that is currently moving towards the development of smart cities. %K genetic algorithms, genetic programming, Evolutionary Computation, CSGP, LSGP, SVM, ANN, RBF, Crime Prediction Urban Security Semantics Local Search %9 journal article %R 10.1007/s12652-015-0334-3 %U http://dx.doi.org/10.1007/s12652-015-0334-3 %P 29-36 %0 Journal Article %T Stock index return forecasting: semantics-based genetic programming with local search optimiser %A Castelli, Mauro %A Vanneschi, Leonardo %A Trujillo, Leonardo %A Popovic, Ales %J International Journal of Bio-Inspired Computation %D 2017 %V 10 %N 3 %F journals/ijbic/CastelliVTP17 %X Making accurate stock price predictions is the pillar of effective decisions in high-velocity environments since the successful prediction of future prices could yield significant profit and reduce operational costs. Generally, solutions for this task are based on trend predictions and are driven by various factors. To add to the existing body of knowledge, we propose a semantics-based genetic programming framework. The proposed framework blends a recently developed version of genetic programming that uses semantic genetic operators with a local search method. To analyse the appropriateness of the proposed computational method for stock market price prediction, we analysed data related to the Dow Jones index and to the Istanbul Stock Index. Experimental results confirm the suitability of the proposed method for predicting stock market prices. In fact, the system produces lower errors with respect to the existing state-of-the art techniques, such as neural networks and support vector machines. forecasting; financial markets; genetic programming; semantics; local search. %K genetic algorithms, genetic programming %9 journal article %R 10.1504/IJBIC.2017.10004325 %U http://dx.doi.org/10.1504/IJBIC.2017.10004325 %P 159-171 %0 Conference Proceedings %T EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming %E Castelli, Mauro %E McDermott, James %E Sekanina, Lukas %S LNCS %D 2017 %8 19 21 apr %V 10196 %I Springer Verlag %C Amsterdam %F Castelli:2017:GP %K genetic algorithms, genetic programming %R 10.1007/978-3-319-55696-3 %U http://dx.doi.org/10.1007/978-3-319-55696-3 %0 Journal Article %T The influence of population size in geometric semantic GP %A Castelli, Mauro %A Manzoni, Luca %A Silva, Sara %A Vanneschi, Leonardo %A Popovic, Ales %J Swarm and Evolutionary Computation %D 2017 %V 32 %@ 2210-6502 %F Castelli:2017:SEC %X In this work, we study the influence of the population size on the learning ability of Geometric Semantic Genetic Programming for the task of symbolic regression. A large set of experiments, considering different population size values on different regression problems, has been performed. Results show that, on real-life problems, having small populations results in a better training fitness with respect to the use of large populations after the same number of fitness evaluations. However, performance on the test instances varies among the different problems: in datasets with a high number of features, models obtained with large populations present a better performance on unseen data, while in datasets characterized by a relative small number of variables a better generalization ability is achieved by using small population size values. When synthetic problems are taken into account, large population size values represent the best option for achieving good quality solutions on both training and test instances. %K genetic algorithms, genetic programming, Semantics, Population size %9 journal article %R 10.1016/j.swevo.2016.05.004 %U http://www.sciencedirect.com/science/article/pii/S2210650216300256 %U http://dx.doi.org/10.1016/j.swevo.2016.05.004 %P 110-120 %0 Journal Article %T An expert system for extracting knowledge from customers’ reviews: The case of Amazon.com, Inc. %A Castelli, Mauro %A Manzoni, Luca %A Vanneschi, Leonardo %A Popovic, Ales %J Expert Systems with Applications %D 2017 %V 84 %@ 0957-4174 %F Castelli:2017:ESA %X E-commerce has proliferated in the daily activities of end-consumers and firms alike. For firms, consumer satisfaction is an important indicator of e-commerce success. Today, consumers’ reviews and feedback are increasingly shaping consumer intentions regarding new purchases and repeated purchases, while helping to attract new customers. In our work, we use an expert system to predict the sentiment of a product considering a subset of available customers’ reviews. %K genetic algorithms, genetic programming, Semantics, E-commerce, Customers’ feedback %9 journal article %R 10.1016/j.eswa.2017.05.008 %U http://www.sciencedirect.com/science/article/pii/S0957417417303263 %U http://dx.doi.org/10.1016/j.eswa.2017.05.008 %P 117-126 %0 Journal Article %T An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming %A Castelli, Mauro %A Trujillo, Leonardo %A Goncalves, Ivo %A Popovic, Ales %J Computers and Concrete %D 2017 %8 jun %V 19 %N 6 %F Castelli:2017:CandC %X High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as super-plasticiser. Hence, it is a highly complex material and modelling its behaviour represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process. %K genetic algorithms, genetic programming, high performance concrete, concrete strength, local search, semantics %9 journal article %R 10.12989/cac.2017.19.6.651 %U http://www.techno-press.org/?page=container&journal=cac&volume=19&num=6 %U http://dx.doi.org/10.12989/cac.2017.19.6.651 %P 651-658 %0 Journal Article %T An evolutionary system for ozone concentration forecasting %A Castelli, Mauro %A Goncalves, Ivo %A Trujillo, Leonardo %A Popovic, Ales %J Information Systems Frontiers %D 2017 %8 January %V 19 %N 5 %I Springer %@ 1572-9419 %F castelli2017evolutionary %X Nowadays, with more than half of the world’s population living in urban areas, cities are facing important environmental challenges. Among them, air pollution has emerged as one of the most important concerns, taking into account the social costs related to the effect of polluted air. According to a report of the World Health Organization, approximately seven million people die each year from the effects of air pollution. Despite this fact, the same report suggests that cities could greatly improve their air quality through local measures by exploiting modern and efficient solutions for smart infrastructures. Ideally, this approach requires insights of how pollutant levels change over time in specific locations. To tackle this problem, we present an evolutionary system for the prediction of pollutants levels based on a recently proposed variant of genetic programming. This system is designed to predict the amount of ozone level, based on the concentration of other pollutants collected by sensors disposed in critical areas of a city. An analysis of data related to the region of Yuen Long (one of the most polluted areas of China), shows the suitability of the proposed system for addressing the problem at hand. In particular, the system is able to predict the ozone level with greater accuracy with respect to other techniques that are commonly used to tackle similar forecasting problems. %K genetic algorithms, genetic programming, Evolutionary computation, Smart cities, Forecasting, Air quality %9 journal article %R 10.1007/s10796-016-9706-2 %U http://dx.doi.org/10.1007/s10796-016-9706-2 %P 1123-1132 %0 Conference Proceedings %T EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming %E Castelli, Mauro %E Sekanina, Lukas %E Zhang, Mengjie %S LNCS %D 2018 %8 April 6 apr %V 10781 %I Springer Verlag %C Parma, Italy %F Castelli:2018:GP %K genetic algorithms, genetic programming %R 10.1007/978-3-319-77553-1 %U https://link.springer.com/book/10.1007%2F978-3-319-77553-1 %U http://dx.doi.org/10.1007/978-3-319-77553-1 %0 Conference Proceedings %T Pruning Techniques for Mixed Ensembles of Genetic Programming Models %A Castelli, Mauro %A Goncalves, Ivo %A Manzoni, Luca %A Vanneschi, Leonardo %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 Castelli:2018:EuroGP %X The objective of this paper is to define an effective strategy for building an ensemble of Genetic Programming (GP) models. Ensemble methods are widely used in machine learning due to their features: they average out biases, they reduce the variance and they usually generalize better than single models. Despite these advantages, building ensemble of GP models is not a well-developed topic in the evolutionary computation community. To fill this gap, we propose a strategy that blends individuals produced by standard syntax-based GP and individuals produced by geometric semantic genetic programming, one of the newest semantics-based method developed in GP. In fact, recent literature showed that combining syntactic and semantics could improve the generalization ability of a GP model. Additionally, to improve the diversity of the GP models used to build up the ensemble, we propose different pruning criteria that are based on correlation and entropy, a commonly used measure in information theory. Experimental results, obtained over different complex problems, suggest that the pruning criteria based on correlation and entropy could be effective in improving the generalization ability of the ensemble model and in reducing the computational burden required to build it. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-77553-1_4 %U http://dx.doi.org/10.1007/978-3-319-77553-1_4 %P 52-67 %0 Conference Proceedings %T Extending Local Search in Geometric Semantic Genetic Programming %A Castelli, Mauro %A Manzoni, Luca %A Mariot, Luca %A Saletta, Martina %Y Oliveira, Paulo Moura %Y Novais, Paulo %Y Reis, Luis Paulo %S Progress in Artificial Intelligence - 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Vila Real, Portugal, September 3-6, 2019, Proceedings, Part I %S Lecture Notes in Computer Science %D 2019 %V 11804 %I Springer %F DBLP:conf/epia/CastelliMMS19 %K genetic algorithms, genetic programming %R 10.1007/978-3-030-30241-2_64 %U https://doi.org/10.1007/978-3-030-30241-2_64 %U http://dx.doi.org/10.1007/978-3-030-30241-2_64 %P 775-787 %0 Journal Article %T GSGP-C++ 2.0: A geometric semantic genetic programming framework %A Castelli, Mauro %A Manzoni, Luca %J SoftwareX %D 2019 %V 10 %@ 2352-7110 %F CASTELLI:2019:SoftwareX %X Geometric semantic operators (GSOs) for Genetic Programming have been widely investigated in recent years, producing competitive results with respect to standard syntax based operator as well as other well-known machine learning techniques. The usage of GSOs has been facilitated by a C++ framework that implements these operators in a very efficient manner. This work presents a description of the system and focuses on a recently implemented feature that allows the user to store the information related to the best individual and to evaluate new data in a time that is linear with respect to the number of generations used to find the optimal individual. The paper presents the main features of the system and provides a step by step guide for interested users or developers %K genetic algorithms, genetic programming, Semantics, Machine learning %9 journal article %R 10.1016/j.softx.2019.100313 %U http://www.sciencedirect.com/science/article/pii/S2352711019301736 %U http://dx.doi.org/10.1016/j.softx.2019.100313 %P 100313 %0 Journal Article %T Forecasting Electricity Prices: A Machine Learning Approach %A Castelli, Mauro %A Groznik, Ales %A Popovic, Ales %J Algorithms %D 2020 %V 13 %N 5 %@ 1999-4893 %F castelli:2020:Algorithms %X The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique—namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/a13050119 %U https://www.mdpi.com/1999-4893/13/5/119 %U http://dx.doi.org/10.3390/a13050119 %0 Journal Article %T The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming %A Castelli, Mauro %A Manzoni, Luca %A Mariot, Luca %A Menara, Giuliamaria %A Pietropolli, Gloria %J Applied Sciences %D 2022 %V 12 %N 10 %@ 2076-3417 %F castelli:2022:AS %X Among the evolutionary methods, one that is quite prominent is genetic programming. In recent years, a variant called geometric semantic genetic programming (GSGP) was successfully applied to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all its evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations. We show that a limited ability to use old generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP. %K genetic algorithms, genetic programming, evolutionary computation, geometric operators, geometric semantic genetic programming %9 journal article %R 10.3390/app12104836 %U https://www.mdpi.com/2076-3417/12/10/4836 %U http://dx.doi.org/10.3390/app12104836 %P ArticleNo.4836 %0 Journal Article %T Commentary for the GPEM peer commentary special section on W. B. Langdon’s “Jaws 30” %A Castelli, Mauro %J Genetic Programming and Evolvable Machines %D 2023 %8 dec %V 24 %N 2 %@ 1389-2576 %F castelli:2023:GPEM %O Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-023-09468-w %U https://rdcu.be/drZcv %U http://dx.doi.org/10.1007/s10710-023-09468-w %P Articlenumber:20 %0 Conference Proceedings %T Symbolic Regression In Design Of Experiments: A Case Study With Linearizing Transformations %A Castillo, Flor A. %A Marshall, Ken A. %A Green, James L. %A Kordon, Arthur K. %Y Langdon, W. B. %Y Cantú-Paz, E. %Y Mathias, K. %Y Roy, R. %Y Davis, D. %Y Poli, R. %Y Balakrishnan, K. %Y Honavar, V. %Y Rudolph, G. %Y Wegener, J. %Y Bull, L. %Y Potter, M. A. %Y Schultz, A. C. %Y Miller, J. F. %Y Burke, E. %Y Jonoska, N. %S GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %D 2002 %8 September 13 jul %I Morgan Kaufmann Publishers %C New York %@ 1-55860-878-8 %F castillo:2002:gecco %X The paper presents the potential of genetic programming (GP)-generated symbolic regression for linearising the response in statistical design of experiments when significant Lack of Fit is detected and no additional experimental runs are economically or technically feasible because of extreme experimental conditions. An application of this approach is presented with a case study in an industrial setting at The Dow Chemical Company. %K genetic algorithms, genetic programming, real world applications, design of experiment (DoE), lack of fit, linearizing transformations, symbolic regression %U http://gpbib.cs.ucl.ac.uk/gecco2002/RWA194.pdf %P 1043-1047 %0 Conference Proceedings %T A Methodology for Combining Symbolic Regression and Design of Experiments to Improve Empirical Model Building %A Castillo, Flor %A Marshall, Kenric %A Green, James %A Kordon, Arthur %Y Cantú-Paz, E. %Y Foster, J. A. %Y Deb, K. %Y Davis, D. %Y Roy, R. %Y O’Reilly, U.-M. %Y Beyer, H.-G. %Y Standish, R. %Y Kendall, G. %Y Wilson, S. %Y Harman, M. %Y Wegener, J. %Y Dasgupta, D. %Y Potter, M. A. %Y Schultz, A. C. %Y Dowsland, K. %Y Jonoska, N. %Y Miller, J. %S Genetic and Evolutionary Computation – GECCO-2003 %S LNCS %D 2003 %8 December 16 jul %V 2724 %I Springer-Verlag %C Chicago %@ 3-540-40603-4 %F Castillo:2003:gecco %X A novel methodology for empirical model building using GP-generated symbolic regression in combination with statistical design of experiments as well as undesigned data is proposed. The main advantage of this methodology is the maximum data usage when extrapolation is necessary. The methodology offers alternative non-linear models that can either linearize the response in the presence of Lack or Fit or challenge and confirm the results from the linear regression in a cost effective and time efficient fashion. The economic benefit is the reduced number of additional experiments in the presence of Lack of Fit. %K genetic algorithms, genetic programming, symbolic regression, design of experiments, Real World Applications %R 10.1007/3-540-45110-2_96 %U http://dx.doi.org/10.1007/3-540-45110-2_96 %P 1975-1985 %0 Book Section %T Using Genetic Programming in Industrial Statistical Model Building %A Castillo, Flor %A Kordon, Arthur %A Sweeney, Jeff %A Zirk, Wayne %E O’Reilly, Una-May %E Yu, Tina %E Riolo, Rick L. %E Worzel, Bill %B Genetic Programming Theory and Practice II %D 2004 %8 13 15 may %I Springer %C Ann Arbor %@ 0-387-23253-2 %F castillo:2004:GPTP %X The chapter summarises the practical experience of integrating genetic programming and statistical modelling at The Dow Chemical Company. A unique methodology for using Genetic Programming in statistical modeling of designed and undesigned data is described and illustrated with successful industrial applications. As a result of the synergistic efforts, the building technique has been improved and the model development cost and time can be significantly reduced. In case of designed data Genetic Programming reduced costs by suggesting transformations as an alternative to doing additional experimentation. In case of undesigned data Genetic Programming was instrumental in reducing the model building costs by providing alternative models for consideration. %K genetic algorithms, genetic programming, statistical model building, symbolic regression, undesigned data %R 10.1007/0-387-23254-0_3 %U http://dx.doi.org/10.1007/0-387-23254-0_3 %P 31-48 %0 Conference Proceedings %T G-Prop-III: Global Optimization of Multilayer Perceptrons using an Evolutionary Algorithm %A Castillo, P. A. %A Rivas, V. %A Merelo, J. J. %A Gonzalez, J. %A Prieto, A. %A Romero, G. %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 castillo:1999:GGOMPEA %K evolution strategies and evolutionary programming, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/G-Prop-III_poster.ps.gz %P 942 %0 Conference Proceedings %T Comparing hybrid systems to design and optimize artificial neural networks %A Castillo, Pedro A. %A Arenas, Maribel G. %A Merelo, J. J. %A Romero, Gustavo %A Rateb, Fatima %A Prieto, Alberto %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 castillo:2004:eurogp %X We conduct a comparative study between hybrid methods to optimise multi-layer perceptrons: a model that optimises the architecture and initial weights of multi layer perceptrons; a parallel approach to optimise the architecture and initial weights of multilayer perceptrons; a method that searches for the parameters of the training algorithm, and an approach for cooperative co-evolutionary optimisation of multi layer perceptrons. Obtained results show that a co-evolutionary model obtains similar or better results than specialised approaches, needing much less training epochs and thus using much less simulation time. %K genetic algorithms, genetic programming: Poster %R 10.1007/978-3-540-24650-3_22 %U http://dx.doi.org/10.1007/978-3-540-24650-3_22 %P 240-249 %0 Conference Proceedings %T Using Evolutionary Algorithms to Suggest Variable Transformations in Linear Model Lack-of-Fit Situations %A Castillo, Flor %A Sweeney, Jeff %A Zirk, Wayne %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 castillo:2004:ueatsvtilmls %X When significant model lack of fit (LOF) is present in a second-order linear regression model, it is often difficult to propose the appropriate parameter transformation that will make model LOF insignificant. This paper presents the potential of genetic programming (GP) symbolic regression for reducing or eliminating significant second-order linear model LOF. A case study in an industrial setting at The Dow Chemical Company is presented to illustrate this methodology. %K genetic algorithms, genetic programming, Evolutionary Computing in the Process Industry %R 10.1109/CEC.2004.1330906 %U http://dx.doi.org/10.1109/CEC.2004.1330906 %P 556-560 %0 Book Section %T Robust Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data %A Castillo, Flor %A Kordon, Arthur %A Smits, Guido %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 Castillo:2006:GPTP %X Symbolic regression based on Pareto front GP is a very effective approach for generating high-performance parsimonious empirical models acceptable for industrial applications. The chapter addresses the issue of finding the optimal parameter settings of Pareto front GP which direct the simulated evolution toward simple models with acceptable prediction error. A generic methodology based on statistical design of experiments is proposed. It includes determination of the number of replicates by half-width confidence intervals, determination of the significant factors by fractional factorial design of experiments, approaching the optimum by steepest ascent/descent, and local exploration around the optimum by Box Behnken design of experiments. The results from implementing the proposed methodology to different types of industrial data sets show that the statistically significant factors are the number of cascades, the number of generations, and the population size. The optimal values for the three parameters have been defined based on second order regression models with R2 higher than 0.97 for small, medium, and large-sized data sets. The robustness of the optimal parameters toward the types of data sets was explored and a robust setting for the three significant parameters was obtained. It reduces the calculation time by 30per cent to 50per cent without statistically significant reduction in the mean response. %K genetic algorithms, genetic programming, symbolic regression, industrial applications, design of experiments, parameter selection %R 10.1007/978-0-387-49650-4_10 %U http://dx.doi.org/10.1007/978-0-387-49650-4_10 %P 149-166 %0 Conference Proceedings %T Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data %A Castillo, Flor %A Kordon, Arthur %A Smits, Guido %A Christenson, Ben %A Dickerson, Dee %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 2 %I ACM Press %C Seattle, Washington, USA %@ 1-59593-186-4 %F 1144264 %X Symbolic regression based on Pareto Front GP is the key approach for generating high-performance parsimonious empirical models acceptable for industrial applications. The paper addresses the issue of finding the optimal parameter settings of Pareto Front GP which direct the simulated evolution toward simple models with acceptable prediction error. A generic methodology based on statistical design of experiments is proposed. It includes statistical determination of the number of replicates by half-width confidence intervals, determination of the significant inputs by fractional factorial design of experiments, approaching the optimum by steepest ascent/descent, and local exploration around the optimum by Box Behnken or by central composite design of experiments. The results from implementing the proposed methodology to a small-sized industrial data set show that the statistically significant factors for symbolic regression, based on Pareto Front GP, are the number of cascades, the number of generations, and the population size. A second order regression model with high R2 of 0.97 includes the three parameters and their optimal values have been defined. The optimal parameter settings were validated with a separate small sized industrial data set. The optimal settings are recommended for symbolic regression applications using data sets with up to 5 inputs and up to 50 data points. %K genetic algorithms, genetic programming, Real-World Applications, industrial applications, Pareto front, statistical design of experiments, symbolic regression %R 10.1145/1143997.1144264 %U http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1613.pdf %U http://dx.doi.org/10.1145/1143997.1144264 %P 1613-1620 %0 Book Section %T Genetic Programming Transforms in Linear Regression Situations %A Castillo, Flor %A Kordon, Arthur %A Villa, Carlos %E Riolo, Rick %E McConaghy, Trent %E Vladislavleva, Ekaterina %B Genetic Programming Theory and Practice VIII %S Genetic and Evolutionary Computation %D 2010 %8 20 22 may %V 8 %I Springer %C Ann Arbor, USA %F Castillo:2010:GPTP %X The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR. %K genetic algorithms, genetic programming, Multiple Linear Regression, multicollinearity, soft sensor %R 10.1007/978-1-4419-7747-2_11 %U http://www.springer.com/computer/ai/book/978-1-4419-7746-5 %U http://dx.doi.org/10.1007/978-1-4419-7747-2_11 %P 175-194 %0 Book Section %T Symbolic Regression Model Comparison Approach Using Transmitted Variation %A Castillo, Flor A. %A Villa, Carlos M. %A Kordon, Arthur K. %E Riolo, Rick %E Vladislavleva, Ekaterina %E Ritchie, Marylyn D. %E Moore, Jason H. %B Genetic Programming Theory and Practice X %S Genetic and Evolutionary Computation %D 2012 %8 December 14 may %I Springer %C Ann Arbor, USA %F Castillo:2012:GPTP %X Model evaluation in symbolic regression generated by GP is of critical importance for successful industrial applications. Typically this model evaluation is achieved by a tradeoff between model complexity and R squared. The chapter introduces a model comparison approach based on the transmission of variation from the inputs to the output. The approach is illustrated with three different data sets from real industrial applications. %K genetic algorithms, genetic programming, Symbolic regression, Model comparison, Transmitted variation, Pareto front, Interpolation, Monte Carlo %R 10.1007/978-1-4614-6846-2_10 %U http://dx.doi.org/10.1007/978-1-4614-6846-2_10 %P 139-154 %0 Journal Article %T Document Clustering with Evolutionary Systems through Straight-Line Programs ’slp’ %A Castillo Sequera, Jose Luis %A Fernandez del Castillo Diez, Jose Raul %A Gonzalez Sotos, Leon %J Journal of Intelligent Learning Systems and Applications %D 2012 %8 nov %V 4 %N 4 %I Scientific Research Publishing %@ 2150-8402 %G eng %F Castillo:2012:JILSA %X In this paper, we show a clustering method supported on evolutionary algorithms with the paradigm of linear genetic programming. The Straight-Line Programs slp, which uses a data structure which will be useful to represent collections of documents. This data structure can be seen as a linear representation of programs, as well as representations in the form of graphs. It has been used as a theoretical model in Computer Algebra, and our purpose is to reuse it in a completely different context. In this case, we apply it to the field of grouping library collections through evolutionary algorithms. We show its efficiency with experimental data we got from traditional library collections. %K genetic algorithms, genetic programming, Data Mining %9 journal article %R 10.4236/jilsa.2012.44032 %U http://www.scirp.org/journal/PaperDownload.aspx?DOI=10.4236/jilsa.2012.44032 %U http://dx.doi.org/10.4236/jilsa.2012.44032 %P 303-318 %0 Conference Proceedings %T Positional Effect of Crossover and Mutation in Grammatical Evolution %A Castle, Tom %A Johnson, Colin G. %Y Esparcia-Alcazar, Anna Isabel %Y Ekart, Aniko %Y Silva, Sara %Y Dignum, Stephen %Y Uyar, A. Sima %S Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010 %S LNCS %D 2010 %8 July 9 apr %V 6021 %I Springer %C Istanbul %F Castle:2010:EuroGP %X An often-mentioned issue with Grammatical Evolution is that a small change in the genotype, through mutation or crossover, may completely change the meaning of all of the following genes. This paper analyses the crossover and mutation operations in GE, in particular examining the constructive or destructive nature of these operations when occurring at points throughout a genotype. The results we present show some strong support for the idea that events occurring at the first positions of a genotype are indeed more destructive, but also indicate that they may be the most constructive crossover and mutation points too. We also demonstrate the sensitivity of this work to the precise definition of what is constructive/destructive. %K genetic algorithms, genetic programming, Grammatical Evolution, crossover, mutation, position, bias %R 10.1007/978-3-642-12148-7_3 %U http://dx.doi.org/10.1007/978-3-642-12148-7_3 %P 26-37 %0 Conference Proceedings %T Evolving High-Level Imperative Program Trees with Strongly Formed Genetic Programming %A Castle, Tom %A Johnson, Colin G. %Y Moraglio, Alberto %Y Silva, Sara %Y Krawiec, Krzysztof %Y Machado, Penousal %Y Cotta, Carlos %S Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012 %S LNCS %D 2012 %8 November 13 apr %V 7244 %I Springer Verlag %C Malaga, Spain %F castle:2012:EuroGP %X We present a set of extensions to Montana’s popular Strongly Typed Genetic Programming system that introduce constraints on the structure of program trees. It is demonstrated that these constraints can be used to evolve programs with a naturally imperative structure, using common high-level imperative language constructs such as loops. A set of three problems including factorial and the general even-n-parity problem are used to test the system. Experimental results are presented which show success rates and required computational effort that compare favourably against other systems on these problems, while providing support for this imperative structure. %K genetic algorithms, genetic programming, Imperative programming, Loops %R 10.1007/978-3-642-29139-5_1 %U http://www.cs.kent.ac.uk/pubs/2012/3202/content.pdf %U http://dx.doi.org/10.1007/978-3-642-29139-5_1 %P 1-12 %0 Conference Proceedings %T Evolving Program Trees with Limited Scope Variable Declarations %A Castle, Tom %A Johnson, Colin G. %Y Li, Xiaodong %S Proceedings of the 2012 IEEE Congress on Evolutionary Computation %D 2012 %8 October 15 jun %I IEEE Press %C Brisbane, Australia %@ 0-7803-8515-2 %F Castle:2012:CEC %X Variables are a fundamental component of computer programs. However, rarely has the construction of new variables been left to the evolutionary process of a tree- based Genetic Programming system. We present a series of modifications to an existing GP approach to allow the evolution of high-level imperative programs with limited scope variables. We make use of several new program constructs made possible by the modifications and experimentally compare their use. Our results suggest the impact of variable declarations is problem dependent, but can potentially improve performance. It is proposed that the use of variable declarations can reduce the degree of insight required into potential solutions. %K genetic algorithms, genetic programming, imperative, sfgp, variables %R 10.1109/CEC.2012.6256547 %U http://www.cs.kent.ac.uk/pubs/2012/3213/index.html %U http://dx.doi.org/10.1109/CEC.2012.6256547 %P 2250-2257 %0 Thesis %T Evolving High-Level Imperative Program Trees with Genetic Programming %A Castle, Thomas Anthony %D 2012 %8 jun %C UK %C University of Kent %F Castle12 %X Genetic Programming (GP) is a technique which uses an evolutionary metaphor to automatically generate computer programs. Although GP proclaims to evolve computer programs, historically it has been used to produce code which more closely resembles mathematical formulae than the well structured programs that modern programmers aim to produce. The objective of this thesis is to explore the use of GP in generating high-level imperative programs and to present some novel techniques to progress this aim. A novel set of extensions to Montana’s Strongly Typed Genetic Programming system are presented that provide a mechanism for constraining the structure of program trees. It is demonstrated that these constraints are sufficient to evolve programs with a naturally imperative structure and to support the use of many common high-level imperative language constructs such as loops. Further simple algorithm modifications are made to support additional constructs, such as variable declarations that create new limited-scope variables. Six non-trivial problems, including sorting and the general even parity problem, are used to experimentally compare the performance of the systems and configurations proposed. Software metrics are widely used in the software engineering process for many purposes, but are largely unused in GP. A detailed analysis of evolved programs is presented using seven different metrics, including cyclomatic complexity and Halstead’s program effort. The relationship between these metrics and a program’s fitness and evaluation time is explored. It is discovered that these metrics are poorly suited for application to improve GP performance, but other potential uses are proposed. %K genetic algorithms, genetic programming, SBSE, STGP, Verification, local variables, loops, software metrics, computer programming %9 PhD Thesis %9 Ph.D. thesis %U http://kar.kent.ac.uk/34799/ %0 Journal Article %T Genetic programming and floating boom performance %A Castro, A. %A Perez, J. L. %A Rabunal, J. R. %A Iglesias, G. %J Ocean Engineering %D 2015 %V 104 %@ 0029-8018 %F Castro:2015:OE %X In this paper the performance of floating booms under waves and currents is investigated by means of genetic programming (GP). This artificial intelligence (AI) technique is used to establish a mathematical expression of the significant effective draft, an essential parameter in predicting the containment capability of floating booms, and more specifically the occurrence of drainage failure. Obtained by applying GP to a comprehensive dataset of wave-current flume experiments, the expression makes the relationships among the relevant variables explicit - an advantage relative to other AI techniques such as artificial neural networks (ANN). The expression was selected as the most adequate to represent this physical problem from various expressions generated in two different stages in which dimensional and dimensionless variables were considered as input and output variables respectively. The most representative expressions obtained in both stages are presented and compared taking into account their goodness-of-fit, physical meaning, coherence and complexity. In addition, the adjustment with the experimental data obtained with these expressions is also discussed and compared with a previously developed ANN model. %K genetic algorithms, genetic programming, Floating booms, Drainage failure, Effective draft, Physical model %9 journal article %R 10.1016/j.oceaneng.2015.05.023 %U http://www.sciencedirect.com/science/article/pii/S0029801815002073 %U http://dx.doi.org/10.1016/j.oceaneng.2015.05.023 %P 310-318 %0 Conference Proceedings %T AIMED: Evolving Malware with Genetic Programming to Evade Detection %A Castro, Raphael Labaca %A Schmitt, Corinna %A Dreo, Gabi %S 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) %D 2019 %8 May 8 aug %C Rotorua, New Zealand %F Castro:2019:BigDataSE %X Genetic Programming (GP) has previously proved to achieve valuable results on the fields of image processing and arcade learning. Similarly, it can be used as an adversarial learning approach to evolve malware samples until static learning classifiers are no longer able to detect it. While the implementation is relatively simple compared with other Machine Learning approaches, results proved that GP can be a competitive solution to find adversarial malware examples comparing with similar methods. Thus, AIMED (Automatic Intelligent Malware Modifications to Evade Detection) was designed and implemented using genetic algorithms to evade malware classifiers. Our experiments suggest that the time to achieve adversarial malware samples can be reduced up to 50percent compared to classic random approaches. Moreover, we implemented AIMED to generate adversarial examples using individual malware scanners as target and tested the evasive files against further classifiers from both research and industry. The generated examples achieved up to 82percent of cross-evasion rates among the classifiers. %K genetic algorithms, genetic programming, AIMED, Malware, Byte-level perturbations, Adversarial learning %R 10.1109/TrustCom/BigDataSE.2019.00040 %U http://dx.doi.org/10.1109/TrustCom/BigDataSE.2019.00040 %P 240-247 %0 Generic %T A Python library for nonlinear system identification using Multi-Gene Genetic Programming algorithm %A Carvalho de Castro, Henrique %A Groenner Barbosa, Bruno Henrique %D 2022 %8 October %I arXiv %F DBLP:journals/corr/abs-2211-05723 %X Models can be built directly from input and output data trough a process known as system identification. The Nonlinear AutoRegressive with eXogenous inputs (NARMAX) models are among the most used mathematical representations in the area and has many successful applications on data-driven modeling in different fields. Such models become extremely large when they have high degree of non-linearity and long-term dependencies. Hence, a structure selection process must be performed to make them parsimonious. In the present paper, it is introduced a toolbox in Python that performs the structure selection process using the evolutionary algorithm named Multi-Gene Genetic Programming (MGGP). The toolbox encapsulates basic tools for parameter estimation, simulation and validation, and it allows the users to customize their evaluation function including prior knowledge and constraints in the individual structure (gray-box identification). %K genetic algorithms, genetic programming, System identification, NARMAX, MGGP, Nonlinear systems %R 10.48550/arXiv.2211.05723 %U https://doi.org/10.48550/arXiv.2211.05723 %U http://dx.doi.org/10.48550/arXiv.2211.05723 %0 Conference Proceedings %T Genetic Programming design of wire antennas %A Casula, G. A. %A Mazzarella, G. %A Sirena, N. %S IEEE Antennas and Propagation Society International Symposium, APSURSI ’09 %D 2009 %8 jun %F Casula:2009:APSURSI %X Genetic optimization has been used in the last years for solving different electromagnetic problems. However, this technique assumes, and binary-codes, a fixed structure from the beginning, so it has a limited use in antenna design. On the other hand, Genetic Programming is able to determine the antenna shape as an outcome of the procedure. This work describes how to use genetic programming to design wire antennas. The performances of each antenna generated by the genetic programming during the optimization process are evaluated by a standard method of moments code, NEC-2. %K genetic algorithms, genetic programming, genetic programming design, wire antennas %R 10.1109/APS.2009.5171505 %U http://dx.doi.org/10.1109/APS.2009.5171505 %P 1-4 %0 Book Section %T Structure-Based Evolutionary Design Applied to Wire Antennas %A Casula, Giovanni Andrea %A Mazzarella, Giuseppe %E Ventura, Sebastian %B Genetic Programming - New Approaches and Successful Applications %D 2012 %I InTech %F Casula:2012:GPnew %K genetic algorithms, genetic programming %R 10.5772/48249 %U http://dx.doi.org/10.5772/48249 %P 117-140 %0 Conference Proceedings %T Distributed Genetic Programming for Obtaining Formulas: Application to Concrete Strength %A Catoira, Alba %A Perez, Juan %A Rabunal, Juan %Y de Leon F. de Carvalho, Andre %Y Rodriguez-Gonzalez, Sara %Y De Paz Santana, Juan %Y Rodriguez, Juan %S 7th International Symposium Distributed Computing and Artificial Intelligence %S Advances in Intelligent and Soft Computing %D 2010 %8 July 10 sep %V 79 %I Springer %C Valencia, Spain %F conf/dcai/CatoiraPR10 %X This paper presents a Genetic Programming algorithm which applies a clustering algorithm. The method evolves a population of trees for a fixed number of rounds or generations and applies a clustering algorithm to the population, in a way that in the selection process of trees their structure is taken into account. The proposed method, named DistClustGP, runs in a parallel environment, according to the model master-slave , so that it can evolve simultaneously different populations, and evolve together the best individuals from each cluster. DistClustGP favours the analysis of the parameters involved in the genetic process, decreases the number of generations necessary to obtain satisfactory results through evolution of different populations, due to its parallel nature, and allows the evolution of the best individuals taking into account their structure. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-14883-5_46 %U http://dx.doi.org/10.1007/978-3-642-14883-5_46 %P 357-364 %0 Conference Proceedings %T Typed Cartesian Genetic Programming for Image Classification %A Cattani, Phil T. %A Johnson, Colin G. %S UK workshop on Computational Intelligence %D 2009 %C Nottingham %G en %F Cattani:2009:UKCI %X This paper introduces an extension to Cartesian Genetic Programming (CGP), aimed at image classification problems. Individuals in the population consist of two layers of functions: image processing functions, and traditional mathematical functions. Information can be passed between these layers, and the final result can either be an image or a numerical value. This has been applied to image classification, by using CGP to evolve image processing algorithms for feature extraction. This paper presents results which show that these automatically extracted features can substantially increase classification accuracy on a medical problem concerned with the analysis of potentially cancerous cells. %K genetic algorithms, genetic programming, cartesian genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.9907 %0 Conference Proceedings %T ME-CGP: Multi Expression Cartesian Genetic Programming %A Cattani, Phil T. %A Johnson, Colin G. %S IEEE Congress on Evolutionary Computation (CEC 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Cattani:2010:cec %X Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. In this paper we propose a way of structuring a CGP algorithm to make use of the multiple phenotypes which are implicitly encoded in a genome string. We show that this leads to a large increase in efficiency compared with standard CGP where genomes are translated into only one phenotype. We call this method Multi Expression CGP (ME-CGP), based on Mihai Oltean’s work on Multi Expression Programming using linear GP. %K genetic algorithms, genetic programming, Cartesian Genetic Programming %R 10.1109/CEC.2010.5586478 %U https://kar.kent.ac.uk/id/eprint/30650 %U http://dx.doi.org/10.1109/CEC.2010.5586478 %0 Thesis %T Extending Cartesian genetic programming : multi-expression genomes and applications in image processing and classification %A Cattani, Philip Thomas %D 2014 %C UK %C University of Kent %F Cattani:thesis %X Genetic Programming (GP) is an Evolutionary Computation technique. Genetic Programming refers to a programming strategy where an artificial population of individuals represent solutions to a problem in the form of programs, and where an iterative process of selection and reproduction is used in order to evolve increasingly better solutions. This strategy is inspired by Charles Darwin theory of evolution through the mechanism of natural selection. Genetic Programming makes use of computational procedures analogous to some of the same biological processes which occur in natural evolution, namely, crossover, mutation, selection, and reproduction. Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. It is called Cartesian, because this representation uses a grid of nodes that are addressed using a Cartesian co-ordinate system. This stands in contrast to GP systems which typically use a tree-based system to represent programs. In this thesis, we will show how it is possible to enhance and extend Cartesian Genetic Programming in two ways. Firstly, we show how CGP can be made to evolve programs which make use of image manipulation functions in order to create image manipulation programs. These programs can then be applied to image classification tasks as well as other image manipulation tasks such as segmentation, the creation of image filters, and transforming an input image in to a target image. Secondly, we show how the efficiency, the time it takes to solve a problem, of a CGP program can sometimes be increased by reinterpreting the semantics of a CGP genome string. We do this by applying Multi-Expression Programming to CGP. %K genetic algorithms, genetic programming, Cartesian genetic programming, MEP %9 Ph.D. thesis %U https://mepx.org/papers.html#2014 %0 Conference Proceedings %T Rule Acquisition with a Genetic Algorithm %A Cattral, Robert %A Oppacher, Franz %A Deugo, Dwight %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 cattral:1999:RAGA %X Data mining, applied to poisonous mushroom machine learning benchmark %K genetic algorithms, genetic programming, classifier systems, poster papers %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco1999/cattral_1999_raga.pdf %P 778 %0 Conference Proceedings %T LIGO detector characterization with genetic programming %A Cavaglia, Marco %A Staats, Kai %A Errico, Luciano %A Mogushi, Kentaro %A Gabbard, Hunter %S APS April Meeting 2017 %D 2017 %8 jan 28 31 %C Washington, DC, USA %F Cavaglia:2017:APS %O Bulletin of the American Physical Society %X Genetic Programming (GP) is a supervised approach to Machine Learning. GP has for two decades been applied to a diversity of problems, from predictive and financial modelling to data mining, from code repair to optical character recognition and product design. GP uses a stochastic search, tournament, and fitness function to explore a solution space. GP evolves a population of individual programs, through multiple generations, following the principals of biological evolution (mutation and reproduction) to discover a model that best fits or categorizes features in a given data set. We apply GP to categorization of LIGO noise and show that it can effectively be used to characterize the detector non-astrophysical noise both in low latency and offline searches. %K genetic algorithms, genetic programming, Laser Interferometer Gravitational-Wave %U http://absimage.aps.org/image/APR17/MWS_APR17-2016-000316.pdf %P Abstract:X6.00008 %0 Generic %T Finding the origin of noise transients in LIGO data with machine learning %A Cavaglia, Marco %A Staats, Kai %A Gill, Teerth %D 2018 %8 13 dec %I arXiv %F Cavaglia:2018:arXiv %X Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artefacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the data sets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered. %K genetic algorithms, genetic programming, Data Analysis, Statistics and Probability %U https://arxiv.org/abs/1812.05225 %0 Journal Article %T Finding the origin of noise transients in LIGO data with machine learning %A Cavaglia, Marco %A Staats, Kai %A Gill, Teerth %J Communications in Computational Physics %D 2019 %8 apr %V 25 %N 4 %@ 1815-2406 %F Cavaglia:2019:CCP %X Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the data sets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered. %K genetic algorithms, genetic programming, instrumentation, astrophysics, mechanical couplings, ligo, Machine learning, gravitational waves, noise mitigation %9 journal article %R 10.4208/cicp.OA-2018-0092 %U https://arxiv.org/abs/1812.05225 %U http://dx.doi.org/10.4208/cicp.OA-2018-0092 %P 963-987 %0 Conference Proceedings %T Learning Initialisation Heuristic for Large Scale Vehicle Routing Problem with Genetic Programming %A Cavalcanti Costa, Joao Guilherme %A Mei, Yi %A Zhang, Mengjie %Y Ong, Yew-Soon %S 2021 IEEE Congress on Evolutionary Computation (CEC) %D 2021 %8 28 jun 1 jul %C Krakow, Poland %F Cavalcanti-Costa:2021:CEC %X The Large Scale Vehicle Routing Problem is a classical NP-hard problem. It has several applications in the industry and has always been the focus of studies and development of new, ever more complex, techniques to solve it. An important group of these techniques are Local Search-based, which are sensitive to the initial solution given to them. However, finding effective initial solutions is not a trivial task, requiring domain knowledge for building them. Although some Genetic Programming Hyper-Heuristics (GPHH) have tried to build better heuristics automatically, they barely give an advantage for improving the solution afterwards. This paper aims to show that Genetic Programming can identify better regions of the search space, where the initial solutions can be improved more efficiently with optimisation steps. This is done by developing new terminals and a new fitness function, which are based on the width of the routes, a metric that was recently found to be an important feature for good solutions. The obtained results show that the proposed approach finds better final solutions than when using classical initial heuristics or other GPHH, for both time efficiency and effectiveness. %K genetic algorithms, genetic programming, Measurement, Space vehicles, Industries, NP-hard problem, Vehicle routing, Search problems, Large Scale Vehicle Routing, Hyper-Heuristic, Initialisation %R 10.1109/CEC45853.2021.9504938 %U http://dx.doi.org/10.1109/CEC45853.2021.9504938 %P 1864-1871 %0 Conference Proceedings %T Learning Penalisation Criterion of Guided Local Search for Large Scale Vehicle Routing Problem %A Cavalcanti Costa, Joao Guilherme %A Mei, Yi %A Zhang, Mengjie %S 2021 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2021 %8 dec %F Cavalcanti-Costa:2021:SSCI %X A recent case of success, the Knowledge-Guided Local Search was able to efficiently and effectively solve several (Large-Scale) Vehicle Routing Problems. This method presents an interesting concept of route compactness in their search process and uses it to penalise the solutions instead of using the traditional distance measure. Although mostly being successful, this measure sometimes leads to underperforming solutions when compared to the distance aspect. Based on the assumption that the best Guide Local Search penalisation criterion depends on the VRP instance, we make an analysis on how the algorithm behaves across different instances and also propose a Machine Learning model to learn to predict the best penalty criterion for a given instance. Genetic Programming, Support-Vector Machines and Random Forests are used in this classification task. Additionally, we also consider a regression model in order to estimate the improvement given for each mode. Results show that it is possible to find the correct class using the selected features and, in fact, some models were able to classify the majority of instances correctly. However, this is not consistent across different instances. %K genetic algorithms, genetic programming, Machine learning algorithms, Computational modelling, Vehicle routing, Predictive models, Search problems, Prediction algorithms %R 10.1109/SSCI50451.2021.9659939 %U http://dx.doi.org/10.1109/SSCI50451.2021.9659939 %0 Thesis %T Hyper-Heuristics for Automatic Configuration of Local Search-based Heuristics for the Large-Scale Vehicle Routing Problem %A Cavalcanti Costa, Joao Guilherme %D 2023 %8 25 sep %C New Zealand %C School of Engineering and Computer Science, Victoria University of Wellington %F Cavalcanti-Costa:thesis %X In this thesis we tackle one of the issues of a well-known hard-to-solve problem. The problem is the Vehicle Routing Problem (VRP), specifically in its large-scale version, also known as the Large Scale VRP (LSVRP). The LSVRP has a number of customers much larger than what traditional approaches can solve with ease, bringing extra challenges due to the amount of resources required to compute the solutions. Even when considering non-exact approaches, the scale of the problem demands reductions to the size of the solution search space. A challenge arises when doing this reduction while trying to maintain most or all of the very good solutions. The issue we tackle is the number of manual decisions that exist in current method design. Most of the existing literature for the VRP uses decisions that are seemingly arbitrary or based on expertise design, sometimes with little experimentation. This leads to methods that fail to generalise well for some instances or, especially, for large-scale problems. Considering the goal of minimisation problems, such as the VRP, to be reducing costs as much as possible, in several scenarios these manually designed fixed decisions often lead to a higher cost. We argue that it is possible to improve the effectiveness of the methods without manually setting up parameter values for each instance. We approach this issue by considering the local search framework, which is arguably the most used engine in most methods for the VRP. We then consider its main design components: initialisation, improvement and acceptance. We apply several machine learning and evolutionary computation approaches as hyper-heuristics to reduce the amount of manual decisions in these three design components. Hence, the overall goal of this thesis is to build hyper-heuristics as techniques for automatically improving and configuring local search-based methods in the context of the LSVRP. For the initialisation step, first we consider its impact to the search process by analysing several baseline methods’ performances. We then introduce ways to use known and new features to predict best performing existing heuristic or build new solutions that can be easily improved. For predicting, we use several machine learning techniques to validate the results independently. The results show that cost is not the main feature in an initial solution. We show that some other characteristics, such as the compactness or width of a solution, have a stronger correlation than cost, leading to faster or better improvement phases. This thesis also develops three evolutionary hyper-heuristic methods to automate the improvement phase. We consider new chromosomes incorporating pruning strategies that evolve to automatically design a heuristic configuration. These strategies minimise the amount of manual decisions leading to more generalisable methods. The results show that the improvement phase can be positively affected when automatically optimised, often outranking the manually designed methodologies. We also consider an adaptive heuristic strategy which improves the efficiency of the local search. We apply this stochastic online approach to a robust framework, increasing its effectiveness due to the extra efficiency. Among the results, we observe a significant increase in the number of iterations given the same time-frame. We also observe a significant cost reduction for most instances considered, especially very-large cases. The proposed strategy also works independently of the instance, increasing the framework generality. The fourth and final contribution regards how to predict which acceptance criteria can be used to escape local optima more effectively. We use machine learning and evolutionary computation to make such predictions by considering the same robust local search-based framework. The problem was modeled as both classification and regression tasks. The latter was added in an attempt to avoid bias on the labeled data. However, the results show that this task is difficult to correlate and predict. We are still able to find success for several cases, improving the quality in a number of scenarios. In summary, this thesis develops strategies and methods that can be used in combination with existing and new local search-based methods to solve the LSVRP. The developed techniques have shown ability to reduce the search space effectively and improve the efficiency of the considered approaches for several cases, whilst minimising manual decisions in method design. %K genetic algorithms, genetic programming, GPHH, VRP, KGLS* %9 Ph.D. thesis %R 10.26686/wgtn.24188859 %U https://openaccess.wgtn.ac.nz/articles/thesis/Hyper-Heuristics_for_Automatic_Configuration_of_Local_Search-based_Heuristics_for_the_Large-Scale_Vehicle_Routing_Problem/24188859?file=42442161 %U http://dx.doi.org/10.26686/wgtn.24188859 %0 Conference Proceedings %T Parkinson’s Disease Diagnosis: Towards Grammar-based Explainable Artificial Intelligence %A Cavaliere, Federica %A Della Cioppa, Antonio %A Marcelli, Angelo %A Parziale, Antonio %A Senatore, Rosa %Y Montavont, Nicolas %Y Douligeris, Christos %S IEEE ISCC 2020: IEEE Symposium on Computers and Communications 2020 %D 2020 %8 jul 7 10 %I EasyChair %C internet, Rennes, France %F Cavaliere:2020:ISCC %X The basic technology that reinvents machines to personalize human experiences is Machine Learning (ML), a branch of Artificial Intelligence (AI) and a strong buzzword in today’s digital world. Despite its success, the most significant limitation of ML is the lack of transparency behind its behavior, which leaves users with a poor understanding of how it makes decisions, such it is the case for Deep Learning models. If the final user does not trust a model, he will not use it. This is especially true in medical diagnosis practice: physicians cannot simply use the predictions of the model but must trust the results it provides. This work focuses on the automatic early detection of Parkinson’s disease (PD), whose impact on both the individual’s quality of life and social well-being is constantly increasing with the aging of the population. To this end, we propose an explainable approach based on Genetic Programming, called Grammar Evolution (GE). This technique uses context-free grammar to describe the language of the programs to be generated and evolved. In this case, the generated programs are the explicit classification rules for the diagnosis of the subjects. The results of the experiments obtained on the publicly available H and PD data set show GE’s high expressive power and performance comparable to those of several ML models that have been proposed in the literature. %K genetic algorithms, genetic programming, Grammatical Evolution, Explainable Artificial Intelligence, XAI, Parkinsons Disease, Supervised Learning by Classification, e-Health, Sociology, Machine learning, Learning (artificial intelligence), Predictive models, Germanium, Grammar %R 10.1109/ISCC50000.2020.9219616 %U https://easychair.org/publications/preprint_download/1Sh4 %U http://dx.doi.org/10.1109/ISCC50000.2020.9219616 %0 Conference Proceedings %T Data Mining using Genetic Programming: The Implications of Parsimony on Generalization Error %A Cavaretta, Michael J. %A Chellapilla, Kumar %Y Angeline, Peter J. %Y Michalewicz, Zbyszek %Y Schoenauer, Marc %Y Yao, Xin %Y Zalzala, Ali %S Proceedings of the Congress on Evolutionary Computation %D 1999 %8 June 9 jul %V 2 %I IEEE Press %C Mayflower Hotel, Washington D.C., USA %@ 0-7803-5536-9 (softbound) %F cavaretta:1999:DMGPTIPGE %X A common data mining heuristic is, ’when choosing between models with the same training error, less complex models should be preferred as they perform better on unseen data’. This heuristic may not always hold. In genetic programming a preference for less complex models is implemented as: (i) placing a limit on the size of the evolved program; (ii) penalising more complex individuals, or both. The paper presents a GP-variant with no limit on the complexity of the evolved program that generates highly accurate models on a common dataset %K genetic algorithms, genetic programming, data mining, GP-variant, common dataset, data mining heuristic, generalisation error, less complex models, program complexity, training error, unseen data, computational complexity, data mining, generalisation (artificial intelligence) %R 10.1109/CEC.1999.782602 %U http://dx.doi.org/10.1109/CEC.1999.782602 %P 1330-1337 %0 Book Section %T A Genetic Algorithm Approach to Discovering an Optimal Blackjack Strategy %A Caverlee, James B. %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 caverlee:2000:AGAADOBS %K genetic algorithms %P 70-79 %0 Conference Proceedings %T Multi-chromosomal genetic programming %A Cavill, Rachel %A Smith, Steve %A Tyrrell, Andy %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 1068300 %K genetic algorithms, genetic programming, design, performance, representations, team evolution %R 10.1145/1068009.1068300 %U http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1753.pdf %U http://dx.doi.org/10.1145/1068009.1068300 %P 1753-1759 %0 Conference Proceedings %T The performance of polyploid evolutionary algorithms is improved both by having many chromosomes and by having many copies of each chromosome on symbolic regression problems %A Cavill, Rachel %A Smith, Stephen L. %A Tyrrell, Andy %Y Corne, David %Y Michalewicz, Zbigniew %Y McKay, Bob %Y Eiben, Gusz %Y Fogel, David %Y Fonseca, Carlos %Y Greenwood, Garrison %Y Raidl, Gunther %Y Tan, Kay Chen %Y Zalzala, Ali %S Proceedings of the 2005 IEEE Congress on Evolutionary Computation %D 2005 %8 February 5 sep %V 1 %I IEEE Press %C Edinburgh, Scotland, UK %@ 0-7803-9363-5 %F Cavill:Tpo:cec2005 %X This paper presents important new findings for a new method for evolving individual programs with multiple chromosomes. Previous results have shown that evolving individuals with multiple chromosomes produced improved results over evolving individuals with a single chromosome. The multiple chromosomes are organised along two axes; there are a number of different chromosomes and a number of copies of each chromosome. This paper investigates the effects which these two axes have on the performance of the algorithm; whether the improvement in performance comes from just one of these features or whether it is a combination of them both %K genetic algorithms, genetic programming, biology, cellular biophysics, evolutionary computation, regression analysis, multiple chromosomes, polyploid evolutionary algorithm, symbolic regression problem %R 10.1109/CEC.2005.1554783 %U http://ieeexplore.ieee.org/servlet/opac?punumber=10417&isvol=1 %U http://dx.doi.org/10.1109/CEC.2005.1554783 %P 935-941 %0 Conference Proceedings %T Variable length genetic algorithms with multiple chromosomes on a variant of the Onemax problem %A Cavill, Rachel %A Smith, Stephen L. %A Tyrrell, Andy M. %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 2 %I ACM Press %C Seattle, Washington, USA %@ 1-59593-186-4 %F 1144217 %K Genetic Algorithms: Poster, algorithms performance design, representation(s), size %R 10.1145/1143997.1144217 %U http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1405.pdf %U http://dx.doi.org/10.1145/1143997.1144217 %P 1405-1406 %0 Thesis %T Multi-Chromosomal Genetic Programming %A Cavill, Rachel %D 2006 %C UK %C Department of Electronics, University of York %F cavill_mcgp %X Typically, computational models inspired by evolution have comprised a single large structure, such as a tree, string or graph, representing a single chromosome. Through the use of evolutionary operators, as mutation and recombination, over a number of generations a satisfactory solution to the problem may be found and the evolution halted. In natural, biological systems, it is not so common to find organisms which have only a single chromosome. Indeed, it is only bacteria and other relatively simple life-forms which can survive with only a single chromosome structure. All animals and plants have a much richer and more complex chromosome space, with not only multiple chromosomes, but multiple copies of each chromosome. Within artificial systems, sometimes, often for very problem-specific reasons, multiple structures are used which each make up part of the final solution. More recently, with the area of co-evolution successfully exploring the evolution of teams, further steps along this path towards a richer representation space have been investigated. This thesis investigates the exploitation of evolution with multiple chromosomes within computational models. By studying the biological model presented to us in nature, and attempting to extract the key mechanisms of multi chromosomal evolution an artificial system which imitates these mechanisms is developed. The system is designed to allow evolution with any number of chromosomes, so that experiments comparing evolution with a single chromosome to that with many may be performed. This work is not attempting to model biological evolution but is inspired by it. As well as presenting a richer representation space, the presence of multiple chromosomes also permits more complex evolutionary operators. For instance crossover may work between any pair of chromosomes, or may be restricted to be allowed only to occur between particular pairs of chromosomes. Natural systems too, display a range of crossover operators acting in different ways; therefore it is important to study the implications of using different crossover operators and to assess their relative characteristics and advantages. To this end, this thesis presents a system which allows evolution to occur with a specified number of chromosomes, conforming to k sets of n chromosomes. Using this system, experiments are done over a range of standard genetic programming benchmark problems to ascertain the affects of increasing the number of chromosomes along each of these two axis of variation. Further experiments are conducted into the behaviour of the crossover operator with this more complex representation and various crossover operators are evaluated within the system. Overall, it was found that multiple chromosomes increase the performance of the evolutionary system, insofar as better solutions were obtained more quickly in the simulations. However, in order to attain optimal increases both the number of chromosomes and the number of copies of copies of each in the system, need to be considered. The optimal number of chromosomes is shown to be problem dependent, but initial conclusions about how many chromosomes different types of problems are likely to use are also presented. Additionally, the crossover operator is shown to work best when it is restricted only to work with the exact same chromosome from the other parent. %K genetic algorithms, genetic programming %9 PhD Dissertation %9 Ph.D. thesis %U http://ethos.bl.uk/OrderDetails.do?did=7&uin=uk.bl.ethos.437617 %0 Journal Article %T Genetic algorithms for simultaneous variable and sample selection in metabonomics %A Cavill, Rachel %A Keun, Hector C. %A Holmes, Elaine %A Lindon, John C. %A Nicholson, Jeremy K. %A Ebbels, Timothy M. D. %J Bioinformatics %D 2009 %V 25 %N 1 %F journals/bioinformatics/CavillKHLNE09 %9 journal article %R 10.1093/bioinformatics/btn586 %U http://dx.doi.org/10.1093/bioinformatics/btn586 %P 112-118 %0 Journal Article %T Hardware spiking neural network prototyping and application %A Cawley, Seamus %A Morgan, Fearghal %A McGinley, Brian %A Pande, Sandeep %A McDaid, Liam %A Carrillo, Snaider %A Harkin, Jim %J Genetic Programming and Evolvable Machines %D 2011 %8 sep %V 12 %N 3 %@ 1389-2576 %F Cawley:2011:GPEM %O Special Issue Title: Evolvable Hardware Challenges %X EMBRACE has been proposed as a scalable, reconfigurable, mixed signal, embedded hardware Spiking Neural Network (SNN) device. EMBRACE, which is yet to be realised, targets the issues of area, power and scalability through the use of a low area, low power analogue neuron/synapse cell, and a digital packet-based Network on Chip (NoC) communication architecture. The paper describes the implementation and testing of EMBRACE-FPGA, an FPGA-based hardware SNN prototype. The operation of the NoC inter-neuron communication approach and its ability to support large scale, reconfigurable, highly interconnected SNNs is illustrated. The paper describes an integrated training and configuration platform and an on-chip fitness function, which supports GA-based evolution of SNN parameters. The practicalities of using the SNN development platform and SNN configuration toolset are described. The paper considers the impact of latency jitter noise introduced by the NoC router and the EMBRACE-FPGA processor-based neuron/synapse model on SNN accuracy and evolution time. Benchmark SNN applications are described and results demonstrate the evolution of high quality and robust solutions in the presence of noise. The reconfigurable EMBRACE architecture enables future investigation of adaptive hardware applications and self repair in evolvable hardware. %K genetic algorithms, evolvable hardware, EMBRACE, Spiking neural networks, Network on chip, Intrinsic evolution, FPGA %9 journal article %R 10.1007/s10710-011-9130-9 %U http://dx.doi.org/10.1007/s10710-011-9130-9 %P 257-280 %0 Journal Article %T Monte-Carlo Expression Discovery %A Cazenave, Tristan %J International Journal on Artificial Intelligence Tools %D 2013 %8 feb %V 22 %N 1 %@ 0218-2130 %F DBLP:journals/ijait/Cazenave13 %X Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Programming evaluates and combines trees to discover expressions that maximise a given fitness function. In this paper Monte-Carlo Tree Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Monte-Carlo Tree Search is transformed in order to search expression trees rather than lists of moves. We compare Nested Monte-Carlo Search to UCT (Upper Confidence Bounds for Trees) for various problems. Monte-Carlo Tree Search achieves state of the art results on multiple benchmark problems. The proposed approach is simple to program, does not suffer from expression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelise. %K genetic algorithms, genetic programming, MCTS, Monte-Carlo tree search, expression discovery, nested Monte-Carlo search, upper confidence bounds for trees, UCT, bloat %9 journal article %R 10.1142/S0218213012500352 %U http://www.lamsade.dauphine.fr/~cazenave/papers/MCExpression.pdf %U http://dx.doi.org/10.1142/S0218213012500352 %0 Conference Proceedings %T Forecasting Financial Volatility Using Nested Monte Carlo Expression Discovery %A Cazenave, Tristan %A Hamida, Sana Ben %S 2015 IEEE Symposium Series on Computational Intelligence %D 2015 %8 July 10 dec %C Cape Town, South Africa %F Cazenave:2015: %X We are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learn from financial time series to generate non linear functions for market volatility prediction. The input data, that is a series of daily prices of European S&P500 index, is filtered and sampled in order to improve the training process. Using some assessment metrics, the best generated models given by both approaches for each training sub sample, are evaluated and compared. Results show that Nested Monte Carlo is able to generate better forecasting models than Genetic Programming for the majority of learning samples. %K genetic algorithms, genetic programming %R 10.1109/SSCI.2015.110 %U http://dx.doi.org/10.1109/SSCI.2015.110 %P 726-733 %0 Book Section %T Acceleration of a procedure to generate fractal curves of a given dimension through the probabilistic analysis of execution time %A Cebrian, Manuel %A de la Puente, Alfonso Ortega %A Alfonseca, Manuel %E Dagli, C. H. %E Buczak, A. L. %E Enke, D. L. %E Embrecht, M. J. %B Intelligent Engineering Systems Through Artificial Neural Networks %D 2004 %V 14 %I ASME Press %C New York %@ 0-7918-0228-0 %F cebrian:2004:IESANN %K genetic algorithms, genetic programming %U http://www.ii.uam.es/~alfonsec/docs/annie.pdf %P 265-270 %0 Conference Proceedings %T Automatic generation of benchmarks for plagiarism detection tools using grammatical evolution %A Cebrian, Manuel %A Alfonseca, Manuel %A Ortega, Alfonso %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 1277388 %K genetic algorithms, genetic programming, grammatical evolution, Real-World Applications: Poster, human factors, reliability, source code plagiarism detection tool assessment %R 10.1145/1276958.1277388 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2253.pdf %U http://dx.doi.org/10.1145/1276958.1277388 %P 2253-2253 %0 Thesis %T Using Algorithmic Information Theory and Stochastic Modeling to Improve Classification and Evolutionary Computation %A Cebrian Ramos, Manuel %D 2007 %8 13 jul %C Spain %C Department of Computer Science, Universidad Autonoma de Madrid %F Cebrian_Ramos:thesis %X This thesis presents theoretical and practical contributions in Algorithmic Information Theory and (Algorithmic) Stochastic Modelling. Algorithmic Information Theory is the theory concerned with obtaining an absolute measure of the information contained in an object. Stochastic Modelling is a methodology to improve an algorithm’s performance by means of the introduction of random elements in its logic. One of the most interesting advances of Algorithmic Information Theory is the development of an absolute measure of similarity between objects. This measure can only be estimated, as it is incomputable by definition. The typical estimation relies on the use of data compression algorithms, being this estimation known as the compression distance. The two theoretical contributions of this thesis analyse the quality of this estimation. The first quantifies the estimation robustness when the information contained in the objects is noise-altered, concluding that it is considerably resistant to noise. The second studies the impact of the compression algorithm implementation on the estimation, yielding some practical recipes for making this choice. We use variants of the compression distance to develop two applications for classification and one for evolutionary computation. The first application addresses the problem of detecting similarities in objects which have been generated by a predecessor common source, independently of whether they use or not the same coding scheme: this includes detecting document translation and reconstructing phylogenetic threes from genetic material. We make use of the already proved usefulness of compression based similarity distances for educational plagiarism detection to develop our second application: AC, an integrated source code plagiarism detection environment. The third application makes use of this distance as a fitness function, which is used by evolutionary algorithms to automatically generate music in a given pre-defined style. Another three new applications are derived using Stochastic Modeling, two for evolutionary computation and one for classification. Two of them are intimately related and make use of the presence of Heavy Tail probability distributions in the optimisation processes involved in the generation of fractals by an evolutionary algorithm, and in the training process of a multilayer perceptron. This discovery is used to improve the performance of both algorithms by means of restart strategies. The last application presented in this thesis is a successful story of the use of a special randomised heuristic in a simple genetic algorithm to yield a state-of-the-art evolutionary algorithm for solving Constraint Satisfaction Problems. %K genetic algorithms, genetic programming, grammatical evolution %9 Sobresaliente Cum Laude %9 Ph.D. thesis %U http://digitool-uam.greendata.es:1801/webclient/DeliveryManager?pid=3411.pdf %0 Journal Article %T Towards the Validation of Plagiarism Detection Tools by Means of Grammar Evolution %A Cebrian, Manuel %A Alfonseca, Manuel %A Ortega, Alfonso %J IEEE Transactions on Evolutionary Computation %D 2009 %8 jun %V 13 %N 3 %@ 1089-778X %F Cebrian:2009:ieeeTEC %X Student plagiarism is a major problem in universities worldwide. In this paper, we focus on plagiarism in answers to computer programming assignments, where students mix and/or modify one or more original solutions to obtain counterfeits. Although several software tools have been developed to help the tedious and time consuming task of detecting plagiarism, little has been done to assess their quality, because determining the real authorship of the whole submission corpus is practically impossible for markers. In this paper, we present a Grammar Evolution technique which generates benchmarks for testing plagiarism detection tools. Given a programming language, our technique generates a set of original solutions to an assignment, together with a set of plagiarisms of the former set which mimic the basic plagiarism techniques performed by students. The authorship of the submission corpus is predefined by the user, providing a base for the assessment and further comparison of copy-catching tools. We give empirical evidence of the suitability of our approach by studying the behavior of one advanced plagiarism detection tool (AC) on four benchmarks coded in APL2, generated with our technique. %K genetic algorithms, genetic programming, Grammar Evolution, Automatic programming, Benchmark testing, Data mining, Distance measurement, Evolution (biology), Genetics, Plagiarism, Probability density function, computer science education, educational technology %9 journal article %R 10.1109/TEVC.2008.2008797 %U http://dx.doi.org/10.1109/TEVC.2008.2008797 %P 477-485 %0 Book Section %T The evolution of Cooperation: The Genetic Algorithm Applied to Three Normal-Form Games %A Cederberg, Scott %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 cederberg:2002:TCTGAATNG %K genetic algorithms %U http://www.genetic-programming.org/sp2002/Cederberg.pdf %P 45-51 %0 Journal Article %T Adaptation Strategies for Automated Machine Learning on Evolving Data %A Celik, Bilge %A Vanschoren, Joaquin %J IEEE Transactions on Pattern Analysis and Machine Intelligence %D 2021 %8 sep %V 43 %N 9 %@ 1939-3539 %F Celik:2021:PAMI %X Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of concept drift on the performance of AutoML methods, and which adaptation strategies can be employed to make them more robust to changes in the underlying data. To that end, we propose 6 concept drift adaptation strategies and evaluate their effectiveness on a variety of AutoML approaches for building machine learning pipelines, including Bayesian optimization, genetic programming, and random search with automated stacking. These are evaluated empirically on real-world and synthetic data streams with different types of concept drift. Based on this analysis, we propose ways to develop more sophisticated and robust AutoML techniques. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TPAMI.2021.3062900 %U http://dx.doi.org/10.1109/TPAMI.2021.3062900 %P 3067-3078 %0 Journal Article %T Unintended effects and their detection in genetically modified crops %A Cellini, F. %A Chesson, A. %A Colquhoun, I. %A Constable, A. %A Davies, H. V. %A Engel, K. H. %A Gatehouse, A. M. R. %A Karenlampi, S. %A Kok, E. J. %A Leguay, J.-J. %A Lehesranta, S. %A Noteborn, H. P. J. M. %A Pedersen, J. %A Smith, M. %J Food and Chemical Toxicology %D 2004 %8 jul %V 42 %N 7 %@ 0278-6915 %F Cellini:2004:FCT %X The commercialisation of GM crops in Europe is practically non-existent at the present time. The European Commission has instigated changes to the regulatory process to address the concerns of consumers and member states and to pave the way for removing the current moratorium. With regard to the safety of GM crops and products, the current risk assessment process pays particular attention to potential adverse effects on human and animal health and the environment. This document deals with the concept of unintended effects in GM crops and products, i.e. effects that go beyond that of the original modification and that might impact primarily on health. The document first deals with the potential for unintended effects caused by the processes of transgene insertion (DNA rearrangements) and makes comparisons with genetic recombination events and DNA rearrangements in traditional breeding. The document then focuses on the potential value of evolving profiling or omics technologies as non-targeted, unbiased approaches, to detect unintended effects. These technologies include metabolomics (parallel analysis of a range of primary and secondary metabolites), proteomics (analysis of polypeptide complement) and transcriptomics (parallel analysis of gene expression). The technologies are described, together with their current limitations. Importantly, the significance of unintended effects on consumer health are discussed and conclusions and recommendations presented on the various approaches outlined. %K genetic algorithms, genetic programming, Genetic modification, GM, Substantial equivalence, Comparative analysis, Targeted analysis, Non-targeted analysis, Unpredictable effects, Unexpected effects %9 journal article %R 10.1016/j.fct.2004.02.003 %U http://www.entransfood.com/products/publications/WG2_paper_rev1_19jan2004_unmarked.pdf %U http://dx.doi.org/10.1016/j.fct.2004.02.003 %P 1089-1125 %0 Conference Proceedings %T Limitations of Genetic Programming Applied to Incipient Fault Detection: SFRA as Example %A Cerda, Jaime %A Avalos, Alberto %A Graff, Mario %S 2015 International Conference on Computational Science and Computational Intelligence (CSCI) %D 2015 %8 dec %F Cerda:2015:CSCI %X This document deals with the application of genetic programming to the fault detection task, specifically with the power transformer fault detection problem of incipient faults. To this end we use genetic programming to obtain an highly approximated model of the a power transformer. The sweep frequency response analysis test represents the response of the transformer to a discrete variable frequency stimuli. We have been able to obtain a highly precision model which improves the precision of a commercial PG system. This result would be good if we only needed to identify the system. However, for the fault detection task, we should be able to identify the components within the transformer to assert where the fault has taken place. This is because the SFRA test when an incipient fault is present are similar but different as the fault advance. The tree generated for the model after the fault is evolved from the tree defining the power transformer model before the fault. Both trees are similar but the evolution seems to take place in a very specific random place. There is no way we can relate such changes with the physical model of the transformer. This shows the limitations of genetic programming to deal with this task and calls for extensions to the genetic programming paradigm or the merge of paradigms in order to deal with such task. %K genetic algorithms, genetic programming %R 10.1109/CSCI.2015.168 %U http://dx.doi.org/10.1109/CSCI.2015.168 %P 498-503 %0 Conference Proceedings %T Using differential evolution for symbolic regression and numerical constant creation %A Cerny, Brian M. %A Nelson, Peter C. %A Zhou, Chi %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 Cerny:2008:gecco %K genetic algorithms, genetic programming, combinatorial search, constant creation, differential evolution, gene expression programming, neutral mutations, optimisation, prefix gene expression programming, Redundant representations, symbolic regression %R 10.1145/1389095.1389331 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1195.pdf %U http://dx.doi.org/10.1145/1389095.1389331 %P 1195-1202 %0 Thesis %T Evolutionary Design of Robot Motion Patterns %A Cerny, Jan %D 2012 %8 13 may %C Czech Technical University in Prague %F Cerny:mastersthesis %X This thesis is focused on the use and implementation of Genetic Programming for generating viable motion patterns for robotic creatures. SYMBRION and REPLICATOR are two European projects whose research is focused on application of biological knowledge in robotics. One of the robots developed as a part of these projects is used in this work as a building block for two larger four legged robotic organisms. A co-evolution algorithm has been developed to generate single leg movements and adapt them to the three remaining legs. This approach of dividing the problem into two smaller sub-problems simplifies the evolution and saves the processing time. It is shown that the implemented Evolution Algorithm is indeed capable of generating motion patterns for robots very similar to those seen in nature and that by using them the robots are able to efficiently reach their predefined targets. All the experiments are conducted in a simulated environment. %K genetic algorithms, genetic programming, robotics %9 Masters thesis %U http://cyber.felk.cvut.cz/research/theses/detail.phtml?id=226 %0 Conference Proceedings %T Co-evolutionary Approach to Design of Robotic Gait %A Cerny, Jan %A Kubalik, Jiri %Y Esparcia-Alcazar, Anna I. %Y Cioppa, Antonio Della %Y De Falco, Ivanoe %Y Tarantino, Ernesto %Y Cotta, Carlos %Y Schaefer, Robert %Y Diwold, Konrad %Y Glette, Kyrre %Y Tettamanzi, Andrea %Y Agapitos, Alexandros %Y Burrelli, Paolo %Y Merelo, J. J. %Y Cagnoni, Stefano %Y Zhang, Mengjie %Y Urquhart, Neil %Y Sim, Kevin %Y Ekart, Aniko %Y Fernandez de Vega, Francisco %Y Silva, Sara %Y Haasdijk, Evert %Y Eiben, Gusz %Y Simoes, Anabela %Y Rohlfshagen, Philipp %S Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC %S LNCS %D 2013 %8 March 5 apr %V 7835 %I Springer Verlag %C Vienna %F Cerny:evoapps13 %X Manual design of motion patterns for legged robots is difficult task often with suboptimal results. To automate this process variety of approaches have been tried including various evolutionary algorithms. In this work we present an algorithm capable of generating viable motion patterns for multi-legged robots. This algorithm consists of two evolutionary algorithms working in co-evolution. The GP is evolving motion of a single leg while the GA deploys the motion to all legs of the robot. Proof-of-concept experiments show that the co-evolutionary approach delivers significantly better results than those evolved for the same robot with simple genetic programming algorithm alone. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-37192-9_55 %U http://dx.doi.org/10.1007/978-3-642-37192-9_55 %P 550-559 %0 Conference Proceedings %T A Grammatical Evolution Algorithm for Generation of Hierarchical Multi-Label Classification Rules %A Cerri, Ricardo %A Barros, Rodrigo %A Carvalho, Andre %A Freitas, Alex %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 Cerri:2013:CEC %K genetic algorithms, genetic programming, grammatical evolution %R 10.1109/CEC.2013.6557604 %U http://dx.doi.org/10.1109/CEC.2013.6557604 %P 454-461 %0 Conference Proceedings %T Estimation of Interstitial Glucose from Physical Activity Measures Using Grammatical Evolution %A Cervigon, Carlos %A Hidalgo, J. Ignacio %Y Petke, Justyna %Y Ekart, Aniko %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F cervigon:2023:GECCOcomp %X People with diabetes need to have their glucose levels under control, and it is essential for them to be able to know or estimate their glucose levels at any time. Continuous glucose monitors are commonly used, which measure interstitial glucose, an approximation of blood glucose, by means of a small catheter. Although most devices are not very intrusive, they do present some discomfort, and it would be preferable if these glucose levels could be estimated non-invasively, for example, through other physiological measurements collected in a simple way. This abstract describes our research on the performance of different grammatical evolution techniques to obtain accurate estimations of actual subcutaneous glucose values from non-invasive physiological measures, steps, calories and heart rates obtained with commercial smartwatches. %K genetic algorithms, genetic programming, grammatical evolution, blood glucose estimation, symbolic regression %R 10.1145/3583133.3596432 %U http://dx.doi.org/10.1145/3583133.3596432 %P 57-58 %0 Book Section %T A Review of Soft Computing Methods Application in Rock Mechanic Engineering %A Ceryan, Nurcihan %B Civil and Environmental Engineering: Concepts, Methodologies, Tools, and Applications %D 2016 %8 jan %I IGI Global %@ 1-4666-9619-2 %F Ceryan:2016:CEECMTA %X Engineering behavior of rock mass is controlled by many factors, related to its nature and the environmental conditions. Determining all the parameters, ranking their weights, and clarifying their relative effects are very difficult tasks to accomplish. To overcome these difficulties, many researchers have employed soft computing methods in rock mechanics engineering. The soft computing methods have taken an important role in rock mechanics, and their abilities to address uncertainties, insufficient information and ambiguous linguistic expressions stand out in treating complex natural rock mass. This chapter briefly will review the development of soft computing techniques in rock mechanics engineering, especially in predicting of rock engineering classification system and mechanical properties of rock material and rock mass, determination weathering degree of rock material, evolution of rock performance, blasting and, rock slope stability. In addition, the future of the development and application of soft computing in rock mechanics engineering is discussed. %K genetic algorithms, genetic programming %R 10.4018/978-1-4666-9619-8.ch027 %U https://www.igi-global.com/chapter/a-review-of-soft-computing-methods-application-in-rock-mechanic-engineering/144518 %U http://dx.doi.org/10.4018/978-1-4666-9619-8.ch027 %P 606-673 %0 Conference Proceedings %T Approximating Complex Arithmetic Circuits with Formal Error Guarantees: 32-bit Multipliers Accomplished %A Ceska, Milan %A Matyas, Jiri %A Mrazek, Vojtech %A Sekanina, Lukas %A Vasicek, Zdenek %A Vojnar, Tomas %Y Bahar, Iris %Y Parameswaran, Sri %S Proceedings of 36th IEEE/ACM International Conference On Computer Aided Design (ICCAD) %D 2017 %8 nov 13 16 %I Institute of Electrical and Electronics Engineers %C Irvine, CA, USA %G english %F Ceska:2017:ICCAD %X We present a novel method allowing one to approximate complex arithmetic circuits with formal guarantees on the approximation error. The method integrates in a unique way formal techniques for approximate equivalence checking into a search-based circuit optimisation algorithm. The key idea of our approach is to employ a novel search strategy that drives the search towards promptly verifiable approximate circuits. The method was implemented within the ABC tool and extensively evaluated on functional approximation of multipliers (with up to 32-bit operands) and adders (with up to 128-bit operands). Within a few hours, we constructed a high-quality Pareto set of 32-bit multipliers providing trade-offs between the circuit error and size. This is for the first time when such complex approximate circuits with formal error guarantees have been derived, which demonstrates an outstanding performance and scalability of our approach compared with existing methods that have either been applied to the approximation of multipliers limited to 8-bit operands or statistical testing has been used only. Our approach thus significantly improves capabilities of the existing methods and paves a way towards an automated design process of provably correct circuit approximations. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, approximate computing, logical synthesis, %R 10.1109/ICCAD.2017.8203807 %U http://www.fit.vutbr.cz/research/view_pub.php?id=11420 %U http://dx.doi.org/10.1109/ICCAD.2017.8203807 %P 416-423 %0 Conference Proceedings %T ADAC: Automated Design of Approximate Circuits %A Ceska, Milan %A Matyas, Jiri %A Mrazek, Vojtech %A Sekanina, Lukas %A Vasicek, Zdenek %A Vojnar, Tomas %Y Chockler, Hana %Y Weissenbacher, Georg %S Computer Aided Verification %S LNCS %D 2018 %8 jul 14 17 %V 10981 %I Springer %C Oxford %F Ceska:2018:CAV %X Approximate circuits with relaxed requirements on functional correctness play an important role in the development of resource-efficient computer systems. Designing approximate circuits is a very complex and time-demanding process trying to find optimal trade-offs between the approximation error and resource savings. In this paper, we present ADAC, a novel framework for automated design of approximate arithmetic circuits. ADAC integrates in a unique way efficient simulation and formal methods for approximate equivalence checking into a search-based circuit optimisation. To make ADAC easily accessible, it is implemented as a module of the ABC tool: a state-of-the-art system for circuit synthesis and verification. Within several hours, ADAC is able to construct high-quality Pareto sets of complex circuits (including even 32-bit multipliers), providing useful trade-offs between the resource consumption and the error that is formally guaranteed. This demonstrates outstanding performance and scalability compared with other existing approaches. %K genetic algorithms, genetic programming, Cartesian genetic programming %R 10.1007/978-3-319-96145-3_35 %U http://dx.doi.org/10.1007/978-3-319-96145-3_35 %P 612-620 %0 Journal Article %T Adaptive verifiability-driven strategy for evolutionary approximation of arithmetic circuits %A Ceska, Milan %A Matyas, Jiri %A Mrazek, Vojtech %A Sekanina, Lukas %A Vasicek, Zdenek %A Vojnar, Tomas %J Applied Soft Computing %D 2020 %V 95 %@ 1568-4946 %F CESKA:2020:ASC %X We present a novel approach for designing complex approximate arithmetic circuits that trade correctness for power consumption and play important role in many energy-aware applications. Our approach integrates in a unique way formal methods providing formal guarantees on the approximation error into an evolutionary circuit optimisation algorithm. The key idea is to employ a novel adaptive search strategy that drives the evolution towards promptly verifiable approximate circuits. As demonstrated in an extensive evaluation including several structurally different arithmetic circuits and target precisions, the search strategy provides superior scalability and versatility with respect to various approximation scenarios. Our approach significantly improves capabilities of the existing methods and paves a way towards an automated design process of provably-correct circuit approximations %K genetic algorithms, genetic programming, Approximate computing, Energy efficiency, Circuit optimisation %9 journal article %R 10.1016/j.asoc.2020.106466 %U http://www.sciencedirect.com/science/article/pii/S1568494620304063 %U http://dx.doi.org/10.1016/j.asoc.2020.106466 %P 106466 %0 Conference Proceedings %T Approximating Complex Arithmetic Circuits with Guaranteed Worst-Case Relative Error %A Ceska jr., Milan %A Ceska, Milan %A Matyas, Jiri %A Pankuch, Adam %A Vojnar, Tomas %Y Moreno-Diaz, Roberto %Y Pichler, Franz %Y Quesada-Arencibia, Alexis %S International Conference on Computer Aided Systems Theory, EUROCAST 2019 %S Lecture Notes in Computer Science %D 2019 %8 17 22 feb %V 12013 %I Springer %C Las Palmas de Gran Canaria, Spain %F Ceska:2019:EUROCAST %X We present a novel method allowing one to approximate complex arithmetic circuits with formal guarantees on the worst-case relative error, abbreviated as WCRE. WCRE represents an important error metric relevant in many applications including, e.g., approximation of neural network HW architectures. The method integrates SAT-based error evaluation of approximate circuits into a verifiability-driven search algorithm based on Cartesian genetic programming. We implement the method in our framework ADAC that provides various techniques for automated design of arithmetic circuits. Our experimental evaluation shows that, in many cases, the method offers a superior scalability and allows us to construct, within a few hours, high-quality approximations (providing trade-offs between the WCRE and size) for circuits with up to 32-bit operands. As such, it significantly improves the capabilities of ADAC. %K genetic algorithms, genetic programming, Cartesian genetic programming %R 10.1007/978-3-030-45093-9_58 %U http://dx.doi.org/10.1007/978-3-030-45093-9_58 %P 482-490 %0 Journal Article %T SagTree: Towards efficient mutation in evolutionary circuit approximation %A Ceska, Milan %A Matyas, Jiri %A Mrazek, Vojtech %A Sekanina, Lukas %A Vasicek, Zdenek %A Vojnar, Tomas %J Swarm and Evolutionary Computation %D 2022 %8 mar %V 69 %@ 2210-6502 %F CESKA:2022:SEC %X Approximate circuits that trade the chip area for the quality of results play a key role in the development of energy-aware systems. Designing complex approximate circuits is, however, a very difficult and computationally demanding process. Evolutionary approximation-in particular, the method of Cartesian Genetic Programming (CGP)-currently represents one of the most successful approaches for automated circuit approximation. In this paper, we thoroughly investigate mutation operators for CGP with respect to the performance of circuit approximation. We design a novel dedicated operator that combines the classical single active gene mutation with a node deactivation operation (eliminating a part of the circuit forming a tree from an active gate). We show that our new operator significantly outperforms other operators on a wide class of approximation problems (such as 16 bit multipliers and dividers) and thus improves the performance of the state-of-the-art approximation techniques. Our results are grounded on a rigorous statistical evaluation including 39 approximation scenarios and 14000 runs %K genetic algorithms, genetic programming, Approximate computing, Arithmetic circuit design, Mutation operators %9 journal article %R 10.1016/j.swevo.2021.100986 %U https://www.sciencedirect.com/science/article/pii/S2210650221001486 %U http://dx.doi.org/10.1016/j.swevo.2021.100986 %P 100986 %0 Conference Proceedings %T Regular expression generation through grammatical evolution %A Cetinkaya, Ahmet %Y Yu, Tina %S Genetic and Evolutionary Computation Conference (GECCO2007) workshop program %D 2007 %8 July 11 jul %I ACM Press %C London, United Kingdom %F 1274089 %X This study investigates automatic regular expression generation using Grammatical Evolution. The software implementation is based on a subset of POSIX regular expression rules. For fitness calculation, a multiline text file is supplied. Lines which are required to match with generated regular expressions are specified beforehand. Fitness is evaluated according to the successful match results. Using this fitness evaluation strategy, preliminary tests have been performed on different files. Results indicate that the Grammatical Evolution approach to automatic generation of regular expressions is promising. %K genetic algorithms, genetic programming, grammatical evolution, regular expressions %R 10.1145/1274000.1274089 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2643.pdf %U http://dx.doi.org/10.1145/1274000.1274089 %P 2643-2646 %0 Conference Proceedings %T Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach %A Cetto, Tomaso %A Byrne, Jonathan %A Xu, Xiaofan %A Moloney, David %Y Oneto, Luca %Y Navarin, Nicolo %Y Sperduti, Alessandro %Y Anguita, Davide %S Recent Advances in Big Data and Deep Learning, Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL 2019 %D 2019 %8 16 18 apr %I Springer %C Sestri Levante, Genova, Italy %F DBLP:conf/inns/CettoBXM19 %X In recent years, the shift from hand-crafted design of Convolutional Neural Networks (CNN) to an automatic approach (AutoML) has garnered much attention. However, most of this work has been concentrated on generating state of the art (SOTA) architectures that set new standards of accuracy. In this paper, we use the NSGA-II algorithm for multi-objective optimization to optimize the size/accuracy trade-off in CNNs. This approach is inspired by the need for simple, effective, and mobile-sized architectures which can easily be re-trained on any datasets. This optimization is carried out using a Grammatical Evolution approach, which, implemented alongside NSGA-II, automatically generates valid network topologies which can best optimize the size/accuracy trade-off. Furthermore, we investigate how the algorithm responds to an increase in the size of the search space, moving from strictly topology optimization (number of layers, size of filter, number of kernels,etc.) and then expanding the search space to include possible variations in other hyper-parameters such as the type of optimizer, dropout rate, batch size, or learning rate, amongst others. %K genetic algorithms, genetic programming, Grammatical evolution , ANN, CNN %R 10.1007/978-3-030-16841-4_3 %U http://dx.doi.org/10.1007/978-3-030-16841-4_3 %P 17-26 %0 Journal Article %T A soft computing based approach for the prediction of ultimate strength of metal plates in compression %A Cevik, Abdulkadir %A Guzelbey, Ibrahim H. %J Engineering Structures %D 2007 %8 mar %V 29 %N 3 %F Cevik:2007:ES %X This paper presents two plate strength formulations applicable to metals with nonlinear stress-strain curves, such as aluminium and stainless steel alloys, obtained by soft computing techniques, namely Neural Networks (ANN) and Genetic Programming (GP). The proposed soft computing formulations are based on well-defined FE results available in the literature. The proposed formulations enable determination of the buckling strength of rectangular plates in terms of RambergOsgood parameters. The strength curves obtained by the proposed soft computing formulations show perfect agreement with FE results. The formulations are later compared with related codes and results are found to be quite satisfactory. %K genetic algorithms, genetic programming, Soft computing, Neural networks, Buckling, Plates %9 journal article %R 10.1016/j.engstruct.2006.05.005 %U http://dx.doi.org/10.1016/j.engstruct.2006.05.005 %P 383-394 %0 Journal Article %T A new formulation for web crippling strength of cold-formed steel sheeting using genetic programming %A Cevik, Abdulkadir %J Journal of Constructional Steel Research %D 2007 %8 jul %V 63 %N 7 %F Cevik:2007:JCSR %X This study presents Genetic programming (GP) as a new tool for the formulation of web crippling strength of cold-formed steel decks for various loading cases. There is no well established analytical solution of the problem due to complex plastic behaviour. The objective of this study is to provide an alternative robust formulation to related design codes and to verify the robustness of GP for the formulation of such structural engineering problems. The training and testing patterns of the proposed GP formulation are based on well established experimental results from the literature. The GP based formulation results are compared with experimental results and current design codes and found to be more accurate. %K genetic algorithms, genetic programming, gene expression programming, Web crippling, Cold-formed steel decks, Formulation %9 journal article %R 10.1016/j.jcsr.2006.08.012 %U http://dx.doi.org/10.1016/j.jcsr.2006.08.012 %P 867-883 %0 Journal Article %T Genetic programming based formulation of rotation capacity of wide flange beams %A Cevik, Abdulkadir %J Journal of Constructional Steel Research %D 2007 %8 jul %V 63 %N 7 %F Cevik:2007:JCSRa %X This study is a pioneer work that proposes genetic programming (GP) as a new approach for the explicit formulation of available rotation capacity of wide-flange beams which is an important phenomenon that determines the plastic behaviour of steel structures. The database for the GP formulation is based on extensive experimental results from literature. The results of the GP-based formulation are compared with numerical results obtained by a specialised computer program and existing analytical equations. The results indicate that the proposed GP formulation performs quite well compared to numerical results and existing analytical equations and is quite practical for use. %K genetic algorithms, genetic programming, Rotation capacity, Beams, Formulation %9 journal article %R 10.1016/j.jcsr.2006.09.004 %U http://dx.doi.org/10.1016/j.jcsr.2006.09.004 %P 884-893 %0 Journal Article %T A new formulation for longitudinally stiffened webs subjected to patch loading %A Cevik, A. %J Journal of Constructional Steel Research %D 2007 %V 63 %F Cevik:2007:JCSRb %X This study proposes a new formulation for patch loading longitudinally stiffened webs using genetic programming (GP) for the first time in the literature. The database for the GP formulation is based on extensive experimental results from the literature. The results of the GP based formulation are compared with existing models and design codes. The results indicate that the proposed GP formulation performs quite well compared to existing models and design codes. %K genetic algorithms, genetic programming, Patch loading, Formulation, Girders, Webs, Longitudinal stiffeners %9 journal article %R 10.1016/j.jcsr.2006.12.004 %U http://dx.doi.org/10.1016/j.jcsr.2006.12.004 %P 1328-1340 %0 Journal Article %T Unified formulation for ultimate capacity of shear failure of arc spot welding using genetic programming %A Cevik, Abdulkadir %J Journal of Materials Processing Technology %D 2008 %V 204 %N 1-3 %@ 0924-0136 %F Cevik2008117 %X This study addresses genetic programming (GP) for the formulation of ultimate capacity of shear failure of arc spot welding. The proposed GP formulation is based on experimental results. The ultimate shear capacity of arc spot welding is formulated in terms of tensile strength, average welding thickness and diameter. The results of the proposed GP model are later compared with results of existing codes and are found to more accurate. In existing design codes four different equations are used whereas the proposed GP model is a unified formulation valid for all governing shear failures at the same time %K genetic algorithms, genetic programming, Arc spot welding, Ultimate capacity, Shear failure %9 journal article %R 10.1016/j.jmatprotec.2007.10.064 %U http://www.sciencedirect.com/science/article/B6TGJ-4R2H7VY-3/2/b16ece537522603ec7cc693ad17fd283 %U http://dx.doi.org/10.1016/j.jmatprotec.2007.10.064 %P 117-124 %0 Journal Article %T Modelling damping ratio and shear modulus of sand-mica mixtures using genetic programming %A Cevik, Abdulkadir %A Cabalar, Ali Firat %J Expert Systems with Applications %D 2009 %8 may %V 36 %N 4 %@ 0957-4174 %F Cevik2008:ESwA1 %X This study presents two Genetic Programming (GP) models for damping ratio and shear modulus of sand-mica mixtures based on experimental results. The experimental database used for GP modeling is based on a laboratory study of dynamic properties of saturated coarse rotund sand and mica mixtures with various mix ratios under different effective stresses. In the tests, shear modulus, and damping ratio of the geomaterials have been measured for a strain range of 0.001% up to 0.1% using a Stokoe resonant column testing apparatus. The input variables in the developed NN models are the mica content, effective stress and strain, and the outputs are damping ratio and shear modulus. The performance of accuracies of proposed NN models are quite satisfactory (R2=0.95 for damping ratio and R2=0.98 for shear modulus). %K genetic algorithms, genetic programming, Leighton Buzzard sand, Mica, Resonant column testing %9 journal article %R 10.1016/j.eswa.2008.09.010 %U http://www.sciencedirect.com/science/article/B6V03-4TGHN90-2/2/78164c859cf3127425aedcca7e6f7d21 %U http://dx.doi.org/10.1016/j.eswa.2008.09.010 %P 7749-7757 %0 Journal Article %T Flexural buckling load prediction of aluminium alloy columns using soft computing techniques %A Cevik, Abdulkadir %A Atmaca, Nihat %A Ekmekyapar, Talha %A Guzelbey, Ibrahim H. %J Expert Systems with Applications %D 2009 %8 apr %V 36 %N 3, Part 2 %@ 0957-4174 %F Cevik:2008:ESwA2 %X This paper presents the application of soft computing techniques for strength prediction of heat-treated extruded aluminium alloy columns failing by flexural buckling. Neural networks (NN) and genetic programming (GP) are presented as soft computing techniques used in the study. Gene-expression programming (GEP) which is an extension to GP is used. The training and test sets for soft computing models are obtained from experimental results available in literature. An algorithm is also developed for the optimal NN model selection process. The proposed NN and GEP models are presented in explicit form to be used in practical applications. The accuracy of the proposed soft computing models are compared with existing codes and are found to be more accurate. %K genetic algorithms, genetic programming, gene expression programming, Soft computing, Neural networks, Flexural buckling, Aluminium alloy columns %9 journal article %R 10.1016/j.eswa.2008.08.011 %U http://www.sciencedirect.com/science/article/B6V03-4TB6X28-1/2/3f64ccc54bc41be648922dc688ccad4a %U http://dx.doi.org/10.1016/j.eswa.2008.08.011 %P 6332-6342 %0 Journal Article %T Soft computing based formulation for strength enhancement of CFRP confined concrete cylinders %A Cevik, Abdulkadir %A Gogus, M. Tolga %A Guzelbey, Ibrahim H. %A Filiz, Huzeyin %J Advances in Engineering Software %D 2010 %V 41 %N 4 %@ 0965-9978 %F Cevik2010527 %X This study presents the application of soft computing techniques namely as genetic programming (GP) and stepwise regression (SR) for formulation of strength enhancement of carbon-fiber-reinforced polymer (CFRP) confined concrete cylinders. The proposed soft computing based formulations are based on experimental results collected from literature. The accuracy of the proposed GP and SR formulations are quite satisfactory as compared to experimental results. Moreover, the results of proposed soft computing based formulations are compared with 15 existing models proposed by various researchers so far and are found to be more accurate. %K genetic algorithms, genetic programming, Soft computing, Stepwise regression, FRP confinement, Concrete cylinder, Strength enhancement %9 journal article %R 10.1016/j.advengsoft.2009.10.015 %U http://www.sciencedirect.com/science/article/B6V1P-4XPBSMR-1/2/fce8b7ee023873cc437bf1c86ee3eb19 %U http://dx.doi.org/10.1016/j.advengsoft.2009.10.015 %P 527-536 %0 Journal Article %T Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network %A Cevik, Abdulkadir %A Akcapinar Sezer, Ebru %A Cabalar, Ali Firat %A Gokceoglu, Candan %J Applied Soft Computing %D 2011 %V 11 %N 2 %@ 1568-4946 %F Cevik20112587 %O The Impact of Soft Computing for the Progress of Artificial Intelligence %X Uniaxial compressive strength of intact rock is significantly important for engineering geology and geotechnics, because it is an important design parameter for tunnels, rock slopes rock foundations, and it is also used as input parameter in some rock mass classification systems. This paper documents the results of laboratory experiments and numerical simulations (i.e. neural network) conducted to estimate the uniaxial compressive strength of some clay-bearing rocks selected from Turkey. Emphasis was placed on assessing the role of slake durability indices and clay contents. The input variables in developed neural network (NN) model are the origin of rocks, two/four-cycle slake durability indices and clay contents, and the output is uniaxial compressive strength. It is shown that the performance of capacities of proposed NN model is quite satisfactory. However, the NN model including four cycle slake durability index yielded slightly more precise results than that including two cycle slake durability index as input parameter. The paper also presents a comparative study on the accuracy of NN model and genetic programming (GP) in the results. %K genetic algorithms, genetic programming, Clay-bearing rock, Uniaxial compressive strength, Neural network, Slake durability index %9 journal article %R 10.1016/j.asoc.2010.10.008 %U http://www.sciencedirect.com/science/article/B6W86-51F7PJN-1/2/29835a31bf86c4e457cfa3e0ae15bae5 %U http://dx.doi.org/10.1016/j.asoc.2010.10.008 %P 2587-2594 %0 Journal Article %T Neuro-fuzzy modeling of rotation capacity of wide flange beams %A Cevik, Abdulkadir %J Expert Systems with Applications %D 2011 %V 38 %N 5 %@ 0957-4174 %F Cevik20115650 %X This study is a pioneer work that investigates the feasibility of neuro-fuzzy (NF) approach for the modeling of rotation capacity of wide flange beams. The database for the NF modeling is based on experimental studies from literature. The results of the NF model are compared with numerical results obtained by a specialised computer programme and existing analytical and genetic programming based equations. The results indicate that the proposed NF model performs better. By using the proposed NF model, a wide range of parametric studies are also performed to evaluate the main effects of each variable on rotation capacity. %K genetic algorithms, genetic programming, Rotation capacity, Beams, Neuro-fuzzy, Modelling %9 journal article %R 10.1016/j.eswa.2010.10.070 %U http://www.sciencedirect.com/science/article/B6V03-51CJ387-K/2/ce5fff4acc0b21a9cd4c1ac3c5afe7df %U http://dx.doi.org/10.1016/j.eswa.2010.10.070 %P 5650-5661 %0 Journal Article %T Modeling strength enhancement of FRP confined concrete cylinders using soft computing %A Cevik, Abdulkadir %J Expert Systems with Applications %D 2011 %V 38 %N 5 %@ 0957-4174 %F Cevik20115662 %X This study presents the application of soft computing techniques namely as genetic programming (GP) and stepwise regression (SR), neuro-fuzzy (NF) and neural networks (NN) for modelling of strength enhancement of FRP (fibre-reinforced polymer) confined concrete cylinders. The proposed soft computing models are based on experimental results collected from literature. The accuracy of the proposed soft computing models are quite satisfactory as compared to experimental results. Moreover the results of proposed soft computing formulations are compared with 10 models existing in the literature proposed by various researchers so far and are found to be by far more accurate. %K genetic algorithms, genetic programming, Soft computing, Neural networks, Neuro-fuzzy, Stepwise regression, FRP confinement, Concrete cylinder, Strength enhancement %9 journal article %R 10.1016/j.eswa.2010.10.069 %U http://www.sciencedirect.com/science/article/B6V03-51CJ387-J/2/4b0e7942a4c46980f638964d442e332a %U http://dx.doi.org/10.1016/j.eswa.2010.10.069 %P 5662-5673 %0 Conference Proceedings %T Induction Of Governing Differential Equations From Hydrologic Time Series Data Using Genetic Programming %A Chadalawada, Jayashree %A Babovic, Vladan %S 11th International Conference on Hydroinformatics %D 2014 %8 aug 17 21 %C New York, USA %F Chadalawada:2014:HIC %X This contribution describes an evolutionary method for identifying causal model from the observed time-series data. In the present case, we use a system of ordinary differential equations (ODEs) as the causal model. Usefulness of the approach is demonstrated on real-world time series of hydrologic processes and the unknown function of governing factors are determined. To explore the evolutionary search space more effectively, the right hand sides of ODEs are inferred by genetic programming (GP). The importance of different fitness criteria, as well as introduction of background knowledge about underlying processes are also being discussed and assessed. The method is applied on several cases and empirically demonstrated how successfully GP infers the systems of ODEs. %K genetic algorithms, genetic programming %U http://www.hic2014.org/xmlui/Chadalawada_2014_HIC.pdf %0 Journal Article %T Genetic Programming Based Approach Towards Understanding the Dynamics of Urban Rainfall-runoff Process %A Chadalawada, Jayashree %A Havlicek, Vojtech %A Babovic, Vladan %J Procedia Engineering %D 2016 %V 154 %@ 1877-7058 %F Chadalawada:2016:PE %O 12th International Conference on Hydroinformatics (HIC 2016) - Smart Water for the Future %X Genetic Programming (GP) is an evolutionary-algorithm based methodology that is the best suited to model non-linear dynamic systems. The potential of GP has not been exploited to the fullest extent in the field of hydrology to understand the complex dynamics involved. The state of the art applications of GP in hydrological modelling involve the use of GP as a short-term prediction and forecast tool rather than as a framework for the development of a better model that can handle current challenges. In today’s scenario, with increasing monitoring programmes and computational power, the techniques like GP can be employed for the development and evaluation of hydrological models, balancing, prior information, model complexity, and parameter and output uncertainty. In this study, GP based data driven model in a single and multi-objective framework is trained to capture the dynamics of the urban rainfall-runoff process using a series of tanks, where each tank is a storage unit in a watershed that corresponds to varying depths below the surface. The hydro-meteorological data employed in this study belongs to the Kent Ridge catchment of National University Singapore, a small urban catchment (8.5 hectares) that receives a mean annual rainfall of 2500 mm and consists of all the major land uses of Singapore. %K genetic algorithms, genetic programming, Multi-objective optimization, System Identification, Data driven modelling in Hydrology, Urban Rainfall-Runoff modelling %9 journal article %R 10.1016/j.proeng.2016.07.601 %U http://www.sciencedirect.com/science/article/pii/S1877705816319907 %U http://dx.doi.org/10.1016/j.proeng.2016.07.601 %P 1093-1102 %0 Journal Article %T A Genetic Programming Approach to System Identification of Rainfall-Runoff Models %A Chadalawada, Jayashree %A Havlicek, Vojtech %A Babovic, Vladan %J Water Resources Management %D 2017 %V 31 %N 12 %F chadalawada:2017:WRM %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s11269-017-1719-1 %U http://link.springer.com/article/10.1007/s11269-017-1719-1 %U http://dx.doi.org/10.1007/s11269-017-1719-1 %0 Book Section %T Development of a Computer Controller Players for Daleks using Genetic Programming %A Chai, Daniel %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 chai:2000:DCCPDGP %K genetic algorithms, genetic programming %P 80-89 %0 Conference Proceedings %T Practical tuning of an OTA-C bandpass biquad via recurrent geometric programming %A Chaisricharoen, Roungsan %A Chipipop, Boonruk %S IEEE 8th International Conference on ASIC, ASICON ’09 %D 2009 %8 20 23 oct %F Chaisricharoen:2009:ASICON %X The geometric programming which can be globally solved special cases of nonlinear problems is operated recurrently with calibrated %K geometric programming, HSPICE simulations, OTA-C bandpass biquad tuning, evolutionary algorithms, heuristic algorithms, operational amplifiers, recurrent geometric programming, second-order bandpass requirement, band-pass filters, biquadratic filters, operational amplifiers %R 10.1109/ASICON.2009.5351182 %U http://dx.doi.org/10.1109/ASICON.2009.5351182 %P 1193-1196 %0 Conference Proceedings %T The Genetic Algorithms Approach for Proving Logical Arguments in Natural Language %A Chakraborti, C. %A Sastry, K. K. N. %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 %F chakraborti:1998:GAplaNLP %K genetic algorithms %P 463-470 %0 Book Section %T Evolutionary Data-Driven Modeling %A Chakraborti, Nirupam %E Rajan, Krishna %B Informatics for Materials Science and Engineering %D 2013 %I Butterworth-Heinemann %C Oxford %F Chakraborti:2013:IMSE %X Artificial neural networks (ANNs) and genetic programming (GP) have already emerged as two very effective computing strategies for constructing data-driven models for systems of scientific and engineering interest. However, coming up with accurate models or meta-models from noisy real-life data is often a formidable task due to their frequent association with high degrees of random noise, which might render an ANN or GP model either over- or underfitted. This problem has recently been tackled in two emerging algorithms, Evolutionary Neural Net (EvoNN) and Bi-objective Genetic Programming (BioGP), which use Pareto tradeoff and apply a bi-objective genetic algorithm (GA) in the basic framework of both ANNs and GP. %K genetic algorithms, genetic programming, Neural network, Multi-objective optimisation, Evolutionary computation %R 10.1016/B978-0-12-394399-6.00005-9 %U http://www.sciencedirect.com/science/article/pii/B9780123943996000059 %U http://dx.doi.org/10.1016/B978-0-12-394399-6.00005-9 %P 71-95 %0 Conference Proceedings %T Data-driven paradigms of EvoNN and BioGP %A Chakraborti, Nirupam %Y Bourgine, Paul %Y Collet, Pierre %S Complex Systems Digital Campus E-conference, CS-DC’15 %D 2015 %8 sep 30 oct 1 %F Chakraborti:2015:csdc %O Invited talk %X This paper will present the operational details of two recent algorithms EvoNN (Evolutionary Neural net) and BioGP (Bi-objective Genetic Programming) which are developed for modelling and optimization tasks pertinent to noisy data. EvoNN uses a neural net architecture while BioGP is based upon a tree structure typical of Genetic Programming. A bi-objective Genetic Algorithm acts on a population of either trees or neural nets, seeking a trade-off between the accuracy and complexity of the candidate models, ultimately leading to the optimum models along a Pareto frontier. Both the paradigms are tailor-made for constructing models of right complexity, and in the process of evolution they exclude the non-essential inputs. By default, an optimum model satisfying the Corrected Akaike Information Criterion (AICc) is recommended in case of EvoNN, and for BioGP the optimum model with the minimum training error is recommended. However, a Decision Maker (DM) can select a suitable model from the Pareto frontier by appropriate one can be easily picked up by applying some external criteria, if necessary. Both the algorithms tend to avoid over fitting or under fitting of any noisy data and in case of BioGP special procedures have been implemented to avoid bloat. Any pair of mutually conflicting objectives created through this procedure can also be optimized here using a built-in evolutionary strategy, incorporated as a module. %K genetic algorithms, genetic programming, ANN %U http://cs-dc-15.org/ %P PaperID:356 %0 Book Section %T Data-Driven Bi-Objective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain %A Chakraborti, Nirupam %E Datta, Shubhabrata %E Davim, J. Paulo %B Computational Approaches to Materials Design: Theoretical and Practical Aspects %D 2016 %8 jun %I IGI Global %F Chakraborti:2016:camdtpa %X Data-driven modeling and optimization are now of utmost importance in computational materials research. This chapter presents the operational details of two recent algorithms EvoNN (Evolutionary Neural net) and BioGP (Bi-objective Genetic Programming) which are particularly suitable for modeling and optimization tasks pertinent to noisy data. In both the approaches a tradeoff between the accuracy and complexity of the candidate models are sought, ultimately leading to some optimum tradeoffs. These novel strategies are tailor-made for constructing models of right complexity, excluding the non-essential inputs. They are constructed to implement the notion of Pareto-optimality using a predator-prey type genetic algorithm, providing the user with a set of optimum models, out of which an appropriate one can be easily picked up by applying some external criteria, if necessary. Several materials related problems have been solved using these algorithms in recent times and a couple of typical examples are briefly presented %K genetic algorithms, genetic programming %R 10.4018/978-1-5225-0290-6.ch012 %U http://dx.doi.org/10.4018/978-1-5225-0290-6.ch012 %P 346-368 %0 Book %T Data-Driven Evolutionary Modeling in Materials Technology %A Chakraborti, Nirupam %D 2023 %I Routledge %F Chakraborti:book %X Chapter 1: Introduction Chapter 2: Data with random noise and its modeling Chapter 3: Nature inspired non-calculus optimization Chapter 4: Single-objective evolutionary algorithms Chapter 5: Multi-objective evolutionary optimization Chapter 6: Evolutionary learning and optimization using Neural Net paradigm Chapter 7: Evolutionary learning and optimization using Genetic Programming paradigm Chapter 8: The challenge of big data and Evolutionary Deep Learning Chapter 9: Software available in public domain and the commercial software Chapter 10: Applications in Iron and Steel making Chapter 11: Applications in chemical and metallurgical unit processing Chapter 12: Applications in Materials Design Chapter 13: Applications in Atomistic Materials Design Chapter 14: Applications in Manufacturing Chapter 15: Miscellaneous Applications %K genetic algorithms, genetic programming, BioGP, matlab %R 10.1201/9781003201045 %U https://www.routledge.com/Data-Driven-Evolutionary-Modeling-in-Materials-Technology/Chakraborti/p/book/9781032061733 %U http://dx.doi.org/10.1201/9781003201045 %0 Journal Article %T Mechanism discovery and model identification using genetic feature extraction and statistical testing %A Chakraborty, Arijit %A Sivaram, Abhishek %A Samavedham, Lakshminarayanan %A Venkatasubramanian, Venkat %J Computer & Chemical Engineering %D 2020 %V 140 %@ 0098-1354 %F CHAKRABORTY:2020:CCE %X One main drawback of many machine learning-based regression models is that they are difficult to interpret and explain. Mechanism-based first-principles models, on the other hand, can be interpreted and hence preferable. However, as they are often quite challenging to develop, the appeal of machine learning-based black-box models is natural. Here, we report a genetic algorithm-based machine learning system that automatically discovers mechanistic models from data using limited human guidance. The advantage of this approach is that it yields simple, interpretable, features and can be used to identify model forms and fundamental mechanisms that are often seen in chemical engineering. We demonstrate our system on several case studies in reaction kinetics and transport phenomena, and discuss its strengths and limitations %K genetic algorithms, genetic programming, Mechanism discovery and model identification, Statistical testing, Feature extraction %9 journal article %R 10.1016/j.compchemeng.2020.106900 %U http://www.sciencedirect.com/science/article/pii/S009813542030123X %U http://dx.doi.org/10.1016/j.compchemeng.2020.106900 %P 106900 %0 Journal Article %T Binary Decision Diagram Assisted Modeling of FPGA-based Physically Unclonable Function by Genetic Programming %A Chakraborty, Rajat Subhra %A Jeldi, Ratan Rahul %A Saha, Indrasish %A Mathew, Jimson %J IEEE Transactions on Computers %D 2017 %8 jun %V 66 %N 6 %@ 0018-9340 %F Chakraborty:2017:ieeeTC %X We present a computationally efficient technique to build concise and accurate computational models for large (60 or more inputs, 1 output) Boolean functions, only a very small fraction of whose truth table is known during model building.We use Genetic Programming with Boolean logic operators, and enhance the accuracy of the technique using Reduced Ordered Binary Decision Diagram based representations of Boolean functions, whereby we exploit their canonical forms. We demonstrate the effectiveness of the proposed technique by successfully modelling several common Boolean functions, and ultimately by accurately modelling a 63-input Physically Unclonable Function circuit design on Xilinx Field Programmable Gate Array. We achieve better accuracy (at lesser computational overhead) in predicting truth table entries not seen during model building, than a previously proposed machine learning based modelling technique for similar Physically Unclonable Function circuits using Support Vector Machines. The success of this modelling technique has important implications in determining the acceptability of Physically Unclonable Functions as useful hardware security primitives, in applications such as anti-counterfeiting of integrated circuits. %K genetic algorithms, genetic programming, Binary Decision Diagrams, Boolean Function Learning, Physically Unclonable Functions %9 journal article %R 10.1109/TC.2016.2603498 %U http://dx.doi.org/10.1109/TC.2016.2603498 %P 971-981 %0 Journal Article %T Genetic and evolutionary computing %A Chakraborty, Uday K. %J Information Sciences %D 2008 %8 January %V 178 %N 23 %@ 0020-0255 %F Chakraborty:2008:IS %O Introduction to special section on Genetic and Evolutionary Computing %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.ins.2008.07.026 %U http://www.sciencedirect.com/science/article/pii/S0020025508002855 %U http://dx.doi.org/10.1016/j.ins.2008.07.026 %P 4419-4420 %0 Journal Article %T Genetic programming model of solid oxide fuel cell stack: first results %A Chakraborty, Uday K. %J International Journal of Information and Communication Technology (IJICT) %D 2008 %V 1 %N 3/4 %I Inderscience Publishers %@ 1741-8070 %G eng %F Chakraborty:2008:IJICT %X Models that predict performance are important tools in understanding and designing solid oxide fuel cells (SOFCs). Modelling of SOFC stack-based systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the electrochemical and thermal properties. Several algorithmic approaches have already been reported for the modelling of solid oxide fuel cell stack-based systems. This paper presents a new, genetic programming approach to SOFC modelling. Initial simulation results obtained with the proposed approach outperform the state-of-the-art radial basis function neural network method for this task. %K genetic algorithms, genetic programming, solid oxide fuel cells, SOFC stack, modelling, nonlinear dynamics, simulation %9 journal article %R 10.1504/IJICT.2008.024015 %U http://www.inderscience.com/link.php?id=24015 %U http://dx.doi.org/10.1504/IJICT.2008.024015 %P 453-461 %0 Conference Proceedings %T An Evolutionary Computation Approach to Predicting Output Voltage from Fuel Utilization in SOFC Stacks %A Chakraborty, Uday K. %Y Tyrrell, Andy %S 2009 IEEE Congress on Evolutionary Computation %D 2009 %8 18 21 may %I IEEE Press %C Trondheim, Norway %F Chakraborty2:2009:cec %X Modeling of solid oxide fuel cell (SOFC) stack based systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the electrochemical and thermal properties. This paper presents an efficient genetic programming approach for modeling and simulation of SOFC output voltage versus fuel burn behavior. This method is shown to outperform the state-of-the-art radial basis function neural network approach for SOFC modeling. %K genetic algorithms, genetic programming, RBFANN %R 10.1109/CEC.2009.4983209 %U P686.pdf %U http://dx.doi.org/10.1109/CEC.2009.4983209 %P 2165-2171 %0 Journal Article %T Static and dynamic modeling of solid oxide fuel cell using genetic programming %A Chakraborty, Uday Kumar %J Energy %D 2009 %V 34 %N 6 %@ 0360-5442 %F Chakraborty2009740 %X Modeling of solid oxide fuel cell (SOFC) systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the electrochemical and thermal properties. Several algorithmic approaches have in the past been reported for the modeling of solid oxide fuel cell stacks. However, all of these models have their limitations. This paper presents an efficient genetic programming approach to SOFC modeling and simulation. This method, belonging to the computational intelligence paradigm, is shown to outperform the state-of-the-art radial basis function neural network approach for SOFC modeling. Both static (fixed load) and dynamic (load transient) analyses are provided. Statistical tests of significance are used to validate the improvement in solution quality. %K genetic algorithms, genetic programming, Solid oxide fuel cell, SOFC stack, Dynamic model, Transient response, Neural network %9 journal article %R 10.1016/j.energy.2009.02.012 %U http://www.sciencedirect.com/science/article/B6V2S-4W32975-1/2/c334dcacd8fee2c381ecd788e82d33fc %U http://dx.doi.org/10.1016/j.energy.2009.02.012 %P 740-751 %0 Journal Article %T CFD-based genetic programming model for liquid entry pressure estimation of hydrophobic membranes %A Chamani, Hooman %A Yazgan-Birgi, Pelin %A Matsuura, Takeshi %A Rana, Dipak %A Hassan Ali, Mohamed I. %A Arafat, Hassan A. %A Lan, Christopher Q. %J Desalination %D 2020 %V 476 %@ 0011-9164 %F CHAMANI:2020:Desalination %X Wetting phenomenon inside the pore is a significant obstacle hindering membrane distillation (MD) from being fully industrialized. Herein, a new equation is provided for the users, using the combination of computational fluid dynamics (CFD) and genetic programming (GP) tools for estimation of liquid entry pressure (LEP), a parameter closely related to pore wetting. CFD was applied to model the wetting process inside the pore during the gradual increase in feed pressure at different scenarios in which contact angle, pore radius and membrane thickness were changed. Afterwards, GP as an intelligent method was employed to provide a computer program estimating LEP in the whole ranges in which CFD modeling was carried out. Moreover, validation was done using experimental data and then the influence of effective parameters on LEP was studied. This work provides an explicit formula for estimation of LEP in a closer agreement with the experimental data in comparison to the Young-Laplace equation. In addition, the influence of the membrane thickness was added to the equation, providing a more realistic formula for LEP estimation %K genetic algorithms, genetic programming, Membrane distillation, Liquid entry pressure, Computational fluid dynamics, Modeling %9 journal article %R 10.1016/j.desal.2019.114231 %U http://www.sciencedirect.com/science/article/pii/S0011916419318430 %U http://dx.doi.org/10.1016/j.desal.2019.114231 %P 114231 %0 Thesis %T Pore Wetting in Desalination of Brine Using Membrane Distillation Process %A Chamani, Hooman %D 2021 %C Canada %C Department of Chemical and Biological Engineering, University of Ottawa %F Chamani_Hooman_2021_thesis %X It goes without saying that water scarcity is a widespread and increasingly pressing global challenge. One of the methods which can mitigate water shortage is to increase freshwater production via desalination of saline waters. Seawater and saline aquifer sources represent 97.5% of all water on Earth. Hence, treating even a small portion of saline water could significantly reduce water shortage. Although reverse osmosis is one of the state-of-the-art pressure-driven membrane desalination technologies, it is incapable of desalinating high-salinity streams due to the very high osmotic pressure to overcome. Membrane distillation (MD) is one of the emerging methods, which has attracted much attention for desalinating highly saline brines. MD is a thermally driven process in which only vapor molecules pass through the pores of a microporous hydrophobic membrane. This process, however, has not been fully commercialized due to a number of challenges, including pore wetting. Pore wetting refers to the presence of liquid, instead of just water vapor, inside the membrane pores, which may cause a decrease in MD flux and/or deterioration of distillate quality. Herein, a comprehensive review on pore wetting is presented, and then this phenomenon is investigated from four aspects. In the first phase of this project, a theoretical model is presented according to which the pore size distribution of membrane, a parameter affecting pore wetting risk, is estimated by employing only a few experimental data points in accordance with the wet/dry method, reducing the number of data required to be recorded largely. In the next phase, an equation is presented for the estimation of liquid entry pressure (LEP), a membrane parameter closely related to pore wetting, using computational fluid dynamics (CFD) tools and genetic programming (GP) as an intelligent technique. This equation can estimate LEP in closer agreement to experimental values in comparison to the Young-Laplace equation. In the third phase, movement of liquid-gas interface inside the membrane pore is tracked using a well-founded model, and consequently, the pressure and velocity at the interface and the required time for replacement are studied. Finally, in the last phase, a model is developed for pore wetting in vacuum MD, considering heat and mass balances at the vapor-liquid interface. This model assumes that heat only enters the pore inlet and is removed due to liquid vaporization at the vapor-liquid interface, with heat transfer through the pore wall neglected. This model shows that partial pore wetting is possible since the vapor-liquid interface might remain within the pore at the steady-state condition. Further, this model can predict the decrease in temperature from the pore inlet to the vapor-liquid interface, a phenomenon that has been reported in the literature without any proof. %K genetic algorithms, genetic programming, Pore Wetting, Membrane Distillation, Desalination, Modeling %9 Ph.D. thesis %R 10.20381/ruor-27163 %U https://ruor.uottawa.ca/items/00271b9a-3d2a-4a10-b169-57afe2194750 %U http://dx.doi.org/10.20381/ruor-27163 %0 Journal Article %T Book Review: Genetic Programming and Data Structures: Genetic Programming+Data Structures=Automatic Programming %A Chambers, Lance D. %J Genetic Programming and Evolvable Machines %D 2001 %8 sep %V 2 %N 3 %@ 1389-2576 %F chambers:2001:GPEM %K genetic algorithms, genetic programming %9 journal article %R 10.1023/A:1011957528066 %U https://rdcu.be/dR8hd %U http://dx.doi.org/10.1023/A:1011957528066 %P 301-303 %0 Journal Article %T Inversion of oceanic constituents in case I and II waters with genetic programming algorithms %A Chami, Malik %A Robilliard, Denis %J Applied Optics %D 2002 %8 20 oct %V 41 %N 30 %@ 1559-128X %F 2002ApOpt..41.6260C %X A stochastic inverse technique based on a genetic programming (GP) algorithm was developed to invert oceanic constituents from simulated data for case I and case II water applications. The simulations were carried out with the Ordre Successifs Ocean Atmosphere (OSOA) radiative transfer model. They include the effects of oceanic substances such as algal-related chlorophyll, nonchlorophyllous suspended matter, and dissolved organic matter. The synthetic data set also takes into account the directional effects of particles through a variation of their phase function that makes the simulated data realistic. It is shown that GP can be successfully applied to the inverse problem with acceptable stability in the presence of realistic noise in the data. GP is compared with neural network methodology for case I waters; GP exhibits similar retrieval accuracy, which is greater than for traditional techniques such as band ratio algorithms. The application of GP to real satellite data [a Sea-viewing Wide Field-of-view Sensor (SeaWiFS)] was also carried out for case I waters as a validation. Good agreement was obtained when GP results were compared with the SeaWiFS empirical algorithm. For case II waters the accuracy of GP is less than 33percent, which remains satisfactory, at the present time, for remote-sensing purposes. %K genetic algorithms, genetic programming, ARTIFICIAL SATELLITES, ATMOSPHERIC OPTICS, COLOUR, INFRARED SPECTROSCOPY, LIGHT TRANSMISSION, OPTICAL PROPERTIES, RADIATIVE TRANSFER, REFLECTANCE, REMOTE SENSING, SEA WATER, SPECTROSCOPIC ANALYSIS, STOCHASTIC PROCESSES, WAVE PROPAGATION %9 journal article %R 10.1364/AO.41.006260 %U http://ao.osa.org/ViewMedia.cfm?id=70258&seq=0 %U http://dx.doi.org/10.1364/AO.41.006260 %P 6260-6275 %0 Conference Proceedings %T A Three-Stage Genetic Algorithm for Compiler Flag and Library Version Selection to Minimize Execution Time %A Chan, Chi Ho %A Nita, Spyro %S "14th International Workshop on Genetic Improvement %F chan:2025:GI %0 Journal Article %D 2025 %8 27 apr %C Ottawa %F 2025"e %X Existing research in compiler autotuning mainly focuses on selecting optimization flags without configurable values. However, the potential of selecting optimization flags with configurable values, alongside using directory and link flags for library version selection to improve performance, remains largely unexplored. We propose a three-stage Genetic Algorithm (GA) that incrementally selects optimization flags without configurable values, then optimization flags with configurable values, and finally library versions, to minimize software execution time. We also discuss the implementation challenges of the proposed algorithm and outline potential future work. %K genetic algorithms, genetic programming, Genetic Improvement, genetic algorithm, compiler optimization, compiler flag selection, library version selection %9 journal article %R 10.1109/GI66624.2025.00009 %U https://gpbib.cs.ucl.ac.uk/gi2025/chan_2025_GI.pdf %U http://dx.doi.org/10.1109/GI66624.2025.00009 %P 1-2 %0 Journal Article %T Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms %A Chan, Wai Sum %A Recknagel, Friedrich %A Cao, Hongqing %A Park, Ho-Dong %J Water Research %D 2007 %8 may %V 41 %N 10 %F chan:2007:WR %X Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of microcystin concentrations based on limnological and meteorological input data, achieving an r2=0.74 for testing. %K genetic algorithms, genetic programming, Lake Suwa, Microcystis, Microcystin, Ordination, Clustering, Forecasting, Explanation %9 journal article %R 10.1016/j.watres.2007.02.001 %U http://dx.doi.org/10.1016/j.watres.2007.02.001 %P 2247-2255 %0 Thesis %T Spatial and temporal features of hydrodynamics and biogeochemistry in Myponga Reservoir, South Australia %A Chan, Wai Sum %D 2011 %8 oct %C Australia %C University of Adelaide, School of Earth and Environmental Sciences %F Grace_Chan_thesis %X Understanding hydrodynamic and biogeochemical processes in lakes is fundamentally important to the management of phytoplankton population and the improvement of water quality. Physical processes such as wind-driven surface mixing, thermal stratification and differential heating and cooling can affect the distribution of water, phytoplankton and sediments and the availability of nutrients and light. These lake processes, which are highly variable in space and time, affect phytoplankton dynamics in the field. This study aims to determine the spatial and temporal variability of phytoplankton and processes that either contribute to or override the variability in the artificially mixed Myponga Reservoir, South Australia. A sediment survey showed that sediments underlying deep water were richer in organic matter, carbon, nitrogen and phosphorus than the sediments underlying shallow water. This may lead to different nutrient release rates between the shallow and deep areas. ... %9 Ph.D. thesis %U https://digital.library.adelaide.edu.au/dspace/handle/2440/76100 %0 Conference Proceedings %T Minimum-Allele-Reserve-Keeper (MARK): A Fast and Effective Mutation Scheme for Genetic Algorithm (GA) %A Chan, Zeke S. H. %A Ngan, H. W. %A Rad, A. B. %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 chan:1999:MAFEMSGA %K genetic algorithms and classifier systems %P 106-113 %0 Conference Proceedings %T A new method to resist premature convergence: Synchonising gene-convergence with correlated recombination %A Chan, Zeke S. H. %A Ngan, H. W. %A Rad, A. B. %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 chan:1999:AS %K Genetic Algorithms %P 74-79 %0 Book Section %T Valid English Word Classifier Using Genetic Programming %A Chan, King Choi %E Koza, John R. %B Genetic Algorithms and Genetic Programming at Stanford 1995 %D 1995 %8 November %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-195720-5 %F chan:1995:VEWCUGP %K genetic algorithms, genetic programming %P 39-48 %0 Book Section %T Automatic Generation of Prime Factorization Algorithms using Genetic Programming %A Chan, David Michael %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 chan:2002:AGPFAGP %X the application of the principles of genetic programming to the field of prime factorization. Any prime factorization algorithm is given one integer and must generate a complete list of primes such that, when multiplied together in varying degrees, produces the original integer. Constructing even a limited factoring algorithm in GP turns out to be extremely challenging and potentially impossible. %K genetic algorithms, genetic programming, PTC2, ECJ %U http://www.genetic-programming.org/sp2002/Chan.pdf %P 52-57 %0 Conference Proceedings %T New Factorial Design Theoretic Crossover Operator for Parametrical Problem %A Chan, Kit Yan %A Aydin, M. Emin %A Fogarty, Terence C. %Y Ryan, Conor %Y Soule, Terence %Y Keijzer, Maarten %Y Tsang, Edward %Y Poli, Riccardo %Y Costa, Ernesto %S Genetic Programming, Proceedings of EuroGP’2003 %S LNCS %D 2003 %8 14 16 apr %V 2610 %I Springer-Verlag %C Essex %@ 3-540-00971-X %F chan03 %X Recent research shows that factorial design methods improve the performance of the crossover operator in evolutionary computation. However the methods employed so far ignore the effects of interaction between genes on fitness, i.e. “epistasis”. Here we propose the application of a systematic method for interaction effect analysis to enhance the performance of the crossover operator. It is shown empirically that the proposed method significantly outperforms existing crossover operators on benchmark problems with high interaction between the variables. %K genetic algorithms, genetic programming %R 10.1007/3-540-36599-0_3 %U http://dx.doi.org/10.1007/3-540-36599-0_3 %P 22-33 %0 Conference Proceedings %T Experimental design based multi-parent crossover operator %A Chan, Kit Yan %A Fogarty, Terence C. %Y Ryan, Conor %Y Soule, Terence %Y Keijzer, Maarten %Y Tsang, Edward %Y Poli, Riccardo %Y Costa, Ernesto %S Genetic Programming, Proceedings of EuroGP’2003 %S LNCS %D 2003 %8 14 16 apr %V 2610 %I Springer-Verlag %C Essex %@ 3-540-00971-X %F chan03b %X Recently, the methodologies of multi-parent crossover have been developed by performing the crossover operation with multi-parent. Some studies have indicated the high performance of multi-parent crossover on some numerical optimization problems. Here a new crossover operator has been proposed by integrating multi-parent crossover with the approach of experimental design. It is based on experimental design method in exploring the solution space that compensates the random search as in traditional genetic algorithm. By replacing the inbuilt randomness of crossover operator with a more systematical method, the proposed method outperforms the classical GA strategy on several GA benchmark problems. %K genetic algorithms, genetic programming: Poster %R 10.1007/3-540-36599-0_27 %U http://dx.doi.org/10.1007/3-540-36599-0_27 %P 297-306 %0 Conference Proceedings %T An Evolutionary Algorithm for the Input-Output Block Assignment Problem %A Chan, Kit Yan %A Fogarty, Terence C. %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 chan:2004:eurogp %X A procedure for system decomposition is developed for decentralised multi-variable systems. Optimal input-output pairing techniques are used to rearrange a large multi variable system into a structure that is closer to the block-diagonal decentralised form. The problem is transformed into a block assignment problem. An evolutionary algorithm is developed to solve this hard IP problem. The result shows that the proposed algorithm is simple to implement and efficient to find the reasonable solution. %K genetic algorithms, genetic programming: Poster %R 10.1007/978-3-540-24650-3_23 %U http://dx.doi.org/10.1007/978-3-540-24650-3_23 %P 250-258 %0 Journal Article %T Modelling customer satisfaction for product development using genetic programming %A Chan, Kit Yan %A Kwong, C. K. %A Wong, T. C. %J Journal of Engineering Design %D 2011 %8 jan %V 22 %N 1 %I Taylor & Francis %@ 0954-4828 %F Chan:2009:JED %X Product development involves several processes in which product planning is the first one. Several tasks normally are required to be conducted in the product-planning process and one of them is to determine settings of design attributes for products. Facing with fierce competition in marketplaces, companies try to determine the settings such that the best customer satisfaction of products could be obtained.To achieve this, models that relate customer satisfaction to design attributes need to be developed first. Previous research has adopted various modelling techniques to develop the models, but those models are not able to address interaction terms or higher-order terms in relating customer satisfaction to design attributes, or they are the black-box type models. In this paper, a method based on genetic programming (GP) is presented to generate models for relating customer satisfaction to design attributes. The GP is first used to construct branches of a tree representing structures of a model where interaction terms and higher-order terms can be addressed. Then an orthogonal least-squares algorithm is used to determine the coefficients of the model. The models thus developed are explicit and consist of interaction terms and higher-order terms in relating customer satisfaction to design attributes. A case study of a digital camera design is used to illustrate the proposed method. %K genetic algorithms, genetic programming, SBSE, SPL, interaction terms, higher-order terms, customer satisfaction, design attributes %9 journal article %R 10.1080/09544820902911374 %U http://dx.doi.org/10.1080/09544820902911374 %P 55-68 %0 Journal Article %T A genetic programming based fuzzy regression approach to modelling manufacturing processes %A Chan, K. Y. %A Kwong, C. K. %A Tsim, Y. C. %J International Journal of Production Research %D 2010 %8 apr %V 48 %N 7 %F Chan:2010:IJPR %X Fuzzy regression has demonstrated its ability to model manufacturing processes in which the processes have fuzziness and the number of experimental data sets for modelling them is limited. However, previous studies only yield fuzzy linear regression based process models in which variables or higher order terms are not addressed. In fact, it is widely recognised that behaviours of manufacturing processes do often carry interactions among variables or higher order terms. In this paper, a genetic programming based fuzzy regression GP-FR, is proposed for modelling manufacturing processes. The proposed method uses the general outcome of GP to construct models the structure of which is based on a tree representation, which could carry interaction and higher order terms. Then, a fuzzy linear regression algorithm is used to estimate the contributions and the fuzziness of each branch of the tree, so as to determine the fuzzy parameters of the genetic programming based fuzzy regression model. To evaluate the effectiveness of the proposed method for process modelling, it was applied to the modelling of a solder paste dispensing process. Results were compared with those based on statistical regression and fuzzy linear regression. It was found that the proposed method can achieve better goodness-of-fitness than the other two methods. Also the prediction accuracy of the model developed based on GP-FR is better than those based on the other two methods. %K genetic algorithms, genetic programming fuzzy regression, process modelling, solder paste dispensing %9 journal article %R 10.1080/00207540802644845 %U http://www.tandfonline.com/doi/abs/10.1080/00207540802644845 %U http://dx.doi.org/10.1080/00207540802644845 %P 1967-1982 %0 Journal Article %T Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers %A Chan, K. Y. %A Kwong, C. K. %A Fogarty, T. C. %J Information Sciences %D 2010 %V 180 %N 4 %@ 0020-0255 %F Chan2010506 %X Fuzzy regression (FR) been demonstrated as a promising technique for modeling manufacturing processes where availability of data is limited. FR can only yield linear type FR models which have a higher degree of fuzziness, but FR ignores higher order or interaction terms and the influence of outliers, all of which usually exist in the manufacturing process data. Genetic programming (GP), on the other hand, can be used to generate models with higher order and interaction terms but it cannot address the fuzziness of the manufacturing process data. In this paper, genetic programming-based fuzzy regression (GP-FR), which combines the advantages of the two approaches to overcome the deficiencies of the commonly used existing modeling methods, is proposed in order to model manufacturing processes. GP-FR uses GP to generate model structures based on tree representation which can represent interaction and higher order terms of models, and it uses an FR generator based on fuzzy regression to determine outliers in experimental data sets. It determines the contribution and fuzziness of each term in the model by using experimental data excluding the outliers. To evaluate the effectiveness of GP-FR in modeling manufacturing processes, it was used to model a non-linear system and an epoxy dispensing process. The results were compared with those based on two commonly used FR methods, Tanka’s FR and Peters’ FR. The prediction accuracy of the models developed based on GP-FR was shown to be better than that of models based on the other two FR methods. %K genetic algorithms, genetic programming, Fuzzy regression, Outlier detection, Epoxy dispensing process %9 journal article %R 10.1016/j.ins.2009.10.007 %U http://www.sciencedirect.com/science/article/B6V0C-4XFPR3M-3/2/1f27ff77e40dc7d917de59d3555abf36 %U http://dx.doi.org/10.1016/j.ins.2009.10.007 %P 506-518 %0 Conference Proceedings %T Using an evolutionary fuzzy regression for affective product design %A Chan, K. Y. %A Dillon, T. S. %A Kwong, C. K. %S IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Chan:2010:ieee-fuzz %X In affective product design, one of the main goals is to maximise customers’ affective satisfaction by optimising design variables of a new product. To achieve this, a model in relating customers’ affective responses and design variables of a new product is required to be developed based on customers’ survey data. However, previous research on modelling the relationship between affective response and design variables cannot address the development of explicit models either involving nonlinearity or fuzziness, which exist in customers’ survey data. In this paper, an evolutionary fuzzy regression approach is proposed to generate explicit models to represent this nonlinear and fuzzy relationship between affective responses and design variables. In the approach, genetic programming is used to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. Fuzzy coefficients of the model, which is represented by the tree, are determined based on a fuzzy regression algorithm. As a result, the fuzzy nonlinear regression model can be obtained to relate affective responses and design variables. %K genetic algorithms, genetic programming %R 10.1109/FUZZY.2010.5584493 %U http://dx.doi.org/10.1109/FUZZY.2010.5584493 %0 Conference Proceedings %T Classification of hypoglycemic episodes for Type 1 diabetes mellitus based on neural networks %A Chan, Kit Yan %A Ling, Sing Ho %A Dillon, Tharam Singh %A Nguyen, Hung %S IEEE Congress on Evolutionary Computation (CEC 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Chan:2010:cec %X Hypoglycemia is dangerous for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, we have developed a classification unit with hybridising the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed classification unit is built and is validated by using the real T1DM patients’ data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based classification unit can achieve more accurate results on both trained and unseen T1DM patients’ data sets compared with those developed based on the commonly used classification methods for medical diagnosis including statistical regression, fuzzy regression and genetic programming. %K genetic algorithms, genetic programming %R 10.1109/CEC.2010.5586320 %U http://dx.doi.org/10.1109/CEC.2010.5586320 %0 Conference Proceedings %T Polynomial modeling for manufacturing processes using a backward elimination based genetic programming %A Chan, Kit Yan %A Dillon, Tharam Singh %A Kwong, Che Kit %S IEEE Congress on Evolutionary Computation (CEC 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Chan:2010:cec2 %X Even if genetic programming (GP) has rich literature in development of polynomial models for manufacturing processes, the polynomial models may contain redundant terms which may cause the overfitted models. In other words, those models have good accuracy on training data sets but poor accuracy on untrained data sets. In this paper, a mechanism which aims at avoiding overfitting is proposed based on a statistical method, backward elimination, which intends to eliminate insignificant terms in polynomial models. By modeling a solder paste dispenser for electronic manufacturing, results show that the insignificant terms in the polynomial model can be eliminated by the proposed mechanism. Results also show that the polynomial model generated by the proposed GP can achieve better predictions than the existing methods. %K genetic algorithms, genetic programming %R 10.1109/CEC.2010.5586309 %U http://dx.doi.org/10.1109/CEC.2010.5586309 %0 Journal Article %T Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm %A Chan, Kit Yan %A Dillon, Tharam S. %A Kwong, C. K. %J Information Sciences %D 2011 %V 181 %N 9 %@ 0020-0255 %F Chan20111623 %X In this paper, an effective particle swarm optimization (PSO) is proposed for polynomial models for time varying systems. The basic operations of the proposed PSO are similar to those of the classical PSO except that elements of particles represent arithmetic operations and variables of time-varying models. The performance of the proposed PSO is evaluated by polynomial modelling based on various sets of time-invariant and time-varying data. Results of polynomial modeling in time-varying systems show that the proposed PSO outperforms commonly used modelling methods which have been developed for solving dynamic optimisation problems including genetic programming (GP) and dynamic GP. An analysis of the diversity of individuals of populations in the proposed PSO and GP reveals why the proposed PSO obtains better results than those obtained by GP. %K genetic algorithms, genetic programming, PSO, Particle swarm optimisation, Time-varying systems, Polynomial modelling %9 journal article %R 10.1016/j.ins.2011.01.006 %U http://www.sciencedirect.com/science/article/B6V0C-51X1VSV-7/2/12b12f977248967cf70b6cfd1dc37507 %U http://dx.doi.org/10.1016/j.ins.2011.01.006 %P 1623-1640 %0 Journal Article %T Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming %A Chan, K. Y. %A Kwong, C. K. %A Dillon, T. S. %A Tsim, Y. C. %J Applied Soft Computing %D 2011 %V 11 %N 2 %@ 1568-4946 %F Chan20111648 %O The Impact of Soft Computing for the Progress of Artificial Intelligence %X Genetic programming (GP) has demonstrated as an effective approach in polynomial modelling of manufacturing processes. However, polynomial models with redundant terms generated by GP may depict over fitting, while the developed models have good accuracy on trained data sets but relatively poor accuracy on testing data sets. In the literature, approaches of avoiding overfitting in GP are handled by limiting the number of terms in polynomial models. However, those approaches cannot guarantee terms in polynomial models produced by GP are statistically significant to manufacturing processes. In this paper, a statistical method, backward elimination (BE), is proposed to incorporate with GP, in order to eliminate insignificant terms in polynomial models. The performance of the proposed GP has been evaluated by modeling three real-world manufacturing processes, epoxy dispenser for electronic packaging, solder paste dispenser for electronic manufacturing, and punch press system for leadframe downset in IC packaging. Empirical results show that insignificant terms in the polynomial models can be eliminated by the proposed GP and also the polynomial models generated by the proposed GP can achieve results with better predictions than the other commonly used existent methods, which are commonly used in GP for avoiding overfitting in polynomial modeling. %K genetic algorithms, genetic programming, Process modelling, Polynomial modelling, Overfitting %9 journal article %R 10.1016/j.asoc.2010.04.022 %U http://www.sciencedirect.com/science/article/B6W86-501FPF7-6/2/4bf5179fccc0bf3772b121aef439e062 %U http://dx.doi.org/10.1016/j.asoc.2010.04.022 %P 1648-1656 %0 Journal Article %T Diagnosis of hypoglycemic episodes using a neural network based rule discovery system %A Chan, K. Y. %A Ling, S. H. %A Dillon, T. S. %A Nguyen, H. T. %J Expert Systems with Applications %D 2011 %V 38 %N 8 %@ 0957-4174 %F Chan20119799 %X Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients’ physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have developed a neural network based rule discovery system with hybridising the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed neural network based rule discovery system is built and is validated by using the real T1DM patients’ data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based rule discovery system can achieve more accurate results on both trained and unseen T1DM patients’ data sets compared with those developed based on the commonly used classification methods for medical diagnosis, statistical regression, fuzzy regression and genetic programming. Apart from the achievement of these better results, the proposed neural network based rule discovery system can provide explicit information in the form of production rules which compensate for the deficiency of traditional neural network method which do not provide a clear understanding of how they work in prediction as they are in an implicit black-box structure. This explicit information provided by the product rules can convince medical doctors to use the neural networks to perform diagnosis of hypoglycemia on T1DM patients. %K genetic algorithms, genetic programming, Neural networks, Hypoglycemic episodes, Medical diagnosis, Type 1 diabetes mellitus %9 journal article %R 10.1016/j.eswa.2011.02.020 %U http://www.sciencedirect.com/science/article/B6V03-524WF2N-4/2/d9f5c30581fa33cc25387714abbbc4b6 %U http://dx.doi.org/10.1016/j.eswa.2011.02.020 %P 9799-9808 %0 Conference Proceedings %T Using genetic programming for developing relationship between engineering characteristics and customer requirements in new products %A Chan, K. Y. %A Dillon, T. S. %A Kwong, C. K. %A Ling, S. H. %S 6th IEEE Conference on Industrial Electronics and Applications (ICIEA 2011) %D 2011 %8 21 23 jun %C Beijing, China %F Chan:2011:ICIEA %X In product planning, development of models of relationship between engineering characteristics and customer requirements in new products is an important process in quality function deployment (QFD), which is a widely used customer driven approach. In this paper, a methodology based on genetic programming (GP) is presented to generate a reliable model that can be used to predict the customer requirements from the engineering characteristics. The proposed GP based method, which has the capability to carry out simultaneous optimisation of model relationship structures and parameters, is used to automatically generate accurate nonlinear models relating the two requirements. A case study of the digital camera design shows that the proposed GP based method produce a more accurate and interpretable models than the other commonly used methods, which ignore nonlinear terms in the model development. %K genetic algorithms, genetic programming, GP based method, QFD, accurate nonlinear models, customer driven approach, customer requirements, digital camera design, engineering characteristics, interpretable models, model development, model relationship structures, new products, nonlinear terms, product development, product planning, quality function deployment, reliable model, simultaneous optimisation, customer services, product development, production planning, quality function deployment, reliability %R 10.1109/ICIEA.2011.5975642 %U http://dx.doi.org/10.1109/ICIEA.2011.5975642 %P 526-531 %0 Conference Proceedings %T Manufacturing modeling using an evolutionary fuzzy regression %A Chan, K. Y. %A Dillon, T. S. %A Ling, S. H. %A Kwong, C. K. %S IEEE International Conference on Fuzzy Systems (FUZZ 2011) %D 2011 %8 27 30 jun %C Taipei, Taiwan %F Chan:2011:ieeeFUZZ %X Fuzzy regression is a commonly used approach for modelling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can only address linearity in manufacturing process systems, but nonlinearity, which is unavoidable in the process, cannot be addressed. In this paper, an evolutionary fuzzy regression which integrates the mechanism of a fuzzy regression and genetic programming is proposed to generate manufacturing process models. It intends to overcome the deficiency of the fuzzy regression, which cannot address nonlinearities in manufacturing processes. The evolutionary fuzzy regression uses genetic programming to generate the structural form of the manufacturing process model based on tree representation which can address both linearity and nonlinearities in manufacturing processes. Then it uses a fuzzy regression to determine outliers in experimental data sets. By using experimental data excluding the outliers, the fuzzy regression can determine fuzzy coefficients which indicate the contribution and fuzziness of each term in the structural form of the manufacturing process model. To evaluate the effectiveness of the evolutionary fuzzy regression, a case study regarding modelling of epoxy dispensing process is carried out. %K genetic algorithms, genetic programming, evolutionary fuzzy regression, fuzzy coefficients, manufacturing modelling, manufacturing process model, fuzzy set theory, manufacturing processes, regression analysis %R 10.1109/FUZZY.2011.6007322 %U http://dx.doi.org/10.1109/FUZZY.2011.6007322 %P 2261-2267 %0 Book Section %T Polynomial Modeling in a Dynamic Environment based on a Particle Swarm Optimization %A Chan, Kit Yan %A Dillon, Tharam S. %E Lam, H. K. %E Ling, Steve S. H. %E Nguyen, Hung T. %B Computational Intelligence and Its Applications %D 2012 %I World Scientific %F chan:2012:cia %X In this chapter, a particle swarm optimisation (PSO) is proposed for polynomial modelling in a dynamic environment. The basic operations of the proposed PSO are identical to the ones of the original PSO except that elements of particles represent arithmetic operations and polynomial variables of polynomial models. The performance of the proposed PSO is evaluated by polynomial modelling based on a set of dynamic benchmark functions in which their optima are dynamically moved. Results show that the proposed PSO can find significantly better polynomial models than genetic programming (GP) which is a commonly used method for polynomial modelling. %K genetic algorithms, genetic programming, particle swarm optimisation, PSO, time-varying modelling, time-varying systems, polynomial modelling, evolutionary computation %R 10.1142/9781848166929_0002 %U http://espace.library.curtin.edu.au/R?func=dbin-jump-full&local_base=gen01-era02&object_id=189166 %U http://dx.doi.org/10.1142/9781848166929_0002 %P 23-38 %0 Journal Article %T Modeling of epoxy dispensing process using a hybrid fuzzy regression approach %A Chan, Kit Yan %A Kwong, C. K. %J The International Journal of Advanced Manufacturing Technology %D 2013 %8 mar %V 65 %N 1-4 %I Springer-Verlag %@ 0268-3768 %G English %F chan:2013:IJAMT %X In the semiconductor manufacturing industry, epoxy dispensing is a popular process commonly used in die-bonding as well as in microchip encapsulation for electronic packaging. Modelling the epoxy dispensing process is important because it enables us to understand the process behaviour, as well as determine the optimum operating conditions of the process for a high yield, low cost, and robust operation. Previous studies of epoxy dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy dispensing is difficult to develop because of its complex behaviour and high degree of uncertainty associated with the process in a real-world environment. Previous studies of modelling the epoxy dispensing process have not addressed the development of explicit models involving high-order and interaction terms, as well as fuzziness between process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two process models are generated for the two quality characteristics of the process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression. %K genetic algorithms, genetic programming, Fuzzy regression, Epoxy dispensing, Microchip encapsulation, Electronic packaging, Process modelling, Semiconductor manufacturing %9 journal article %R 10.1007/s00170-012-4202-4 %U http://espace.library.curtin.edu.au:80/R?func=dbin-jump-full&local_base=gen01-era02&object_id=185726 %U http://dx.doi.org/10.1007/s00170-012-4202-4 %P 589-600 %0 Journal Article %T A Stepwise-Based Fuzzy Regression Procedure for Developing Customer Preference Models in New Product Development %A Chan, Kit Yan %A Lam, Hak Keung %A Dillon, Tharam S. %A Ling, Sai Ho %J IEEE Transactions on Fuzzy Systems %D 2015 %8 oct %V 23 %N 5 %@ 1063-6706 %F Chan:2015:ieeeFUZZ %X too long %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TFUZZ.2014.2375911 %U http://dx.doi.org/10.1109/TFUZZ.2014.2375911 %P 1728-1745 %0 Journal Article %T A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments %A Chan, Kit Yan %A Lam, Hak-Keung %A Yiu, Cedric Ka Fai %A Dillon, Tharam S. %J IEEE Transactions on Systems, Man, and Cybernetics: Systems %D 2017 %8 aug %V 47 %N 8 %@ 2168-2216 %F Chan:2017:ieeeSMCS %X Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence, the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TSMC.2017.2672997 %U https://ieeexplore.ieee.org/document/7907344/ %U http://dx.doi.org/10.1109/TSMC.2017.2672997 %P 2363-2377 %0 Journal Article %T Predicting customer satisfaction based on online reviews and hybrid ensemble genetic programming algorithms %A Chan, Kit Yan %A Kwong, C. K. %A Kremer, Gul E. %J Engineering Applications of Artificial Intelligence %D 2020 %V 95 %@ 0952-1976 %F CHAN:2020:EAAI %X Determination of the design attribute settings of a new product is essential for maximizing customer satisfaction. A model is necessary to illustrate the relation between the design attributes and dimensions of customer satisfaction such as product performance, affection and quality. The model is commonly developed based on customer survey data collected from questionnaires or interviews which require a long deployment time; hence the developed model cannot completely reflect the current marketplace. In this paper, a framework is proposed based on online reviews in which past and current customer opinions are included to develop the model. The proposed framework overcomes the limitation of the aforementioned approaches in which the developed models are not up-to-date. Indeed, the proposed framework develops models based on machine learning technologies, namely genetic programming, which has better generalization capabilities than classical approaches, and has higher transparency capabilities than implicit modelling approaches. To further enhance the prediction capability, committee member selection is proposed. The proposed selection method improves the currently used selection method which trains several models and only selects the best one. The proposed selection method generates a hybrid model which integrates the predictions of the generated models. Each prediction is weighted by how likely the prediction is agreed by others. The proposed framework is implemented on electric hair dryer design of which online reviews in amazon.com are used. Experimental results show that models with more accurate prediction capabilities can be generated by the proposed framework %K genetic algorithms, genetic programming, New product development, Social media, Online customer reviews, Machine learning, Committee member selection %9 journal article %R 10.1016/j.engappai.2020.103902 %U http://www.sciencedirect.com/science/article/pii/S0952197620302396 %U http://dx.doi.org/10.1016/j.engappai.2020.103902 %P 103902 %0 Journal Article %T Analyzing imbalanced online consumer review data in product design using geometric semantic genetic programming %A Chan, Kit Yan %A Kwong, C. K. %A Jiang, Huimin %J Engineering Applications of Artificial Intelligence %D 2021 %V 105 %@ 0952-1976 %F CHAN:2021:EAAI %X To develop a successful product, understanding the relationship between customer satisfaction (CS) and design attributes of a new product is essential. Nowadays IoT technologies are used to collect online review data from social media. More representative CS models are developed using online review data. However, online review data is imbalanced, since popular products receive more online consumer reviews and unpopular products receive less. When imbalanced data is used, CS models learn the characteristics of majority data while rarely learning minority data. Misleading analysis for product development is made since the CS model is biased to popular products. This paper proposes an approach to generate nondominated CS models which learn equally to imbalanced data from popular and unpopular products. A multi-objective optimization problem is formulated to learn equally in imbalanced data. This problem is proposed to be solved by the geometric semantic genetic programming (GSGP); a Pareto set of nondominated CS models is generated by the GSGP. Product designers select the most preferred models in the Pareto set. The preferred nondominated CS model attempts to tradeoff unpopular and popular products, to determine optimal design attributes and maximize the CS. The case study shows that the proposed GSGP is able to generate CS models with more accurate CS predictions compared to the commonly used methods. The proposed GSGP also generates a Pareto set of nondominated CS models which equally learn consumer reviews for those dryers. Based on the Pareto set, the design team selects the most preferred CS model %K genetic algorithms, genetic programming, New product development, Social media, Online customer reviews, Imbalanced data mining, Multi-objective optimization %9 journal article %R 10.1016/j.engappai.2021.104442 %U https://www.sciencedirect.com/science/article/pii/S0952197621002906 %U http://dx.doi.org/10.1016/j.engappai.2021.104442 %P 104442 %0 Journal Article %T A genetic programming-based convolutional neural network for image quality evaluations %A Chan, Kit Yan %A Lam, Hak-Keung %A Jiang, Huimin %J Neural Computing and Applications %D 2022 %V 34 %N 18 %F chan:2022:NCaA %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s00521-022-07218-0 %U http://link.springer.com/article/10.1007/s00521-022-07218-0 %U http://dx.doi.org/10.1007/s00521-022-07218-0 %0 Journal Article %T Discovering multiple realistic TFBS motifs based on a generalized model %A Chan, Tak-Ming %A Li, Gang %A Leung, Kwong-Sak %A Lee, Kin-Hong %J BMC Bioinformatics 2009, 10:321 doi:10.1186/1471-2105-10-321 %D 2009 %V 10 %N 321 %F Chan:2009:BMCbi %X Background Identification of transcription factor binding sites (TFBSs) is a central problem in Bioinformatics on gene regulation. de novo motif discovery serves as a promising way to predict and better understand TFBSs for biological verifications. Real TFBSs of a motif may vary in their widths and their conservation degrees within a certain range. Deciding a single motif width by existing models may be biased and misleading. Additionally, multiple, possibly overlapping, candidate motifs are desired and necessary for biological verification in practice. However, current techniques either prohibit overlapping TFBSs or lack explicit control of different motifs. Results We propose a new generalised model to tackle the motif widths by considering and evaluating a width range of interest simultaneously, which should better address the width uncertainty. Moreover, a meta-convergence framework for genetic algorithms (GAs), is proposed to provide multiple overlapping optimal motifs simultaneously in an effective and flexible way. Users can easily specify the difference amongst expected motif kinds via similarity test. Incorporating Genetic Algorithm with Local Filtering (GALF) for searching, the new GALF-G (G for generalised) algorithm is proposed based on the generalized model and meta-convergence framework. Conclusion GALF-G was tested extensively on over 970 synthetic, real and benchmark datasets, and is usually better than the state-of-the-art methods. The range model shows an increase in sensitivity compared with the single-width ones, while providing competitive precisions on the E. coli benchmark. Effectiveness can be maintained even using a very small population, exhibiting very competitive efficiency. In discovering multiple overlapping motifs in a real liver-specific dataset, GALF-G outperforms MEME by up to 73percent in overall F-scores. GALF-G also helps to discover an additional motif which has probably not been annotated in the data set. http://www.cse.cuhk.edu.hk/ chan/GALFG/ web site %K genetic algorithms %9 journal article %R 10.1186/1471-2105-10-321 %U http://dx.doi.org/10.1186/1471-2105-10-321 %0 Journal Article %T On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems %A Chand, Shelvin %A Huynh, Quang %A Singh, Hemant %A Ray, Tapabrata %A Wagner, Markus %J Information Sciences %D 2018 %V 432 %@ 0020-0255 %F CHAND:2018:IS %X Resource constrained project scheduling is critical in logistic and planning operations across a range of industries. Most businesses rely on priority rules to determine the order in which the activities required for the project should be executed. However, the design of such rules is non-trivial. Even with significant knowledge and experience, human experts are understandably limited in terms of the possibilities they can consider. This paper introduces a genetic programming based hyper-heuristic (GPHH) for producing efficient priority rules targeting the resource constrained project scheduling problem (RCPSP). For performance analysis of the proposed approach, a series of experiments are conducted on the standard PSPLib instances with up to 120 activities. The evolved priority rules are then compared against the existing state-of-the-art priority rules to demonstrate the efficacy of our approach. The experimental results indicate that our GPHH is capable of producing reusable priority rules which significantly out-perform the best human designed priority rules %K genetic algorithms, genetic programming, Resource constrained project scheduling, Heuristic evolution, Evolutionary computation, Generation hyper-heuristics %9 journal article %R 10.1016/j.ins.2017.12.013 %U http://www.sciencedirect.com/science/article/pii/S0020025517311350 %U http://dx.doi.org/10.1016/j.ins.2017.12.013 %P 146-163 %0 Thesis %T Automated Design of Heuristics for the Resource Constrained Project Scheduling Problem %A Chand, Shelvin %D 2018 %8 June %C Australia %C Engineering & Information Technology, Australian Defence Force Academy, University of New South Wales %F Chand:thesis %X Classical project scheduling problem usually involves a set of non-preempt-able and precedence related activities that need to be scheduled. The resource constrained project scheduling problem (RCPSP) extends the classical project scheduling by taking into account constraints on the resources required to complete the activities. One particular approach for solving RCPSP instances is through the use of simple priority heuristics. A priority heuristic can be defined as a function which uses certain instance characteristics to construct a solution. Design of priority heuristics, however, is a non-trivial task. Usually, the process involves problem experts who extensively study instance characteristics in order to construct new heuristics. This approach can be time-consuming as well as being restrictive in terms of the possibilities that can be considered by an expert. As a result, researchers are increasingly exploring methods to automate construction of heuristics, commonly known as hyper-heuristics. Genetic programming based hyper heuristics (GPHH) are more commonly used for this task. GPHH operates on a set of problem attributes and mathematical operators to evolve heuristics. GPHHs have been used in a number of different domains such as job shop scheduling and routing. The same, however, can not be said about RCPSP, for which, the literature is relatively scant. The work presented in this thesis is directed towards addressing the aforementioned gap in the literature. Firstly, a GPHH is presented for evolving different types of priority heuristics for RCPSP. The effect of different representations and attributes are empirically evaluated and an attempt is made to evolve priority heuristics which can out-perform existing human designed priority heuristics. Next, a GPHH framework is proposed for evolving variants of the rollout-justification procedure in order to leverage the strength of this approach in discovering heuristics which can perform on par with state-of-the-art algorithmic methods. Finally, a dynamic variant of the classical RCPSP is formulated and a multi-objective GPHH is proposed for discovering priority heuristics, with strong performance and low complexity, to deal with dynamic instances. Apart from these major contributions, other improvements in GP and RCPSP are also proposed as part of the research undertaken during this PhD. %K genetic algorithms, genetic programming, Resource Constrained Project Scheduling, Optimization, Scheduling, Heuristics, Hyper-Heuristics %9 Ph.D. thesis %U http://unsworks.unsw.edu.au/fapi/datastream/unsworks:52846/SOURCE02?view=true %0 Journal Article %T Evolving heuristics for the resource constrained project scheduling problem with dynamic resource disruptions %A Chand, Shelvin %A Singh, Hemant %A Ray, Tapabrata %J Swarm and Evolutionary Computation %D 2019 %V 44 %@ 2210-6502 %F CHAND:2019:SEC %X Dynamic changes and disruptions are encountered frequently in the domain of project scheduling. The nature of these dynamic events often requires project managers to make quick decisions with regards to effectively re-scheduling the activities. Priority heuristics have a significant potential for such applications due to their simplicity, intuitiveness and low computational cost. In this research, we focus on automated evolution of priority heuristics using a genetic programming hyper-heuristic (GPHH). The proposed approach uses a multi-objective scheme (MO-GPHH) to evolve priority heuristics that can perform better than the existing rules, and at the same time have low complexity. Furthermore, unlike the existing works on evolving priority heuristics that focus on only static problems, this study covers both static and dynamic instances. The proposed approach is tested on a practical dynamic variant of the classical resource constrained project scheduling problem (RCPSP) in which the resource availability varies with time and knowledge about these changes and disruptions only become available as the project progresses. Extensive numerical experiments and benchmarking are performed to demonstrate the efficacy of the proposed approach %K genetic algorithms, genetic programming, Dynamic resource constrained project scheduling, Heuristic evolution, Evolutionary computation, Generation hyper-heuristics %9 journal article %R 10.1016/j.swevo.2018.09.007 %U http://www.sciencedirect.com/science/article/pii/S2210650217308325 %U http://dx.doi.org/10.1016/j.swevo.2018.09.007 %P 897-912 %0 Journal Article %T Evolving rollout-justification based heuristics for resource constrained project scheduling problems %A Chand, Shelvin %A Singh, Hemant %A Ray, Tapabrata %J Swarm and Evolutionary Computation %D 2019 %V 50 %@ 2210-6502 %F CHAND:2019:swarm %X Resource constrained project scheduling is critical in logistic and planning operations across a range of industries. An interesting heuristic for solving this problem is the Rollout-Justification (RJ) procedure. This procedure, which has conceptual similarities with dynamic programming, incrementally builds a solution by identifying the next activity to schedule based on the projections made using a guiding priority rule (heuristic) coupled with forward-backward local search. A critical component that affects the performance of RJ procedure is the guiding priority rule (or a set of rules). In this study, instead of using existing rules from literature, we aim to evolve new priority rules using genetic programming, and systematically investigate their use with the RJ procedure. Apart from evolving new rules, we also investigate new ways of integrating/using the rules within RJ procedure. To this end we consider the use of both forward and backward scheduling, independent and cohesive ensemble rule approaches, limited and unlimited number of function evaluations, among others. We use data from the project scheduling library (PSPLib) to train and test the evolved rules and their integration with RJ. A comprehensive set of numerical experiments are performed to benchmark the rules evolved using the proposed approach against a range of existing rules. The results demonstrate the competence and potential of the proposed approach, both in terms of accuracy and complexity %K genetic algorithms, genetic programming, Resource constrained project scheduling problem, Hyper-heuristics, Priority rules, Rollout, Justification %9 journal article %R 10.1016/j.swevo.2019.07.002 %U http://www.sciencedirect.com/science/article/pii/S2210650218309672 %U http://dx.doi.org/10.1016/j.swevo.2019.07.002 %P 100556 %0 Journal Article %T Environmental Adaption Method: A Heuristic Approach for Optimization %A Chandila, Anuj %A Tiwari, Shailesh %A Mishra, K. K. %A Punhani, Akash %J International Journal of Applied Metaheuristic Computing %D 2019 %V 10 %N 1 %@ 1947-8283 %F Chandila:2019:IJAMC %X This article describes how optimisation is a process of finding out the best solutions among all available solutions for a problem. Many randomized algorithms have been designed to identify optimal solutions in optimisation problems. Among these algorithms evolutionary programming, evolutionary strategy, genetic algorithm, particle swarm optimisation and genetic programming are widely accepted for the optimisation problems. Although a number of randomized algorithms are available in literature for solving optimisation problems yet their design objectives are same. Each algorithm has been designed to meet certain goals like minimising total number of fitness evaluations to capture nearly optimal solutions, to capture diverse optimal solutions in multimodal solutions when needed and also to avoid the local optimal solution in multi modal problems. This article discusses a novel optimisation algorithm named as Environmental Adaption Method (EAM) foable 3r solving the optimisation problems. EAM is designed to reduce the overall processing time for retrieving optimal solution of the problem, to improve the quality of solutions and particularly to avoid being trapped in local optima. The results of the proposed algorithm are compared with the latest version of existing algorithms such as particle swarm optimisation (PSO-TVAC), and differential evolution (SADE) on benchmark functions and the proposed algorithm proves its effectiveness over the existing algorithms in all the taken cases. %K genetic algorithms, genetic programming %9 journal article %R 10.4018/IJAMC.2019010107 %U http://dx.doi.org/10.4018/IJAMC.2019010107 %P Article:7 %0 Journal Article %T Particle Swarm Optimisation for Protein Motif Discovery %A Chang, Bill C. H. %A Ratnaweera, Asanga %A Halgamuge, Saman K. %A Watson, Harry C. %J Genetic Programming and Evolvable Machines %D 2004 %8 jun %V 5 %N 2 %@ 1389-2576 %F billchang:2004:GPEM %X a modified particle swarm optimisation algorithm is proposed for protein sequence motif discovery. Protein sequences are represented as a chain of symbols and a protein sequence motif is a short sequence that exists in most of the protein sequence families. Protein sequence symbols are converted into numbers using a one to one amino acid translation table. The simulation uses EGF protein and C2H2 Zinc Finger protein families obtained from the PROSITE database. Simulation results show that the modified particle swarm optimisation algorithm is effective in obtaining global optimum sequence patterns, achieving 96.9 and 99.5 classification accuracy respectively in EGF and C2H2 Zinc Finger protein families. A better true positive hit result is achieved when compared to the motifs published in PROSITE database. %K PSO, particle swarm optimisation, protein sequence motif, motif discovery, symbolic data optimisation, HPSO-TVAC %9 journal article %R 10.1023/B:GENP.0000023688.42515.92 %U http://dx.doi.org/10.1023/B:GENP.0000023688.42515.92 %P 203-214 %0 Thesis %T Dynamic Proportion Portfolio Insurance with Genetic Programming and Market Volatility Factors Analysis %A Chang, Chia-Lan %D 2005 %8 30 jun %C Taiwan %C National Central University, Jungli %F Chia-Lan.Chang:masters %X This thesis proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. It helps investor easily to understand how to allocate the capital among risky and risk-free assets and straightforward to implement. The risk multiplier in CPPI is predetermined by the investor’s view-point and fixed to the end of investment duration. However, since the market changes constantly, we think that the risk multiplier should change accordingly. When the market becomes volatile, the predetermined large risk multiplier will lead to loss of insurance and DPPI may solve this kind of problem. This research identifies factors relating to market volatility. These factors are built into equation trees by genetic programming. We collected five stocks of American companies’ financial data and the market information of New York Stock Exchange as input data feeding genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. Because the equation trees are all different, there is no method to analyse the factor contributions to the results of the risk multiplier. We use principal component analysis to see the effect of factors, and the experimental results show that among the market volatility factors, risk-free rate influences the variances of risk multiplier most. %K genetic algorithms, genetic programming, DPPI, CPPI, market volatility, principal component analysis, PCA %9 Masters thesis %U http://ir.lib.ncu.edu.tw/handle/987654321/13148 %0 Conference Proceedings %T Taylor Polynomial Enhancer Using Genetic Programming for Symbolic Regression %A Chang, Chi-Hsien %A Chiang, Tu-Chin %A Hsu, Tzu-Hao %A Chuang, Ting-Shuo %A Fang, Wen-Zhong %A Yu, Tian-Li %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F chang:2023:GECCOcomp %X Unlike most research of symbolic regression with genetic programming (GP) concerning black-box optimization, this paper focuses on the scenario where the underlying function is available, but due to limited computational resources or product imperfection, the function needs to be approximated with simplicity to fit measured data. Taylor polynomial (TP) is commonly used in such scenario; however, its performance drops drastically away from the expansion point. On the other hand, solely using GP does not use the knowledge of the underlying function, even though possibly inaccurate. This paper proposes using GP as a TP enhancer, namely TPE-GP, to combine the advantages from TP and GP. Specifically, TPE-GP uses infinite-order operators to compensate the power of TP with finite order. Empirically, on functions that are expressible by TP, TP outperformed both gplearn and TPE-GP as expected, while TPE-GP outperformed gplearn due to the use of TP. On functions that are not expressible by TP but expressible by the function set (FS), TPE-GP was competitive with gplearn while both outperformed TP. Finally, on functions that are not expressible by both TP and FS, TPE-GP outperformed both TP and gplearn, indicating the hybrid did achieve the synergy effect from TP and GP. %K genetic algorithms, genetic programming, symbolic regression, taylor polynomial: Poster %R 10.1145/3583133.3590591 %U http://dx.doi.org/10.1145/3583133.3590591 %P 543-546 %0 Conference Proceedings %T A Genetic Programming Based Scheme for Combining Image Operators %A Chang, Feng-Cheng %A Huang, Hsiang-Cheh %S Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2012) %D 2012 %8 18 20 jul %C Piraeus %F Chang:2012:IIH-MSP %X Sophisticated image processing is usually nonlinear and difficult to model. In addition to the conventional image processing tools, we need some alternatives to bridge the gap between low-level and semantic level computation. This paper presents an idea of image processing scheme. We transform an image into different representations; feed the representations to the proper cellular automaton (CA) components to produce the information images; use the information images as the inputs to the combination program; and finally get the processed result. To identify the needed transforms, the CA transition rules, and the combination expression, we adopt genetic programming (GP) and cellular programming (CP) to search for the configuration. The searched configuration separates the parallelisable and sequential parts of the program. We don’t enforce the linearity of the program, and it is likely that the searched result matches to the nonlinear nature of human semantics. %K genetic algorithms, genetic programming, cellular automata, image representation, search problems, transforms, CA transition rules, cellular automaton, cellular programming, combination expression, combination program, configuration search, image operators, image processing, image representation, image transformation, information images, low-level computation, parallelizable program parts, semantic level computation, sequential program parts, Image edge detection, Programming, Semantics, Training, cellular programming, image processing %R 10.1109/IIH-MSP.2012.58 %U http://dx.doi.org/10.1109/IIH-MSP.2012.58 %P 215-218 %0 Conference Proceedings %T Experiments on Genetic Programming Based Image Artefact Detection %A Chang, Feng-Cheng %A Huang, Hsiang-Cheh %S Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2013) %D 2013 %8 oct %F Chang:2013:IIH-MSP %X One of the interesting image processing applications is to detect and/or restore a damaged image. Because image damage would vary in different ways, a straightforward method is to use a program to represent the damage. Then, the type of artefact can be searched by applying programs to the original image and comparing with the target image. The run-time environment of a program is the structure of the execution resources. In this paper, we define a cellular automaton based structure as the run-time environment and use genetic programming (GP) to find the proper program for the given image artefacts. The results show that an effective GP engine requires careful configuration. The important lesson learnt from the experiments is also discussed. %K genetic algorithms, genetic programming %R 10.1109/IIH-MSP.2013.11 %U http://dx.doi.org/10.1109/IIH-MSP.2013.11 %P 9-12 %0 Conference Proceedings %T A Design of Genetic Programming Scheme with VLIW Concepts %A Chang, Feng-Cheng %A Huang, Hsiang-Cheh %S Advances in Intelligent Information Hiding and Multimedia Signal Processing %D 2017 %I Springer %F chang:2017:AIIHMSP %K genetic algorithms, genetic programming %R 10.1007/978-3-319-50212-0_37 %U http://link.springer.com/chapter/10.1007/978-3-319-50212-0_37 %U http://dx.doi.org/10.1007/978-3-319-50212-0_37 %0 Conference Proceedings %T Conditionals Support in Binary Expression Tree Based Genetic Programming %A Chang, Feng-Cheng %A Huang, Hsiang-Cheh %S 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech) %D 2022 %8 mar %F Chang:2022:LifeTech %X Inspired by the genetic algorithm (GA), the genetic programming (GP) was proposed for searching a program that fits a certain behavior. There are many aspects that distinguish GP from GA a lot, though GP concepts were originating from GA. In this paper, we focus on the representation scheme for a GP program. A GP program contains both operators and operands. Without proper encoding, the GP crossover and mutation are likely to produce invalid programs. Based on our previous design experiences, we proposed an alternative approach. It is a binary expression tree based representation with conditional behavior of each node. Therefore, the scheme supports unary, binary, and ternary operators. It also reduce the probability of producing invalid programs. A feature of the scheme is that conditional operators are first-class member because each evaluation embeds conditional processing. A few image-processing experiments were conducted to show the effectiveness of the design. The experimental results are also discussed %K genetic algorithms, genetic programming, Conferences, Life sciences, Encoding, binary expression tree, conditional expression, image processing %R 10.1109/LifeTech53646.2022.9754834 %U http://dx.doi.org/10.1109/LifeTech53646.2022.9754834 %P 310-313 %0 Conference Proceedings %T Exploring Genetic Programming in Image Processing: Challenges and Enhanced Techniques %A Chang, Feng-Cheng %A Huang, Hsiang-Cheh %S 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM) %D 2025 %8 jan %F Chang:2025:IMCOM %X In recent years, the advancement of AI has been primarily driven by neural networks, which, despite their success, pose challenges in terms of explainability and high-power consumption. Genetic Programming (GP) offers an interpretable alternative, although its complexity has limited its practical application. This paper explores the potential of GP through experiments on a simple image filtering task, aiming to understand its properties and limitations. We also investigate the integration of transformer concepts into the GP process. Preliminary results suggest that while GP faces convergence challenges, the introduction of symbolic transformers may enhance its effectiveness in image processing tasks. These findings open up new possibilities for optimising GP in future applications. %K genetic algorithms, genetic programming, Training, Refining, Neural networks, Transformers, Image filtering, Information management, Reliability, Optimisation, Convergence, symbolic transformer %R 10.1109/IMCOM64595.2025.10857586 %U http://dx.doi.org/10.1109/IMCOM64595.2025.10857586 %0 Journal Article %T Load Identification of Non-intrusive Load-monitoring System in Smart Home %A Chang, Hsueh-Hsien %J WSEAS Transactions on Systems %D 2010 %8 jan %V 9 %@ 1109-2777 %G en %F Chang:2010:WSEAS %X In response to the governmental policy of saving energy sources and reducing CO2, and carry out the resident quality of local; this paper proposes a new method for a non-intrusive load-monitoring (NILM) system in smart home to implement the load identification of electric equipments and establish the electric demand management. Non-intrusive load-monitoring techniques were often based on power signatures in the past, these techniques are necessary to be improved for the results of reliability and accuracy of recognition. By using neural network (NN) in combination with genetic programming (GP) and turn-on transient energy analysis, this study attempts to identify load demands and improve recognition accuracy of non-intrusive load-monitoring results. The turn-on transient energy signature can improve the efficiency of load identification and computational time under multiple operations. %K genetic algorithms, genetic programming, load identification, artificial neural networks, non-intrusive load monitoring, turn-on transient energy analysis, smart home %9 journal article %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.455.3952 %0 Conference Proceedings %T A New Method for Load Identification of Nonintrusive Energy Management System in Smart Home %A Chang, Hsueh-Hsien %A Lin, Ching-Lung %S 2010 IEEE 7th International Conference on e-Business Engineering (ICEBE) %D 2010 %8 October 12 nov %F Chang:2010:ICEBE %X In response to the governmental policy of saving energy sources and reducing CO2, and carry out the resident quality of local; this paper proposes a new method for a non-intrusive energy management (NIEM) system in smart home to implement the load identification of electric equipments and establish the electric demand management. Non-intrusive energy management techniques were often based on power signatures in the past, these techniques are necessary to be improved for the results of reliability and accuracy of recognition. By using neural network (NN) in combination with genetic programming (GP) and turn-on transient energy analysis, this study attempts to identify load demands and improve recognition accuracy of non-intrusive energy-managing results. The turn-on transient energy signature can improve the efficiency of load identification and computational time under multiple operations. %K genetic algorithms, genetic programming, GP, NIEM system, electric demand management, electric equipments, energy sources, governmental policy, load demands, load identification, neural network, non-intrusive energy management system, non-intrusive energy management techniques, non-intrusive energy-managing results, nonintrusive energy management system, power signatures, recognition accuracy, smart home, turn-on transient energy analysis, turn-on transient energy signature, demand side management, home automation, neural nets, power engineering computing, power system transients %R 10.1109/ICEBE.2010.24 %U http://dx.doi.org/10.1109/ICEBE.2010.24 %P 351-357 %0 Conference Proceedings %T Rutting Prediction Model Developed by Genetic Programming Method Through Full Scale Accelerated Pavement Testing %A Chang, Jia-Ruey %A Chen, Shun-Hsing %A Chen, Dar-Hao %A Liu, Yao-Bin %S Fourth International Conference on Natural Computation, ICNC ’08 %D 2008 %8 oct %V 6 %F Chang:2008:ICNC %X The application of genetic programming (GP) to pavement performance evaluation is relatively new. This paper both describes and demonstrates how to develop a model to predict the pavement rutting by using GP method. Results from closely controlled full-scale Accelerated Pavement Testing (APT) - 7 test pavements (264 records) from CRREL’s HVS and 1 test pavement (8 records) from TxDOT’s MLS - were employed to establish a rutting prediction model. For model evaluation purposes, additional test pavements (94 records) from both CRREL’s HVS and TxDOT’s MLS were used. GP was applied successfully to develop a rutting prediction model that uses wheel load, load repetitions and the pavement Structural Number (SN) as inputs. The overall R2 for 272 records is 0.8140. The model and algorithms proposed in this study provide a good foundation for further refinement when additional data is available. %K genetic algorithms, genetic programming, accelerated pavement testing, load repetitions, model evaluation, pavement performance evaluation, pavement rutting, pavement structural number, rutting prediction model, test pavements, wheel load, structural engineering computing %R 10.1109/ICNC.2008.673 %U http://dx.doi.org/10.1109/ICNC.2008.673 %P 326-330 %0 Conference Proceedings %T Pavement maintenance and rehabilitation decisions derived by genetic programming %A Chang, Jia-Ruey %A Chao, Sao-Jeng %S Sixth International Conference on Natural Computation (ICNC), 2010 %D 2010 %8 October 12 aug %V 5 %C Yantai, Shandong, China %F Chang:2010:ICNC %X The application of genetic programming (GP) to pavement performance evaluation is relatively new. GP was first proposed by John R. Koza as an evolutionary computation technique: a stochastic search method based on the Darwinian principle of ‘survival of the fittest’, whereby intelligible relationships in a system are automatically extracted and used to generate mathematical expressions or ‘programs’. Nowadays, GP has been used as an important problem-solving method for function fitting and classification. In this paper, an empirical study is performed to develop a pavement maintenance and rehabilitation (M and R) decision model by using GP. As part of the research, experienced pavement engineers from the Taiwan Highway Bureau (THB) conducted pavement distress surveys on seven county roads. For each road section, the severity and coverage of existing distresses that required M and R treatments were separately identified and collated into an analytical database containing 2,340 records. These records were then used to train, validate, and apply the M and R decision model. The finding shows that the total accuracy of the evolved M and R decision model was 0.903, 0.877, and 0.878 for the training, validation, and application data set, respectively. It proves that the GP-based M and R decision model process makes the pavement knowledge extraction process more systematic, easier to use and solvable with a higher probability of success - even for complex M and R decision problems. %K genetic algorithms, genetic programming, Darwinian principle, GP-based M amp, R decision model, Taiwan highway bureau, evolutionary computation technique, pavement distress surveys, pavement knowledge extraction process, pavement maintenance, pavement performance evaluation, problem-solving method, rehabilitation decisions, stochastic search method, maintenance engineering, road building, search problems, stochastic processes %R 10.1109/ICNC.2010.5583502 %U http://dx.doi.org/10.1109/ICNC.2010.5583502 %P 2439-2443 %0 Journal Article %T Nonlinear vibrations and critical angular velocity information of the high-speed rotating eccentric disk made of Gori-metamaterials: Introducing data-driven solution for solving the nonlinear problems %A Chang, Lei %A Khademi, Kia %A Sharaf, Mohamed %J Thin-Walled Structures %D 2024 %V 202 %@ 0263-8231 %F Chang:2024:tws %X Eccentric discs in rotational motion are commonly used in various technical fields, including gas turbine engines, flywheels, gears, and brakes. So, improving its critical angular velocity and frequency characteristics is a challenging issue for engineers. So, in this work for the first time, nonlinear vibrations and buckling analysis of the high-speed rotating eccentric disks using mathematical simulation and data-driven solutions are presented. One of the suggestions for improving its mechanical properties is considering metamaterials in the construction of the eccentric disk. Metamaterial is a novel synthetic material that has distinctive physical and mechanical capabilities that are unattainable in natural materials due to its well-designed structure. The properties of the eccentric disks are controlled by the amount of graphene and the degree of folding of graphene origami (GOri) across the thickness of the eccentric disks. These properties, such as Poisson’s ratio, vary depending on the position and can be estimated using micromechanical models assisted by genetic programming (GP). Using von-Karman nonlinearity, transformed differential quadrature method (TDQM), and Newton’s method the nonlinear governing equations are obtained and solved, respectively. The results show that when the radius ratio of the rotating eccentric disk increases by 8percent, the critical point is decreased from 420 HZ to 370 HZ, a reduction of around 12 percent. Another suggestion for improving its mechanical properties is controlling the geometrics and physics of the presented structure according to the designer’s purposes %K genetic algorithms, genetic programming, Rotating eccentric disk, Nonlinear vibrations, Critical angular velocity, Origami metamaterial, Data-driven solution %9 journal article %R 10.1016/j.tws.2024.112077 %U https://www.sciencedirect.com/science/article/pii/S0263823124005202 %U http://dx.doi.org/10.1016/j.tws.2024.112077 %P 112077 %0 Book Section %T Swarm and Evolutionary Intelligence %A Chang, Mark %B Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare %D 2020 %I Chapman and Hall/CRC %C New York %F Chang:2020:AIdrug %X ... Genetic programming is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. The idea of genetic programming is to evolve a population of computer programs. Genetic programming does not have to be for mathematical function fitting. Evolutionary algorithms include genetic algorithms and genetic programming. Genetic programming and genetic algorithms are very similar in that they both use evolution by comparing the fitness of each candidate in a population of potential candidates over many generations. Swarm intelligence and evolutionary artificial intelligence have found applications in drug discovery and development and in artificial general intelligence. %K genetic algorithms, genetic programming %R 10.1201/9780429345159 %U https://www.taylorfrancis.com/books/mono/10.1201/9780429345159/artificial-intelligence-drug-development-precision-medicine-healthcare-mark-chang %U http://dx.doi.org/10.1201/9780429345159 %0 Conference Proceedings %T Comparative Data Fusion between Genetic Programing and Neural Network Models for Remote Sensing Images of Water Quality Monitoring %A Chang, Ni-Bin %A Vannah, Benjamin %S IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013) %D 2013 %8 oct %F Chang:2013:SMC %X Historically, algal blooms have proliferated throughout Western Lake Erie as a result of eutrophic conditions caused by urban growth and agricultural activities. Of great concern is the blue-green algae Microcystis that thrives in eutrophic conditions and generates microcystin, a powerful hepatotoxin. Microcystin poses a threat to the delicate ecosystem of Lake Erie, and it threatens commercial fishing operations and water treatment plants using the lake as a water source. Integrated Data Fusion and Machine-learning (IDFM) is an early warning system proposed by this paper for the prediction of microcystin concentrations and distribution by measuring the surface reflectance of the water body using satellite sensors. The fine spatial resolution of Landsat is fused with the high temporal resolution of MODIS to create a synthetic image possessing both high temporal and spatial resolution. As a demonstration, the spatiotemporal distribution of microcystin within western Lake Erie is reconstructed using the band data from the fused products and applied machine-learning techniques. The performance of Artificial Neural Networks (ANN) and Genetic Programming (GP) are compared and tested against traditional two-band model regression techniques. It was found that the GP model performed slightly better at predicting microcystin with an R-squared value of 0.6020 compared to 0.5277 for ANN. %K genetic algorithms, genetic programming, Data fusion, machine-learning, remote sensing, surface reflectance, microcystin, harmful algal bloom %R 10.1109/SMC.2013.182 %U http://dx.doi.org/10.1109/SMC.2013.182 %P 1046-1051 %0 Conference Proceedings %T Intercomparisons between empirical models with data fusion techniques for monitoring water quality in a large lake %A Chang, Ni-Bin %A Vannah, Benjamin %S 10th IEEE International Conference on Networking, Sensing and Control (ICNSC 2013) %D 2013 %8 apr %F Chang:2013:ieeeICNSCerie %X Lake Erie has a history of algal blooms, due to eutrophic conditions attributed to urban and agricultural activities. Blue-green algae or cyanobacteria thrive in these eutrophic conditions, since they require little energy for cell maintenance and growth. Microcystis are a type of blue-green algae of particular concern, because they produce microcystin, a potent hepatotoxin. Microcystin not only presents a threat to the ecosystem, but it threatens commercial fishing operations and water treatment plants using the lake as a water source. In this paper, we have proposed an early warning system using Integrated Data Fusion and Machine-learning (IDFM) techniques to determine microcystin concentrations and distribution by measuring the surface reflectance of the water body using satellite sensors. The fine spatial resolution of Landsat is fused with the high temporal resolution of MODIS to create a synthetic image possessing both high temporal and spatial resolution. As a demonstration, the spatiotemporal distribution of microcystin within western Lake Erie is reconstructed using the band data from the fused products and applied machine-learning techniques. Analysis of the results through statistical indices confirmed that the Genetic Programming (GP) model has potential accurately estimating microcystin concentrations in the lake (R2 = 0.5699). %K genetic algorithms, genetic programming, environmental science computing, geophysical image processing, image fusion, image resolution, lakes, learning (artificial intelligence), microorganisms, statistical analysis, water pollution control, water quality, GP model, IDFM technique, Lake Erie, Landsat, blue-green algae, cell growth, cell maintenance, cyanobacteria, data fusion technique, eutrophic condition, hepatotoxin, machine learning technique, microcystin, spatial resolution, statistical index, synthetic image possessing, temporal resolution, water quality monitoring, Data fusion, harmful algal bloom, machine-learning, microcystin, remote sensing, surface reflectance %R 10.1109/ICNSC.2013.6548747 %U http://dx.doi.org/10.1109/ICNSC.2013.6548747 %P 258-263 %0 Conference Proceedings %T Monitoring nutrient concentrations in Tampa Bay with MODIS images and machine learning models %A Chang, Ni-Bin %A Xuan, Zhemin %S 10th IEEE International Conference on Networking, Sensing and Control (ICNSC 2013) %D 2013 %8 apr %F Chang:2013:ieeeICNSCtampa %X This paper explores the spatiotemporal nutrient patterns in Tampa Bay, Florida with the aid of Moderate Resolution Imaging Spectroradiometer (MODIS) images and Genetic Programming (GP) models that are designed to link Total Phosphorus (TP) levels and remote sensing reflectance bands in aquatic environments. In-situ data were drawn from a local database to support the calibration and validation of the GP model. The GP models show the effective capacity to demonstrating the snapshots of spatio-temporal distributions of TP across the Bay, which helps to delineate the short-term seasonality effect and the global trend of TP in the coastal bay. The model output can provide informative reference for the establishment of contingency plans in treating nutrients-rich runoff. %K genetic algorithms, genetic programming, environmental science computing, geophysical image processing, learning (artificial intelligence), phosphorus, remote sensing, water treatment, GP model, MODIS image, TP, Tampa Bay, aquatic environment, coastal bay, machine learning model, moderate resolution imaging spectroradiometer, nutrient concentration monitoring, remote sensing reflectance band, short-term seasonality effect, total phosphorus, MODIS, Remote sensing, coastal bay, nutrient monitoring, wastewater treatment %R 10.1109/ICNSC.2013.6548824 %U http://dx.doi.org/10.1109/ICNSC.2013.6548824 %P 702-707 %0 Journal Article %T Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with MODIS images and machine learning models %A Chang, Ni-Bin %A Xuan, Zhemin %A Yang, Y. Jeffrey %J Remote Sensing of Environment %D 2013 %V 134 %@ 0034-4257 %F Chang:2013:RSE %X This paper explores the spatiotemporal patterns of total phosphorus (TP) in Tampa Bay (Bay), Florida, with the aid of Moderate Resolution Imaging Spectroradiometer (MODIS) images and genetic programming (GP) models. The study was designed to link TP concentrations with relevant water quality parameters and remote sensing reflectance bands in aquatic environments using in-situ data from a local database to support the calibration and validation of the GP model. The GP models show the effective capacity to demonstrate snapshots of spatiotemporal distributions of TP across the Bay, which helps to delineate the short-term seasonality effects and the decadal trends of TP in an environmentally sensitive coastal bay area. In the past decade, urban development and agricultural activities in the Bay area have substantially increased the use of fertilisers. Landfall hurricanes, including Frances and Jeanne in 2004 and Wilma in 2005, followed by continuous droughts from 2006 to 2008 in South Florida, made the Bay area an ideal place for a remote sensing impact assessment. A changing hydrological cycle, triggered by climate variations, exhibited unique regional patterns of varying TP waste loads into the Bay over different time scales ranging from seasons to years. With the aid of the derived GP model in this study, we were able to explore these multiple spatiotemporal distributions of TP concentrations in the Tampa Bay area aquatic environment and to elucidate these coupled dynamic impacts induced by both natural hazards and anthropogenic perturbations. This advancement enables us to identify the hot moments and hot spots of TP concentrations in the Tampa Bay region. %K genetic algorithms, genetic programming, Remote sensing, Coastal bay, Nutrient monitoring, MODIS %9 journal article %R 10.1016/j.rse.2013.03.002 %U http://www.sciencedirect.com/science/article/pii/S0034425713000746 %U http://dx.doi.org/10.1016/j.rse.2013.03.002 %P 100-110 %0 Journal Article %T Comparative Sensor Fusion Between Hyperspectral and Multispectral Satellite Sensors for Monitoring Microcystin Distribution in Lake Erie %A Chang, Ni-Bin %A Vannah, Benjamin %A Yang, Y. Jeffrey %J IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing %D 2014 %8 jun %V 7 %N 6 %@ 1939-1404 %F Chang:2014:ieeeSTAEORS %X Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophication in the water body. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. The hepatotoxin microcystin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This study demonstrates the prototype of a near real-time early warning system using integrated data fusion and mining (IDFM) techniques with the aid of both hyperspectral (MERIS) and multispectral (MODIS and Landsat) satellite sensors to determine spatiotemporal microcystin concentrations in Lake Erie. In the proposed IDFM, the MODIS images with high temporal resolution are fused with the MERIS and Landsat images with higher spatial resolution to create synthetic images on a daily basis. The spatiotemporal distributions of microcystin within western Lake Erie were then reconstructed using the band data from the fused products with machine learning or data mining techniques such as genetic programming (GP) models. The performance of the data mining models derived using fused hyperspectral and fused multispectral sensor data are quantified using four statistical indices. These data mining models were further compared with traditional two-band models in terms of microcystin prediction accuracy. This study confirmed that GP models outperformed traditional two-band models, and additional spectral reflectance data offered by hyperspectral sensors produces a noticeable increase in the prediction accuracy especially in the range of low microcystin concentrations. %K genetic algorithms, genetic programming, Harmful algal bloom, image fusion, machine learning, microcystin, remote sensing %9 journal article %R 10.1109/JSTARS.2014.2329913 %U http://dx.doi.org/10.1109/JSTARS.2014.2329913 %P 2426-2442 %0 Conference Proceedings %T Improving the control of water treatment plant with remote sensing-based water quality forecasting model %A Chang, N. B. %A Imen, S. %S 12th IEEE International Conference on Networking, Sensing and Control (ICNSC) %D 2015 %8 apr %F Chang:2015:ICNSC %X When Total Organic Carbon (TOC) in the source water is in contact with disinfectants in a drinking water treatment process, it often times causes the formation of disinfection by-products such as Trihalomethanes which have harmful effects on human health. As a result of the potential health risk of Trihalomethanes for drinking water, proper monitoring and forecasting of high TOC episodes in the source water body can be helpful for the operators who are in charge of the decisions when they have to start the removal procedures for TOC in surface water treatment plants. This issue is of great importance in Lake Mead in the United States which provides drinking water for 25 million people, while it is considered as an important recreational area and wildlife habitat as well. In this study, artificial neural network, extreme learning machine, and genetic programming are examined using the long-term observations of TOC concentration throughout the lake. Among these models, the model with the best performance was applied in the development of a forecasting model to predict TOC values on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory of the past states with nonlinear autoregressive neural network with external input (NARXNET) on a rolling basis onwards. The best input scenario of NARXNET was selected with respect to several statistical indices. Numerical outputs of the forecasting process confirm the fidelity of the iterative scheme in predicting water quality status one day ahead of the time. %K genetic algorithms, genetic programming %R 10.1109/ICNSC.2015.7116009 %U http://dx.doi.org/10.1109/ICNSC.2015.7116009 %P 51-57 %0 Journal Article %T Diagnosis of the artificial intelligence-based predictions of flow regime in a constructed wetland for stormwater pollution control %A Chang, Ni-Bin %A Mohiuddin, Golam %A Crawford, A. James %A Bai, Kaixu %A Jin, Kang-Ren %J Ecological Informatics %D 2015 %V 28 %@ 1574-9541 %F Chang:2015:EI %X Monitoring the velocity field and stage variations in heterogeneous aquatic environments, such as constructed wetlands, is critical for understanding hydrodynamic patterns, nutrient removal capacity, and hydrographic impact on the wetland ecosystem. Obtaining low velocity measurements representative of the entire wetland system may be challenging, expensive, and even infeasible in some cases. Data-driven modelling techniques in the computational intelligence regime may provide fast predictions of the velocity field based on a handful of local measurements. They can be a convenient tool to visualize the general spatial and temporal distribution of flow magnitude and direction with reasonable accuracy in case regular hydraulic models suffer from insufficient baseline information and longer run time. In this paper, a comparison between two types of bio-inspired computational intelligence models including genetic programming (GP) and artificial neural network (ANN) models was implemented to estimate the velocity field within a constructed wetland (i.e., the Storm-water Treatment Area in South Florida) in the Everglades, Florida. Two different ANN-based models, including back propagation algorithm and extreme learning machine, were used. Model calibration and validation were driven by data collected from a local sensor network of Acoustic Doppler Velocimeters (ADVs) and weather stations. In general, the two ANN-based models outperformed the GP model in terms of several indices. Findings may improve the design and operation strategies for similar wetland systems. %K genetic algorithms, genetic programming, Constructed wetland, Stormwater Management, Artificial neural network, Velocity Flow Field, Acoustic Doppler Velocimeter %9 journal article %R 10.1016/j.ecoinf.2015.05.001 %U http://www.sciencedirect.com/science/article/pii/S1574954115000795 %U http://dx.doi.org/10.1016/j.ecoinf.2015.05.001 %P 42-60 %0 Journal Article %T Automated synthesis of passive filter circuits including parasitic effects by genetic programming %A Chang, Shoou-Jinn %A Hou, Hao-Sheng %A Su, Yan-Kuin %J Microelectronics Journal %D 2006 %8 aug %V 37 %N 8 %F Chang:2006:mej %X In this paper, we propose a genetic programming method to synthesise passive filter circuits including parasitic effects, which are very common in high-frequency application. This approach allows circuit topology and component values to be evolved simultaneously; therefore, novel circuits different from those generated by traditional methods can be explored. Experimental results show the proposed method can effectively generate not only compliant but also efficient solutions of such problems where the traditional approaches fail. %K genetic algorithms, genetic programming, Parasitic effects, Passive filter synthesis %9 journal article %R 10.1016/j.mejo.2005.12.012 %U http://dx.doi.org/10.1016/j.mejo.2005.12.012 %P 792-799 %0 Journal Article %T Nonlinear model for ECG R-R interval variation using genetic programming approach %A Chang, Yun Seok %A Park, Kwang Suk %A Kim, Bo Yeon %J Future Generation Computer Systems %D 2005 %8 jul %V 21 %N 7 %F Chang:2005:FGCS %X We propose a nonlinear system modelling method, which predicts characteristics of the ECG R-R interval variation. For determining model equation, we adopted a genetic programming method in which the chromosome represents the model equation consisting of time-delayed variables, constants, and four arithmetic operators, and determines the fitness function. By genetic programming, sequences of regressive nonlinear equations are produced and evolved until the finding of the optimal model equation, which could simulate the spectral, statistical and nonlinear behaviour of the given R-R interval dynamics. Experimental results showed that the evolutionary approach could find the equation which simulates the spectral and chaotic dynamics of the given signal. Therefore, the proposed evolutionary approach is useful for the system identification of the nonlinear biological system. %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.future.2004.03.011 %U http://www.sciencedirect.com/science/article/B6V06-4CVX0RT-1/2/111fea795562435e39023c448749d96a %U http://dx.doi.org/10.1016/j.future.2004.03.011 %P 1117-1123 %0 Journal Article %T Automated passive filter synthesis using a novel tree representation and genetic programming %A Chang, Shoou-Jinn %A Hou, Hao-Sheng %A Su, Yan-Kuin %J IEEE Transactions on Evolutionary Computation %D 2006 %8 feb %V 10 %N 1 %@ 1089-778X %F CHS06 %X This paper proposes a novel tree representation which is suitable for the analysis of RLC (i.e., resistor, inductor, and capacitor) circuits. Genetic programming (GP) based on the tree representation is applied to passive filter synthesis problems. The GP is optimised and then incorporated into an algorithm which can automatically find parsimonious solutions without predetermining the number of the required circuit components. The experimental results show the proposed method is efficient in three aspects. First, the GP-evolved circuits are more parsimonious than those resulting from traditional design methods in many cases. Second, the proposed method is faster than previous work and can effectively generate parsimonious filters of very high order where conventional methods fail. Third, when the component values are restricted to a set of preferred values, the GP method can generate compliant solutions by means of novel circuit topology. %K genetic algorithms, genetic programming, RLC circuits, circuit optimisation, network topology, passive filters, GP-evolved circuits, RLC circuit analysis, automated passive filter synthesis, circuit topology, tree representation, Circuit analysis, circuit representation, passive filter synthesis %9 journal article %R 10.1109/TEVC.2005.861415 %U http://dx.doi.org/10.1109/TEVC.2005.861415 %P 93-100 %0 Conference Proceedings %T Prediction of dissolved gas Content in transformer oil based on Genetic Programming and DGA %A Chang, Wei %A Hao, Ning %S International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE 2011) %D 2011 %8 16 18 dec %C Changchun, China %F WeiChang:2011:TMEE %X Genetic Programming (GP), which is suitable for prediction, is combined with transformer oil dissolved gas analysis (DGA), and also a method of the prediction of dissolved gas Content in transformer oil based on GP classification algorithm is proposed, so as to predicting the operational status and the latent faults of a power transformer effectively. The comparative results show that GP model can improve the prediction accuracy effectively. %K genetic algorithms, genetic programming, DGA, GP classification algorithm, dissolved gas content prediction, power transformer, transformer oil dissolved gas analysis, chemical analysis, power transformer insulation, transformer oil %R 10.1109/TMEE.2011.6199404 %U http://dx.doi.org/10.1109/TMEE.2011.6199404 %P 1133-1136 %0 Thesis %T The Evolutionary Emergence route to Artificial Intelligence %A Channon, Alastair D. %D 1996 %C UK %C School of Cognitive and Computing Sciences, University of Sussex %F Channon:masters %X The artificial evolution of intelligence is discussed with respect to current methods. An argument for withdrawal of the traditional fitness function in genetic algorithms is given on the grounds that this would better enable the emergence of intelligence, necessary because we cannot specify what intelligence is. A modular developmental system is constructed to aid the evolution of neural structures and a simple virtual world with many of the properties believed beneficial is set up to test these ideas. Resulting emergent properties are given, along with a brief discussion. %K genetic algorithms, genetic programming, Artificial Intelligence, Emergence, Artificial Life, Neural Networks, Development, Modularity, Fractals, Lindenmayer Systems, Recurrence %9 Masters thesis %U http://www.channon.net/alastair/msc/adc_msc.pdf %0 Unpublished Work %T The Artificial Evolution of Real Intelligence by Natural Selection %A Channon, Alastair %A Damper, Bob %D 1997 %C Brighton, UK %F ChaDam97 %O Published on the web site of and poster presented at the Fourth European Conference on Artificial Life (ECAL97), Brighton %X This paper outlines a preliminary step towards the long-term goal of intelligent artificial life. Evolutionary emergence via natural selection is proposed as the way forward, in combination with other biologically-inspired principles including the developmental modularity of neural networks. In order to develop and test the ideas, an artificial world containing autonomous organisms has been created. Its underlying theory and construction are described. Resulting emergent strategies are reported both from an observer’s perspective and in terms of their neural mechanisms. The results prove that the proposed approach is viable and show it to be an exciting area for further research. %K genetic algorithms, genetic programming %9 unpublished %U http://www.channon.net/alastair/geb/ecal1997/channon_ad_ecal97.pdf %0 Conference Proceedings %T Evolving Novel Behaviors via Natural Selection %A Channon, A. D. %A Damper, R. I. %Y Adami, Christoph %Y Belew, Richard K. %Y Kitano, Hiroaki %Y Taylor, Charles %S Proceedings of the 6th International Conference on Artificial Life (ALIFE-98) %D 1998 %8 jun 27–29 %I MIT Press %C Cambridge, MA, USA %@ 0-262-51099-5 %F ALIFE98*384 %K genetic algorithms, genetic programming, natural selection %U http://www.channon.net/alastair/geb/alife6/channon_ad_alife6.pdf %P 384-388 %0 Conference Proceedings %T Perpetuating evolutionary emergence %A Channon, A. D. %A Damper, R. I. %Y Pfeifer, Rolf %Y Blumberg, Bruce %Y Meyer, Jean-Arcady %Y Wilson, Stewart W. %S From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior %D 1998 %8 aug 17 21 %I MIT Press %C Zurich, Switzerland %@ 0-262-66144-6 %F Channon_sab98 %X Perpetuating evolutionary emergence is the key to artificially evolving increasingly complex systems. In order to generate complex entities with adaptive behaviours beyond our manual design capability, long term incremental evolution with continuing emergence is called for. Purely artificial selection models, such as traditional genetic algorithms, are argued to be fundamentally inadequate for this calling and existing natural selection systems are evaluated. Thus some requirements for perpetuating evolutionary emergence are revealed. A new environment containing simple virtual autonomous organisms has been created to satisfy these requirements. Resulting evolutionary emergent behaviors are reported alongside of their neural correlates. In one example, the collective behaviour of one species clearly provides a selective force which is overcome by another species, demonstrating the perpetuation of evolutionary emergence via naturally arising coevolution. %K genetic algorithms, genetic programming, natural selection %R 10.7551/mitpress/3119.001.0001 %U http://www.channon.net/alastair/geb/sab98/channon_ad_sab98_nc.pdf %U http://dx.doi.org/10.7551/mitpress/3119.001.0001 %P 534-539 %0 Journal Article %T Towards the evolutionary emergence of increasingly complex advantageous behaviours %A Channon, A. D. %A Damper, R. I. %J International Journal of Systems Science %D 2000 %V 31 %N 7 %@ 0020-7721 %F ChaDam00 %O Special issue on Emergent Properties of Complex Systems %X The generation of complex entities with advantageous behaviours beyond our manual design capability requires long-term incremental evolution with continuing emergence. In this paper, we argue that artificial selection models, such as traditional genetic algorithms, are fundamentally inadequate for this goal. Existing natural selection systems are evaluated, revealing both significant achievements and pitfalls. Thus, some requirements for the perpetuation of evolutionary emergence are established. An (artificial) environment containing simple virtual autonomous organisms with neural controllers has been created to satisfy these requirements and to aid in the development of an accompanying theory of evolutionary emergence. Resulting behaviours are reported alongside their neural correlates. In a particular example, the collective behaviour of one species provides a selective force which is overcome by another species, demonstrating the incremental evolutionary emergence of advantageous behaviours via naturally arising coevolution. While the results fall short of the ultimate goal, experience with the system has provided some useful lessons for the perpetuation of emergence towards increasingly complex advantageous behaviours. %K genetic algorithms, genetic programming, geb %9 journal article %R 10.1080/002077200406570 %U http://www.channon.net/alastair/geb/ijssepcs/channon_ad_ijssepcs.pdf %U http://dx.doi.org/10.1080/002077200406570 %P 843-860 %0 Thesis %T Evolutionary Emergence: The Struggle for Existence in Artificial Biota %A Channon, Alastair %D 2001 %8 nov %C UK %C University of Southampton %F channon_ad_phdthesis %X The generation of complex entities with advantageous behaviours beyond our manual design capability requires long-term incremental evolution with continuing emergence. This thesis presents the argument that artificial selection models, such as traditional genetic algorithms, are fundamentally inadequate for this goal. Existing natural selection systems are evaluated, revealing both significant achievements and pitfalls. Thus, some requirements for the perpetuation of evolutionary emergence are established. An (artificial) environment containing simple virtual autonomous organisms with neural controllers has been created to satisfy these requirements and to aid in the development of an accompanying theory of evolutionary emergence. Resulting behaviours are reported alongside their neural correlates. In one example, the collective behaviour of one species provides a selective force which is overcome by another species, demonstrating the incremental evolutionary emergence of advantageous behaviours via naturally-arising coevolution. Further behavioural or neural analysis is infeasible in this environment, so evolutionary statistical methods are employed and extended in order to classify the evolutionary dynamics. This qualitative analysis indicates that evolution is unbounded in the system. As well as validating the theory behind it, work with the system has provided some useful lessons and directions towards the evolution of increasingly complex advantageous behaviours. %K genetic algorithms, genetic programming, natural selection %9 Ph.D. thesis %U http://www.channon.net/alastair/geb/phdthesis/channon_ad_phdthesis.pdf %0 Conference Proceedings %T Passing the ALife Test: Activity Statistics Classify Evolution in Geb as Unbounded %A Channon, Alastair %Y Kelemen, Jozef %Y Sosik, Petr %S Advances in Artificial Life: Proceedings of the Sixth European Conference on Artificial Life (ECAL2001) %S Lecture Notes in Computer Science %D 2001 %V 2159 %I Springer-Verlag %F Channon:2001:PAT %X Bedau and Packard’s evolutionary activity statistics [1,2] are used to classify the evolutionary dynamics in Geb [3,4], a system designed to verify and extend theories behind the generation of evolutionary emergent systems. The result is that, according to these statistics, Geb exhibits unbounded evolutionary activity, making it the first autonomous artificial system to pass this test. However, having passed it, the most prudent course of action is to look for weaknesses in the test. Two weaknesses are identified and approaches for overcoming them are proposed. %K genetic algorithms, genetic programming, natural selection %R 10.1007/3-540-44811-X_45 %U http://www.channon.net/alastair/geb/ecal2001/channon_ad_ecal2001.pdf %U http://dx.doi.org/10.1007/3-540-44811-X_45 %P 417-426 %0 Conference Proceedings %T Improving and still passing the ALife test: Component-normalised activity statistics classify evolution in Geb as unbounded %A Channon, Alastair %Y Standish, Russell K. %Y Bedau, Mark A. %Y Abbass, Hussein A. %S Proceedings of Artificial Life VIII, the 8th International Conference on the Simulation and Synthesis of Living Systems %D 2002 %8 September 13th dec %I The MIT Press %C University of New South Wales, Sydney, NSW, Australia %F Channon:2002:alife %X Bedau’s (1998a) classification system for long-term evolutionary dynamics provides a test for open-ended evolution. Making this ALife test more rigorous, and passing it, are two of the most important open problems in the field. Previously (Channon 2001) I presented the result that Geb, a system designed to verify and extend theories behind the generation of evolutionary emergent systems (Channon & Damper 2000), has passed this test. However I also criticised the test, most significantly with regard to its normalisation method for artificial systems. This paper details a modified normalisation method, based on component activity normalisation, that overcomes these criticisms. It then presents the results of the revised test when applied to Geb, which indicate that this system does indeed exhibit open-ended evolution. %K genetic algorithms, genetic programming, natural selection %U http://www.channon.net/alastair/geb/alife8/channon_ad_alife8.pdf %P 173-181 %0 Journal Article %T Unbounded evolutionary dynamics in a system of agents that actively process and transform their environment %A Channon, Alastair %J Genetic Programming and Evolvable Machines %D 2006 %8 oct %V 7 %N 3 %@ 1389-2576 %F Channon:2006:GPEM %X Bedau et al.’s statistical classification system for long-term evolutionary dynamics provides a test for open-ended evolution. Making this test more rigorous, and passing it, are two of the most important open problems for research into systems of agents that actively process and transform their environment. This paper presents a detailed description of the application of this test to Geb, a system designed to verify and extend theories behind the generation of evolutionarily emergent systems. The result is that, according to these statistics, Geb exhibits unbounded evolutionary dynamics, making it the first autonomous artificial system to pass this test. However, having passed it, the most prudent course of action is to look for weaknesses in the test. The test is criticised, most significantly with regard to its normalisation method for artificial systems. Furthermore, this paper presents a modified normalisation method, based on component activity normalisation, that overcomes these criticisms. The results of the revised test, when applied to Geb, indicate that this system does indeed exhibit open-ended evolution. %K artificial life, Evolutionary dynamics, Variable-size genomes, Coevolution, Biotic selection, Emergence %9 journal article %R 10.1007/s10710-006-9009-3 %U http://www.channon.net/alastair/papers/channon_ad_gpem.pdf %U http://dx.doi.org/10.1007/s10710-006-9009-3 %P 253-281 %0 Journal Article %T Information Immune Systems %A Chao, Dennis L. %A Forrest, Stephanie %J Genetic Programming and Evolvable Machines %D 2003 %8 dec %V 4 %N 4 %@ 1389-2576 %F chao:2003:GPEM %X The concept of an information immune system (IIS) is introduced, in which undesirable information is eliminated before it can reach the user. The IIS is inspired by the natural immune systems that protect us from pathogens. IISs from multiple individuals can be combined to form a group IIS which filters out information undesirable to any of the members. The relationship between our proposed IIS architecture and the natural immune system is outlined, and potential applications, including information filtering, interactive design, and collaborative design, are discussed. %K artificial immune systems, collaborative design, collaborative filtering, evolutionary art, information filtering, biomorphs, sonomorphs, muzak %9 journal article %R 10.1023/A:1026139027539 %U http://dx.doi.org/10.1023/A:1026139027539 %P 311-331 %0 Journal Article %T A genetic programming hyper-heuristic with whale optimization algorithm for the dynamic resource-constrained multi-project scheduling problems %A Chao, Yutong %A Zhuang, Cunbo %A Guo, Haoxin %A Liu, Jianhua %J Expert Syst. Appl. %D 2026 %8 January %V 295 %F DBLP:journals/eswa/ChaoZGL26 %K genetic algorithms, genetic programming %9 journal article %R 10.1016/J.ESWA.2025.128881 %U https://doi.org/10.1016/j.eswa.2025.128881 %U http://dx.doi.org/10.1016/J.ESWA.2025.128881 %P 128881 %0 Conference Proceedings %T Genetic programming for inverse kinematics approximation %A Chapelle, Frederic %A Chocron, O. %A Bidaud, Philippe %S International Symposium on Robotics (ISR’00) %D 2000 %8 14 17 may %I Canadian Society for Robotics, 2000 %C Montreal, Canada %@ 0-9687044-0-9 %F Chapelle:2000:isr %K genetic algorithms, genetic programming %U http://books.google.co.uk/books/about/ISR_2000.html?id=u6zpAAAAMAAJ&redir_esc=y %P 5-11 %0 Conference Proceedings %T Prototypage virtuel de micro-endoscopes par algorithmes evolutionnaires %A Chapelle, Frederic %A Dumont, G. %A Chocron, O. %S Journees Jeunes Chercheurs en Robotique (JJCR 13) %D 2000 %8 sep %C Rennes, France %F Chapelle:2000:jjcr %O in french %K genetic algorithms %U http://www.irisa.fr/manifestations/2000/jjcr/Papiers/chapelle.pdf %0 Conference Proceedings %T A closed form for inverse kinematics approximation of general 6R manipulators using genetic programming %A Chapelle, Frederic %A Bidaud, Philippe %S IEEE International Conference on Robotics and Automation (ICRA’01) %D 2001 %8 21 28 may %V 4 %I IEEE %C Seoul, Korea %F Chapelle:2001:icra %X We present an original use of evolutionary algorithms in order to approximate by a closed form the inverse kinematic model of analytical (non-analytical) and general manipulators. The objective is to provide a fast and general solution to the inverse kinematic problem when it is extensively evaluated in the design processes of manipulators. A mathematical function is evolved through genetic programming according to the known direct kinematic model to determine an analytical expression which approximates the joint variable solution for a given end-effector configuration. As an illustration of this evolutionary symbolic regression process, the inverse kinematic models of the PUMA and GMF Arc Mate are approximated before applying the algorithm to general 6R manipulators. %K genetic algorithms, genetic programming, industrial manipulators, manipulator kinematics, symbol manipulation, 6R manipulators, approximation, evolutionary algorithms, industrial manipulators, inverse kinematics, joint variables, symbolic regression, steady state, demes, ADF, parsimony preasure, subsample training data, learning base %R 10.1109/ROBOT.2001.933137 %U http://dx.doi.org/10.1109/ROBOT.2001.933137 %P 3364-3369 %0 Thesis %T Evaluation de systemes robotiques et comportements complexes par algorithmes evolutionnaires %A Chapelle, Frederic %D 2002 %8 sep %C France %C University Pierre et Marie Curie, Paris VI %F Chapelle:2002:thesis %O in french %X Evaluation of robotic systems and complex behaviours using evolutionary algorithms : in this thesis, an original approach for evaluation of robotic systems in the context of simultaneous structure/control design is presented. It relies on the evolutionary algorithms. The initial procedures for evaluation are usually difficult to implement and expensive in computing time. The developed method uses genetic programming within an evolutionary symbolic regression algorithm, to generate expressions with various levels of refinement which are intended to approximate the original evaluations (according to the concept of metamodels). The interest of this approach is illustrated by various applications of gradual complexity where the initial evaluation methods can be simple functions, algorithms or a value drawn from a simulation considering the globality of the system to be designed, its interactions with the environment and its tasks. Reliable and fast generic models, which are solutions of the inverse kinematic problem for any 6R manipulator geometry (analytical or not), have been produced via approximating functions. The application of these techniques to a problem with dynamics resulted in fixing restrictions to the use of our method for direct approximation of constrained behaviours. Evolutionary symbolic regression is then applied within the framework of optimisations by genetic algorithms (GA), for simple cases like when a GA seeks a solution of the 2D inverse kinematic problem, or more complex like preliminary design of smart active endoscopes for minimally invasive surgery. Additionally, an extension allowing to increase the evolutionarity of GA is deduced. %K genetic algorithms, genetic programming, Computer-aided design, robotic systems, simultaneous structure/control evaluation, symbolic regression, inverse models, inverse kinematic problem, programming, control, simulation, medical devices, minimally invasive surgery %9 Ph.D. thesis %U http://www.sudoc.fr/069898715 %0 Conference Proceedings %T Conception et evaluation de micro-endoscopes basees sur les algorithmes evolutionnaires %A Chapelle, Frederic %A Bidaud, Philippe %A Dumont, G. %S Journees du Reseau Thematique Pluri-disciplinaire Micro-robotique CNRS %D 2002 %8 June %C Rennes, France %F Chapelle:2002:jrtpm %O in french %K genetic algorithms, genetic programming %0 Journal Article %T Closed form solutions for inverse kinematics approximation of general 6R manipulators %A Chapelle, Frederic %A Bidaud, Philippe %J Mechanism and Machine Theory %D 2004 %8 mar %V 39 %N 3 %F Chapelle:2004:MMT %X This paper presents an original use of Evolutionary Algorithms in order to approximate by a closed form the inverse kinematic model (IKM) of analytical, non-analytical and general (i.e. with an arbitrary geometry) manipulators. The objective is to provide a fast and general solution to the inverse kinematic problem when it is extensively evaluated as in design processes of manipulators. A mathematical function is evolved through Genetic Programming according to the known direct kinematic model to determine an analytical expression which approximates the joint variable solution for a given end-effector configuration. As an illustration of this evolutionary symbolic regression process, the inverse kinematic models of the PUMA and the GMF Arc Mate are approximated before to apply the algorithm to general 6R manipulators. %K genetic algorithms, genetic programming, Inverse kinematics, Mechanical design, Manipulators, Genetic programming, Symbolic regression %9 journal article %R 10.1016/j.mechmachtheory.2003.09.003 %U http://www.sciencedirect.com/science/article/B6V46-4B1XNXT-1/2/2bf40af1f930c87f19d6fcc130f2f57a %U http://dx.doi.org/10.1016/j.mechmachtheory.2003.09.003 %P 323-338 %0 Journal Article %T Evaluation functions synthesis for optimal design of hyper-redundant robotic systems %A Chapelle, Frederic %A Bidaud, Philippe %J Mechanism and Machine Theory %D 2006 %8 oct %V 41 %N 10 %F Chapelle:2006:MMT %X Simultaneous structure/control optimisation in a robotic system design is addressed through Genetic Algorithms. Both aspects are here evolved in the same algorithm through simulations for task oriented evaluations. Moreover, a technique based on Genetic Programming is proposed to generate approximated evaluation functions. Its aim is to significantly speed the design process up, while leading to robust evaluation. A specific adaptation of these principles is investigated for the design of hyper-redundant robotic systems such as smart active endoscopes intended for minimally invasive surgery. The design of these micro-robots is based on a serial arrangement of articulated rings with associated antagonist SMA micro-actuators, whose configuration has to be adapted to the surgical operation constraints. The control strategies for an adaptation of the system geometry to the environment are based on a multi-agent approach to minimise the inter-module communication requirements. The results obtained for the particular application of colonoscopy show the consistency of the solutions. %K genetic algorithms, genetic programming, Mechanical design, Simultaneous structure/control evaluation, Functions synthesis, Hyper-redundant micro-robotics, Minimally invasive surgery %9 journal article %R 10.1016/j.mechmachtheory.2005.11.006 %U http://dx.doi.org/10.1016/j.mechmachtheory.2005.11.006 %P 1196-1212 %0 Conference Proceedings %T Constructivist AI with GP %A Char, K. Govinda %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 char:1997:caiGP %K genetic algorithms, genetic programming %P 28-34 %0 Conference Proceedings %T Evolution of Learning with Genetic Programming - Constructivist AI with Genetic Programming %A Char, K. Govinda %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 char:1997:elGPcAI %K genetic algorithms, genetic programming %P 289 %0 Book Section %T Pattern recognition %A Char, K. Govinda %A Tackett, Walter Alden %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 Char:1997:HEC %X Pattern recognition is one of the most important components of any intelligent system. The traditional methodologies in pattern recognition are inadequate to provide optimal solutions to a variety of pattern recognition and classification problems that are inherently complex. In recent years, evolutionary algorithms have been successfully applied to a wide range of diverse sets of problems in the field of pattern recognition. In a number of applications, evolutionary paradigms, in hybrid with the traditional techniques or in isolation, have outperformed traditional techniques. In this section we provide an overview of various pattern recognition techniques that are currently in use, the role of evolutionary computation in adaptive pattern recognition and the future trends. %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf %0 Conference Proceedings %T Constructive Learning with Genetic Programming %A Char, K. Govinda %Y Poli, Riccardo %Y Langdon, W. B. %Y Schoenauer, Marc %Y Fogarty, Terry %Y Banzhaf, Wolfgang %S Late Breaking Papers at EuroGP’98: the First European Workshop on Genetic Programming %D 1998 %8 14 15 apr %I CSRP-98-10, The University of Birmingham, UK %C Paris, France %F char:1998:clGP %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf %P 1-5 %0 Thesis %T Constructivist Artificial Intelligence with Genetic Programming %A Char, Kalyani Govinda %D 1998 %C Oakfield Avenue, Glasgow G12 8LT, Scotland, UK %C Department of Electronics and Electrical Engineering, University of Glasgow %F char:thesis %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://ethos.bl.uk/OrderDetails.do?did=10&uin=uk.bl.ethos.265641 %0 Conference Proceedings %T A study of fitness functions for data classification using grammatical evolution %A Chareka, Tatenda %A Pillay, Nelishia %S 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech) %D 2016 %8 nov %F Chareka:2016:PRASA %X Data classification is a well studied area with various techniques such as support vector machines, decision trees, neural networks and evolutionary algorithms, amongst others successfully applied to this domain. The research presented in this paper forms part of an initiative aimed at evaluating grammatical evolution, a recent variation of genetic programming, for data classification. The paper reports on a study conducted to compare six different measures, namely, accuracy, true positive rate, false positive rate, precision, F-score and Matthew’s correlation coefficient, as fitness functions for grammatical evolution. The performance of grammatical evolution using the six measures as a fitness function is evaluated for multi-class data classification. The study has shown that the accuracy and F-score are effective as fitness functions outperforming all other measures. In some instances accuracy produced better results than F-score. Future work will examine the correlation between the characteristics of the data set and the best performing measure. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1109/RoboMech.2016.7813165 %U http://dx.doi.org/10.1109/RoboMech.2016.7813165 %0 Journal Article %T Soft and hard computing approaches for real-time prediction of currents in a tide-dominated coastal area %A Charhate, S. B. %A Deo, M. C. %A Kumar, V. Sanil %J Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment %D 2007 %V 221 %N 4 %@ 1475-0902 %F Charhate:2007:JEME %X The prediction of tidal currents in the coastal region on a real-time or online basis is useful in taking operation- and planning-related decisions such as towing of vessels and monitoring of oil slick movements. Currently, however, this is done in offline mode on the basis of the statistical method of harmonic analysis involving fitting of harmonic functions to measured data. Alternatively, numerical solutions of hydrodynamic models can also provide spatial and temporal information on currents. Owing to the complex real sea conditions, such methods may not always yield satisfactory results. This paper discusses a few alternative approaches based on the soft computing tools of artificial neural networks (ANNs) and genetic programming (GP), as well as the hard mathematical approaches of stochastic and statistical methods. The suggested schemes use only a univariate time series of currents to forecast their future values. The measurements of coastal currents made at two locations in the Gulf of Khambhat along the west coast of India have been analysed. The current predictions over a time step of 20 min, a few hours, and a day at the specified locations were carried out. It was found that the soft computing schemes of GP and ANN performed better than the traditional hard technique of harmonic analysis in the present application. This work should initiate more application of GP in coastal engineering. Addressing the problem of current predictions in real-time mode based on analysis of observed time series of ocean currents is a specialty of this work. %K genetic algorithms, genetic programming, tidal currents, neural networks, harmonic analysis, current measurements %9 journal article %R 10.1243/14750902JEME77 %U http://dx.doi.org/10.1243/14750902JEME77 %P 147-163 %0 Journal Article %T Inverse modeling to derive wind parameters from wave measurements %A Charhate, S. B. %A Deo, M. C. %A Londhe, S. N. %J Applied Ocean Research %D 2008 %V 30 %N 2 %@ 0141-1187 %F Charhate2008120 %X The problem of deriving wind parameters from measured waves is discussed in this paper. Such a need reportedly arises in the field when the wind sensor attached to a wave rider buoy at high elevation from the sea level gets disconnected during rough weather, or otherwise needs repairs. This task is viewed as an inverse modeling approach as against the direct and common one of evaluating the wind-wave relationship. Two purely nonlinear approaches of soft computing, namely genetic programming (GP) and artificial neural network (ANN) have been used. The study is oriented towards measurements made at five different offshore locations in the Arabian Sea and around the western Indian coastline. It is found that although the results of both soft approaches rival each other, GP has a tendency to produce more accurate results than the adopted ANN. It was also noticed that the equation-based GP model could be equally useful as the one based on computer programs, and hence for the sake of simplicity in implementation, the former can be adopted. In case the entire wave rider buoy does not function for some period, a common regional GP model prescribed in this work can still produce the desired wind parameters with the help of wave observations available from anywhere in the region. A graphical user interface is developed that puts the derived models to their actual use in the field. %K genetic algorithms, genetic programming, Wave buoy, Wave data, Wind data, Neural networks %9 journal article %R 10.1016/j.apor.2008.08.002 %U http://www.sciencedirect.com/science/article/B6V1V-4TCGM50-1/2/69dcf477c9fc85235d0cc5df25e6a54a %U http://dx.doi.org/10.1016/j.apor.2008.08.002 %P 120-129 %0 Thesis %T Applications of soft computing techniques to solve coastal and ocean problems %A Charhate, Shrikant Bhauraoji %D 2008 %C India %C Department of Civil Engineering, Indian Institute of Technology, Bombay %F Charhate:thesis %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://www.civil.iitb.ac.in/~mcdeo/thesis.html %0 Book Section %T Genetic Programming to Forecast Stream Flow %A Charhate, S. B. %A Dandawat, Y. H. %A Londhe, S. N. %B Advances in Water Resources and Hydraulic Engineering %D 2009 %I Springer %G English %F Charhate200929 %X Prediction of stream flow plays a vital role in design, construction, operation and maintenance of many hydraulic structures. The present study aims at predicting stream flow at Rajghat in Narmada river basin of India using the technique of genetic programming (GP). The GP models are developed based on monsoon and non-monsoon seasons. The present paper describes 5 separate GP models, 4 for monsoon months and 1 for non-monsoon months for predicting stream flow at Rajghat 1 day in advance. The performance of the GP models especially at peaks is the point of interest along with general prediction accuracy of the models. %K genetic algorithms, genetic programming, stream flow, peak flow %R 10.1007/978-3-540-89465-0_6 %U http://dx.doi.org/10.1007/978-3-540-89465-0_6 %P 29-34 %0 Journal Article %T Genetic programming for real-time prediction of offshore wind %A Charhate, S. B. %A Deo, M. C. %A Londhe, S. N. %J Ships and Offshore Structures %D 2009 %8 mar %V 4 %N 1 %@ 1744-5302 %F Charhate:2009:SOS %X Wind speed and its direction at two offshore locations along the west coast of India are predicted over future time-steps of 3 to 24 hrs based on a sequence past wind measurements made by floating buoys. This is done based on a relatively new soft computing tool using genetic programming. The attention of investigators has recently been drawn to the application of this approach that differs from the well-known technique of genetic algorithms in basic coding and application of genetic operators. Unlike most of the past works dealing with causative modelling or spatial correlations, this study explores the usefulness of genetic programming to carry out temporal regression. It is found that the resulting predictions of wind movements rival those made by an equivalent and more traditional artificial neural network and sometimes appear more attractive when multiple-error criteria were applied. The success of genetic programming as a modelling tool reported in this study may inspire similar applications in future in the problem domain of offshore engineering, and more research in the computing domain as well. %K genetic algorithms, genetic programming, artificial neural networks, wind speed, wind direction, wind prediction %9 journal article %R 10.1080/17445300802492638 %U http://dx.doi.org/10.1080/17445300802492638 %P 77-88 %0 Journal Article %T Prediction of welding responses using AI approach: adaptive neuro-fuzzy inference system and genetic programming %A Chatterjee, Suman %A Mahapatra, Siba Sankar %A Lamberti, Luciano %A Pruncu, Catalin I. %J Journal of the Brazilian Society of Mechanical Sciences and Engineering %D 2022 %V 44 %N 2 %F chatterjee:2022:JBSMSE %X Laser welding of thin sheets has widespread application in various fields such as battery manufacturing, automobiles, aviation, electronics circuits and medical sciences. Hence, it is very essential to develop a predictive model using artificial intelligence in order to achieve high-quality weldments in an economical manner. In the present study, two advanced artificial intelligence techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and multi-gene genetic programming (MGGP), were implemented to predict the welding responses such as heat-affected zone, surface roughness and welding strength during joining of thin sheets using Nd:YAG laser. The study attempts to develop an appropriate predictive model for the welding process. In the proposed methodology, 70 percent of the experimental data constitutes the training set whereas remaining 30 percent data is used as testing set. The results of this study indicated that the root-mean-square error (RMSE) of tested data set ranges between 7 and 16 percent for MGGP model, while RMSE for testing data set lies 18 to 35 percent for ANFIS model. The study indicates that the MGGP predicts the welding responses in a superior manner in laser welding process and can be applied for accurate prediction of performance measures. %K genetic algorithms, genetic programming, MGGP, Laser welding, Nd, YAG laser, ANFIS, Titanium alloy, Stainless steel %9 journal article %R 10.1007/s40430-021-03294-w %U http://link.springer.com/article/10.1007/s40430-021-03294-w %U http://dx.doi.org/10.1007/s40430-021-03294-w %P Articlenumber:53 %0 Journal Article %T Just How (Un)realistic are Evolutionary Algorithms as Representations of Social Processes? %A Chattoe, Edmund %J The Journal of Artificial Societies and Social Simulation %D 1998 %8 30 jun %V 1 %N 3 %@ 1460-7425 %F chattoe:1998:uEArsp %X This paper attempts to illustrate the importance of a coherent behavioural interpretation in applying evolutionary algorithms like Genetic Algorithms and Genetic Programming to the modelling of social processes. It summarises and draws out the implications of the Neo-Darwinian Synthesis for processes of social evolution and then discusses the extent to which evolutionary algorithms capture the aspects of biological evolution which are relevant to social processes. The paper uses several recent papers in the field as case studies, discussing more and less successful uses of evolutionary algorithms in social science. The key aspects of evolution discussed in the paper are that it is dependent on relative rather than absolute fitness, it does not require global knowledge or a system level teleology, it avoids the credit assignment problem, it does not exclude Lamarckian inheritance and it is both progressive and open ended. %K genetic algorithms, genetic programming, evolutionary algorithms, social evolution, selectionist paradigm %9 journal article %U http://jasss.soc.surrey.ac.uk/1/3/2.html %0 Book Section %T The Prospects for Artificial Intelligence Techniques in Understanding Economic Behaviour: An Overview %A Chattoe, Edmund %E de Gijsen, Peter %E Schmid-Schoenbein, Thomas %E Schneider, Johannes %B Oekonomie und Gesellschaft (Economics and Society), Jahrbuch 17: Komplexitaet und Lernen %D 2001 %I Metropolis-Verlag %C Marburg, Germany %@ 3-89518-997-9 %F Chattoe:2001:OundG %K genetic algorithms, genetic programming %U http://www.metropolis-publisher.com/Komplexitaet-und-Lernen/997/book.do %P 135-162 %0 Thesis %T The Evolution of Expectations in Boundedly Rational Agents %A Chattoe, Edmund %D 2003 %C UK %C Department of Economics, University of Oxford %F Chattoe:thesis %X The thesis uses a technique called Genetic Programming to show how variation and selective retention of pricing rules of thumb can produce self-organised markets even when each firm has to learn against a very noisy background of simultaneous adaptation by other firms %K genetic algorithms, genetic programming %9 DPhil %9 Ph.D. thesis %U https://www.academia.edu/8906840/The_Evolution_of_Expectations_in_Boundedly_Rational_Agents_Front_Material %0 Journal Article %T Genetic Algorithms and Genetic Programming in Computational Finance, Chen, Shu-Heng (ed.) %A Chattoe, Edmund %J Journal of Artificial Societies and Social Simulation %D 2004 %8 31 oct %V 7 %N 4 %@ 1460-7425 %F chattoe:2004:gagpf %O Book review %K genetic algorithms, genetic programming %9 journal article %U http://jasss.soc.surrey.ac.uk/7/4/reviews/chattoe.html %0 Book Section %T Modelling Evolutionary Mechanisms in Social Systems %A Chattoe-Brown, Edmund %A Edmonds, Bruce %E Edmonds, Bruce %E Meyer, Ruth %B Simulating Social Complexity %S Understanding Complex Systems %D 2003 %V VII %I Springer %F Chattoe-Brown:2013:SSC %K genetic algorithms, genetic programming %U http://www.springer.com/computer/information+systems+and+applications/book/978-3-540-93812-5 %0 Conference Proceedings %T Genetic Programming for Domain Adaptation in Product Reviews %A Chaturvedi, Iti %A Cambria, Erik %A Cavallari, Sandro %A Welsch, Roy E. %Y Jin, Yaochu %S 2020 IEEE Congress on Evolutionary Computation, CEC 2020 %D 2020 %8 19 24 jul %I IEEE Press %C internet %F Chaturvedi:2020:CEC %X There is a large variety of products sold online and the websites are in several languages. Hence, it is desirable to train a model that can predict sentiments in different domains simultaneously. Previous authors have used deep learning to extract features from multiple domains. Here, each word is represented by a vector that is determined using co-occurrence data. Such a model requires that all sentences have the same length resulting in low accuracy. To overcome this challenge, we model the features in each sentence using a variable length tree called a Genetic Program. The polarity of clauses can be represented using mathematical operators such as plus or minus at internal nodes in the tree. The proposed model is evaluated on Amazon product reviews for different products and in different languages. We are able to outperform the accuracy of baseline multi-domain models in the range of 5-20percent. %K genetic algorithms, genetic programming, Sentiment Analysis %R 10.1109/CEC48606.2020.9185713 %U http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_WCCI_2020/CEC/Papers/E-24673.pdf %U http://dx.doi.org/10.1109/CEC48606.2020.9185713 %P paperid24673 %0 Journal Article %T Fuzzy Aggregated Topology Evolution for Cognitive Multi-tasks %A Chaturvedi, Iti %A Su, Chit L. %A Welsch, Roy E. %J Cognitive Computation %D 2021 %V 13 %I Springer US %G en %F Chaturvedi:2021:CognComput %X Evolutionary optimisation aims to tune the hyper-parameters during learning in a computationally fast manner. For optimisation of multi-task problems, evolution is done by creating a unified search space with a dimensionality that can include all the tasks. Multi-task evolution is achieved via selective imitation where two individuals with the same type of skill are encouraged to crossover. Due to the relatedness of the tasks, the resulting offspring may have a skill for a different task. In this way, we can simultaneously evolve a population where different individuals excel in different tasks. In this paper, we consider a type of evolution called Genetic Programming (GP) where the population of genes have a tree-like structure and can be of different lengths and hence can naturally represent multiple tasks. We apply the model to multi-task neuroevolution that aims to determine the optimal hyper-parameters of a neural network such as number of nodes, learning rate, and number of training epochs using evolution. Here each gene is encoded with the hyper parameters for a single neural network. Previously, optimisation was done by enabling or disabling individual connections between neurons during evolution. This method is extremely slow and does not generalise well to new neural architectures such as Seq2Seq. To overcome this limitation, we follow a modular approach where each sub-tree in a GP can be a sub-neural architecture that is preserved during crossover across multiple tasks. Lastly, in order to leverage on the inter-task covariance for faster evolutionary search, we project the features from both tasks to common space using fuzzy membership functions. The proposed model is used to determine the optimal topology of a feed-forward neural network for classification of emotions in physiological heart signals and also a Seq2seq chatbot that can converse with kindergarten children. We can outperform baselines by over 10percent in accuracy. %K genetic algorithms, genetic programming, multi-task optimisation, fuzzy logic, neuroevolution %9 journal article %R 10.1007/s12559-020-09807-4 %U https://hdl.handle.net/1721.1/131981 %U http://dx.doi.org/10.1007/s12559-020-09807-4 %P 96-107 %0 Conference Proceedings %T A multiclass classifier using Genetic Programming %A Chaudhari, Narendra S. %A Purohit, Anuradha %A Tiwari, Aruna %S 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 %D 2008 %8 17 20 dec %I IEEE %C Hanoi, Vietnam %F DBLP:conf/icarcv/ChaudhariPT08 %X his paper presents an approach for designing classifiers for a multiclass problem using Genetic Programming (GP). The proposed approach takes an integrated view of all classes when GP evolves. An individual of the population will be represented using multiple trees. The GP is trained with a set of N training samples in steps. A concept of unfitness of a tree is used in order to improve genetic evolution. Weak trees having poor performance are given more chance to participate in the genetic operations, and thus improve themselves. In this context, a new mutation operation called nondestructive directed point mutation is used, which reduces the destructive nature of mutation operation. The approach is being demonstrated by experimenting on some datasets. %K genetic algorithms, genetic programming %R 10.1109/ICARCV.2008.4795815 %U http://dx.doi.org/10.1109/ICARCV.2008.4795815 %P 1884-1887 %0 Journal Article %T Estimation of pan evaporation using soft computing tools %A Chaudhari, Narhari %A Londhe, Shreenivas %A Khare, Kanchan %J International Journal of Hydrology Science and Technology %D 2013 %8 feb 28 %V 2 %N 4 %I Inderscience Publishers %@ 2042-7816 %G eng %F Chaudhari:2012:IJHST %X Estimation of evaporation plays a key role in managing water resources projects. Traditionally, evaporation is determined using theoretical and empirical techniques as well as by pan observations. Practically, it is difficult to install evaporation pans at every location and empirical approach like Penman’s equation is data intensive. This necessitates the use of an alternative approach, which can make use of readily available data and estimate evaporation reasonably with limited data. Major objective of the present study is to estimate evaporation using the soft computing tools of artificial neural networks (ANN) and genetic programming (GP) making use of the measured climatic parameters and to compare the results with traditional empirical techniques. The results indicate that the models developed using the soft computing tools of ANN and GP worked reasonably well for estimation of evaporation compared to empirical methods. GP works slightly better for higher values of pan evaporation compared to ANN. %K genetic algorithms, genetic programming, pan evaporation, soft computing, artificial neural networks, ANNs, penman’s equation, water resources management, water management, evaporation estimation. %9 journal article %R 10.1504/IJHST.2012.052375 %U http://www.inderscience.com/link.php?id=52375 %U http://dx.doi.org/10.1504/IJHST.2012.052375 %P 373-390 %0 Journal Article %T Spatial mapping of pan evaporation using linear genetic programming %A Chaudhari, Narhari %A Londhe, Shreenivas %A Khare, Kanchan %J Int. J. of Hydrology Science and Technology %D 2015 %8 mar 07 %V 4 %N 3 %I Inderscience Publishers %@ 2042-7816 %F Chaudhari:2015:IJHST %X Daily pan evaporation is of utmost importance in planning and managing water resources. The present paper involves estimation of daily pan evaporation at a particular climatic station using daily pan evaporations of surrounding ten climatic stations covering six districts of Maharashtra state (India) with variation in elevations and weather. The surrounding stations were added one by one based on the correlation of each station with the output station. The soft computing technique of linear genetic programming was employed for this spatial mapping exercise. The models were developed for each station as output station (total 11) with the remaining stations (1 to 10) as inputs added one by one. In all 110 LGP models were developed to examine the ability of linear genetic programming to work as virtual pan as and when existing evaporimeters become inoperative. The best LGP model was for Suksale station with coefficient of correlation (r = 0.94) between observed and estimated pan evaporation. This will retrieve the missing evaporation data at one location using data at other locations. %K genetic algorithms, genetic programming, evaporimeters, linear genetic programming, LGP, pan evaporation, spatial mapping, hydrology science, water resources, water management, India %9 journal article %R 10.1504/IJHST.2014.067731 %U http://www.inderscience.com/link.php?id=67731 %U http://dx.doi.org/10.1504/IJHST.2014.067731 %P 234-244 %0 Conference Proceedings %T One Day Ahead Forecast of Pan Evaporation at Pali Using Genetic Programming %A Chaudhari, Narhari Dattatraya %A Chaudhari, Neha Narhari %S Proceedings of Fifth International Conference on Soft Computing for Problem Solving %D 2016 %I Springer %F chaudhari:2016:FICSCPS %K genetic algorithms, genetic programming %R 10.1007/978-981-10-0448-3_10 %U http://link.springer.com/chapter/10.1007/978-981-10-0448-3_10 %U http://dx.doi.org/10.1007/978-981-10-0448-3_10 %0 Conference Proceedings %T Determination of optimum genetic parameters for symbolic non-linear regression-like problems in genetic programming %A Chaudhary, U. K. %A Iqbal, M. %S IEEE 13th International Multitopic Conference, INMIC 2009 %D 2009 %8 14 15 dec %C Islamabad, Pakistan %F Chaudhary:2009:INMIC %X Parametric studies have been carried out for the quartic-polynomial regression problem demonstrated in the Genetic Programming (GP) v3 toolbox of Matlab. Many classification schemes and modeling issues are polynomial based. Every possible combination originating from all available options between the two genetic parameters namely ’elitism’ and ’sampling’ has been analyzed while keeping all other parameters as fixed. Three performance parameters namely, execution time of a given GP run, quickness of convergence to reach the required fitness and the most important, fitness improvement factor per generation have been studied. In terms of the last mentioned performance parameter, being an indicative of diversity, it is shown that the best particular combination is ’halfelitism-sus’ if naming in the general format of ’elitism-sampling’ is used. On the average, this combination went on improving the fitness value (of the best so far individual) in more than 78percent of generations as the GP simulations progressed towards the required solution. Secondly, halfelitism-roulette took, on the average, as less as 6.8 generations to complete a GP run outperforming other combinations in terms of quickness of convergence with again, halfelitism-sus as second best consuming 7.4 generations to reach at the desired quartic genre. In spite of its promising average values, this combination showed a contrasting behavior depending upon the auto-evolution process at the start of a given GP run. Either it took on a right track and converged to the solution efficiently or it de-tracked in the very beginning and lost its performance regarding the three aforementioned parameters. Furthermore, it was found that for the combinations replace-doubletour and keepbest-doubletour giving the best two execution times (in seconds) to complete a given GP run, their results were least encouraging regarding the other performance parameters. Also, in contrast to some combinations such as, replace-tournament and replace-lexictour, other combinations worked satisfactorily well in at least one of the three performances studied. %K genetic algorithms, genetic programming, Matlab, elitism, halfelitism-roulette, keepbest-doubletour, optimum genetic parameters, replace-doubletour, replace-lexictour, replace-tournament, symbolic non-linear regression-like problems, mathematics computing, regression analysis %R 10.1109/INMIC.2009.5383162 %U http://dx.doi.org/10.1109/INMIC.2009.5383162 %P 1-5 %0 Conference Proceedings %T Characterizing a Tunably Difficult Problem in Genetic Programming %A Chaudhri, Omer A. %A Daida, Jason M. %A Khoo, Jonathan C. %A Richardson, Wendell S. %A Harrison, Rachel B. %A Sloat, William J. %Y Whitley, Darrell %Y Goldberg, David %Y Cantu-Paz, Erick %Y Spector, Lee %Y Parmee, Ian %Y Beyer, Hans-Georg %S Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000) %D 2000 %8 October 12 jul %I Morgan Kaufmann %C Las Vegas, Nevada, USA %@ 1-55860-708-0 %F Chaudhri:2000:GECCO %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2000/GP206.pdf %P 395-402 %0 Thesis %T Image Restoration using Machine Learning %A Chaudhry, Asmatullah %D 2007 %8 mar %C Topi, NWFP, Pakistan %C Ghulam Ishaq Khan Institute of Engineering Sciences & Technology %F Chaudhry:thesis %X Restoration of degraded images has become an important and effective tool for many technological applications like space imaging, medical imaging and many other post-processing techniques. Most of the image restoration techniques model the degradation phenomena, usually blur and noise, and then obtain an approximation of the image. Whereas, in realistic situation, one has to estimate both the true image and the blur from the degraded image characteristics in the absence of any a priori information about the blurring system. The objective of this thesis is to develop a new punctual kriging based image restoration approach using machine-learning techniques. To achieve this objective, the research concentrates on the restoration of images corrupted with Gaussian noise by making good tradeoffs between two contradicting properties; smoothness versus edge preservation. This thesis makes the following contributions: (1) Quantitative analysis of the at hand punctual kriging based image restoration technique is carried out, (2) Fuzzy logic, punctual kriging and fuzzy averaging are used intelligently to develop a better image restoration technique, (3) A new image quality measure is proposed in terms of the semi-variograms to judge the performance of image restoration techniques, (4) Analysis of the effect of neighbourhood size on negative weights and the subsequent improvement in punctual kriging based image restoration is performed, (5) To avoid both the problems of matrix inversion failure and the negative weights in punctual kriging, artificial neural network is used to develop a neuro-fuzzy filter for image denoising, (6) Further, using genetic programming, a hybrid technique for image restoration based on fuzzy punctual kriging is developed, the developed machine learning technique uses local statistical measures along with kriged information for subsequent pixel estimation. Main parameters considered for evaluation of the proposed technique are image quality measure and computational cost. The image quality measures used for evaluation and comparison include MSE, PSNR, SSIM, wPSNR, VMSE and VPSNR. A series of empirical investigations have been made to evaluate the performance of the proposed techniques using database of standard images that show the effectiveness of our methodology. %K genetic algorithms, genetic programming, Image Restoration %9 Ph.D. thesis %U https://fac.ksu.edu.sa/ammirza/page/22439 %0 Journal Article %T A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming %A Chaudhry, Asmatullah %A Khan, Asifullah %A Ali, Asad %A Mirza, Anwar M. %J International Journal of Imaging Systems and Technology %D 2007 %V 17 %N 4 %@ 1098-1098 %F Chaudhry:2007:IJIST %X We present an intelligent technique for image denoising problem of gray level images degraded with Gaussian white noise in spatial domain. The proposed technique consists of using fuzzy logic as a mapping function to decide whether a pixel needs to be krigged or not. Genetic programming is then used to evolve an optimal pixel intensity-estimation function for restoring degraded images. The proposed system has shown considerable improvement when compared both qualitatively and quantitatively with the adaptive Wiener filter, methods based on fuzzy kriging, and a fuzzy-based averaging technique. Experimental results conducted using an image database confirms that the proposed technique offers superior performance in terms of image quality measures. This also validates the use of hybrid techniques for image restoration. %K genetic algorithms, genetic programming, image restoration, fuzzy logic, punctual kriging, structure similarity index measure, SSIM, adaptive spatial filtering %9 journal article %R 10.1002/ima.20105 %U http://dx.doi.org/10.1002/ima.20105 %P 224-231 %0 Journal Article %T Fusion of Linear and Non-Linear Image Restoration Filters Using Genetic Programming %A Chaudhry, Asmatullah %A Mirza, Anwar M. %A Memon, Nisar Ahmed %J Mehran university Research Journal of Engineering and Technology %D 2009 %8 oct %V 28 %N 4 %I Mehran University of Engineering and Technology %C Pakistan %@ 0254-7821 %F Chaudhry:2009:murjet %X In this paper, we present an intelligent technique for image de-noising of gray level still images degraded with Gaussian white noise. The proposed technique consists of using E-median filter in wavelet domain and adaptive Wiener filter to restore the noisy image. Genetic programming is then used to evolve an optimal pixel intensity estimation function used to restore the degraded images. The proposed method has shown considerable improvement in the image quality as compared to the adaptive Wiener and E-median filter approaches. Experimental results carried out on several standard images and a database consisting of 450 images confirm the superiority of the proposed technique in terms of image quality. This also validates the use of hybrid techniques for image restoration. %K genetic algorithms, genetic programming, Image restoration, E-median filter, Adaptive Wiener filter (AWF) %9 journal article %P 429-436 %0 Conference Proceedings %T Automated Machine Learning: The New Wave of Machine Learning %A Chauhan, Karansingh %A Jani, Shreena %A Thakkar, Dhrumin %A Dave, Riddham %A Bhatia, Jitendra %A Tanwar, Sudeep %A Obaidat, Mohammad S. %S 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) %D 2020 %8 May 7 mar %I IEEE %C Bangalore, India %F Chauhan:2020:ICIMIA %X With the explosion in the use of machine learning in various domains, the need for an efficient pipeline for the development of machine learning models has never been more critical. However, the task of forming and training models largely remains traditional with a dependency on domain experts and time-consuming data manipulation operations, which impedes the development of machine learning models in both academia as well as industry. This demand advocates the new research era concerned with fitting machine learning models fully automatically i.e., AutoML. Automated Machine Learning(AutoML) is an end-to-end process that aims at automating this model development pipeline without any external assistance. First, we provide an insights of AutoML. Second, we delve into the individual segments in the AutoML pipeline and cover their approaches in brief. We also provide a case study on the industrial use and impact of AutoML with a focus on practical applicability in a business context. At last, we conclude with the open research issues, and future research directions. %K genetic algorithms, genetic programming, TPOT %R 10.1109/ICIMIA48430.2020.9074859 %U http://dx.doi.org/10.1109/ICIMIA48430.2020.9074859 %P 205-212 %0 Journal Article %T Evolution of sustained foraging in three-dimensional environments with physics %A Chaumont, Nicolas %A Adami, Christoph %J Genetic Programming and Evolvable Machines %D 2016 %8 dec %V 17 %N 4 %@ 1389-2576 %F Chaumont:2016:GPEM %X Artificially evolving foraging behavior in simulated articulated animals has proved to be a notoriously difficult task. Here, we co-evolve the morphology and controller for virtual organisms in a three-dimensional physical environment to produce goal-directed locomotion in articulated agents. We show that following and reaching multiple food sources can evolve de novo, by evaluating each organism on multiple food sources placed on a basic pattern that is gradually randomized across generations. We devised a strategy of evolutionary “staging”, where the best organism from a set of evolutionary experiments using a particular fitness function is used to seed a new set, with a fitness function that is progressively altered to better challenge organisms as evolution improves them. We find that an organism’s efficiency at reaching the first food source does not predict its ability at finding subsequent ones because foraging efficiency crucially depends on the position of the last food source reached, an effect illustrated by “foraging maps” that capture the organism’s controller state, body position, and orientation. Our best evolved foragers are able to reach multiple food sources over 90percent of the time on average, a behavior that is key to any biologically realistic simulation where a self-sustaining population has to survive by collecting food sources in three-dimensional, physical environments. %K genetic algorithms, genetic programming, alife, Sustainable foraging, 3D environment, Physics simulator, Body-brain co-evolution, Foraging map EVO %9 journal article %R 10.1007/s10710-016-9270-z %U http://dx.doi.org/10.1007/s10710-016-9270-z %P 359-390 %0 Journal Article %T Shear Strength of Slender Reinforced Concrete Beams without Web Reinforcement %A Chavan, R. S. %A Pawar, P. M. %J International Journal of Engineering Research and Applications %D 2013 %8 nov dec %V 3 %N 6 %@ 2248-9622 %F Chavan:2013:IJERA %X This paper attempt to predict the shear strength of high strength concrete beam with different shear span of depth ratio without web reinforcement. The large data base available has been clustered and a linear equation analysis has been performed . The prepared models are functions of compressive strength , percentage of flexural reinforcement and depth of beam. The proposed models have been validated with existence of popular models as well as with design code provisions. %K genetic algorithms, genetic programming, Shear strength, fuzzy rule, shear span to depth ratio (a/d) %9 journal article %U https://www.ijera.com/pages/v3-no6.html %P 554-559 %0 Conference Proceedings %T Una Herramienta de Programacion Genetica Paralela que Aprovecha Recursos Publicos de Computacion %A Chavez de la O, Francisco %A Guisado Lizar, Jose Luis %A Lombrana Gonzalez, Daniel %A Fernandez de Vega, Francisco %Y Rodriguez, Francisco Almeida %Y Batista, Maria Belen Melian %Y Perez, Jose Andres Moreno %Y Vega, Jose Marcos Moreno %S MAEB’2007, V Congreso Espanol sobre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados %D 2007 %8 feb %I La Laguna %C Puerto de la Cruz, Spain %F chavez:2007:MAEB %X Eeste articulo presenta una primera implementacion de una herramienta generica de programacion genetica capaz de aprovechar recursos publicos de computacion. Dadas las altas necesidades de recursos de computacion requeridos por los algoritmos evolutivos, la aplicacion del paralelismo ha sido habitual recientemente, aunque las herramientas paralelas requieren infraestructuras costosas para su aprovechamiento. El modelo que se presenta en este articulo, permite utilizar computadores distribuidos en Internet, cuyos usuarios ceden altruistamente para colaborar en proyectos de investigacion. El proceso de donacion de recursos es simple e inmediato por parte del usuario, afectando solamente a los ciclos de CPU que no son consumidos por el propio usuario. Se estudia la mejora de las prestaciones obtenidas gracias al uso de estos recursos en Programacion Genetica Distribuida. %K genetic algorithms, genetic programming, Palabras clave, Algoritmos Paralelos, Programacion Genetica %U http://icaro.eii.us.es/~jlguisado/publicaciones/MAEB2007_preprint.pdf %P 167-173 %0 Journal Article %T Deploying massive runs of evolutionary algorithms with ECJ and Hadoop: Reducing interest points required for face recognition %A Chavez, Francisco %A Fernandez de Vega, Francisco %A Lanza, Daniel %A Benavides, Cesar %A Villegas, Juan %A Trujillo, Leonardo %A Olague, Gustavo %A Roman, Graciela %J The International Journal of High Performance Computing Applications %D 2018 %8 sep %V 32 %N 5 %F Chavez:2018:IJHPCA %X In this paper we present a new strategy for deploying massive runs of evolutionary algorithms with the well-known Evolutionary Computation Library (ECJ) tool, which we combine with the MapReduce model so as to allow the deployment of computing intensive runs of evolutionary algorithms on big data infrastructures. Moreover, by addressing a hard real life problem, we show how the new strategy allows us to address problems that cannot be solved with more traditional approaches. Thus, this paper shows that by using the Hadoop framework ECJ users can, by means of a new parameter, choose where the run will be launched, whether in a Hadoop based infrastructure or in a desktop computer. Moreover, together with the performed tests we address the well-known face recognition problem with a new purpose: to allow a genetic algorithm to decide which are the more relevant interest points within the human face. Massive runs have allowed us to reduce the set from about 60 to just 20 points. In this way, recognition tasks based on the solution provided by the genetic algorithm will work significantly quicker in the future, given that just 20 points will be required. Therefore, two goals have been achieved: (a) to allow ECJ users to launch massive runs of evolutionary algorithms on big data infrastructures and also (b) to demonstrate the capabilities of the tool to successfully improve results regarding the problem of face recognition. %K genetic algorithms, genetic programming, ECJ, face recognition, Hadoop, parallel evolutionary algorithm %9 journal article %R 10.1177/1094342016678302 %U https://evovision.cicese.mx/Olague-JHPCA2018.pdf %U http://dx.doi.org/10.1177/1094342016678302 %P 706-720 %0 Conference Proceedings %T Energy-consumption prediction of genetic programming algorithms using a fuzzy rule-based system %A Chavez, F. %A Fdez de Vega, F. %A Diaz, J. %A Garcia, J. A. %A Rodriguez, F. J. %A Castillo, P. A. %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 Chavez:2018:GECCOcomp %X Energy awareness has gained momentum over the last decade in the software industry, as well as in environmentally concious society. Thus, algorithm designers and programmers are paying increasing attention this issue, particularly when hand-held devices are considered, given their battery-consuming characteristics. When we focus on Evolutionary Algorithms, few works have attempted to study the relationship between the main features of the algorithm, the problem to be solved and the underlying hardware where it runs. This work presents a preliminary analysis and modelling of energy consumption of EAs. We try to predict it by means of a fuzzy rule-based system, so that different devices are considered as well as a number of problems and Genetic Programming parameters. Experimental results performed show that the proposed model can predict energy consumption with very low error values. %K genetic algorithms, genetic programming, Energy Consumption, Raspberry-Pi, Laptop, Tablet %R 10.1145/3205651.3208216 %U http://dx.doi.org/10.1145/3205651.3208216 %P 9-10 %0 Conference Proceedings %T Applying Genetic Programming for Estimating Software Development Effort of Short-scale Projects %A Chavoya, Arturo %A Lopez-Martin, Cuauhtemoc %A Meda-Campana, M. E. %S Eighth International Conference on Information Technology: New Generations (ITNG 2011) %D 2011 %8 November 13 apr %C Las Vegas, NV, USA %F Chavoya:2011:ITNG %X Statistical regression and neural networks have frequently been used to estimate the development effort of both short and large software projects. In this paper, a genetic programming technique is used with the goal of estimating the effort required in the development of short-scale projects. Results obtained are compared with those generated using the first two techniques. A sample of 132 short-scale projects developed by 40 programmers was used for generating the three models, whereas another sample of 77 projects developed by 24 programmers was used for validating those three models. Accuracy results from the model obtained with genetic programming suggest that it could be used to estimate software development effort of short-scale projects when these projects are developed in a disciplined manner within a development controlled environment. %K genetic algorithms, genetic programming, SBSE, development controlled environment, neural networks, short scale projects, software development effort, statistical regression, software development management %R 10.1109/ITNG.2011.37 %U http://dx.doi.org/10.1109/ITNG.2011.37 %P 174-179 %0 Journal Article %T Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects %A Chavoya, Arturo %A Lopez-Martin, Cuauhtemoc %A Andalon-Garcia, Irma R. %A Meda-Campana, M. E. %J PLoS ONE %D 2012 %8 nov 30 %V 7 %N 11 %G en %F Chavoya:2012:PLOS %X Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment. %K genetic algorithms, genetic programming, SBSE %9 journal article %R 10.1371/journal.pone.0050531 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1035.6477 %U http://dx.doi.org/10.1371/journal.pone.0050531 %P e50531 %0 Journal Article %T Software Development Effort Estimation by Means of Genetic Programming %A Chavoya, Arturo %A Lopez-Martin, Cuauhtemoc %A Meda-Campana, M. E. %J International Journal of Advanced Computer Science and Applications %D 2013 %V 4 %N 11 %I The Science and Information (SAI) Organization %G eng %F Chavoya:2013:IJACSA %X In this study, a genetic programming technique was used with the goal of estimating the effort required in the development of individual projects. Results obtained were compared with those generated by a statistical regression and by a neural network that have already been used to estimate the development effort of individual software projects. A sample of 132 projects developed by 40 programmers was used for generating the three models and another sample of 77 projects developed by 24 programmers was used for validating the three models. Results in the accuracy of the model obtained from genetic programming suggest that it could be used to estimate software development effort of individual projects. %K genetic algorithms, genetic programming, SBSE, feedforward neural network, software effort estimation, statistical regression %9 journal article %R 10.14569/IJACSA.2013.041115 %U http://thesai.org/Downloads/Volume4No11/Paper_15-Software_Development_Effort_Estimation_by_Means_of_Genetic_Programming.pdf %U http://dx.doi.org/10.14569/IJACSA.2013.041115 %0 Conference Proceedings %T Data Classification Using Genetic Parallel Programming %A Cheang, Sin Man %A Lee, Kin Hong %A Leung, Kwong Sak %Y Cantú-Paz, E. %Y Foster, J. A. %Y Deb, K. %Y Davis, D. %Y Roy, R. %Y O’Reilly, U.-M. %Y Beyer, H.-G. %Y Standish, R. %Y Kendall, G. %Y Wilson, S. %Y Harman, M. %Y Wegener, J. %Y Dasgupta, D. %Y Potter, M. A. %Y Schultz, A. C. %Y Dowsland, K. %Y Jonoska, N. %Y Miller, J. %S Genetic and Evolutionary Computation – GECCO-2003 %S LNCS %D 2003 %8 December 16 jul %V 2724 %I Springer-Verlag %C Chicago %@ 3-540-40603-4 %F cheang2:2003:gecco %X A novel Linear Genetic Programming (LGP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. It is found that GPP can evolve parallel programs for Data Classification problems. In this paper, five binary-class UCI Machine Learning Repository databases are used to test the effectiveness of the proposed GPP-classifier. The main advantages of employing GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; and 2) discovering parallel algorithms automatically. Experimental results show that the GPP-classifier evolves simple classification programs with good generalization performance. The accuracies of these evolved classifiers are comparable to other existing classification algorithms. %K genetic algorithms, genetic programming, Learning Classifier Systems, poster %R 10.1007/3-540-45110-2_88 %U http://dx.doi.org/10.1007/3-540-45110-2_88 %P 1918-1919 %0 Conference Proceedings %T Improving Evolvability of Genetic Parallel Programming Using Dynamic Sample Weighting %A Cheang, Sin Man %A Lee, Kin Hong %A Leung, Kwong Sak %Y Cantú-Paz, E. %Y Foster, J. A. %Y Deb, K. %Y Davis, D. %Y Roy, R. %Y O’Reilly, U.-M. %Y Beyer, H.-G. %Y Standish, R. %Y Kendall, G. %Y Wilson, S. %Y Harman, M. %Y Wegener, J. %Y Dasgupta, D. %Y Potter, M. A. %Y Schultz, A. C. %Y Dowsland, K. %Y Jonoska, N. %Y Miller, J. %S Genetic and Evolutionary Computation – GECCO-2003 %S LNCS %D 2003 %8 December 16 jul %V 2724 %I Springer-Verlag %C Chicago %@ 3-540-40603-4 %F cheang:2003:gecco %X sample weighting effect on Genetic Parallel Programming (GPP) that evolves parallel programs to solve the training samples captured directly from a real-world system. The distribution of these samples can be extremely biased. Standard GPP assigns equal weights to all samples. It slows down evolution because crowded regions of samples dominate the fitness evaluation and cause premature convergence. This paper compares the performance of four sample weighting (SW) methods, namely, Equal SW (ESW), Class-equal SW (CSW), Static SW (SSW) and Dynamic SW (DSW) on five training sets. Experimental results show that DSW is superior in performance on tested problems. %K genetic algorithms, genetic programming, poster %R 10.1007/3-540-45110-2_72 %U http://dx.doi.org/10.1007/3-540-45110-2_72 %P 1802-1803 %0 Conference Proceedings %T An Empirical Study of the Accelerating Phenomenon in Genetic Parallel Programming %A Cheang, Sin Man %A Lee, Kin Hong %A Leung, Kwong Sak %Y Rylander, Bart %S Genetic and Evolutionary Computation Conference Late Breaking Papers %D 2003 %8 December %C Chicago, USA %F cheang:gecco03lbp %K genetic algorithms, genetic programming %P 54-61 %0 Conference Proceedings %T Evolving data classification programs using genetic parallel programming %A Cheang, Sin Man %A Lee, Kin Hong %A Leung, Kwong Sak %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 cheang:2003:edcpugpp %X A novel Linear Genetic Programming (Linear GP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. The GPP Accelerating Phenomenon, i.e. parallel programs are easier to be evolved than sequential programs, opens up a new two-step approach: 1) evolves a parallel program solution; and 2) serialises the parallel program to a equivalent sequential program. In this paper, five two-class UCI Machine Learning Repository databases are used to investigate the effectiveness of GPP. The main advantages to employ GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; 2) discovering parallel algorithms automatically; and 3) boosting evolutionary performance by the GPP Accelerating Phenomenon. Experimental results show that GPP evolves simple classification programs with good generalisation performance. The accuracies of these evolved classification programs are comparable to other existing classification algorithms. %K genetic algorithms, genetic programming, Acceleration, Classification algorithms, Concurrent computing, Data mining, Databases, Machine learning, Machine learning algorithms, Parallel programming, Registers, data analysis, learning (artificial intelligence), parallel programming, pattern classification, tree data structures, GPP-classifier, UCI machine learning repository databases, classification algorithms, data classification problems, data classification programs, evolutionary process, generalization performance, genetic parallel programming, linear genetic programming paradigm, multiALU processor, parallel algorithms, parallel hardware fitness evaluation, parallel programs %R 10.1109/CEC.2003.1299582 %U http://dx.doi.org/10.1109/CEC.2003.1299582 %P 248-255 %0 Conference Proceedings %T Applying sample weighting methods to genetic parallel programming %A Cheang, Sin Man %A Lee, Kin Hong %A Leung, Kwong Sak %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 Man:2003:Aswmtgpp %X We investigate the sample weighting effect on Genetic Parallel Programming (GPP). GPP evolves parallel programs to solve the training samples in a training set. Usually, the samples are captured directly from a real-world system. The distribution of samples in a training set can be extremely biased. Standard GPP assigns equal weights to all samples. It slows down evolution because crowded regions of samples dominate the fitness evaluation causing premature convergence. This paper presents 4 sample weighting (SW) methods, i.e. Equal SW, Class-equal SW, Static SW (SSW) and Dynamic SW (DSW). We evaluate the 4 methods on 7 training sets (3 Boolean functions and 4 UCI medical data classification databases). Experimental results show that DSW is superior in performance on all tested problems. In the 5-input Symmetry Boolean function experiment, SSW and DSW boost the evolutionary performance by 465 and 745 times respectively. Due to the simplicity and effectiveness of SSW and DSW, they can also be applied to different population-based evolutionary algorithms. %K genetic algorithms, genetic programming, Boolean functions, Clocks, Computer science, Computer science education, Concurrent computing, Educational programs, Evolutionary computation, Parallel programming, Silicon compounds, Boolean functions, learning (artificial intelligence), parallel programming, Boolean function, DSW, GPP, SSW, UCI medical data classification database, class-equal SW method, dynamic SW method, equal SW method, evolutionary algorithm, genetic parallel programming, real-world system, sample weighting method, static SW method, training sample, training set %R 10.1109/CEC.2003.1299766 %U http://dx.doi.org/10.1109/CEC.2003.1299766 %P 928-935 %0 Conference Proceedings %T An Empirical Study of the GPP Accelerating Phenomenon %A Cheang, Sin Man %Y Vadakkepat, P. %Y Wan, T. W. %Y Chen, T. K. %Y Poh, L. A. %S Proceedings of the second International Conference on Computational Intelligence, Robotics and Autonomous Systems – CIRAS-2003 %D 2003 %8 15 18 dec %I National Univ. of Singapore %C Singapore %F cheang:2003:CIRAS %X The Genetic Parallel Programming (GPP) is a novel Linear-structure Genetic Programming paradigm that learns parallel programs. We discover the GPP Accelerating Phenomenon, i.e. parallel programs are evolved faster than their counterpart sequential programs of identical functions. This paper presents an empirical study of Boolean function regression based on a Multi-ALU Processor that results in the phenomenon. We performed a series of random search experiments using different numbers of ALUs (w) and instructions (l). We identify that w (the degree of parallelism of the program) is the dominant factor that affects the searching performance. In a 3-input Boolean function experiment, searching a single-ALU program requires 875 times on average of the computational effort of an 8-ALU program. An investigation on the probabilities of finding solutions to different problem instances shows that parallel representation of programs can increase the probabilities of finding solutions to hard problems. %K genetic algorithms, genetic programming %P PS04-4–03 %0 Conference Proceedings %T Designing Optimal Combinational Digital Circuits Using a Multiple Logic Unit Processor %A Cheang, Sin Man %A Lee, Kin Hong %A Leung, Kwong Sak %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 cheang:2004:eurogp %X Genetic Parallel Programming (GPP) is a novel Genetic Programming paradigm. The GPP Accelerating Phenomenon, i.e. parallel programs are easier to be evolved than sequential programs, opens up a new approach to evolve solution programs in parallel forms. Based on the GPP paradigm, we developed a combinational digital circuit learning system, the GPP+MLP system. An optimal Multiple Logic Unit Processor (MLP) is designed to evaluate genetic parallel programs. To show the effectiveness of the proposed GPP+MLP system, four multi-output Binary arithmetic circuits are used. Experimental results show that both the gate counts and the propagation gate delays of the evolved circuits are less than conventional designs. For example, in a 3-bit multiplier experiment, we obtained a combinational digital circuit with 26 two-input logic gates in 6 gate levels. It uses 4 gates less than a conventional design. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-24650-3_3 %U http://dx.doi.org/10.1007/978-3-540-24650-3_3 %P 23-34 %0 Thesis %T Genetic parallel programming %A Cheang, Sin Man %D 2005 %8 mar %C Hong Kong %C The Chinese University of Hong Kong %G English %F cheang:thesis %X This thesis investigates the design and implementation of a novel linear-structured Genetic Programming (GP) paradigm, Genetic Parallel Programming (GPP), in which a parallel architecture, Multi-Arithmetic-Logic-Unit Processor (MAP) is employed. The MAP is a MIMD, general-purpose register machine that can be implemented on modern Field Programmable Gate Arrays so that genetic parallel programs can be evaluated at high speed. Based on the parallel architecture of MAP, GPP evolves genetic programs in parallel form. This thesis presents a number of benchmark problems. The evolved solution programs are precise and compact. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, an accelerating phenomenon in GPP, the GPP accelerating phenomenon, is observed. Experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. This creates a new approach to evolving a feasible problem solution program in parallel form and then serializes it into a sequential form if required. Since serialization is mechanical and its processing time is linear with respect to the size of the parallel program, the total learning time can be reduced significantly. In order to evolve parallel programs effectively and efficiently, this thesis also investigates different genetic operators to assist the evolution. These operators include Dynamic Sample Weighting (DSW), dual-phase fitness functions and special types of mutation for parallel programs. Since the samples in a training set are captured directly from a real-world system, the distribution of these samples can be extremely biased. DSW adjusts the weights of training samples dynamically according to their past frequency of hits. Experimental results show that DSW boosts the evolutionary performance significantly... To demonstrate the applicability of GPP, two application systems have been developed: (1) GPP Data Classification System (GPP-Classifier); and (2) GPP Logic Circuit Synthesizer (GPPLCS). The GPP-Classifier evolves MAP programs to classify data records in a database. The GPPLCS synthesizes combinational logic circuits directly from a truth table with different logic gates or RAM-based lookup-tables. High performance logic circuits are evolved and both their gate counts and propagation gate delays are less than that of the conventional designs... ... this thesis has made four major contributions: 1) parallel ; 2) revealing the GPP accelerating phenomenon; 3) inventing DSW 4) GPP Data Classification System a n d th e GPP Logic Circuit Synthesizer. %K genetic algorithms, genetic programming, Applied sciences, Parallel computing, Sample weighting, Computer science %9 Ph.D. thesis %U https://search.proquest.com/docview/305346245 %0 Journal Article %T Genetic Parallel Programming: Design and Implementation %A Cheang, Sin Man %A Leung, Kwong Sak %A Lee, Kin Hong %J Evolutionary Computation %D 2006 %8 Summer %V 14 %N 2 %@ 1063-6560 %F Cheang:2006:EC %X This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serialises it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially. %K genetic algorithms, genetic programming, linear genetic programming, parallel processor architecture, MIMD, parallel assembly program, ALU MAP, GPP, Fibonacci recursive sequence %9 journal article %R 10.1162/evco.2006.14.2.129 %U http://dx.doi.org/10.1162/evco.2006.14.2.129 %P 129-156 %0 Journal Article %T Applying Genetic Parallel Programming to Synthesize Combinational Logic Circuits %A Cheang, Sin Man %A Lee, Kin Hong %A Leung, Kwong Sak %J IEEE Transactions on Evolutionary Computation %D 2007 %8 aug %V 11 %N 4 %@ 1389-2576 %F Cheang:2007:tec %X Experimental results show that parallel programs can be evolved more easily than sequential programs in genetic parallel programming (GPP). GPP is a novel genetic programming paradigm which evolves parallel program solutions. With the rapid development of lookup-table-based (LUT-based) field programmable gate arrays (FPGAs), traditional circuit design and optimisation techniques cannot fully exploit the LUTs in LUT-based FPGAs. Based on the GPP paradigm, we have developed a combinational logic circuit learning system, called GPP logic circuit synthesiser (GPPLCS), in which a multilogic-unit processor is used to evaluate LUT circuits. To show the effectiveness of the GPPLCS, we have performed a series of experiments to evolve combinational logic circuits with two- and four-input LUTs. In this paper, we present eleven multi-output Boolean problems and their evolved circuits. The results show that the GPPLCS can evolve more compact four-input LUT circuits than the well-known LUT-based FPGA synthesis algorithms. %K genetic algorithms, genetic programming, FPGA, Circuit design, digital circuits, evolvable hardware, parallel programming %9 journal article %R 10.1109/TEVC.2006.884044 %U http://dx.doi.org/10.1109/TEVC.2006.884044 %P 503-520 %0 Conference Proceedings %T Using a co-evolutionary approach to automatically generate vertical undulation and lateral rolling motions for snake-like modular robot %A Chee, Wei Shun %A Teo, Jason %S 2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA) %D 2014 %8 dec %F Chee:2014:ROMA %X This paper explores the use of evolutionary algorithm approach to automatically design and optimise the snake-like modular robot to acquire with the vertical undulation locomotion and lateral rolling moving behaviour. A hybridized Genetic Programming and self-adaptive Differential Evolution algorithm is implemented in this work to simultaneously co-evolve both the morphology and controller of the snake-like modular robot throughout the artificial evolutionary process. This paper also illustrates on how the overall structure and control strategy of the snake-like modular robot is being designed in order for the snake-like modular robot to perform the particular locomotion. Moreover, different fitness functions had also been modelled for each locomotion experiment in computing the performance score of the snake-like modular robot. Interestingly, it was found out that the snake-like modular robot can actually travel for longer distance using vertical undulation locomotion. It was also found out that the rolling movement of the snake-like modular robot can be achieved by motors attached only in pitch and yaw axis. In conclusion, promising results were obtained in this work showing that the co-evolving evolutionary algorithm illustrated in this work is feasible to be implemented to automatically design and optimise the modular robot to evolve with various locomotion capabilities. %K genetic algorithms, genetic programming %R 10.1109/ROMA.2014.7295894 %U http://dx.doi.org/10.1109/ROMA.2014.7295894 %P 236-241 %0 Conference Proceedings %T Simultaneous Evolutionary-Based Optimization of Controller and Morphology of Snake-Like Modular Robots %A Chee, Wei Shun %A Teo, Jason %S 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology (ICAIET) %D 2014 %8 dec %F Chee:2014:ICAIET %X This paper explores the use of evolutionary algorithm approach to automatically design and optimise the snake-like modular robot to automatically design and optimise the snake-like modular robot to acquire the forward moving behaviour. A hybridized Genetic Programming and self-adaptive Differential Evolution algorithm is implemented to co-evolving both the morphology and controller of the robot throughout the artificial evolutionary process. Two different artificial evolutionary experiments have been conducted in this paper by using the classic DE mutation technique (DE/rand/1/bin) and a customized DE mutation technique with different mutation differential operation. It was found out that the customized DE mutation approach is more effective in co-evolving both the morphology and controller for the snake-like modular robot to acquire forward moving behaviour. Moreover, from the analysis conducted on the results obtained throughout the evolutionary process, interesting findings were discovered on the evolved morphology and moving behaviour of the snake-like modular robot. In conclusion, promising results were shown in this work which suggests that the co-evolving evolutionary algorithm presented in this work is an alternative method and feasible to be implemented to automatically design and optimise the modular robot for the moving behaviour by co-evolving both the morphology and controller of the modular robot. %K genetic algorithms, genetic programming %R 10.1109/ICAIET.2014.16 %U http://dx.doi.org/10.1109/ICAIET.2014.16 %P 37-42 %0 Journal Article %T Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation %A Cheema, Jitender Jit Singh %A Sankpal, Narendra V. %A Tambe, Sanjeev S. %A Kulkarni, Bhaskar D. %J Biotechnology Progress %D 2002 %V 18 %N 6 %@ 8756-7938 %F cheema:2002:BTP %X This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses. %K genetic algorithms, genetic programming %9 journal article %R 10.1021/bp015509s %U http://www3.interscience.wiley.com/journal/121399381/abstract %U http://dx.doi.org/10.1021/bp015509s %P 1356-1365 %0 Conference Proceedings %T Evolutionary Programming with Tree Mutations: Evolving Computer Programs without Crossover %A Chellapilla, Kumar %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 Chellapilla:1997:eptm %K evolutionary programming and evolution strategies %P 431-438 %0 Conference Proceedings %T Evolving Nonlinear Controllers for Backing up a Truck-and-Trialer Using Evolutionary Programming %A Chellapilla, Kumar %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 chellapilla:1998:enlbtatuEP %K evolutionary programming %R 10.1007/BFb0040753 %U http://dx.doi.org/10.1007/BFb0040753 %P 417-426 %0 Conference Proceedings %T Automatic Generation of Nonlinear Optimal Control Laws for Broom Balancing using Evolutionary Programming %A Chellapilla, Kumar %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 chellapilla:1998:agnoclbbEP %X This paper explores the use of mutation operators with evolutionary programming (EP) to automatically generate time optimal ’bang-bang’ type control laws for the three dimensional broom balancing (inverted pendulum) problem. EP produces a time optimal nonlinear control strategy that takes the state variables as input and determines the direction of the ’bang-bang’ force to be applied. Preliminary results indicate that the control laws generated are capable of generalising over previously unseen input states and compare well with nonlinear control laws that were generated using other evolutionary computation methods. %K genetic algorithms, genetic programming, 3D broom balancing problem, automatic nonlinear optimal control law generation, bang-bang force direction, bang-bang type control laws, broom balancing, evolutionary computation methods, evolutionary programming, inverted pendulum problem, mutation operators, state variables, time optimal nonlinear control strategy, unseen input states, bang-bang control, nonlinear control systems, optimal control, optimisation %R 10.1109/ICEC.1998.699500 %U c034.pdf %U http://dx.doi.org/10.1109/ICEC.1998.699500 %P 195-200 %0 Conference Proceedings %T A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover %A Chellapilla, Kumar %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 chellapilla:1998:piempwsx %X ADF like modularity in evolutionary programming demonstrated on parity problems of various sizes %K genetic algorithms, genetic programming, EP, ADF, parity %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/chellapilla_1998_piempwsx.pdf %P 23-31 %0 Conference Proceedings %T Effectivenss of Local Search Operators in Evolutionary Programming %A Chellapilla, Kumar %A Birru, Hemanth %A Sathyanarayan, Rao %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 chellapilla:1998:elsoEP %K evolutionary programming %P 753-761 %0 Journal Article %T Evolving Computer Programs without Subtree Crossover %A Chellapilla, Kumar %J IEEE Transactions on Evolutionary Computation %D 1997 %8 sep %V 1 %N 3 %@ 1089-778X %F Chellapilla:1998:eptm %X An evolutionary programming procedure is used for optimising computer programs in the form of symbolic expressions. Six tree mutation operators are proposed. Recombination operators such as crossover are not included. The viability and efficiency of the method is extensively investigated on a set of well-studied problems. The evidence indicates that the technique is not only viable but is indeed capable of evolving good computer programs. The results compare well with other evolutionary methods that rely on crossover to solve the same problems %K genetic algorithms, genetic programming, symbolic expressions, Evolutionary Programming, variation operators %9 journal article %R 10.1109/4235.661552 %U http://dx.doi.org/10.1109/4235.661552 %P 209-216 %0 Thesis %T Designing Effective Evolutionary Computations %A Chellapilla, Kumar H. %D 2005 %C USA %C Electrical Engineering, University of California, San Diego %F Chellapilla:thesis %X Evolutionary algorithms offer a practical approach to solving difficult real-world problems. In many problem domains, these are the only possible approaches with potential for effectively searching through complex solution spaces. For novel problem domains wherein previous research efforts are sparse or problem domain expertise is in its infancy, evolutionary algorithms offer strong alternatives for exploring the solution spaces and also gaining insights into effectively solving the problem. The principal roadblock in conventional practice is the lack of a specific approach which permits one to simultaneously control an algorithm’s representation, population variation operators and population selection operators. An approach based on mathematically sound principles is adopted in this thesis to provide asymptotic guarantees on evolutionary algorithm performance followed by useful real-time methods for improving the rate of convergence. In particular, the evolutionary algorithm is decomposed into its constituent representation, population variation, and population selection operators. The population variation operators are further broken down into solution variation operators. Each component is independently analysed without being constrained by an overall architecture for the evolutionary algorithm. Each component presents several alternatives that can be chosen independently to control desired properties of the evolutionary algorithm. A new mathematical model for analysing evolutionary algorithms is developed, and necessary and sufficient conditions on the variation and selection operators for asymptotic convergence are derived. Fitness distributions and fitness distribution feature based heuristics are presented to improve the rate of convergence of an evolutionary algorithm. This thesis also presents a wide array of empirical results to demonstrate the utility, effectiveness, and applicability of the new theory. Within the new framework, evolutionary algorithms are applied to solve real, discrete and mixed parameter optimization problems. Evolutionary algorithms that guarantee asymptotic convergence are designed to solve problems involving structures such as parse trees and finite state machines. Co-evolutionary algorithms are designed to evolve an expert checkers player that rated 2045 against human checkers players. Fitness distribution heuristics are used to tune an evolutionary algorithm for improved rate of convergence for solving the travelling salesman problem. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://search.proquest.com/docview/305003505 %0 Conference Proceedings %T Applications of artificial intelligence technologies in credit scoring: A survey of literature %A Chen, Bili %A Zeng, Wenhua %A Lin, Yangbin %S 10th International Conference on Natural Computation (ICNC 2014) %D 2014 %8 aug %F Chen:2014:ICNC %X We covers support vector machines, artificial neural networks, genetic algorithms, genetic programming algorithms and their hybrids. %K genetic algorithms, genetic programming %R 10.1109/ICNC.2014.6975914 %U http://dx.doi.org/10.1109/ICNC.2014.6975914 %P 658-664 %0 Conference Proceedings %T A Self-adapting Algorithm for Identifying Rheology Model and Its Parameters of Rock Mass %A Chen, Bing-Rui %A Feng, Xia-Ting %A Yang, Cheng-Xiang %S International Conference on Computational Intelligence and Natural Computing, CINC ’09 %D 2009 %8 jun %V 2 %F Chen:2009:CINC %X As it is difficult to previously determine rockmass rheology constitutive model using phenomena methods of mechanics, so a new self-adapting system identification method, a hybrid genetic programming (GP) with the chaos-genetic algorithm (CGA) based on self-rheological characteristic of rock mass, is proposed. Genetic programming is used for exploring the model’s structure and the chaos-genetic algorithm is produced to identify parameters (coefficients) in the tentative model. The optimal rheological model is determined by mechanical and rheological characteristic, important expertise etc and can describe rheological behavior of identified rock mass perfectly. The assistant tunnel B of Jinping-2 hydropower station is used as an example for verifying the proposed method. The results show that the algorithm is feasible and has great potential in finding new rheological models. %K genetic algorithms, genetic programming, Jinping-2 hydropower station, chaos-genetic algorithm, hybrid genetic programming, optimal rheological model, rheology model identification, rock mass parameters, self-adapting system identification method, tentative model, identification, natural resources, rheology %R 10.1109/CINC.2009.39 %U http://dx.doi.org/10.1109/CINC.2009.39 %P 478-481 %0 Thesis %T Bayesian methodology for genetics of complex diseases %A Chen, Carla Chia-Ming %D 2010 %C Australia %C Past, QUT Faculties & Divisions, Faculty of Science and Technology, Queensland University of Technology %F Carla_Chen_Thesis %X Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression. %K genetic algorithms, genetic programming, gene expression programming, Bayesian, statistics, genetics, phenotype analysis, complex diseases, complex etiology, model comparison, latent class analysis, grade of membership, fuzzy clustering, item response theory, migraine, twin study, heritability, genome-wide linkage analysis, deviance information criteria, model averaging, MCMC, genomewide association studies, epistasis, logistic regression, stochastic search algorithm, case-control studies, Type I diabetes, single nucleotide polymorphism, logic tree, logicFS, Monte Carlo logic regression, genetic programming for association study, random forest, GENICA %9 Ph.D. thesis %U http://eprints.qut.edu.au/43357/ %0 Journal Article %T Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression %A Chen, Carla Chia-Ming %A Schwender, Holger %A Keith, Jonathan %A Nunkesser, Robin %A Mengersen, Kerrie %A Macrossan, Paula %J IEEE/ACM Transactions on Computational Biology and Bioinformatics %D 2011 %8 nov dec %V 8 %N 6 %@ 1545-5963 %F Chen:2011:TCBB %X Due to advancements in computational ability, enhanced technology and a reduction i the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modelling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection. %K genetic algorithms, genetic programming, Gene Expression Programming, Logic regressions, Genetic Programming for Association Studies, Modified Logic Regression-Gene Expression Programming, Random Forest, Bayesian logistic regression with stochastic search algorithm, candidate gene search %9 journal article %R 10.1109/TCBB.2011.46 %U http://dx.doi.org/10.1109/TCBB.2011.46 %P 1580-1591 %0 Journal Article %T Typhoon event-based evolutionary fuzzy inference model for flood stage forecasting %A Chen, Chang-Shian %A Jhong, You-Da %A Wu, Ting-Ying %A Chen, Shien-Tsung %J Journal of Hydrology %D 2013 %8 20 may %V 490 %@ 0022-1694 %F Chen:2013:JH %X This study proposes an evolutionary fuzzy inference model that combines a fuzzy inference model, genetic programming (GP), and a genetic algorithm (GA) to forecast flood stages during typhoons. The number of fuzzy inference rules in the proposed approach is based on the number of typhoon flood events. The consequent part of the rule was formed by constructing GP models that depict the rainfall-stage relationship of a specific flood event, whereas the GA was used to search the parameters of the fuzzy membership functions in the premise part of the rule. This study uses the proposed event-based evolutionary fuzzy inference model to forecast the typhoon flood stages of Wu River in Taiwan. Forecasting results based on stage hydrographs and performance indices verify the forecasting ability of the proposed model. This study also identifies the weights of triggered fuzzy rules during the fuzzy inference process, showing that a fuzzy rule is triggered according to the characteristics of the flood event that forms the rule. Moreover, physical explanation of the proposed evolutionary fuzzy inference model was discussed. %K genetic algorithms, genetic programming, Fuzzy inference, Flood stage forecasting %9 journal article %R 10.1016/j.jhydrol.2013.03.033 %U http://www.sciencedirect.com/science/article/pii/S0022169413002424 %U http://dx.doi.org/10.1016/j.jhydrol.2013.03.033 %P 134-143 %0 Conference Proceedings %T Elite bases regression: A real-time algorithm for symbolic regression %A Chen, Chen %A Luo, Changtong %A Jiang, Zonglin %S 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) %D 2017 %8 jul %F Chen:2017:ICNC-FSKD %X Symbolic regression is an important but challenging research topic in data mining. It can detect the underlying mathematical models. Genetic programming (GP) is one of the most popular methods for symbolic regression. However, its convergence speed might be too slow for large scale problems with a large number of variables. This drawback has become a bottleneck in practical applications. In this paper, a new non-evolutionary real-time algorithm for symbolic regression, Elite Bases Regression (EBR), is proposed. EBR generates a set of candidate basis functions coded with parse-matrix in specific mapping rules. Meanwhile, a certain number of elite bases are preserved and updated iteratively according to the correlation coefficients with respect to the target model. The regression model is then spanned by the elite bases. A comparative study between EBR and a recent proposed machine learning method for symbolic regression, Fast Function eXtraction (FFX), are conducted. Numerical results indicate that EBR can solve symbolic regression problems more effectively. %K genetic algorithms, genetic programming %R 10.1109/FSKD.2017.8393325 %U http://dx.doi.org/10.1109/FSKD.2017.8393325 %P 529-535 %0 Conference Proceedings %T Fast Modeling Methods for Complex System with Separable Features %A Chen, Chen %A Luo, Changtong %A Jiang, Zonglin %S 2017 10th International Symposium on Computational Intelligence and Design (ISCID) %D 2017 %8 dec %V 1 %F Chen:2017:ISCID %X Data-driven modelling plays an increasingly important role in different areas of engineering. For most of existing methods, such as genetic programming (GP), the convergence speed might be too slow for large scale problems with a large number of variables. Fortunately, in many applications, the target models are separable in some sense. In this paper, we analyse different types of separability and establish a generalised separable model (GSM). In order to get the structure of the GSM, a multi-level block search method is proposed, in which the target model is decomposed into a number of blocks, further into minimal blocks and factors. Compare to the conventional GP, the new method can make large reductions to the search space. The minimal blocks and factors are optimised and assembled with a global optimisation search engine, low dimensional simplex evolution (LDSE). An extensive study between the proposed method and a state-of-the-art data-driven fitting tool, Eureqa, has been presented with several man-made problems. Test results indicate that the proposed method is more effective and efficient under all the investigated cases. %K genetic algorithms, genetic programming %R 10.1109/ISCID.2017.144 %U http://dx.doi.org/10.1109/ISCID.2017.144 %P 201-204 %0 Journal Article %T Block building programming for symbolic regression %A Chen, Chen %A Luo, Changtong %A Jiang, Zonglin %J Neurocomputing %D 2018 %8 31 jan %V 275 %@ 0925-2312 %F CHEN20181973 %X Symbolic regression that aims to detect underlying data-driven models has become increasingly important for industrial data analysis. For most existing algorithms such as genetic programming (GP), the convergence speed might be too slow for large-scale problems with a large number of variables. This situation may become even worse with increasing problem size. The aforementioned difficulty makes symbolic regression limited in practical applications. Fortunately, in many engineering problems, the independent variables in target models are separable or partially separable. This feature inspires us to develop a new approach, block building programming (BBP). BBP divides the original target function into several blocks, and further into factors. The factors are then modelled by an optimization engine (e.g. GP). Under such circumstances, BBP can make large reductions to the search space. The partition of separability is based on a special method, block and factor detection. Two different optimization engines are applied to test the performance of BBP on a set of symbolic regression problems. Numerical results show that BBP has a good capability of structure and coefficient optimization with high computational efficiency. %K genetic algorithms, genetic programming, Symbolic regression, Separable function, Block building programming %9 journal article %R 10.1016/j.neucom.2017.10.047 %U http://www.sciencedirect.com/science/article/pii/S0925231217316983 %U http://dx.doi.org/10.1016/j.neucom.2017.10.047 %P 1973-1980 %0 Journal Article %T A multilevel block building algorithm for fast modeling generalized separable systems %A Chen, Chen %A Luo, Changtong %A Jiang, Zonglin %J Expert Systems with Applications %D 2018 %V 109 %@ 0957-4174 %F CHEN:2018:ESA %X Symbolic regression is an important application area of genetic programming (GP), aimed at finding an optimal mathematical model that can describe and predict a given system based on observed input-response data. However, GP convergence speed towards the target model can be prohibitively slow for large-scale problems containing many variables. With the development of artificial intelligence, convergence speed has become a bottleneck for practical applications. In this paper, based on observations of real-world engineering equations, generalized separability is defined to handle repeated variables that appear more than once in the target model. To identify the structure of a function with a possible generalized separability feature, a multilevel block building (MBB) algorithm is proposed in which the target model is decomposed into several blocks and then into minimal blocks and factors. The minimal factors are relatively easy to determine for most conventional GP or other non-evolutionary algorithms. The efficiency of the proposed MBB has been tested by comparing it with Eureqa, a state-of-the-art symbolic regression tool. Test results indicate MBB is more effective and efficient; it can recover all investigated cases quickly and reliably. MBB is thus a promising algorithm for modeling engineering systems with separability features %K genetic algorithms, genetic programming, Symbolic regression, Generalized separability, Multilevel block building %9 journal article %R 10.1016/j.eswa.2018.05.021 %U http://www.sciencedirect.com/science/article/pii/S0957417418303142 %U http://dx.doi.org/10.1016/j.eswa.2018.05.021 %P 25-34 %0 Conference Proceedings %T Nonlinear Deterministic Frontier Model Using Genetic Programming %A Chen, Chin-Yi %A Huang, Jih-Jeng %A Tzeng, Gwo-Hshiung %S Cutting-Edge Research Topics on Multiple Criteria Decision Making %D 2009 %I Springer %F chen:2009:CRTMCDM %K genetic algorithms, genetic programming %R 10.1007/978-3-642-02298-2_111 %U http://link.springer.com/chapter/10.1007/978-3-642-02298-2_111 %U http://dx.doi.org/10.1007/978-3-642-02298-2_111 %0 Thesis %T A New Fitness Function for Evaluating the Quality of Predicted Protein Structures %A Chen, Chun-jen %D 2010 %8 February %C Taiwan %C National Sun Yat-sen University %F oai:NSYSU:etd-0902110-103900 %X For understanding the function of a protein, the protein structure plays an important role. The prediction of protein structure from its primary sequence has significant assistance in bioinformatics. Generally, the real protein structures can be reconstructed by some costly techniques, but predicting the protein structures helps us guess the functional expression of a protein in advance. In this thesis, we develop three terms as the materials of the fitness function that can be successfully used in protein backbone structure prediction. In the result of this thesis, it shows that over 80 percent of good values calculated from our fitness function, which are generated by the genetic programming, are better than the average in the CASP8. %K genetic algorithms, genetic programming, prediction, tertiary structure, protein %9 Masters thesis %U https://etd.lis.nsysu.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0902110-103900 %0 Conference Proceedings %T Network intrusion detection using fuzzy class association rule mining based on genetic network programming %A Chen, Ci %A Mabu, Shingo %A Yue, Chuan %A Shimada, Kaoru %A Hirasawa, Kotaro %S IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 %D 2009 %8 oct %C San Antonio, Texas, USA %F chen:2009:SMC %X Computer systems are exposed to an increasing number and type of security threats due to the expanding of Internet in recent years. How to detect network intrusions effectively becomes an important techniques. This paper presents a novel fuzzy class association rule mining method based on Genetic Network Programming (GNP) for detecting network intrusions. GNP is an evolutionary optimization techniques, which uses directed graph structures as genes instead of strings (Genetic Algorithm) or trees (Genetic Programming), leading to creating compact programs and implicitly memorizing past action sequences. By combining fuzzy set theory with GNP, the proposed method can deal with the mixed database which contains both discrete and continuous attributes. And it can be flexibly applied to both misuse and anomaly detection in Network Intrusion Detection Problem. Experimental results with KDD99Cup and DAPRA98 databases from MIT Lincoln Laboratory show that the proposed method provides a competitively high detection rate compared with other machine learning techniques. %K genetic algorithms, genetic programming, Internet, anomaly detection, computer systems, directed graph structure, evolutionary optimization, fuzzy class association rule mining, fuzzy set theory, genetic network programming, machine learning, network intrusion detection, Internet, data mining, security of data %R 10.1109/ICSMC.2009.5346328 %U http://dx.doi.org/10.1109/ICSMC.2009.5346328 %P 60-67 %0 Thesis %T The Studies of Artificial Intelligent Technology and Its Applications %A Chen, Chih-Yung %D 2007 %8 August %C Kaohsiung, Taiwan %C Graduate School of Electrical Engineering, I-Shou University %F etd-0114108-184337 %X This thesis focuses on the concept of system design by using artificial intelligent (AI) techniques. Four different research topics were studied. For each topic, in order to achieve the condition and goal of the system’s request, all the problems were firstly modeled and then solved based on the AI techniques. The proposed approaches could sufficiently evidence the importance of AI design methodology in modern system design area. Firstly, in the research of the evolutionary hardware design, a new digital circuit genetic coding method based on genetic algorithm was proposed. Such a coding method is more flexible in the real application. Its variable structure can make it express the floor plan and routing of digital components easier. In the studies of image processing and computer vision, the first part is about a new face detection method which consists of the fast ellipse detect algorithm and the probabilistic neural network based color classifier. Cooperated with the servo motor controllers designed by fuzzy theory, the proposed face tracking system can reach the goal for the real-time using. The second part is about the study of automatic white balancing. In this part, a hybrid neural model was developed for estimating the illuminate of an image and then performing the automatic white balancing procedure according to estimated illuminate. The third part is about the digital camera auto-focusing system. In this part, the developed passive auto-focusing system could measure the sharpness value of a capture scene, and then predict the best focused position by a self-organized map based lens controller. Such a focusing system not only can move the adjustable lens to the best position, but also can save the time of focus searching. Through the examples of real work design we proposed, AI techniques in each application could be clearly described and easily understood. These researches not only show the feasibility and superiority of AI algorithm in the real system design, but also make a great improvement in comparison with the traditional design approaches. In our experiments, all studies were implemented by the software, firmware or hardware. In addition, they were also carried out by several ways, including simulation, embedded system or integrated circuit, respectively. %K genetic algorithms, EHW, Image Processing, Fuzzy Control, Neural Network, Evolutionary Hardware, Artificial Intelligence %9 Ph.D. thesis %U http://ethesys.isu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0114108-184337 %0 Journal Article %T A new variable topology for evolutionary hardware design %A Chen, Chih-Yung %A Hwang, Rey-Chue %J Expert Systems with Applications %D 2009 %V 36 %N 1 %@ 0957-4174 %F Chen2009634 %X In this paper, a novel variable topology for evolutionary hardware design is proposed. The slicing structure and routing graph are integrated into the design of evolutionary hardware. With off-line gate-level samples, simulation results clearly demonstrate the validity of this new approach performed as superior as existing methods in the logic circuit optimization. Compare with the random circuit matrix method, our approach uses less code length for evolutionary hardware description. The method we proposed could be taken as an alternative way for possible evolutionary hardware applications in the future. %K genetic algorithms, genetic programming, evolvable hardware, Evolutionary hardware design, Slicing structure, Routing graph %9 journal article %R 10.1016/j.eswa.2007.09.017 %U http://www.sciencedirect.com/science/article/B6V03-4PV2RVX-6/2/6aa751f84c76e323ab6ddab36f70e63d %U http://dx.doi.org/10.1016/j.eswa.2007.09.017 %P 634-642 %0 Journal Article %T Railway turnout system RUL prediction based on feature fusion and genetic programming %A Chen, Cong %A Xu, Tianhua %A Wang, Guang %A Li, Bo %J Measurement %D 2020 %V 151 %@ 0263-2241 %F CHEN:2020:Measurement %X The remaining useful life (RUL) prediction of railway turnout systems (RTS) is very important to avoid unplanned shutdowns and reduce labor costs for the normal operation of railways. One key challenge on RUL prediction is how to construct an appropriate health indicator (HI) that can be used to infer conditions of RTS. Existing methods usually adopt some inherit merits (e.g., monotonicity, trendability, and robustness), and their prediction results lack real-world physical meaning due to their ’black-box-like’ property. In this paper, we present a novel feature fusion method for RUL prediction, which is able to capture the relationship between RUL and HI. A variant correlation-based feature selection method is used to extract features, which has the potential to depict the degradation process optimally, and then the selected features are fused by Auto-Associative Kernel Regression (AAKR) for prediction. To reduce the noise interference, the extracted features and the combined HI are all smoothed by using the locally weighted regression. Finally, a genetic programming (GP) algorithm is employed to predict the RUL of RTS. The proposed method is extensively tested on two turnout machine degradation datasets, and the results show that the proposed approach is effective for RUL prediction of RTS %K genetic algorithms, genetic programming, RUL prediction, Railway turnout system, Feature fusion %9 journal article %R 10.1016/j.measurement.2019.107162 %U http://www.sciencedirect.com/science/article/pii/S0263224119310280 %U http://dx.doi.org/10.1016/j.measurement.2019.107162 %P 107162 %0 Journal Article %T A guided genetic programming with attribute node activation encoding for resource constrained project scheduling problem %A Chen, Haojie %A Li, Xinyu %A Gao, Liang %J Swarm and Evolutionary Computation %D 2023 %V 83 %@ 2210-6502 %F CHEN:2023:swevo %X The large-scale characteristic and complex logic between activities have made priority rules (PRs) are more favoured in actual project scheduling, resulting in the increasing attention of genetic programming (GP) with automatically generating more effective PRs. However, the limitations of encoding and numerous random search operators in existing GPs not only affect the effectiveness of evolved PRs, but also reduce their interpretability. This paper proposes a novel Hyper-Heuristic based Guided Genetic Programming with Attribute Node Activation Encoding for resource constrained project scheduling problem. Uniquely, the proposed method transforms existing single class feature activation encoding into attribute node activation encoding for independently controlling each attribute node, and develops an attribute importance calculation method based on the frequency of attribute occurrence and activation. Based on the importance of subtrees and attributes, four guided and two random local search operators are designed to obtain more characteristic PRs. In addition, a two-stage evolution framework that automatically switches stages through iteration number is constructed to achieve performance sampling and guided generation of PRs. Based on the PSPLIB benchmark, although with fewer attribute inputs, the proposed method can generate more effective PRs with significantly better results compared to 12 existing PRs and PRs evolved from the two latest GPs in all test subsets %K genetic algorithms, genetic programming, Resource constrained project scheduling, Guided search, Attribute Node activation encoding, Priority rule %9 journal article %R 10.1016/j.swevo.2023.101418 %U https://www.sciencedirect.com/science/article/pii/S2210650223001918 %U http://dx.doi.org/10.1016/j.swevo.2023.101418 %P 101418 %0 Journal Article %T Size-dependent nonlinear vibrations of functionally graded origami-enabled auxetic metamaterial plate: Application of artificial intelligence networks for solving the engineering problem %A Chen, Fenghua %A Qiu, Xinguo %A Alnowibet, Khalid A. %J Materials Today Communications %D 2024 %V 38 %@ 2352-4928 %F CHEN:2024:mtcomm %X Auxetic metamaterials are a kind of advanced materials that have distinct mechanical and physical characteristics that are not seen in traditional materials. This study presents a new concept for a microplate composed of graphene origami (GOri)-enabled auxetic metamaterials (GOEAMs) with functionally graded (FG) properties. The research also examines the nonlinear free vibration behavior of the microplate, which is reinforced by the GOEAMs. The microplate is composed of many layers of GOEAMs, with the GOri content varying in a layer-wise manner across the thickness. This variation in content allows for the graded modification of the auxetic property and other material characteristics. These modifications may be accurately determined using micromechanical models helped by genetic programming (GP). The modified couple stress theory (MCST) is used to accurately represent the microstructure of the current plate, given its size. This theory incorporates a single-length scale parameter. This study uses the first-order shear deformation theory and includes von Karman type nonlinearity to establish the nonlinear kinematic equations. These equations are then solved numerically using the generalized differential quadrature (GDQ) method and pseudo-arc-length continuation approach. we use mathematical modeling to collect data on the nonlinear frequency and deflection of the FG microplate made of GOEAMs. The data is then preprocessed by normalizing the input features and splitting the dataset into training and validation sets. Subsequently, an artificial intelligence network (AIN) architecture is constructed, consisting of an input layer, hidden layers, and an output layer. Once the AIN has been used to test, train, and validate the findings, this approach may be used in future studies on the nonlinear frequency and deflection of FG microplates built of GOEAMs, with reduced computational cost. Ultimately, the findings suggest that the nonlinear free vibration characteristics of the microplate may be successfully adjusted by manipulating the GOri parameter and distribution %K genetic algorithms, genetic programming, Nonlinear behavior, Pseudo-arc-length continuation approach, GOEAMs, Microplate, Artificial intelligence network %9 journal article %R 10.1016/j.mtcomm.2024.108232 %U https://www.sciencedirect.com/science/article/pii/S2352492824002125 %U http://dx.doi.org/10.1016/j.mtcomm.2024.108232 %P 108232 %0 Conference Proceedings %T Evolving While-Loop Structures in Genetic Programming for Factorial and Ant Problems %A Chen, Guang %A Zhang, Mengjie %Y Zhang, Shichao %Y Jarvis, Ray %S AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Proceedings %S Lecture Notes in Computer Science %D 2005 %8 dec 5 9 %V 3809 %I Springer %C Sydney, Australia %@ 3-540-30462-2 %F conf/ausai/ChenZ05 %X Loop is an important structure in human written programs. However, it is seldom used in the evolved programs in genetic programming (GP). use of while-loop structure in GP for the factorial and the artificial ant problems. Two different forms of the while-loop structure, count-controlled loop and event-controlled loop, are investigated. The results suggest that both forms of the while-loop structure can be successfully evolved in GP, the system with the while-loop structure is more effective and more efficient than the standard GP system for the two problems, and the evolved genetic programs with the loop-structure are much easier to interpret. %K genetic algorithms, genetic programming, STGP %R 10.1007/11589990_144 %U https://rdcu.be/dgJfN %U http://dx.doi.org/10.1007/11589990_144 %P 1079-1085 %0 Journal Article %T A Genetic Programming-Driven Data Fitting Method %A Chen, Hao %A Guo, Zi Yuan %A Duan, Hong Bai %A Ban, Duo %J IEEE Access %D 2020 %8 jun %V 8 %@ 2169-3536 %F Chen:2020:ACC %X Data fitting is the process of constructing a curve, or a set of mathematical functions, that has the best fit to a series of data points. Different with constructing a fitting model from same type of function, such as the polynomial model, we notice that a hybrid fitting model with multiple types of function may have a better fitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid fitting model depends on a reasonable combination of multiple functions and a set of effective parameters. That is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fitting model construction approach. In this approach, the model is expressed by an improved tree coding expression and constructed through an evolution search process driven by the genetic programming. In order to verify the validity of generated hybrid fitting model, 6 prediction problems are chosen for experiment studies. The experimental results show that the proposed method is superior to 7 typical methods in terms of the prediction accuracy and interpretability. %K genetic algorithms, genetic programming, Data fitting, hybrid model, tree coding, interpretability. %9 journal article %R 10.1109/ACCESS.2020.3002563 %U http://dx.doi.org/10.1109/ACCESS.2020.3002563 %P 111448-111459 %0 Journal Article %T A two-stage genetic programming framework for Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions %A Chen, HaoJie %A Zhang2, Jian %A Li, Rong %A Ding, Guofu %A Qin, Shengfeng %J Applied Soft Computing %D 2022 %V 124 %@ 1568-4946 %F CHEN:2022:asoc %X This study proposes a novel hyper-heuristic based two-stage genetic programming framework (HH-TGP) to solve the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI). It divides the evolution of genetic programming into generation and selection stages, and then establishes a multi-state combination scheduling mode with multiple priority rules (PRs) for the first time to realize resource constrained project scheduling under both stochastic activity duration and new project insertion. In the generation stage, based on a modified attribute set for multi-project scheduling, NSGA-II is hybridized to evolve a non-dominated PR set for forming a selectable PR set. While in the selection stage, the whole decision-making process is divided into multiple states based on the completion activity duration, and a weighted normalized evolution process with two crossovers, two mutations and four local search operators to match the optimal PR for each state from the PR set. Under the existing benchmark, HH-TGP is compared with the existing methods to verify its effectiveness %K genetic algorithms, genetic programming, Multi-state combination scheduling, Hyper-heuristic, Priority rule, Stochastic resource constrained multi-project scheduling %9 journal article %R 10.1016/j.asoc.2022.109087 %U https://www.sciencedirect.com/science/article/pii/S1568494622003751 %U http://dx.doi.org/10.1016/j.asoc.2022.109087 %P 109087 %0 Journal Article %T A filtering genetic programming framework for stochastic resource constrained multi-project scheduling problem under new project insertions %A Chen, HaoJie %A Ding, Guofu %A Zhang2, Jian %A Li, Rong %A Jiang, Lei %A Qin, Shengfeng %J Expert Systems with Applications %D 2022 %V 198 %@ 0957-4174 %F CHEN:2022:eswa %X Multi-project management and uncertain environment are very common factors, and they bring greater challenges to scheduling due to the increase of problem complexity and response efficiency requirements. In this paper, a novel hyper-heuristic based filtering genetic programming (HH-FGP) framework is proposed for evolving priority rules (PRs) to deal with a multi-project scheduling problem considering stochastic activity duration and new project insertion together, namely the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI), within heuristic computation time. HH-FGP is designed to divide traditional evolution into sampling and filtering evolution for simultaneously filtering two kinds of parameters constituting PRs, namely depth range and attribute, to obtain more effective PRs. Based on this, the existing genetic search and local search are improved to meet the depth constraints, and a multi-objective evaluation mechanism is designed to achieve effective filtering. Under the existing benchmark, HH-FGP is compared and analysed with the existing methods to verify its effectiveness %K genetic algorithms, genetic programming, Filtering evolution, Priority rule, Stochastic resource constrained multi-project scheduling %9 journal article %R 10.1016/j.eswa.2022.116911 %U https://www.sciencedirect.com/science/article/pii/S0957417422003487 %U http://dx.doi.org/10.1016/j.eswa.2022.116911 %P 116911 %0 Journal Article %T A hyper-heuristic based ensemble genetic programming approach for stochastic resource constrained project scheduling problem %A Chen, HaoJie %A Ding, Guofu %A Qin, Shengfeng %A Zhang2, Jian %J Expert Systems with Applications %D 2021 %V 167 %@ 0957-4174 %F CHEN:2021:ESA %X In project scheduling studies, to the best of our knowledge, the hyper-heuristic collaborative scheduling is first-time applied to project scheduling with random activity durations. A hyper-heuristic based ensemble genetic programming (HH-EGP) method is proposed for solving stochastic resource constrained project scheduling problem (SRCPSP) by evolving an ensemble of priority rules (PRs). The proposed approach features with (1) integrating the critical path method into the resource-based policy class to generate schedules; (2) improving the existing single hyper-heuristic project scheduling research to construct a suitable solution space for solving SRCPSP; and (3) bettering genetic evolution of each subpopulation from a decision ensemble with three different local searches in corporation with discriminant mutation and discriminant population renewal. In addition, a sequence voting mechanism is designed to deal with collaborative decision-making in the scheduling process for SRCPSP. The benchmark PSPLIB is performed to verify the advantage of the HH-EGP over heuristics, meta-heuristics and the single hyper-heuristic approaches %K genetic algorithms, genetic programming, Ensemble decision, Hyper-heuristics, Priority rule, Stochastic resource constrained project scheduling %9 journal article %R 10.1016/j.eswa.2020.114174 %U https://www.sciencedirect.com/science/article/pii/S0957417420309118 %U http://dx.doi.org/10.1016/j.eswa.2020.114174 %P 114174 %0 Journal Article %T A surrogate-assisted dual-tree genetic programming framework for dynamic resource constrained multi-project scheduling problem %A Chen, Haojie %A Li, Xinyu %A Gao, Liang %J Int. J. Prod. Res. %D 2024 %V 62 %N 16 %F DBLP:journals/ijpr/ChenLG24 %K genetic algorithms, genetic programming %9 journal article %R 10.1080/00207543.2023.2294109 %U https://doi.org/10.1080/00207543.2023.2294109 %U http://dx.doi.org/10.1080/00207543.2023.2294109 %P 5631-5653 %0 Thesis %T Genetic Programming for the Investment of the Mutual Fund with Sortino Ratio and Mean Variance Model %A Chen, Hung-Hsin %D 2010 %8 jul %C Kaohsiung, Taiwan %C Computer Science and Engineering, National Sun Yat-sen University %F Chen:mastersthesis %X In this thesis, we propose two genetic-programming-based models that improve the trading strategies for mutual funds. These two models can help investors get returns and reduce risks. The first model increases the return by selecting funds with high Sortino ratios and allocates the capital equally, achieving the best annualized return. The second model also selects funds with high Sortino ratios, but reduces the risk by allocating the capital with the mean variance model. Most importantly, our model uses the genetic programming to generate feasible trading strategies to gain return, which is suitable for the market that changes anytime. To verify our models, we simulate the investment for mutual funds from January 1999 to December 2009 (11 years in total). The experimental results show that our first model can gain return from 2004/1/1 to 2008/12/31, achieving the best annualized return 9.11%, which is better than the annualized return 6.89% of previous approaches. In addition, our second model with smaller downside volatility can achieve almost the same return as previous results. %K genetic algorithms, genetic programming, trading strategy, return, Sortino ratio, risk %9 Masters thesis %U http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0824110-122030 %0 Journal Article %T The trading on the mutual funds by gene expression programming with Sortino ratio %A Chen, Hung-Hsin %A Yang, Chang-Biau %A Peng, Yung-Hsing %J Applied Soft Computing %D 2014 %8 feb %V 15 %F journals/asc/ChenYP14 %K genetic algorithms, genetic programming, gene expression programming %9 journal article %U http://dx.doi.org/10.1016/j.asoc.2013.09.011 %P 219-230 %0 Journal Article %T Genetic programming for predicting aseismic abilities of school buildings %A Chen2, Hung-Ming %A Kao, Wei-Ko %A Tsai, Hsing-Chih %J Engineering Applications of Artificial Intelligence %D 2012 %V 25 %N 6 %@ 0952-1976 %F Chen2012 %X In general, the aseismic ability of buildings is analysed using nonlinear models. To obtain aseismic abilities of buildings, numerical models are constructed based on the structural configuration and material properties of buildings, and their stress responses and behaviours are simulated. This method is complex, time-consuming, and should only be conducted by professionals. In the past, soft computing techniques have been applied in the construction field to predict the particular stress responses and behaviors; however, only a few studies have been made to predict specific properties of entire buildings. In this study, a weighted genetic programming system is developed to construct the relation models between the aseismic capacity of school buildings, and their basic design parameters. This is based on information from the database of school buildings, as well as information regarding the aseismic capacity of school buildings analysed using complete nonlinear methods. This system can be further applied to predict the aseismic capacity of the school buildings. %K genetic algorithms, genetic programming, Prediction, Aseismic ability, School building, Soft computing %9 journal article %R 10.1016/j.engappai.2012.04.002 %U http://www.sciencedirect.com/science/article/pii/S0952197612000905 %U http://dx.doi.org/10.1016/j.engappai.2012.04.002 %P 1103-1113 %0 Conference Proceedings %T Multi-Valued Stock Valuation Based on Fuzzy Genetic Programming Approach %A Chen, Jiah-Shing %A Lin, Ping-Chen %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 Jiah-ShingChen:2003:CINC %K genetic algorithms, genetic programming, Trading Strategies %U http://www.fin.kuas.edu.tw/people/writing_seminar.php?Sn=127 %P CIEF3-39 %0 Conference Proceedings %T Generate a Single Heuristic for Multiple Dynamic Flexible Job Shop Scheduling Tasks by Genetic Programming %A Chen, Jiayin %A Jia, Ya-Hui %A Bi, Ying %A Chen, Wei-Neng %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F chen:2024:CEC2 %X Genetic programming (GP) hyper-heuristic method has been extensively studied to solve multiple dynamic job shop scheduling tasks by generating an effective heuristic for each task simultaneously. However, a fundamental question has not been answered. Do we need to customize a specific heuristic for each task? To fill this research gap, we propose to generate a single heuristic for handling multiple tasks. Without designing complex evolution mechanisms, only during the evaluation process of GP, the fitness of a heuristic is evaluated by multiple tasks. Since there are multiple tasks, a heuristic has multiple objective values. A rank aggregation (RA) fitness evaluation strategy is designed to convert multiple objective values of multiple tasks into a fitness value for a single heuristic. To validate the effectiveness of the generated solution and the proposed RA strategy, we design multitask scenarios that encompass tasks with diverse objectives, use levels, and maximum operation times. The results demonstrate that the performance of the single heuristic generated in multitask scenarios is comparable to solutions generated by GP using the single-task learning paradigm, meaning that with an appropriate training method, GP can generate a heuristic with good generality. %K genetic algorithms, genetic programming, Training, Technological innovation, Job shop scheduling, Processor scheduling, Heuristic algorithms, Dynamic scheduling, hyperheuristic, dynamic job shop scheduling, multitask optimization %R 10.1109/CEC60901.2024.10611762 %U http://dx.doi.org/10.1109/CEC60901.2024.10611762 %0 Thesis %T Next Generation Hardware Monitoring Infrastructure for Multi-core Resource Auditing %A Chen, Jie %D 2015 %8 17 may %C USA %C The School of Engineering and Applied Science of The George Washington University %F Jie-Chen:thesis %X Thesis Statement: Performance counters in hardware have been very successful in providing feedback about application performance to programmers and compilers. With the growing relevance of understanding power, energy and security (information leakage), we envision that the next generation hardware monitoring infrastructure will support these features. In this work, we study the design and implementation of such hardware monitors. Continuous advances in semiconductor technologies have enabled the integration of billions of transistors in modern multicore processors. This offers software applications with abundant hardware resources to use. To realize more parallelism and higher performance, software developers are concerned about characterizing and optimizing their applications over the usage of hardware resources. At the same time, hardware monitoring infrastructure in most current processors offers a collection of hardware counters for auditing architectural events on hardware units. Such counters can be used by programmers and performance analysts for auditing performance bottlenecks and consequently, optimizing application performance. In recent years, there is a surging demand for improving application power, energy and information leakage beyond performance, due to the increasing complexity in power delivery inside the processor chip and the vast amount of shared hardware resources. As power budget is limited, it becomes necessary to audit software power usages and look for power optimization at all levels to better use the limited power. Optimizing the applications for energy is necessary due its impact to the operational cost in the system. Inefficient software has been often cited as a major reason for wasteful energy consumption in computing systems. It is essential for programmers to audit the energy usage of their program code and apply code optimization to reduce energy consumption. While power and energy are already among the top issues that need to be solved, information leakage using shared resource in multi-core hardware has been becoming a fast growing concern. Over the past years, it has been shown many times that multicore hardware resources are vulnerable and can easily be exploited as covert timing channels to leak sensitive information at high speed. Unfortunately, there is no existing hardware-supported auditing for such types of information leakage. As factors such as power, energy and information leakage are becoming more critical, software developers and system administrators are urgently looking for appropriate tools to address such challenges, just like they used to rely on performance counters for solving performance issues. The next generation hardware monitoring infrastructure should take users need into consideration, and provide sufficient and convenient resource auditing support beyond just performance. It will not only enable the programmers to improve the scalability of their software programs by better using power budget, and to reduce cost by improving the energy efficiency of their program code, but it will also help system administrators enhance the level of trust of their systems by tracking and removing information leakage sources. In this dissertation, we proposed and explored the design of three novel resource auditing techniques as part of the next generation hardware monitoring infrastructure, namely, application power auditing, application energy auditing, and covert timing channel auditing. The design methodologies of the three techniques share the same goal of leveraging hardware support to enable the gathering of resource usage information in an efficient and cost-effective manner. The hardware support is also equipped with lightweight software support to maximize the flexibility of usages. Overall, the goal of this work is to push the hardware monitoring support to the next level, and enable the programmer to efficiently address a spectrum of existing and emerging system issues. %K genetic algorithms, genetic programming, optimization, security, debugging, power, profiling, energy, CSPPV, CDDG, Fmm, Cholesky, L2 cache, SPEC2k6, SPLASH-2, PARSEC-1.0, ASGP, SESC, Intel Core i7, CC-Auditor %9 Ph.D. thesis %U https://scholarspace.library.gwu.edu/concern/gw_etds/qz20ss512 %0 Journal Article %T enDebug: A hardware-software framework for automated energy debugging %A Chen, Jie %A Venkataramani, Guru %J Journal of Parallel and Distributed Computing %D 2016 %8 oct %V 96 %@ 0743-7315 %F Chen:2016:JPDC %X Energy consumption by software applications is one of the critical issues that determine the future of multicore software development. Inefficient software has been often cited as a major reason for wasteful energy consumption in computing systems. Without adequate tools, programmers and compilers are often left to guess the regions of code to optimize, that results in frustrating and unfruitful attempts at improving application energy. In this paper, we propose enDebug, an energy debugging framework that aims to automate the process of energy debugging. It first profiles the application code for high energy consumption using a hardware-software cooperative approach. Based on the observed application energy profile, an automated recommendation system that uses artificial selection genetic programming is used to generate the energy optimizing program mutants while preserving functional accuracy. We demonstrate the usefulness of our framework using several Splash-2, PARSEC-1.0 and SPEC CPU2006 benchmarks, where we were able to achieve up to 7percent energy savings beyond the highest compiler optimization (including profile guided optimization) settings on real-world Intel Core i7 processors. %K genetic algorithms, genetic programming, Energy profiling, Energy optimization %9 journal article %R 10.1016/j.jpdc.2016.05.005 %U http://www.sciencedirect.com/science/article/pii/S0743731516300351 %U http://dx.doi.org/10.1016/j.jpdc.2016.05.005 %P 121-133 %0 Journal Article %T A new multi-tree Genetic Programming approach to feature construction in high-dimensional classification %A Chen, Ke %A Dao, Mingyang %A Bi, Ying %A Liang, Jing %A Wu, Zhenlong %A Wang, Peng %J Knowledge-Based Systems %D 2025 %V 319 %@ 0950-7051 %F Chen:2025:knosys %X Classification, as a key task in machine learning, has been widely studied. However, with the rapid advancement of information technology, features in classification tasks increasingly exhibit low discriminability and strong redundancy. And the classification data has gradually changed from low-dimensional data to high-dimensional data. These characteristics not only significantly increase the complexity of the classification model training, but also lead to a decline in the generalisation ability of the classification model. How to reduce the number of original features and improve their differentiation becomes the key to improve the classification accuracy. Feature construction stands out as a crucial data processing technique for enhancing data quality. Genetic Programming (GP) has found extensive applications in feature construction due to its flexibility and strong interpretability. However, the existing GP-based feature construction methods suffer from the interference of redundant and irrelevant features in the face of high-dimensional data. By eliminating irrelevant and redundant features, reducing the search space of GP can aid in constructing more discriminative features. Motivated by this, we propose a GP algorithm that combines feature selection with feature construction for high-dimensional classification. During the evolution of GP, a subtree archive is introduced to store promising subtrees and these subtrees assist the generation of offspring. The experimental results show that the proposed method can achieve better classification performance than single-tree and multi-tree GP methods %K genetic algorithms, genetic programming, Feature construction, Feature selection, High-dimensional classification %9 journal article %R 10.1016/j.knosys.2025.113643 %U https://www.sciencedirect.com/science/article/pii/S0950705125006896 %U http://dx.doi.org/10.1016/j.knosys.2025.113643 %P 113643 %0 Conference Proceedings %T Dynamic Threshold Selection in Genetic Programming for Imbalanced Fault Diagnosis %A Chen, Ke %A Wu, Tianqing %A Bi, Ying %A Liang, Jing %A Yu, Kunjie %Y Jin, Yaochu %Y Baeck, Thomas %S 2025 IEEE Congress on Evolutionary Computation (CEC) %D 2025 %8 August 12 jun %I IEEE %C Hangzhou, China %F DBLP:conf/cec/ChenWBLY25 %X Imbalanced datasets are a major challenge in industrial fault diagnosis because the majority of data belong to the non-fault class, while fault instances constitute a small minority. Genetic Programming (GP) has shown great potential in handling imbalanced classification tasks due to its ability to evolve classifiers and automatically optimise decision rules. Traditional GP methods for imbalanced classification often rely on fixed decision thresholds (e.g., 0 or 0.5). However, such thresholds fail to adapt to varying data distributions, resulting in limited accuracy in fault diagnosis. While threshold-free methods, such as GP using the Area Under the Curve (AUC) and its variants as fitness functions, have demonstrated effectiveness, practical applications in industrial systems often require explicit thresholds to generate accurate class labels. This paper introduces a GP-based approach with a simplified AUC variant as the fitness function and a dynamic threshold search mechanism. By adaptively optimising thresholds during evolution, the method improves minority class detection. Experiments on public fault diagnosis datasets with varying imbalance ratios demonstrate that the proposed approach consistently outperforms traditional GP methods. %K genetic algorithms, genetic programming, Fault diagnosis, Accuracy, Computational modeling, Evolutionary computation, Dynamic programming, Class Imbalance, Dynamic Threshold Selection %R 10.1109/CEC65147.2025.11043109 %U https://doi.org/10.1109/CEC65147.2025.11043109 %U http://dx.doi.org/10.1109/CEC65147.2025.11043109 %0 Conference Proceedings %T Genetic Programming for Feature Learning and Feature Construction in Glioma Survival Prediction %A Chen, Kunjun %S 2024 20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) %D 2024 %8 jul %F Chen:2024:ICNC-FSKD %X MRI imaging plays a vital role in the initial tumour screening process and also aids in constructing a high-dimensional feature space for survival prediction tasks through the combination of sequences and tumour subregions. Traditional GP methods applied to feature construction treat features as part of the terminal set, which limits their search capabilities. Combining feature learning with GP allows for the direct use of image data, albeit dependent on predefined extraction functions. Our goal is to design a FLFC method that integrates feature learning and construction to automatically locate ROIs, extract features, construct new features, and match classifiers in 3D MRI images. To this end, we have introduced a new GP structure with an interpretable function set. To verify the algorithm, experiments were performed using the publicly available BraTS 2020 dataset. Ours achieved an average accuracy of 94.14percent in the binary classification task (differentiating between HGG and LGG) and 72.49percent in the four-class task (within HGG subtypes). These results underscore the efficacy of the FLFC method in harnessing the complexity of MRI data for meaningful survival prediction, illustrating its potential as a robust tool in medical imaging analysis and cancer prognosis. %K genetic algorithms, genetic programming, Representation learning, Three-dimensional displays, Magnetic resonance imaging, Feature extraction, Prediction algorithms, Classification algorithms, Prognostics and health management, Tumours, glioma, survival prediction, feature learning, feature construction %R 10.1109/ICNC-FSKD64080.2024.10702275 %U http://dx.doi.org/10.1109/ICNC-FSKD64080.2024.10702275 %0 Journal Article %T Multi-Tree Genetic Programming With Deep Contextual Bandits for Learning-Assisted Scheduling of Cluster Tools Integrating Stockers %A Chen, LiangChao %A Lai, YiMing %A Qiao, Yan %A Wu, NaiQi %A Luo, Xin %J IEEE Transactions on Evolutionary Computation %@ 1089-778X %F Chen:2026:ieeeTEC %O Early Access %K genetic algorithms, genetic programming, Job shop scheduling, Dynamic scheduling, Schedules, Cleaning, Real-time systems, Production, Processor scheduling, Robots, Fabrication, Semiconductor device manufacture, Smart manufacturing scheduling, semiconductor manufacturing, cluster tool, genetic programming, deep contextual bandit %9 journal article %R 10.1109/TEVC.2025.3650587 %U http://dx.doi.org/10.1109/TEVC.2025.3650587 %0 Conference Proceedings %T A sensor tagging approach for reusing building blocks of knowledge in learning classifier systems %A Chen, Liang-Yu %A Lee, Po-Ming %A Hsiao, Tzu-Chien %S IEEE Congress on Evolutionary Computation (CEC 2015) %D 2015 %8 may %F Chen:2015:CECa %X During the last decade, the extraction and reuse of building blocks of knowledge for the learning process of Extended Classifier System (XCS) in Multiplexer (MUX) problem domain have been demonstrate feasible by using Code Fragment (CF) (i.e. a tree-based structure ordinarily used in the field of Genetic Programming (GP)) as the representation of classifier conditions (the resulting system was called XCSCFC). However, the use of the tree-based structure may lead to the bloating problem and increase in time complexity when the tree grows deep. Therefore, we proposed a novel representation of classifier conditions for the XCS, named Sensory Tag (ST). The XCS with the ST as the input representation is called XCSSTC. The experiments of the proposed method were conducted in the MUX problem domain. The results indicate that the XCSSTC is capable of reusing building blocks of knowledge in the MUX problems. The current study also discussed about two different aspects of reusing of building blocks of knowledge. Specifically, we proposed the attribution selection’ part and the ’logical relation between the attributes’ part. %K genetic algorithms, genetic programming %R 10.1109/CEC.2015.7257256 %U http://dx.doi.org/10.1109/CEC.2015.7257256 %P 2953-2960 %0 Journal Article %T Development of predictive models for sustainable concrete via genetic programming-based algorithms %A Chen, Lingling %A Wang, Zhiyuan %A Khan, Aftab Ahmad %A Khan, Majid %A Javed, Muhammad Faisal %A Alaskar, Abdulaziz %A Eldin, Sayed M. %J Journal of Materials Research and Technology %D 2023 %V 24 %@ 2238-7854 %F CHEN:2023:jmrt %X Waste foundry sand (WFS), a by-product of the casting industry, is a potential material that may be employed as a substitute for fine aggregate in concrete. In the present study, gene expression programming (GEP) and multi-expression programming (MEP) are used to generate predictive models for the split tensile strength (STS) and elastic modulus (E) of waste foundry sand concrete (WFSC). Therefore, a comprehensive database was collected that contains 146 and 242 values of E and STS, respectively. Seven different variables were chosen as input for the development of the ML-based models. The reliability and accuracy of the proposed model were evaluated by using various statistical indicators. Given the performance assessment, both GEP and MEP accurately predict the E with a correlation of 0.994 and 0.996, respectively. However, GEP performance was much superior in predicting STS (R = 0.987) as compared to the MEP model (R = 0.892). The integrated statistical performance (rho, OF) of both models approaches zero, indicating the excellent performance and generalization potential of the developed models. For the interpretation of machine learning (ML) models, Shapley additive explanation (SHAP) was used to know about the input variables’ importance and influence on the output parameter. The SHAP analysis revealed that a higher ratio of FA/TA results in the enhancement of the elastic modulus, whereas CA/C higher ratio is favorably influencing the split tensile strength up to some extent, however, this trend changes when the ratio is further increased. These soft computing prediction techniques can incentivize the use of WFS in sustainable concrete, reducing waste disposal and promoting environment-friendly construction. Furthermore, it is recommended that the findings of this study be validated with more extensive data sets and that other ML techniques be investigated %K genetic algorithms, genetic programming, Waste foundry sand, Gene expression programming, Multi-expression programming, Solid waste, Sustainable construction %9 journal article %R 10.1016/j.jmrt.2023.04.180 %U https://www.sciencedirect.com/science/article/pii/S223878542300875X %U http://dx.doi.org/10.1016/j.jmrt.2023.04.180 %P 6391-6410 %0 Conference Proceedings %T Dynamical Proportion Portfolio Insurance with Genetic Programming %A Chen, Jiah-Shing %A Chang, Chia-Lan %Y Wang, Lipo %Y Chen, Ke %Y Ong, Yew-Soon %S Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part II %S Lecture Notes in Computer Science %D 2005 %8 aug 27 29 %V 3611 %I Springer %C Changsha, China %@ 3-540-28325-0 %F conf/icnc/ChenC05b %X a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change accordingly. This research identifies factors relating to market volatility. These factors are built into equation trees by genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. %K genetic algorithms, genetic programming %R 10.1007/11539117_104 %U http://dx.doi.org/10.1007/11539117_104 %P 735-743 %0 Journal Article %T Piecewise nonlinear goal-directed CPPI strategy %A Chen, J. S. %A Liao, Benjamin Penyang %J Expert Systems with Applications %D 2007 %8 nov %V 33 %N 4 %F Chen:2007:ESA %X Traditional portfolio insurance (PI) strategy, such as constant proportion portfolio insurance (CPPI), only considers the floor constraint but not the goal aspect. This paper proposes a goal-directed (GD) strategy to express an investor’s goal-directed trading behaviour and combines this floor-less GD strategy with the goal-less CPPI strategy to form a piecewise linear goal-directed CPPI (GDCPPI) strategy. The piecewise linear GDCPPI strategy shows that there is a wealth position M at the intersection of the GD and CPPI strategies. This M position guides investors to apply the CPPI strategy or the GD strategy depending on whether current wealth is less than or greater than M, respectively. In addition, we extend the piecewise linear GDCPPI strategy to a piecewise nonlinear GDCPPI strategy. This paper applies genetic algorithm (GA) technique to find better piecewise linear GDCPPI strategy parameters than those under the Brownian motion assumption. This paper also applies forest genetic programming (GP) technique to generate the piecewise nonlinear GDCPPI strategy. The statistical tests show that the GP strategy outperforms the GA strategy which in turn outperforms the Brownian strategy. %K genetic algorithms, genetic programming, Portfolio insurance strategy, Goal-directed strategy, Piecewise linear GDCPPI strategy, Piecewise nonlinear GDCPPI strategy %9 journal article %R 10.1016/j.eswa.2006.07.001 %U http://dx.doi.org/10.1016/j.eswa.2006.07.001 %P 857-869 %0 Journal Article %T Dynamic proportion portfolio insurance using genetic programming with principal component analysis %A Chen, Jiah-Shing %A Chang, Chia-Lan %A Hou, Jia-Li %A Lin, Yao-Tang %J Expert Systems with Applications %D 2008 %V 35 %N 1-2 %@ 0957-4174 %F Chen2008273 %X This paper proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change according to market conditions. This research identifies risk variables relating to market conditions. These risk variables are used to build the equation tree for the risk multiplier by genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. In addition, principal component analysis of the risk variables in equation trees indicates that among all the risk variables, risk-free interest rate influences the risk multiplier most. %K genetic algorithms, genetic programming, Dynamic proportion portfolio insurance (DPPI), Constant proportion portfolio insurance (CPPI), Principal component analysis (PCA) %9 journal article %R 10.1016/j.eswa.2007.06.030 %U http://www.sciencedirect.com/science/article/B6V03-4P40KHS-4/2/0bbb6228d04a3a1a4d59108b17c37664 %U http://dx.doi.org/10.1016/j.eswa.2007.06.030 %P 273-278 %0 Conference Proceedings %T Displacement prediction model of landslide based on multi-gene genetic programming %A Chen, Jiejie %A Zeng, Zhigang %A Jiang, Ping %S 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC) %D 2016 %8 nov %F Chen:2016:YAC %X In this paper, a new approach is presented for predicting landslide displacement using multi-gene genetic programming (MGGP). For the characteristic of MGGP which does not need specific assumptions, two real cases is used to prove the new approach is feasibility and validity. %K genetic algorithms, genetic programming %R 10.1109/YAC.2016.7804942 %U http://dx.doi.org/10.1109/YAC.2016.7804942 %P 481-485 %0 Journal Article %T Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction %A Chen, Jiejie %A Zeng, Zhigang %A Jiang, Ping %A Tang, Huiming %J Neural Computing and Applications %D 2016 %V 27 %N 6 %F journals/nca/ChenZJT16 %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s00521-015-1976-y %U http://dx.doi.org/10.1007/s00521-015-1976-y %P 1771-1784 %0 Conference Proceedings %T Distributed Service Management Based on Genetic Programming %A Chen, Jing %A Li, Zeng-zhi %A Wang, Yun-lan %Y Szczepaniak, Piotr S. %Y Kacprzyk, Janusz %Y Niewiadomski, Adam %S Advances in Web Intelligence Third International Atlantic Web Intelligence Conference, AWIC 2005, Proceedings %S Lecture Notes in Computer Science %D 2005 %8 June 9 jun %V 3528 %I Springer %C Lodz, Poland %@ 3-540-26219-9 %F conf/awic/ChenLW05 %X An architecture for online discovery quantitative model of distributed service management based on genetic programming (GP) was proposed. The GP system was capable of constructing the quantitative models online without prior knowledge of the managed elements. The model can be updated continuously in response to the changes made in provider configurations and the evolution of business demands. The GP system chose a particular subset from the numerous metrics as the explanatory variables of the model. In order to evaluate the system, a prototype is implemented to estimate the online response times for Oracle Universal Database under a TPC-W workload. Of more than 500 Oracle performance metrics, the system model choose three most influential metrics that weight 76percent of the variability of response time, illustrating the effectiveness of quantitative model constructing system and model constructing algorithms. %K genetic algorithms, genetic programming %R 10.1007/11495772_14 %U http://dx.doi.org/10.1007/11495772_14 %P 83-88 %0 Conference Proceedings %T Distributed Service Performance Management Based on Linear Regression and Genetic Programming %A Chen, Jing %A Li, Zeng-Zhi %A Liao, Zhi-Gang %A Wang, Yun-Lan %S Proceedings of 2005 International Conference on Machine Learning and Cybernetics %D 2005 %8 18 21 aug %V 1 %F Chen:2005:ICMLC %X An architecture for online discovery quantitative models system of service performance management was proposed. The system was capable of constructing the quantitative models without prior knowledge of the managed elements. The model can be updated continuously in response to the changes made in provider configurations and the evolution of business demands. Due to the existence of strong correlation between the distributed service metrics and response times, a linear and a hyper-linear quantitative models are constructed, which respectively use the stepwise multiple linear regression and genetic programming algorithms. The simulation results show that the effectiveness of quantitative model constructing system and model constructing algorithms. %K genetic algorithms, genetic programming %R 10.1109/ICMLC.2005.1527007 %U http://dx.doi.org/10.1109/ICMLC.2005.1527007 %P 560-563 %0 Journal Article %T Study of Applying Macroevolutionary Genetic Programming to Concrete Strength Estimation %A Chen, Li %J Journal of Computing in Civil Engineering %D 2003 %8 oct %V 17 %N 4 %I ASCE %F chen:290 %X This technical note is aimed at demonstrating a mixture-proportioning problem, which uses the macroevolutionary algorithm (MA) combined with genetic programming (GP) to estimate the compressive strength of high-performance concrete (HPC). GP provides system identification in a transparent and structured way; a fittest function type of experimental results will be obtained automatically from this method. MA is a new concept of species evolution at the higher level. It could improve the capability of searching global optima and avoid premature convergence during the selection process of GP. In the study, two appropriate functions have been found to represent the relationships between different ingredients and the compressive strength. The results show that this new model, MAGP, is better than the traditional proportional selection GP for HPC strength estimation. %K genetic algorithms, genetic programming, civil engineering computing, compressive strength, mixtures, concrete %9 journal article %R 10.1061/(ASCE)0887-3801(2003)17:4(290) %U http://link.aip.org/link/?QCP/17/290/1 %U http://dx.doi.org/10.1061/(ASCE)0887-3801(2003)17:4(290) %P 290-294 %0 Journal Article %T Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery %A Chen, Li %A Tan, Chih-Hung %A Kao, Shuh-Ji %A Wang, Tai-Sheng %J Water Research %D 2008 %8 jan %V 42 %N 1-2 %@ 0043-1354 %F Chen2008296 %X Parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on remote sensing images. A cruise was conducted to ground-truth chlorophyll-a (Chl-a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl-a concentrations and remote-sensed imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the traditional LMR model with lower estimating errors. %K genetic algorithms, genetic programming, Grammatical evolution, Parallel genetic algorithm, Water quality monitoring, Chlorophyll-a, Remote-sensed imagery %9 journal article %R 10.1016/j.watres.2007.07.014 %U http://dx.doi.org/10.1016/j.watres.2007.07.014 %P 296-306 %0 Journal Article %T Macro-grammatical evolution for nonlinear time series modeling-a case study of reservoir inflow forecasting %A Chen, Li %J Engineering with Computers %D 2011 %V 27 %N 4 %I Springer %@ 0177-0667 %F journals/ewc/Chen11 %X Streamflow forecasting is significantly important for planning and operating water resource systems. However, stream flow formation is a highly nonlinear, time varying, spatially distributed process and difficult to forecast. This paper proposes a nonlinear model which incorporates improved real-coded grammatical evolution (GE) with a genetic algorithm (GA) to predict the ten-day inflow of the De-Chi Reservoir in central Taiwan. The GE is a recently developed evolutionary-programming algorithm used to express complex relationships among long-term nonlinear time series. The algorithm discovers significant input variables and combines them to form mathematical equations automatically. Using GA with GE optimises an appropriate type of function and its associated coefficients. To enhance searching efficiency and genetic diversity during GA optimisation, the macro-evolutionary algorithm (MA) is processed as a selection operator. The results using an example of theoretical nonlinear time series problems indicate that the proposed GEMA yields an efficient optimal solution. GEMA has the advantages of its ability to learn relationships hidden in data and express them automatically in a mathematical manner. When applied to a real world case study, the fittest equation generated through GEMA used only a single input variable in a reasonable nonlinear form. The predicting accuracies of GEMA were better than those of the traditional linear regression (LR) model and as good as those of the back-propagation neural network (BPNN). In addition, the predicting of ten-day reservoir inflows reveals the effectives of GEMA, and standardisation is beneficial to model for seasonal time series. %K genetic algorithms, genetic programming, grammatical evolution, streamflow forecasting, nonlinear model, macroevolutionary algorithm %9 journal article %R 10.1007/s00366-011-0212-3 %U http://dx.doi.org/10.1007/s00366-011-0212-3 %P 393-404 %0 Journal Article %T Prediction of slump flow of high-performance concrete via parallel hyper-cubic gene-expression programming %A Chen, Li %A Kou, Chang-Huan %A Ma, Shih-Wei %J Eng. Appl. of AI %D 2014 %V 34 %F journals/eaai/ChenKM14 %K genetic algorithms, genetic programming, gene expression programming %9 journal article %U http://dx.doi.org/10.1016/j.engappai.2014.05.005 %P 66-74 %0 Conference Proceedings %T An ant colony optimization-based hyper-heuristic with genetic programming approach for a hybrid flow shop scheduling problem %A Chen, Lin %A Zheng, Hong %A Zheng, Dan %A Li, Dongni %S 2015 IEEE Congress on Evolutionary Computation (CEC) %D 2015 %8 may %F Chen:2015:CECb %X The problem of a k-stage hybrid flow shop (HFS) with one stage composed of non-identical batch processing machines and the others consisting of non-identical single processing machines is analysed in the context of the equipment manufacturing industry. Due to the complexity of the addressed problem, a hyper-heuristic which combines heuristic generation and heuristic search is proposed to solve the problem. For each sub-problem, i.e., part assignment, part sequencing and batch formation, heuristic rules are first generated by genetic programming (GP) off-line and then selected by ant colony optimisation (ACO) correspondingly. Finally, the scheduling solutions are obtained through the above generated combinatorial heuristic rules. Aiming at minimizing the total weighted tardiness of parts, a comparison experiment with the other hyper-heuristic for the same HFS problem is conducted. The result has shown that the proposed algorithm has advantages over the other method with respect to the total weighted tardiness. %K genetic algorithms, genetic programming, ant colony optimization, ACO, scheduling, discrete event systems %R 10.1109/CEC.2015.7256975 %U http://dx.doi.org/10.1109/CEC.2015.7256975 %P 814-821 %0 Journal Article %T Comparing extended classifier system and genetic programming for financial forecasting: an empirical study %A Chen, Mu-Yen %A Chen, Kuang-Ku %A Chiang, Heien-Kun %A Huang, Hwa-Shan %A Huang, Mu-Jung %J Soft Computing %D 2007 %8 oct %V 11 %N 12 %@ 1432-7643 %F journals/soco/ChenCCHH07 %X As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to learn. These methods such as fuzzy logic, neural networks, support vector machines, decision trees and Bayesian learning have been applied to learn meaningful rules; however, the only drawback of these methods is that it often gets trapped into a local optimal. In contrast with machine learning methods, a genetic algorithm (GA) is guaranteeing for acquiring better results based on its natural evolution and global searching. GA has given rise to two new fields of research where global optimization is of crucial importance: genetic based machine learning (GBML) and genetic programming (GP). This article adopts the GBML technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. Moreover, the proposed system and GP are both applied to the theoretical and empirical experiments. Results for both approaches are presented and compared. This paper makes two important contributions: (1) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process which is very effective in selecting an optimal set of rules from a large population; (2) the experiments prove that the rule sets derived by the proposed approach are more accurate than GP. %K genetic algorithms, genetic programming, Learning classifier system, Extended classifier system, Machine learning %9 journal article %R 10.1007/s00500-007-0161-3 %U http://dx.doi.org/10.1007/s00500-007-0161-3 %P 1173-1183 %0 Conference Proceedings %T Automatic Running Planning for Omni-Directional Mobile Robot By Genetic Programming %A Chen, Peng %A Koyama, Shinji %A Mitutake, Shinichiro %A Isoda, Takashi %Y Mastorakis, Nikos %S WSEAS SEPAD-AIKED-ISPRA-EHAC %D 2003 %8 aug 11 13 %C Rethymno, Greece %F WSEAS_466-157_Chen %X This paper presents a omni-directional mobile robot which can run on off-road and run over a obstacle. The robot equipped with crawler-roller running system. The motion analysis is also discussed to realise the autonomic off-road running. In order to automatically control the robot to run in optional direction and an orbit. We have to decide the inputting volts of the motors according to given direction or orbit. Even though we can do this by analysis theory, it is difficult to control the robot in real time. So we propose an intelligent control method using Genetic Programming (GP) to search an optimum route leading the robot to given destination and avoiding obstacles. We have carried out many practical running tests and simulations to verify the efficiency of the mechanism and the intelligent control method. In this paper we show an example of the tests. %K genetic algorithms, genetic programming, off-road running, omni-directional mobile robot, crawler-roller running system, obstacle, running planning %U http://www.wseas.us/e-library/conferences/digest2003/papers/digest.htm %P 5 %0 Journal Article %T Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming %A Chen, Peng %A Taniguchi, Masatoshi %A Toyota, Toshio %A He, Zhengja %J Mechanical Systems and Signal Processing %D 2005 %8 jan %V 19 %N 1 %@ 0888-3270 %F Chen:2005:MSSP %X This paper proposes a fault diagnosis method for plant machinery in an unsteady operating condition using instantaneous power spectrum (IPS) and genetic programming (GP). IPS is used to extract feature frequencies of each machine state from measured vibration signals for distinguishing faults by relative crossing information. Excellent symptom parameters for detecting faults are automatically generated by the GP. The excellent symptom parameters generated by GP can sensitively reflect the characteristics of signals for precise diagnosis. The method proposed is verified by applying it to the fault diagnosis of a rolling bearing. %K genetic algorithms, genetic programming, Machinery fault diagnosis, Unsteady operating condition, Instantaneous power spectrum, Relative crossing information, Rolling bearing %9 journal article %R 10.1016/j.ymssp.2003.11.004 %U http://dx.doi.org/10.1016/j.ymssp.2003.11.004 %P 175-194 %0 Journal Article %T Automatic Design of Robust Optimal Controller for Interval Plants using Genetic Programming and Kharitonov Theorem %A Chen2, Peng %A Lu, Yong-Zai %J International Journal of Computational Intelligence Systems %D 2011 %8 sep %V 4 %N 5 %G en %F Chen:2011:IJCIS %X This paper presents a novel approach to automatic design of a robust optimal controller for interval plants with Genetic Programming based on Kharitonov Theorem (KT), which provides a theoretical foundation in the design of robust controller for interval plants. The structure and parameters of the robust optimal controller for interval plants are optimised by Genetic Programming and the Generalized KT related stability criteria are integrated into the solution to guarantee the stability of the closed-loop system. Consequently, the evolved controller not only minimises time-weighted absolute error (ITAE) of the closed-loop system, but also stabilizes the whole interval plant family robustly. Finally, the simulations on a benchmark problem show that the proposed method can effectively generate a robust optimal controller for interval plants. %K genetic algorithms, genetic programming, interval plant, kharitonov theorem, robust optimal controller %9 journal article %R 10.1080/18756891.2011.9727834 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1010.701 %U http://dx.doi.org/10.1080/18756891.2011.9727834 %P 826-836 %0 Conference Proceedings %T Generalisation and Domain Adaptation in GP with Gradient Descent for Symbolic Regression %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %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 Chen:2015:CEC %X Genetic programming (GP) has been widely applied to symbolic regression problems and achieved good success. Gradient descent has also been used in GP as a complementary search to the genetic beam search to further improve symbolic regression performance. However, most existing GP approaches with gradient descent (GPGD) to symbolic regression have only been tested on the conventional symbolic regression problems such as benchmark function approximations and engineering practical problems with a single (training) data set only and the effectiveness on unseen data sets in the same domain and in different domains has not been fully investigated. This paper designs a series of experiment objectives to investigate the effectiveness and efficiency of GPGD with various settings for a set of symbolic regression problems applied to unseen data in the same domain and adapted to other domains. The results suggest that the existing GPGD method applying gradient descent to all evolved program trees three times at every generation can perform very well on the training set itself, but cannot generalise well on the unseen data set in the same domain and cannot be adapted to unseen data in an extended domain. Applying gradient descent to the best program in the final generation of GP can also improve the performance over the standard GP method and can generalise well on unseen data for some of the tasks in the same domain, but perform poorly on the unseen data in an extended domain. Applying gradient descent to the top 20percent programs in the population can generalise reasonably well on the unseen data in not only the same domain but also in an extended domain. %K genetic algorithms, genetic programming %R 10.1109/CEC.2015.7257017 %U http://dx.doi.org/10.1109/CEC.2015.7257017 %P 1137-1144 %0 Conference Proceedings %T Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation %A Chen, Qi %A Zhang, Mengjie %A Cue, Bing %Y Friedrich, Tobias %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 Chen:2016:GECCO %X Generalisation is one of the most important performance measures for any learning algorithm, no exception to Genetic Programming (GP). A number of works have been devoted to improve the generalisation ability of GP for symbolic regression. Methods based on a reliable estimation of generalisation error of models during evolutionary process are a sensible choice to enhance the generalisation of GP. Structural risk minimisation (SRM), which is based on the VC dimension in the learning theory, provides a powerful framework for estimating the difference between the generalisation error and the empirical error. Despite its solid theoretical foundation and reliability, SRM has seldom been applied to GP. The most important reason is the difficulty in measuring the VC dimension of GP models/programs. This paper introduces SRM, which is based on an empirical method to measure the VC dimension of models, into GP to improve its generalisation performance for symbolic regression. The results of a set of experiments confirm that GP with SRM has a dramatical generalisation gain while evolving more compact/less complex models than standard GP. Further analysis also shows that in most cases, GP with SRM has better generalisation performance than GP with bias-variance decomposition, which is one of the state-of-the-art methods to control overfitting. %K genetic algorithms, genetic programming %R 10.1145/2908812.2908842 %U http://dx.doi.org/10.1145/2908812.2908842 %P 709-716 %0 Conference Proceedings %T Improving Generalisation of Genetic Programming for High-Dimensional Symbolic Regression with Feature Selection %A Chen, Qi %A Xue, Bing %A Niu, Ben %A Zhang, Mengjie %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 Chen:2016:CEC %X Feature selection is a desired process when learning from high-dimensional data. However, it is seldom considered in Genetic Programming (GP) for high-dimensional symbolic regression. This work aims to develop a new method, Genetic Programming with Feature Selection (GPWFS), to improve the generalisation ability of GP for symbolic regression. GPWFS is a two-stage method. The main task of the first stage is to select important/informative features from fittest individuals, and the second stage uses a set of selected features, which is a subset of original features, for regression. To investigate the learning/optimisation performance and generalisation capability of GPWFS, a set of experiments using standard GP as a baseline for comparison have been conducted on six real-world high-dimensional symbolic regression datasets. The experimental results show that GPWFS can have better performance both on the training sets and the test sets on most cases. Further analysis on the solution size, the number of distinguished features and total number of used features in the evolved models shows that using GPWFS can induce more compact models with better interpretability and lower computational costs than standard GP. %K genetic algorithms, genetic programming %R 10.1109/CEC.2016.7744270 %U http://dx.doi.org/10.1109/CEC.2016.7744270 %P 3793-3800 %0 Journal Article %T Feature Selection to Improve Generalisation of Genetic Programming for High-Dimensional Symbolic Regression %A Chen, Qi %A Zhang, Mengjie %A Xue, Bing %J IEEE Transactions on Evolutionary Computation %D 2017 %8 oct %V 21 %N 5 %@ 1089-778X %F Chen:2017:ieeeTEC %X When learning from high-dimensional data for symbolic regression, genetic programming typically could not generalise well. Feature selection, as a data preprocessing method, can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, in genetic programming for high-dimensional symbolic regression, feature selection before learning is seldom considered. In this work, we propose a new feature selection method based on permutation to select features for high dimensional symbolic regression using genetic programming. A set of experiments has been conducted to investigate the performance of the proposed method on the generalisation of genetic programming for high-dimensional symbolic regression. The regression results confirm the superior performance of the proposed method over the other examined feature selection methods. Further analysis indicates that the models evolved by the proposed method are more likely to contain only the truly relevant features and have better interpretability. %K genetic algorithms, genetic programming, Symbolic Regression %9 journal article %R 10.1109/TEVC.2017.2683489 %U http://dx.doi.org/10.1109/TEVC.2017.2683489 %P 792-806 %0 Conference Proceedings %T Geometric Semantic Crossover with an Angle-aware Mating Scheme in Genetic Programming for Symbolic Regression %A Chen, Qi %A Xue, Bing %A Mei, Yi %A Zhang, Mengjie %Y Castelli, Mauro %Y McDermott, James %Y Sekanina, Lukas %S EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming %S LNCS %D 2017 %8 19 21 apr %V 10196 %I Springer Verlag %C Amsterdam %F Chen:2017:EuroGP %X Recent research shows that incorporating semantic knowledge into the genetic programming (GP) evolutionary process can improve its performance. This work proposes an angle-aware mating scheme for geometric semantic crossover in GP for symbolic regression. The angle-awareness guides the crossover operating on parents which have a large angle between their relative semantics to the target semantics. The proposed idea of angle-awareness has been incorporated into one state-of-the-art geometric crossover, the locally geometric semantic crossover. The experimental results show that, compared with locally geometric semantic crossover and the regular GP crossover, the locally geometric crossover with angle-awareness not only has a significantly better learning performance but also has a notable generalisation gain on unseen test data. Further analysis has been conducted to see the difference between the angle distribution of crossovers with and without angle-awareness, which confirms that the angle-awareness changes the original distribution of angles by decreasing the number of parents with zero degree while increasing their counterparts with large angles, leading to better performance. %K genetic algorithms, genetic programming: Poster %R 10.1007/978-3-319-55696-3_15 %U http://dx.doi.org/10.1007/978-3-319-55696-3_15 %P 229-245 %0 Conference Proceedings %T Geometric Semantic Genetic Programming with Perpendicular Crossover and Random Segment Mutation for Symbolic Regression %A Chen, Qi %A Zhang, Mengjie %A Xue, Bing %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/0002ZX17 %X Geometric semantic operators have been a rising topic in genetic programming (GP). For the sake of a more effective evolutionary process, various geometric search operators have been developed to use the knowledge acquired from inspecting the behaviours of GP individuals. While the current exact geometric operators lead to over-grown children in GP, existing approximate geometric operators never consider the theoretical framework of geometric semantic GP explicitly. This work proposes two new geometric search operators, i.e. perpendicular crossover and random segment mutation, to fulfil precise semantic requirements for symbolic regression under the theoretical framework of geometric semantic GP. The two operators approximate the target semantics gradually and effectively. The results show that the new geometric operators bring a notable benefit to both the learning performance and the generalisation ability of GP. In addition, they also have significant advantages over Random Desired Operator, which is a state-of-the-art geometric semantic operator. %K genetic algorithms, genetic programming, Symbolic regression, Geometric semantic operators %R 10.1007/978-3-319-68759-9_35 %U http://dx.doi.org/10.1007/978-3-319-68759-9_35 %P 422-434 %0 Conference Proceedings %T New Geometric Semantic Operators in Genetic Programming: Perpendicular Crossover and Random Segment Mutation %A Chen, Qi %A Zhang, Mengjie %A Xue, Bing %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 Chen:2017:GECCOa %X Various geometric search operators have been developed to explore the behaviours of individuals in genetic programming (GP) for the sake of making the evolutionary process more effective. This work proposes two geometric search operators to fulfil the semantic requirements under the theoretical framework of geometric semantic GP for symbolic regression. The two operators approximate the target semantics gradually but effectively. The results show that the new geometric operators can not only lead to a notable benefit to the learning performance, but also improve the generalisation ability of GP. In addition, they also bring a significant improvement to Random Desired Operator, which is a state-of-the-art geometric semantic operator. %K genetic algorithms, genetic programming, geometric semantic operators, symbolic regression %R 10.1145/3067695.3076008 %U http://doi.acm.org/10.1145/3067695.3076008 %U http://dx.doi.org/10.1145/3067695.3076008 %P 223-224 %0 Conference Proceedings %T Genetic Programming with Embedded Feature Construction for High-Dimensional Symbolic Regression %A Chen, Qi %A Zhang, Mengjie %A Xue, Bing %S Intelligent and Evolutionary Systems %D 2017 %I Springer %F chen:2017:IES %K genetic algorithms, genetic programming %R 10.1007/978-3-319-49049-6_7 %U http://link.springer.com/chapter/10.1007/978-3-319-49049-6_7 %U http://dx.doi.org/10.1007/978-3-319-49049-6_7 %0 Thesis %T Improving the Generalisation of Genetic Programming for Symbolic Regression %A Chen, Qi %D 2018 %C New Zealand %C School of Engineering and Computer Science, Victoria University of Wellington %F QiChen:thesis %X Symbolic regression (SR) is a function identification process, the task of which is to identify and express the relationship between the input and output variables in mathematical models. SR is named to emphasize its ability to find the structure and coefficients of the model simultaneously. Genetic Programming (GP) is an attractive and powerful technique for SR, since it does not require any predefined model and has a flexible representation. However, GP based SR generally has a poor generalisation ability which degrades its reliability and hampers its applications to science and real-world modeling. Therefore, this thesis aims to develop new GP approaches to SR that evolve/learn models exhibiting good generalisation ability. This thesis develops a novel feature selection method in GP for high-dimensional SR. Feature selection can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, feature selection is seldom considered in GP for high-dimensional SR. The proposed new feature selection method uses GPs built-in feature selection ability and relies on permutation to detect the truly relevant features and discard irrelevant/noisy features. The results confirm the superiority of the proposed method over the other examined feature selection methods including random forests and decision trees on identifying the truly relevant features. Further analysis indicates that the models evolved by GP with the proposed feature selection method are more likely to contain only the truly relevant features and have better interpretability. To address the overfitting issue of GP when learning from a relatively small number of instances, this thesis proposes a new GP approach by incorporating structural risk minimisation (SRM), which is a framework to estimate the generalisation performance of models, into GP. The effectiveness of SRM highly depends on the accuracy of the Vapnik-Chervonenkis (VC) dimension measuring model complexity. This thesis significantly extends an experimental method (instead of theoretical estimation) to measure the VC-dimension of a mixture of linear and nonlinear regression models in GP for the first time. The experimental method has been conducted using uniform and non-uniform settings and provides reliable VC-dimension values. The results show that our methods have an impressively better generalisation gain and evolve more compact model, which have a much smaller behavioural difference from the target models than standard GP and GP with bootstrap, The proposed method using the optimised non-uniform setting further improves the one using the uniform setting. This thesis employs geometric semantic GP (GSGP) to tackle the unsatisfied generalisation performance of GP for SR when no overfitting occurs. It proposes three new angle-awareness driven geometric semantic operators (GSO) including selection, crossover and mutation to further explore the geometry of the semantic space to gain a greater generalisation improvement in GP for SR. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, to be resistant to overfitting. The results show that compared with two kinds of state-of-the-art GSOs, the proposed new GSOs not only drive the evolutionary process fitting the target semantics more efficiently but also significantly improve the generalisation performance. A further comparison on the evolved models shows that the new method generally produces simpler models with a much smaller size and containing important building blocks of the target models. %K genetic algorithms, genetic programming, GPWFS, Semantic GP, VC-Dimension, SRM, Geometric Semantic GP, GSGP, GP-C5.0, GP-RF, GP-GPPI, GPWFS, GPSRM, GPOPSRM, Angle-aware Geometric Semantic Crossover, AGSX, LASSO, RF, Keijzer14, NRMSEs, LD50, DLBCL, RSS, Artificial Intelligence, AI %9 Ph.D. thesis %U http://hdl.handle.net/10063/7029 %0 Journal Article %T Improving Generalisation of Genetic Programming for Symbolic Regression with Angle-Driven Geometric Semantic Operators %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2019 %8 jun %V 23 %N 3 %@ 1089-778X %F Chen:ieeeTEC:8462796 %X Geometric semantic genetic programming has recently attracted much attention. The key innovations are inducing a unimodal fitness landscape in the semantic space and providing a theoretical framework for designing geometric semantic operators. The geometric semantic operators aim to manipulate the semantics of programs by making a bounded semantic impact and generating child programs with similar or better behaviour than their parents. These properties are shown to be highly related to a notable generalisation improvement in genetic programming. However, the potential ineffectiveness and difficulties in bounding the variations in these geometric operators still limits their positive effect on generalisation. This work attempts to further explore the geometry and search space of geometric operators to gain a greater generalisation improvement in genetic programming for symbolic regression. To this end, a new angle-driven selection operator and two new angle-driven geometric search operators are proposed. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, be resistant to over fitting. The experiments show that compared with two state-of-the-art geometric semantic operators, our angle-driven geometric operators not only drive the evolutionary process to fit the target semantics more efficiently but also improve the generalisation performance. A further comparison between the evolved models shows that the new method generally produces simpler models with a much smaller size and is more likely to evolve towards the correct structure of the target models. %K genetic algorithms, genetic programming, Geometric Semantic Operator, Symbolic Regression, Generalisation %9 journal article %R 10.1109/TEVC.2018.2869621 %U http://dx.doi.org/10.1109/TEVC.2018.2869621 %P 488-502 %0 Journal Article %T Structural Risk Minimisation-Driven Genetic Programming for Enhancing Generalisation in Symbolic Regression %A Chen, Qi %A Zhang, Mengjie %A Xue, Bing %J IEEE Transactions on Evolutionary Computation %D 2019 %8 aug %V 23 %N 4 %@ 1089-778X %F Chen:ieeeTEVC %X Generalisation ability, which reflects the prediction ability of a learnt model, is an important property in genetic programming for symbolic regression. Structural risk minimisation is a framework providing a reliable estimation of the generalisation performance of prediction models. Introducing the framework into genetic programming has the potential to drive the evolutionary process towards models with good generalisation performance. However, this is tough due to the difficulty in obtaining the Vapnik-Chervonenkis dimension of nonlinear models. To address this difficulty, this paper proposes a structural risk minimisation-driven genetic programming approach, which uses an experimental method (instead of theoretical estimation) to measure the Vapnik-Chervonenkis dimension of a mixture of linear and nonlinear regression models for the first time. The experimental method has been conducted using uniform and non-uniform settings. The results show that our method has impressive generalisation gains over standard genetic programming and genetic programming with the 0.632 bootstrap, and that the proposed method using the non-uniform setting has further improvement than its counterpart using the uniform setting. Further analyses reveal that the proposed method can evolve more compact models, and that the behavioural difference between these compact models and the target models is much smaller than their counterparts evolved by the other genetic programming methods. %K genetic algorithms, genetic programming, Symbolic Regression, Generalisation, Structural Risk Minimisation, Vapnik-Chervonenkis Dimension %9 journal article %R 10.1109/TEVC.2018.2881392 %U http://dx.doi.org/10.1109/TEVC.2018.2881392 %P 703-717 %0 Conference Proceedings %T Instance based Transfer Learning for Genetic Programming for Symbolic Regression %A Chen, Qi %A Xue, Bing %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 Chen:2019:CEC %X Transfer learning aims to use knowledge acquired from the source domain to improve the learning performance in the target domain. It attracts increasing interests and many transfer learning approaches have been proposed. However, studies on transfer learning for genetic programming for symbolic regression are still rare, although clearly desired, due to the difficulty to evolve models with a good cross-domain generalisation ability. This work proposes a new instance weighting framework for transfer learning in genetic programming for symbolic regression. The key idea is to use a local weight updating scheme to identify and learn from more useful source domain instances and reduce the effort on the source domain instances, which are more different from the target domain data. The experimental results show that the proposed method notably enhances the learning capacity and the generalisation performance of genetic programming on the target domain and also outperforms some state-of-the-art regression methods. %K genetic algorithms, genetic programming, Transfer learning, Symbolic Regression %R 10.1109/CEC.2019.8790217 %U http://dx.doi.org/10.1109/CEC.2019.8790217 %P 3006-3013 %0 Conference Proceedings %T Differential evolution for instance based transfer learning in genetic programming for symbolic regression %A Chen, Qi %A Xue, Bing %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 Chen:2019:GECCOcomp %K genetic algorithms, genetic programming %R 10.1145/3319619.3321941 %U http://dx.doi.org/10.1145/3319619.3321941 %P 161-162 %0 Conference Proceedings %T Improving Symbolic Regression Based on Correlation between Residuals and Variables %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 Chen:2020:GECCO %X In traditional regression analysis, a detailed examination of the residuals can provide an important way of validating the model quality. However, it has not been used in genetic programming based symbolic regression. This work aims to fill this gap and propose a new evaluation criterion of minimising the correlation between the residuals of regression models and the independent variables. Based on a recent association detection measure, maximal information coefficient which provides an accurate estimation of the correlation, the new evaluation measure is expected to enhance the generalisation of genetic programming by driving the evolutionary process towards models that are without unnecessary complexity and less likely learning from noise in data. The experiment results show that, compared with standard genetic programming which selects model based on the training error only and two state-of-the-art multiobjective genetic programming methods with mechanisms to prefer models with adequate structures, our new multiobjective genetic programming method minimising both the correlation between residuals and variable, and the training error has a consistently better generalisation performance and evolves simpler models. %K genetic algorithms, genetic programming, generalisation, evaluation measure, symbolic regression %R 10.1145/3377930.3390161 %U https://doi.org/10.1145/3377930.3390161 %U http://dx.doi.org/10.1145/3377930.3390161 %P 922-930 %0 Journal Article %T Genetic Programming for Instance Transfer Learning in Symbolic Regression %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Cybernetics %D 2022 %8 jan %V 52 %N 1 %@ 2168-2275 %F Chen:2020:CYB %X Transfer learning has attracted more attention in the machine-learning community recently. It aims to improve the learning performance on the domain of interest with the help of the knowledge acquired from a similar domain(s). However, there is only a limited number of research on tackling transfer learning in genetic programming for symbolic regression. This article attempts to fill this gap by proposing a new instance weighting framework for transfer learning in genetic programming-based symbolic regression. In the new framework, differential evolution is employed to search for optimal weights for source-domain instances, which helps genetic programming to identify more useful source-domain instances and learn from them. Meanwhile, a density estimation method is used to provide good starting points to help the search for the optimal weights while discarding some irrelevant or less important source-domain instances before learning regression models. The experimental results show that compared with genetic programming and support vector regression that learn only from the target instances, and learning from a mixture of instances from the source and target domains without any transfer learning component, the proposed method can evolve regression models which not only achieve notably better cross-domain generalization performance in stability but also reduce the trend of overfitting effectively. Meanwhile, these models are generally much simpler than those generated by the other GP methods. %K genetic algorithms, genetic programming, Task analysis, Estimation, Multitasking, Machine learning, Data models, Cybernetics, instance weighting, transfer learning %9 journal article %R 10.1109/TCYB.2020.2969689 %U http://dx.doi.org/10.1109/TCYB.2020.2969689 %P 25-38 %0 Journal Article %T Preserving Population Diversity Based on Transformed Semantics in Genetic Programming for Symbolic Regression %A Chen, Qi %A Xue, Bing %A Zhang, Mengjie %J IEEE Transactions on Evolutionary Computation %D 2021 %8 jun %V 25 %N 3 %@ 1089-778X %F Qi_Chen:ieeeTEC %X Population diversity plays an important role in avoiding premature convergence in evolutionary techniques including genetic programming. Obtaining an adequate level of diversity during the evolutionary process has became a concern of many previous researches in genetic programming. This work proposes a new novelty metric for entropy based diversity measure for genetic programming. The new novelty metric is based on the transformed semantics of models in genetic programming, where the semantics are the set of outputs of a model on the training data and principal component analysis is used for a transformation of the semantics. Based on the new novelty metric, a new diversity preserving framework, which incorporates a new fitness function and a new selection operator, is proposed to help genetic programming achieve a good balance between the exploration and the exploitation, thus enhancing its learning and generalisation performance. Compared with two stat-of-the-art diversity preserving methods, the new method can generalise better and reduce the overfitting trend more effectively in most cases. Further examinations on the properties of the search process confirm that the new framework notably enhances the evolvability and locality of genetic programming. %K genetic algorithms, genetic programming, Population Diversity, Symbolic Regression %9 journal article %R 10.1109/TEVC.2020.3046569 %U http://dx.doi.org/10.1109/TEVC.2020.3046569 %P 433-447 %0 Conference Proceedings %T Generalisation in Genetic Programming for Symbolic Regression: Challenges and Future Directions %A Chen, Qi %A Xue, Bing %S Women in Computational Intelligence %D 2022 %I Springer %F chen:2022:WiCI %K genetic algorithms, genetic programming %R 10.1007/978-3-030-79092-9_13 %U http://link.springer.com/chapter/10.1007/978-3-030-79092-9_13 %U http://dx.doi.org/10.1007/978-3-030-79092-9_13 %0 Conference Proceedings %T Relieving Genetic Programming from Coefficient Learning for Symbolic Regression via Correlation and Linear Scaling %A Chen, Qi %A Xue, Bing %A Banzhaf, Wolfgang %A Zhang, Mengjie %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F chen:2023:GECCO %X The difficulty of learning optimal coefficients in regression models using only genetic operators has long been a challenge in genetic programming for symbolic regression. As a simple but effective remedy it has been proposed to perform linear scaling of model outputs prior to a fitness evaluation. Recently, the use of a correlation coefficient-based fitness function with a post-processing linear scaling step for model alignment has been shown to outperform error-based fitness functions in generating symbolic regression models. In this study, we compare the impact of four evaluation strategies on relieving genetic programming (GP) from learning coefficients in symbolic regression and focusing on learning the more crucial model structure. The results from 12 datasets, including ten real-world tasks and two synthetic datasets, confirm that all these strategies assist GP to varying degrees in learning coefficients. Among the them, correlation fitness with one-time linear scaling as post-processing, due to be the most efficient while bringing notable benefits to the performance, is the recommended strategy to relieve GP from learning coefficients. %K genetic algorithms, genetic programming, fitness function, correlation, linear scaling, symbolic regression %R 10.1145/3583131.3595918 %U http://dx.doi.org/10.1145/3583131.3595918 %P 420-428 %0 Book Section %T Evolutionary Regression and Modelling %A Chen, Qi %A Xue, Bing %A Browne, Will %A Zhang, Mengjie %E Banzhaf, Wolfgang %E Machado, Penousal %E Zhang, Mengjie %B Handbook of Evolutionary Machine Learning %S Genetic and Evolutionary Computation (GEVO) %D 2023 %8 February %7 1 %I Springer Nature %C Singapore %F chen:2023:hbeml %X Regression and modelling, which identify the relationship between the dependent and independent variables, play an important role in knowledge discovery from data. Symbolic regression goes a step further by learning explicitly symbolic models from data that are potentially interpretable. This chapter provides an overview of evolutionary computation techniques for regression and modelling including coefficient learning and symbolic regression. We introduce the ideas behind various evolutionary computation methods for regression and present a review of the efforts on enhancing learning capability, generalisation, interpretability and imputation of missing data in evolutionary computation for regression. %K genetic algorithms, genetic programming %R 10.1007/978-981-99-3814-8_5 %U http://dx.doi.org/10.1007/978-981-99-3814-8_5 %P 121-149 %0 Conference Proceedings %T A Modified Genetic Programming for Behavior Scoring Problem %A Chen, Qing-Shan %A Zhang, De-Fu %A Wei, Li-Jun %A Chen, Huo-Wang %S IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 %D 2007 %8 mar 1 apr 5 %I IEEE %C Honolulu, HI, USA %@ 1-4244-0705-2 %F chen:2007:CIDM %X Behavior scoring is an important part of risk management in financial institutions, which is used to help financial institutions make better decisions in managing existing customers by forecasting their future credit performance. In this paper, a modified genetic programming (MGP) is introduced to solve the behavior scoring problems. A real life credit data set in a Chinese commercial bank is selected as the experimental data to demonstrate the classification accuracy of this method. MGP is compared with back-propagation neural network (BPN), and another GP that uses normalized inputs (NGP), the experimental results show that the MGP has slight better classification accuracy rate than NGP, and outperforms BPN in dealing with behavior scoring problems because of less historical samples of credit data in Chinese commercial banks %K genetic algorithms, genetic programming, Chinese commercial bank, backpropagation neural network, behavior scoring problem, financial institutions, future credit performance forecasting, real life credit data set, risk management, backpropagation, customer relationship management, financial data processing %R 10.1109/CIDM.2007.368921 %U http://dx.doi.org/10.1109/CIDM.2007.368921 %P 535-539 %0 Conference Proceedings %T Genetic Programming-Enabled Prediction for Students Academic Performance in Blended Learning %A Chen, Rui %A Lai, Fan %A Li, Yanmei %A Wang, Xuan %S The 15th International Conference on Education Technology and Computers, ICETC 2023 %D 2023 %8 sep 26 28 %I ACM %C Barcelona, Spain %F DBLP:conf/icetc/ChenLLW23 %K genetic algorithms, genetic programming, E-Learning, Students academic performance prediction, Blended Learning, Learning behaviours, ANN, SVR, covid-19, online quizzes, online video watching time %R 10.1145/3629296.3629345 %U https://doi.org/10.1145/3629296.3629345 %U http://dx.doi.org/10.1145/3629296.3629345 %P 297-303 %0 Conference Proceedings %T Neural Network Surrogate based on Binary Classification for Assisting Genetic Programming in Searching Scheduling Heuristic %A Chen, Ruiqi %A Mei, Yi %A Zhang, Fangfang %A Zhang, Mengjie %Y Hadfi, Rafik %Y Anthony, Patricia %Y Sharma, Alok %Y Ito, Takayuki %Y Bai, Quan %S Proceedings of 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Part I %S Lecture Notes in Computer Science %D 2024 %8 nov 18 24 %V 15281 %I Springer %C Kyoto, Japan %F DBLP:conf/pricai/ChenMZZ24 %O Trends in Artificial Intelligence %K genetic algorithms, genetic programming, Dynamic job shop scheduling, DJSS, Surrogate, Neural network, ANN %R 10.1007/978-981-96-0116-5_25 %U https://rdcu.be/ebPrb %U http://dx.doi.org/10.1007/978-981-96-0116-5_25 %P 309-321 %0 Journal Article %T Forecasting container throughputs at ports using genetic programming %A Chen, Shih-Huang %A Chen, Jun-Nan %J Expert Systems with Applications %D 2010 %V 37 %N 3 %@ 0957-4174 %F Chen20102054 %X To accurately forecast container throughput is crucial to the success of any port operation policy. This study attempts to create an optimal predictive model of volumes of container throughput at ports by using genetic programming (GP), decomposition approach (X-11), and seasonal auto regression integrated moving average (SARIMA). Twenty-nine years of historical data from Taiwan’s major ports were collected to establish and validate a forecasting model. The Mean Absolute Percent Error levels between forecast and actual data were within 4percent for all three approaches. The GP model predictions were about 32-36percent better than those of X-11 and SARIMA. These results suggest that GP is the optimal method for this case. GP predicted that container through puts at Taiwan’s major ports would slowly increase in the year 2008. Since Taiwan’s government opened direct transportation with China in July 2008, the issue of container throughput in Taiwan has become even more worthy of discussion. %K genetic algorithms, genetic programming, Container throughput, Forecasting %9 journal article %R 10.1016/j.eswa.2009.06.054 %U http://www.sciencedirect.com/science/article/B6V03-4WNXTWY-M/2/1a5e0fe084ba3ea36303bd280acecc04 %U http://dx.doi.org/10.1016/j.eswa.2009.06.054 %P 2054-2058 %0 Conference Proceedings %T Lateral Jet Interaction Model Identification Based on Genetic Programming %A Chen, Shi-Ming %A Dong, Yun-Feng %A Wang, Xiao-Lei %Y Deng, Hepu %Y Miao, Duoqian %Y Lei, Jingsheng %Y Wang, Fu Lee %S Proceedings Third International Conference on Artificial Intelligence and Computational Intelligence (AICI 2011) Part I %S Lecture Notes in Computer Science %D 2011 %8 sep 24 25 %V 7002 %I Springer %C Taiyuan, China %F conf/aici/ChenDW11 %X Precise lateral jet interaction models are required for missiles’ blending control strategies. Because of the complicated flow field, the interaction models are multivariable, complex and coupled. Traditional aerodynamics coefficients model identification used Maximum-likelihood estimation to adjust the parameters of the postulation model, but it is not good at dealing with complex nonlinear models. A genetic programming (GP) method is proposed to identify the interaction model, which not only can optimise the parameters, but also can identify the model structure. The interaction model’s inputs are altitude, mach number, attack angle and fire number of jets in wind channel experiment results, and its output is interaction force coefficient. The fitness function is root mean square error. Select suitable function set and terminal set for GP, then use GP to evolve model automatically. The identify process with different reproduced probability; crossover probability and mutation probability are compared. Results shows that GP’s result error is decrease 30percent than multi-variable regression method. %K genetic algorithms, genetic programming, lateral jet, model identification, missile %R 10.1007/978-3-642-23881-9_63 %U http://dx.doi.org/10.1007/978-3-642-23881-9_63 %P 484-491 %0 Conference Proceedings %T Predicting Stock Returns with Genetic Programming: Do the Short-Term Nonlinear Regularities Exist? %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %Y Fisher, Doug %S Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %D 1995 %8 jan 4 7 %C Ft. Lauderdale, Florida, U.S.A. %F chen:1995:psmrGP %K genetic algorithms, genetic programming %P 95-101 %0 Conference Proceedings %T On the Competitiveness of the Quantity Theory of Money: A Natural-Selection Test Based on Genetic Programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S 11th International Conference on Advanced Science and Technology %D 1995 %8 25 27 mar %C Chicago, Illinois, U.S.A %F chen:1995:cqtm %K genetic algorithms, genetic programming %0 Conference Proceedings %T On the Coordination and Adaptability of the Large Economy: An Application of Genetic Programming to the Cobweb Model %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S Proceedings of the First International Conference on Applications of Dynamic Models to Economics %S The School of Management National Central University’s International Conference Series %D 1995 %8 jun 17 18 %N 3 %C ChungLi, Taiwan, R.O.C. %F chen:1995:cale %K genetic algorithms, genetic programming %P 121-159 %0 Conference Proceedings %T Genetic Programming, Predictability and Stock Market Efficiency %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S Proceedings of 1995 IFAC/IFIP/IFORS/SEDC Symposium on Modelling and Control of National and Regional Economies %D 1995 %8 jul 3 5 %V II %C Gold Coast, Australia %F chen:1995:GPpsme %K genetic algorithms, genetic programming %0 Conference Proceedings %T Predicting Chaotic Dynamic Systems with Genetic Programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S Proceedings of the 50th International Statistical Institute Session %D 1995 %8 aug 21 29 %C Beijing %F chen:1995:pcdsGP %K genetic algorithms, genetic programming %0 Conference Proceedings %T Information Transmission, Market Efficiency and the Evolution of Information-Processing Technology %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %Y Houng, C. %S Proceedings of the 1995 National Conference on Management of Technology %D 1995 %F chen:1995:itmeeipt %K genetic algorithms, genetic programming %P 339-348 %0 Conference Proceedings %T Modelling Coordination Game as a Multi-Agent Adaptive System by Genetic Programming %A Chen, Shu-Heng %A Duffy, John %A Yeh, Chia-Hsuan %Y Van de Velde, W. %Y Perram, J. W. %S Position Papers of the 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World (MAAMAW’96) %D 1996 %8 jan 22 25 %F chen:1996:MAAMAW %O Technical Report 96-1 %K genetic algorithms, genetic programming %0 Conference Proceedings %T Genetic Programming in Computable Financial Economics %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S Proceedings of the ISCA 11th Conference: Computers and Their Applications %D 1996 %8 mar 7 9 %I ISCA Press %C San Francisco, California, U.S.A. %@ 1-880843-15-3 %F chen:1996:GPcfe %X From a computation-theoretic standpoint, this paper formalises the notion of unpredictability in the efficient market hypothesis (EMH) by a biological-based search program, i.e., genetic programming (GP). This formalisation differs from the traditional notion based on probabilistic independence in its treatment of search. Compared with the traditional notion, a GP-based search provided an explicit and efficient search program upon which an objective measure for predictability can be formalized... %K genetic algorithms, genetic programming %U ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/ISCA96/isca96.ps %P 135-138 %0 Conference Proceedings %T Bridging the Gap between Nonlinearity Tests and the Efficient Market Hypothesis by Genetic Programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering %D 1996 %8 mar 24 26 %I IEEE Press %C Crowne Plaza Manhattan, New York City %@ 0-7803-3236-9 %F chen:1996:bgntemh %K genetic algorithms, genetic programming %P 34-39 %0 Book Section %T Genetic Programming, Predictability, and Stock Market Efficiency %A Chen, Shu-Heng %E Vlacic, L. %E Nguyen, T. %E Cecez-Kecmanovic, D. %B Modelling and Control of National and Regional Economies 1995 %D 1996 %I Pergamon %C Oxford, Great Britain %@ 0-08-042376-0 %F chen:1996:GPpsme %K genetic algorithms, genetic programming %P 283-288 %0 Conference Proceedings %T On the Coordination and Adaptability of the Large Economy: An Application of Genetic Programming to the Cobweb Model %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S Preprints of 13th World Congress International Federation of Automatic Control %D 1996 %8 jun 30 jul 5 %V L %C San Francisco, CA, USA %F chen:1996:cale:GPcm %K genetic algorithms, genetic programming %P 279-284 %0 Book Section %T Genetic Programming Learning and the Cobweb Model %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %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 chen:1996:aigp2 %X Using genetic programming to model the cobweb model as a multiagent system, this chapter generalises the work done by Arifovic (1994), which is based on genetic algorithms. We find that the rational expectations equilibrium price which can be discovered by genetic algorithms can also be discovered by genetic programming. Furthermore, genetic programming requires much less prior knowledge than genetic algorithms. The reasonable upper limit of the price and the characteristic of the equilibrium which is assumed as the prior knowledge in genetic algorithms can all be discovered by genetic programming. In addition, GP-based markets have a self-stabilising force which is capable of bringing any deviations caused by mutation back to the rational expectations equilibrium price. All of these features show that genetic programming can be a very useful tool for economists to model learning and adaptation in multiagent systems. In particular, with respect to the understanding of the dynamics of the market process, it provides us with a visible foundation for the ’invisible hand’. %K genetic algorithms, genetic programming %R 10.7551/mitpress/1109.003.0029 %U http://www.aiecon.org/staff/shc/pdf/AGP2.pdf %U http://dx.doi.org/10.7551/mitpress/1109.003.0029 %P 443-466 %0 Conference Proceedings %T Genetic Programming in the Coordination Game with a Chaotic Best-Response Function %A Chen, Shu-Heng %A Duffy, John %A Yeh, Chia-Hsuan %Y Fogel, Lawrence J. %Y Angeline, Peter J. %Y Baeck, Thomas %S Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming %D 1996 %8 feb 29 mar 3 %I MIT Press %C San Diego %@ 0-262-06190-2 %F chen:1996:GPcgcbr %X By modelling the coordination game as GP (Genetic Programming)-based adaptive multiagent systems, this paper analyses the coordination experiments with human subjects conducted by (Van Huyck et al. 1994). In the model on which their experiments were based, the coordination pattern in the equilibrium crucially depends on the learning schemes adopted by the interactive agents in the society. While, in general, we cannot exclude the possibility of chaotic-like coordination, such a result did not... %K genetic algorithms, genetic programming %U ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/EP96/ep96.ps %P 277-286 %0 Journal Article %T Toward a Computable Approach to the Efficient Market Hypothesis: An Application of Genetic Programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %J Journal of Economic Dynamics and Control %D 1997 %8 January %V 21 %N 6 %F chen:1996:caemh %X From a computation-theoretic standpoint, this paper formalises the notion of unpredictability in the efficient market hypothesis (EMH) by a biological-based search program, i.e., genetic programming (GP). This formalization differs from the traditional notion based on probabilistic independence in its treatment of search. Compared with the traditional notion, a GP-based search provides an explicit and efficient search program upon which an objective measure for predictability can be formalized in terms of search intensity and chance of success in the search. This will be illustrated by an example of applying GP to predict chaotic time series. Then the EMH based on this notion will be exemplified by an application to the Taiwan and US stock market. A short-term sample of TAIEX and S&P 500 with the highest complexity defined by Rissanen’s minimum description length principle (MDLP) is chosen and tested. It is found that, while linear models cannot predict better than the random walk, a GP-based search can beat random walk by 50%. It, therefore, confirms the belief that while the short-term nonlinear regularities might still exist, the search costs of discovering them might be too high to make the exploitation of these regularities profitable, hence the efficient market hypothesis is sustained. %K genetic algorithms, genetic programming, Evolutionary computation, Minimum description length principle, Mean absolute percentage error, Efficient market hypothesis %9 journal article %R 10.1016/S0165-1889(97)82991-0 %U http://dx.doi.org/10.1016/S0165-1889(97)82991-0 %P 1043-1063 %0 Conference Proceedings %T Equilibrium Selection Using Genetic Programming %A Chen, Shu-Heng %A Duffy, John %A Yeh, Chia-Hsuan %Y Amari, S. %Y Xu, L. %Y Chan, L. %Y King, I. %Y Leung, K. %S Progress in Neural Information Precessing: Proceedings of the International Conference on Neural Information Processing (ICONIP’96) %D 1996 %V 2 %I Springer-Verlag %C Hong Kong Convention and Exhibition Center, Hong Kong %@ 981-3083-04-2 %F chen:1996:esGP %X We use genetic programming techniques developed by Koza (1992) to model the behaviour of a population of heterogeneous agents playing a simple coordination game with multiple equilibria. We compare the results from our computational experiments with results obtained from a number of controlled laboratory experiments conducted by Van Huyck et al. (1994) where human subjects played the same coordination game. We nd that the behavior exhibited by our population of artificially intelligent... %K genetic algorithms, genetic programming %U ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/ICONIP96/iconip96.ps %P 1341-1346 %0 Conference Proceedings %T Genetic Programming Learning in the Cobweb Model with Speculators %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S Proceedings of 3rd Conference on Business Education %D 1996 %8 dec 5 %C Department of Business Education, National Changhua University of Education, Chunghua, Taiwan %F chen:1996:GPlcms %K genetic algorithms, genetic programming %P 155-176 %0 Conference Proceedings %T Genetic Programming Learning in the Cobweb Model with Speculators %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S International Computer Symposium (ICS’96). Proceedings of International Conference on Artificial Intelligence %D 1996 %8 dec 19 21 %C National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C. %F chen:1996:GPlcmsICS %K genetic algorithms, genetic programming %P 39-46 %0 Journal Article %T Information Transmission, Market Efficiency and the Evolution of Information-Processing Technology %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %J Journal of Technology Management %D 1996 %V 1 %N 1 %F chen:1996:itmeeipt %K genetic algorithms, genetic programming %9 journal article %P 23-41 %0 Conference Proceedings %T A Comparison of Forcast Accuracy between Genetic Programming and Other Forcasters: A loss-Differential Approach %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %Y Borrajo, Daniel %Y Isasi, Pedro %S The First International Workshop on Machine Learning, Forecasting, and Optimization (MALFO96) %D 1996 %8 October %C Gatafe, Spain %@ 84-89315-04-3 %F chen:1996:cfaGPothers %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/chen_1996_cfaGPothers.pdf %P 39-51 %0 Conference Proceedings %T Genetic Programming and the Efficient Market Hypothesis %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %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 chen:1996:gpemh %X While search plays an important role in the efficient market hypothesis (EMH), the traditional formalisation of the EMH, based on probabilistic independence, fails to capture it. Due to this failure, recent findings of nonlinear tests misled us into concluding that the EMH is rejected. Even though most economists are reluctant to make this conclusion, the traditional formalization leaves us no other choice. This paper reformalizes the EMH with a biologically-based search program, i.e., genetic... %K genetic algorithms, genetic programming %U ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/GP96/gp96.ps %P 45-53 %0 Conference Proceedings %T Speculative Trades and Financial Regulations: Simulations Based on Genetic Programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr’97) %D 1997 %8 mar 24 25 %I IEEE Press %C New York City, U.S.A. %F chen:1997:stfr %X By exploring a two-dimensional parameter space, the paper pinpoints the area where speculative trades can contribute to the reduction of price volatility and are hence imperative for market efficiency. This area is delimited by a rather restrictive financial regulations imposed on an inherently unstable economy. Specifically, depending on the associated financial regulations, the authors’ GP-based simulations of cobweb markets show that speculative trades may reduce price volatility by 20percent to 50percent in an inherently unstable economy; on the other hand they may also increase price volatility by 300percent to 3000percent. The paper generalises the earlier finding by Chen and Yeh (1997), which basically shows that in an inherently stable economy, speculative trades can only be destabilising %K genetic algorithms, genetic programming, 2D parameter space, cobweb markets, financial regulations, market efficiency, price volatility reduction, simulations, speculative trades, unstable economy, economics, financial data processing, mathematical programming, simulation, stock markets %R 10.1109/CIFER.1997.618924 %U http://dx.doi.org/10.1109/CIFER.1997.618924 %P 123-129 %0 Conference Proceedings %T Simulating Economic Transition Processes by Genetic Programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %Y Kulikowski, R. %Y Nahorski, Z. %Y Owsinski, J. W. %S Proceedings of the International Conference on Transition to Advanced Market Institutions and Economies: Systems and Operations Research Challenges (Transition’97) %D 1997 %8 jun 18 21 %C Warsaw, Poland %@ 83-85847-81-2 %F chen:1997:setpGP %K genetic algorithms, genetic programming %P 87-93 %0 Conference Proceedings %T Trading Restrictions, Speculative Trades and Price Volatility: An Application of Genetic Programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S Proceedings of the 3rd International Mendel Conference on Genetic Algorithms, Optimization Problems, Fuzzy Logic, Neural Networks, Rough Sets (Mendel’97) %D 1997 %8 jun 25 27 %I PC-DIR %C Brno, Czech Republic %@ 80-214-0884-7 %F chen:1997:trstpv %X n this paper, genetic programming is employed to explore the significance of speculative activities in economic theory. Unlike most previous studies, this paper explicitly take interaction of speculators into account. Through genetic programming, this interaction processes is modelled as a competitive process which applies the survival-of-the-fittest principle to the selection of trading strategies. There are two interesting findings which make this paper distinctive. Firstly, while markets... %K genetic algorithms, genetic programming %U ftp://econo.nccu.edu.tw/AI-ECON/YEH/1997/MENDEL97/mendel97.ps %P 31-37. %0 Conference Proceedings %T Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data %A Chen, Shu-Heng %A Ni, Chih-Chi %Y Smith, George D. %Y Steele, Nigel C. %Y Albrecht, Rudolf F. %S Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97 %D 1997 %I Springer-Verlag %C University of East Anglia, Norwich, UK %@ 3-211-83087-1 %F chen:1997:eannGPfd %O published in 1998 %X In this paper, the stock index S&P 500 is used to test the predicting performance of genetic programming (GP) and genetic programming neural networks (GPNN). While both GP and GPNN are considered universal approximators, in this practical financial application, they perform differently. GPNN seemed to suffer the overlearning (over fitting) problem more seriously than GP; the latter outdid the former in all the simulations. %K genetic algorithms, genetic programming %R 10.1007/978-3-7091-6492-1_87 %U http://dx.doi.org/10.1007/978-3-7091-6492-1_87 %P 397-400 %0 Conference Proceedings %T Modeling Speculators with Genetic Programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %Y Angeline, Peter J. %Y Reynolds, Robert G. %Y McDonnell, John R. %Y Eberhart, Russ %S Proceedings of the Sixth 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 %F chen:1997:msGP %X In spirit of the earlier works done by Arthur (1992) and Palmer et al. (1993), this paper models speculators with genetic programming (GP) in a production economy (Muthian Economy). Through genetic programming, we approximate the consequences of speculating about the speculations of others, including the price volatility and the resulting welfare loss. Some of the patterns observed in our simulations are consistent with findings in experimental markets with human subjects. For example, we show that GP-based speculators can be noisy by nature. However, when appropriate financial regulations are imposed, GP-based speculators can also be more informative than noisy. %K genetic algorithms, genetic programming, no-trade theorems %R 10.1007/BFb0014807 %U ftp://econo.nccu.edu.tw/AI-ECON/YEH/1997/EP97/ep97.ps %U http://dx.doi.org/10.1007/BFb0014807 %P 137-147 %0 Conference Proceedings %T Using Genetic Programming to Model Volatility in Financial Time Series %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %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 %@ 1-55860-483-9 %F chen:1997:GPmvfts %X RGP tested by using Nikkei 255 and S&P 500 as an example %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/chen_1997_GPmvfts.pdf %P 58-63 %0 Conference Proceedings %T Using Genetic Programming to Model Volatility in Financial Time Series: The Case of Nikkei 225 and S&P 500 %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %S Proceedings of the 4th JAFEE International Conference on Investments and Derivatives (JIC’97) %D 1997 %8 jul 29 31 %C Aoyoma Gakuin University, Tokyo, Japan %F chen:1997:GPmvfts:NS+P %X In this paper we propose a time-variant and non-parametric approach to estimating volatility. This approach is based on recursive genetic programming (RGP). Here, volatility is estimated by a class of non-parametric models which are generated through a recursive competitive process. The essential feature of this approach is that it can estimate volatility by simultaneously detecting and adapting to structural changes. Thus, volatility is estimated by taking possible structural changes into account. When RGP discovers structural changes, it will quickly suggest a new class of models so that overestimation of volatility due to ignorance of structural changes can be avoided. The idea of this work is motivated by two lines of research in two different fields; one is the volatility estimation through articial neural nets in nancial engineering, and the other the design of robust adaptive systems under uncertain circumstances in articial intelligence. In this paper, the idea is ... %K genetic algorithms, genetic programming, recursive genetic programming, structural changes, model-specific structural changes, model-free structural changes, improvement sequence %U ftp://econo.nccu.edu.tw/AI-ECON/YEH/1997/JIC97/jic97.ps %P 288-306 %0 Conference Proceedings %T Speculative Trades and Financial Regulations: Simulation Bassed on Genetic Programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %Y Ghose, A. %S Working Notes of The IJCAI-97: Workshop on Business Applications of AI. Fifteenth International Joint Conference on Artificial Intelligence (IJCAI’97) %D 1997 %8 aug 23 29 %C Nagoya, Japan %F chen:1997:stfr:ICJAI %K genetic algorithms, genetic programming %P 1-8 %0 Conference Proceedings %T Modelling Structural Changes with Genetic Programming: An Outline %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %Y Sydow, A. %S Proceedings of 15th IMACS World Congress on Scientific Computation, Moldelling and Applied Mathematics %D 1997 %8 aug 24 29 %V 2 %I Numerical Mathematics, Wissenschaft & Technik Verlag %C Berlin %@ 3-89685-552-2 %F chen:1997:mscGPo %K genetic algorithms, genetic programming %P 621-626 %0 Book Section %T Competition in ’Quantity theory of money’ : Genetic Programming Application in Knowledge Discovery %A Chen, Shuheng %A Ye, Jiaxuan %E Yang, Wenshan %B Development(s) and Application(s) of Measurement Method(s) in Social Science %S Literature of Sun Yat-Sen Institute for Social Sciences and Philosophy %D 1997 %8 sep %N 41 %I Sun Yat-Sen Institute for Social Sciences and Philosophy %C Taipei, Taiwan %F Chen:1997:SunYatSen %K genetic algorithms, genetic programming %U http://www.issp.sinica.edu.tw/chinese/book/ebook/pdf1/bk41/charp-7.pdf %P 139-183 %0 Conference Proceedings %T Genetic programming in the overlapping generations model: An illustration with the dynamics of the inflation rate %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %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 chen:1998:GPogmidir %X In this paper, genetic programming (GP) is employed to model learning and adaptation in the overlapping generations model, one of the most popular dynamic economic models. Using a model of inflation with multiple equilibria as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior equilibrium (the low-inflation steady state) rather than the Pareto-inferior equilibrium (the high-inflation steady state). We also test the robustness of this result with different initial conditions, economic parameters, and GP control parameters. %K genetic algorithms, genetic programming %R 10.1007/BFb0040833 %U http://link.springer.com/chapter/10.1007/BFb0040833 %U http://dx.doi.org/10.1007/BFb0040833 %P 829-837 %0 Conference Proceedings %T Option Pricing with Genetic Programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %A Lee, Woh-Chiang %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 chen:1998:opGP %X One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulas. Earlier studies take the Black-Scholes model as the true model and use the artificial data generated by it to train and to test GP. This paper may be regarded as the first attempt to provide some initial evidence of the empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test two styles of GP, one-stage GP which does not... %K genetic algorithms, genetic programming %U ftp://econo.nccu.edu.tw/AI-ECON/YEH/1998/GP98/gp98.ps %P 32-37 %0 Conference Proceedings %T Hedging Derivative Securities with Genetic Programming %A Chen, Shu-Heng %A Lee, W.-C. %A Yeh, C.-H. %Y Nakhaeizadeh, G. %Y Steurer, E. %S Application of Machine Learning and Data Mining in Finance: Workshop at ECML-98 %D 1998 %8 24 apr %C Dorint-Parkhotel, Chemnitz, Germany %F chen:1998:hdsGP %K genetic algorithms, genetic programming %P 140-151 %0 Conference Proceedings %T Forecasting High-Frequency Financial Time Series with Evolutionary Neural Trees: The Case of Hang-Seng Stock Index %A Chen, Shu-Heng %A Wang, Hung-Shuo %A Zhang, Byoung-Tak %Y Arabnia, Hamid R. %S Proceedings of the International Conference on Artificial Intelligence, IC-AI ’99 %D 1999 %8 28 jun 1 jul %V 2 %I CSREA Press %C Las Vegas, Nevada, USA %@ 1-892512-17-3 %G en %F oai:CiteSeerPSU:454950 %X In this paper, the evolutionary neural trees (ENT) are applied to forecasing the highfrequency stock returns of Heng-Sheng stock index on December, 1998. To understand what may consistute an effective implementation, six experiments are conducted. These experiments are different in data-preprocessing procedures, sample sizes, search intensity and complexity regularization. Our results shows that ENT can perform more efficiently if we can associate ENT with a linear filter so that it can concentrate on searching in the space of nonlinear signals. Also, as well demonstarted in this study, the infrequent bursts (outliers) appearing in the high-frequency data can be very disturbing for the normal operation of ENT. %K genetic algorithms, genetic programming, Evolutionary Artificial Neural Networks, Neural Trees, Sigma-Pi Neural Trees, Breeder Genetic Algorithm %U http://bi.snu.ac.kr/Publications/Conferences/International/ICAI99.ps %P 437-443 %0 Conference Proceedings %T Genetic Algorithms, Trading Strategies and Stochastic Processes: Some New Evidence from Monte Carlo Simulations %A Chen, Shu-Heng %A Lin, Wei-Yuan %A Tsao, Chueh-Iong %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 chen:1999:GATSSPSNEMCS %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-397.pdf %P 114-121 %0 Conference Proceedings %T Genetic Programming in the Agent-Based Modeling of Stock Markets %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %Y Belsley, David A. %Y Baum, Christopher F. %S Fifth International Conference: Computing in Economics and Finance %D 1999 %8 24 26 jun %C Boston College, MA, USA %F SHChen:1999:gpabmsm %O Book of Abstracts %X In this paper, we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called school which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of school, and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders’ search behavior. By simulated annealing, traders’ search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid), and hence supports the efficient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived. %K genetic algorithms, genetic programming, Agent-Based Computational Economics, Social Learning, Business School, Artificial Stock Markets, Simulated Annealing, Peer Pressure %U http://fmwww.bc.edu/cef99/papers/ChenYeh.pdf %P 77 %0 Conference Proceedings %T Towards an Agent-Based Foundation of Financial Econometrics: An Approach Based on Genetic-Programming Artificial Markets %A Chen, Shu-Heng %A Kuo, Tzu-Wen %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 chen:1999:TAFFEAABGAM %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco1999/GP-425c.pdf %P 966 %0 Conference Proceedings %T Genetic Programming in the Agent-Based Artificial Stock Market %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %Y Angeline, Peter J. %Y Michalewicz, Zbyszek %Y Schoenauer, Marc %Y Yao, Xin %Y Zalzala, Ali %S Proceedings of the Congress on Evolutionary Computation %D 1999 %8 June 9 jul %V 2 %I IEEE Press %C Mayflower Hotel, Washington D.C., USA %@ 0-7803-5536-9 (softbound) %F chen:1999:GPAASM %X In this paper, we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called ’school’ which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of school, and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders’ search behaviour. By simulated annealing, traders’ search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid), and hence supports the efficient market hypothesis (EMH). What is interesting though is that this lid series was generated by ’traders’ who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived %K genetic algorithms, genetic programming, algorithms, agent-based, agent-based computational economics, artificial stock markets, business school, peer pressure, simulated annealing, social learning, time series, economics, simulated annealing, software agents, stock markets %R 10.1109/CEC.1999.782509 %U http://dx.doi.org/10.1109/CEC.1999.782509 %P 834-841 %0 Journal Article %T Modeling the expectations of inflation in the OLG model with genetic programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %J Soft Computing - A Fusion of Foundations, Methodologies and Applications %D 1999 %8 sep %V 3 %N 3 %@ 1432-7643 %F chen:1999:SC %X genetic programming (GP) is employed to model learning and adaptation in the overlapping generations model, one of the most popular dynamic economic models. Using a model of inflation with multiple equilibria as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior equilibrium (the low-inflation steady state) rather than the Pareto inferior equilibrium (the high-inflation steady state). We also test the robustness of this result with different initial conditions, economic parameters, GP control parameters, and the selection mechanism. We find that as long as the survival-of-the-fittest principle is maintained, the evolutionary operators are only secondarily important. However, once the survival-of-the-fittest principle is absent, the well-coordinated economy is also gone and the inflation rate can jump quite wildly. To some extent, these results shed light on the biological foundations of economics. %K genetic algorithms, genetic programming, overlapping generations models, bounded rationality, agent-based computational economics, Pareto-superior equilibrium %9 journal article %R 10.1007/s005000050053 %U http://dx.doi.org/10.1007/s005000050053 %P 53-62 %0 Journal Article %T Hedging derivative securities with genetic programming %A Chen, Shu-Heng %A Lee, Wo-Chiang %A Yeh, Chia-Hsuan %J Intelligent Systems in Accounting, Finance and Management %D 1999 %8 dec %V 8 %N 4 %@ 1099-1174 %F Chen:1999:ISAFM %O Special Issue: Machine Learning and Data Mining in Finance %X One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulae. Earlier studies take the BlackScholes model as the true model and use the artificial data generated by it to train and to test GP. The aim of this paper is to provide some initial evidence of the empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test our GP by distinguishing the case in-the-money from the case out-of-the-money. Unlike most empirical studies, we do not evaluate the performance of GP in terms of its pricing accuracy. Instead, the derived GP tree is compared with the Black-Scholes model in its capability to hedge. To do so, a notion of tracking error is taken as the performance measure. Based on the post-sample performance, it is found that in approximately 20percent of the 97 test paths GP has a lower tracking error than the Black–Scholes formula. We further compare our result with the ones obtained by radial basis functions and multilayer perceptrons and one-stage GP %K genetic algorithms, genetic programming, option pricing, Black-Scholes model, tracking error %9 journal article %R 10.1002/(SICI)1099-1174(199912)8:4%3C237::AID-ISAF174%3E3.0.CO%3B2-J %U http://dx.doi.org/10.1002/(SICI)1099-1174(199912)8:4%3C237::AID-ISAF174%3E3.0.CO%3B2-J %P 237-251 %0 Conference Proceedings %T On The Emergent Properties Of Artificial Stock Markets: Some Initial Evidences %A Chen, Shu-Heng %A Liao, Chung-Chi %A Yeh, Chi-Hsuan %S Computing in Economics and Finance %D 2000 %8 June 8 jul %C Universitat Pompeu Fabra, Barcelona, Spain %F RePEc:sce:scecf0:328 %X Using the framework of agent-based artificial stock markets, this paper addresses the two well-known properties frequently observed in financial markets, namely, price-volume relation and sunspots, from a bottom-up perspective. In spirit of “bottom-up”, these two phenomena are pursued in a more fundamental level, i.e., we are asking: is it possible to observed the emergence of these phenomena without explicit references to the assumptions frequently used by the studies in a “top-down” style? Posing it slightly different, would it be enough to generate these phenomena once we model the market as an evolving decentralised system of autonomous interacting agents? Or, can these two phenomenon be coined as “emergent phenomena”, a terminology from complex adaptive systems.To do so, simulation based on AIE-ASM Version 3 (Chen and Yeh, 2000) are conducted for multiple runs. Within the genetic programming framework, we include trading volume and some irrelevant exogenous variables into the terminal sets. This make it possible that trader can choose to believe that trading volume or sunspots can help forecast the future movement of stock returns if they are convinced so from the market behaviour endogenously generated by themselves. To have a further examination on the emergence of sunspot effects, sunspots are generated by deterministic cyclic processes, such as sin curve, and the purely iid random processes. We then test the emergent of these two phenomena by using a new version of the Granger causality test, which does not require an ad-hoc procedure of filtering. %K genetic algorithms, genetic programming %U http://econpapers.repec.org/paper/scescecf0/328.htm %0 Conference Proceedings %T On Bargaining Strategies in the SFI Double Auction Tournaments: Is Genetic Programming the Answer? %A Chen, Shu-Heng %S Computing in Economics and Finance %D 2000 %8 June 8 jul %C Universitat Pompeu Fabra, Barcelona, Spain %F Shu-HengChen:2000:CEF %X While early computational studies of bargaining strategies, such as Rust, Miller and Palmer (1993, 1994) and Andrew and Prager (1996) all indicates the significance of agent-based modeling in the follow-up research, a real agent-based model of bargaining strategies in DA markets has never been taken. This paper attempts to take the fisrt step toward it. In this paper, genetic programming is employed to evolve bargaining strategies within the context of SFI double auction tournaments. We are interested in knowing that given a set of traders, each with a fixed trading strategies, can the automated trader driven by genetic programming eventually develop bargaining strategies which can outperform its competitors’ strategies? To see how GP trader can survive in various environments, different sets of traders characterized by different compositions of bargaining strategies are chosen to compete with the single GP trader. To give a measure of the difficult level of the DA auction markets facing the GP trader, the program length is used to define the intelligence of chosen traders. In one experiment, the chosen traders are all naive; in another experiment, the traders are all sophisticated. Other experiments are placed in the middle of these two extremes. %K genetic algorithms, genetic programming %U http://EconPapers.repec.org/RePEc:sce:scecf0:329 %0 Conference Proceedings %T Toward an Agent-Based Computational Modeling of Bargaining Strategies in Double Auction Markets with Genetic Programming %A Chen, Shu-Heng %Y Leung, Kwong Sak %Y Chan, Lai-Wan %Y Meng, Helen %S Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents %S Lecture Notes in Computer Science %D 2000 %8 13 15 dec %V 1983 %I Springer-Verlag %C Shatin, N.T., Hong Kong, China %@ 3-540-41450-9 %F Chen:2000:TAB %X Using genetic programming, this paper proposes an agent- based computational modelling of double auction (DA) markets in the sense that a DA market is modeled as an evolving market of autonomous interacting traders (automated software agents). The specific DA market on which our modeling is based is the Santa Fe DA market ([12], [13]), which in structure, is a discrete-time version of the Arizona continuous- time experimental DA market ([14], [15]). %K genetic algorithms, genetic programming %R 10.1007/3-540-44491-2_76 %U http://www.aiecon.org/staff/shc/pdf/toward_an_agent.pdf %U http://dx.doi.org/10.1007/3-540-44491-2_76 %P 517-531 %0 Journal Article %T Simulating economic transition processes by genetic programming %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %J Annals of Operations Research %D 2000 %8 dec %V 97 %N 1-4 %@ 0254-5330 %F Chen:2000:AOR %X Recently, genetic programming has been proposed to model agents’ adaptive behaviour in a complex transition process where uncertainty cannot be formalised within the usual probabilistic framework. However, this approach has not been widely accepted by economists. One of the main reasons is the lack of the theoretical foundation of using genetic programming to model transition dynamics. Therefore, the purpose of this paper is two-fold. First, motivated by the recent applications of algorithmic information theory in economics, we would like to show the relevance of genetic programming to transition dynamics given this background. Second, we would like to supply two concrete applications to transition dynamics. The first application, which is designed for the pedagogic purpose, shows that genetic programming can simulate the non-smooth transition, which is difficult to be captured by conventional toolkits, such as differential equations and difference equations. In the second application, genetic programming is applied to simulate the adaptive behavior of speculators. This simulation shows that genetic programming can generate artificial time series with the statistical properties frequently observed in real financial time series. %K genetic algorithms, genetic programming, Kolmogorov complexity, minimum description length principle, bounded rationality, short selling %9 journal article %R 10.1023/A:1018972006990 %U http://dx.doi.org/10.1023/A:1018972006990 %P 265-286 %0 Conference Proceedings %T The Schema Analysis of Emergent Bargaining Strategies in Agent-Based Double Auction Markets %A Chen, Shu-Heng %A Chie, Bin-Tzong %S Fourth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’01) %D 2001 %8 30 oct 1 nov %C Yokusike City, Japan %G en %F oai:CiteSeerPSU:475338 %X In this paper, we simulate the double auction markets with AIE-DA Ver.2. Given that all traders are truth tellers and non-adaptive, we find that the GP trader can always find the most profitable trading strategies. Furthermore, the analysis shows that the trading strategies discovered by GP are very market-specific, which makes our artificial bargaining agent behave quite intelligently. %K genetic algorithms, genetic programming, Double Auctions, Bargaining Strategies, Predatory Pricing, Truth-Tellers %U http://www.aiecon.org/staff/shc/pdf/iccima3.pdf %P 61 %0 Conference Proceedings %T Evolving Bargaining Strategies with Genetic Programming: An Overview of AIE-DA, Ver. 2, Part 1 %A Chen, Shu-Heng %S Fourth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2001 %D 2001 %8 30 oct 1 nov %C Yokusika City, Japan %F Chen:2001:ICCIMA1 %X The purpose of this paper is to introduce the software system A IE-DA, which is designed for the implementation of an agent-based modelling of double auction markets. We shall start this introduction with the current version, Version 2, and then indicate what can be expected from the future of it. %K genetic algorithms, genetic programming %U http://www.aiecon.org/staff/shc/pdf/iccima1.pdf %P 48-54 %0 Conference Proceedings %T Evolving Bargaining Strategies with Genetic Programming: An Overview of AIE-DA, Ver. 2, Part 2 %A Chen, Shu-Heng %A Chie, Bin-Tzong %A Tai, Chung-Ching %S Fourth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2001 %D 2001 %8 30 oct 1 nov %C Yokusika City, Japan %F Chen:2001:ICCIMA2 %X The purpose of this paper is to introduce the software system AIE-DA, which is designed for the implementation of an agent-based modelling of double auction markets. We shall start this introduction with the current version, Version 2, and then indicate what can be expected from the future of it. %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.485.2866 %P 55-60 %0 Journal Article %T Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %J Journal of Economic Dynamics and Control %D 2001 %8 mar %V 25 %N 3-4 %F Shu-HengChen:2001:JEDC %X we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called ‘school’ which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of ‘school’, and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders’ search behavior. By simulated annealing, traders’ search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid), and hence supports the efficient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived. %K genetic algorithms, genetic programming, Agent-based computational economics, Social learning, Business school, Artificial stock markets %9 journal article %R 10.1016/S0165-1889(00)00030-0 %U http://dx.doi.org/10.1016/S0165-1889(00)00030-0 %P 363-393 %0 Journal Article %T Equilibrium Selection via Adaptation: Using Genetic Programming to Model Learning in a Coordination Game %A Chen, Shu-Heng %A Duffy, John %A Yeh, Chia-Hsuan %J The Electronic Journal of Evolutionary Modeling and Economic Dynamics %D 2002 %8 15 jan %@ 1298-0137 %F Chen:2002:EJEMED %X This paper models adaptive learning behavior in a simple coordination game that Van Huyck, Cook and Battalio (1994) have investigated in a controlled laboratory setting with human subjects. We consider how populations of artificially intelligent players behave when playing the same game. We use the genetic programming paradigm, as developed by Koza (1992, 1994), to model how a population of players might learn over time. In genetic programming one seeks to breed and evolve highly fit computer programs that are capable of solving a given problem. In our application, each computer program in the population can be viewed as an individual agent’s forecast rule. The various forecast rules (programs) then repeatedly take part in the coordination game evolving and adapting over time according to principles of natural selection and population genetics. We argue that the genetic programming paradigm that we use has certain advantages over other models of adaptive learning behavior in the context of the coordination game that we consider. We find that the pattern of behavior generated by our population of artificially intelligent players is remarkably similar to that followed by the human subjects who played the same game. In particular, we find that a steady state that is theoretically unstable under a myopic, bestresponse learning dynamic turns out to be stable under our genetic programming based learning system, in accordance with Van Huyck et al.’s (1994) finding using human subjects. We conclude that genetic programming techniques may serve as a plausible mechanism for modelling human behavior, and may also serve as a useful selection criterion in environments with multiple equilibria. %K genetic algorithms, genetic programming, Adaptation, Coordination Game, Equilibrium Selection, Survival of the Fittest %9 journal article %U http://sclab.mis.yzu.edu.tw/faculty/yeh/paper/2002/e-jemed2002.pdf %0 Journal Article %T On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %J Journal of Economic Behavior & Organization %D 2002 %V 49 %N 2 %@ 0167-2681 %F Chen:2002:JEBO %X By studying two well known hypotheses in economics, this paper illustrates how emergent properties can be shown in an agent-based artificial stock market. The two hypotheses considered are the efficient market hypothesis and the rational expectations hypothesis. We inquire whether the macrobehavior depicted by these two hypotheses is consistent with our understanding of the micro-behaviour. In this agent-based model, genetic programming is applied to evolving a population of traders learning over time. We first apply a series of econometric tests to show that the EMH and the REH can be satisfied with some portions of the artificial time series. Then, by analysing traders’ behavior, we show that these aggregate results cannot be interpreted as a simple scaling-up of individual behaviour. A conjecture based on sunspot-like signals is proposed to explain why macrobehavior can be very different from micro-behaviour. We assert that the huge search space attributable to genetic programming can induce sunspot-like signals, and we use simulated evolved complexity of forecasting rules and Granger causality tests to examine this assertion. %K genetic algorithms, genetic programming, Artificial stock markets, Emergent properties, Efficient market hypothesis, Rational expectations hypothesis %9 journal article %R 10.1016/S0167-2681(02)00068-9 %U http://www.sciencedirect.com/science/article/B6V8F-45F900X-8/2/c034ae35c111ca061a11cae1df4b2cd5 %U http://dx.doi.org/10.1016/S0167-2681(02)00068-9 %P 217-239 %0 Book %T Genetic Algorithms and Genetic Programming in Computational Finance %E Chen, Shu-Heng %D 2002 %8 jul %I Kluwer Academic Publishers %C Dordrecht %@ 0-7923-7601-3 %F chen:2002:gagpcf %K genetic algorithms, genetic programming %U http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9 %0 Book Section %T An Overview %A Chen, Shu-Heng %E Chen, Shu-Heng %B Genetic Algorithms and Genetic Programming in Computational Finance %D 2002 %I Kluwer Academic Press %@ 0-7923-7601-3 %F ChenAO:2002:gagpcf %X This chapter reviews some recent advancements in financial applications of genetic algorithms and genetic programming. We start with the more familiar applications, such as forecasting, trading, and portfolio management. We then trace the recent extensions to cash flow management, option pricing, volatility forecasting, and arbitrage. The direction then turns to agent-based computational finance, a bottom-up approach to the study of financial markets. The review also sheds light on a few technical aspects of GAs and GP, which may play a vital role in financial applications. %K genetic algorithms, genetic programming, Agent-based Computational Finance, Financial Engineering %R 10.1007/978-1-4615-0835-9_1 %U http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9 %U http://dx.doi.org/10.1007/978-1-4615-0835-9_1 %P 1-26 %0 Book Section %T Genetic Programming: A Tutorial With The Software Simple GP %A Chen, Shu-Heng %A Kuo, Tzu-Wen %A Shieh, Yuh-Pyng %E Chen, Shu-Heng %B Genetic Algorithms and Genetic Programming in Computational Finance %D 2002 %I Kluwer Academic Press %@ 0-7923-7601-3 %F ChenKuoShieh:2002:gagpcf %X This chapter demonstrates a computer program for tutoring genetic programming (GP). The software, called Simple GP, is developed by the AI-ECON Research Center at National Chengchi University, Taiwan. Using this software, the instructor can help students without programming background to quickly grasp some essential elements of GP. Along with the demonstration of the software is a list of key issues regarding the effective design of the implementation of GP. Some of the issues are already well noticed and studied by financial users of GP, but some are not. While many of the issues do not have a clear-cut answer, the attached software can help beginners to tackle those issues on their own. Once they have a general grasp of how to implement GP effectively, many advanced materials prepared in this volume are there for further exploration. %K genetic algorithms, genetic programming, Simple GP, Symbolic Regression, Data Generating Mechanisms, Chaotic Dynamic Series, Production Function %R 10.1007/978-1-4615-0835-9_3 %U http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9 %U http://dx.doi.org/10.1007/978-1-4615-0835-9_3 %P 55-77 %0 Book Section %T Price Discovery in Agent-Based Computational Modeling of the Artificial Stock Market %A Chen, Shu-Heng %A Liao, Chung-Chih %E Chen, Shu-Heng %B Genetic Algorithms and Genetic Programming in Computational Finance %D 2002 %I Kluwer Academic Press %@ 0-7923-7601-3 %F ChenLiao:2002:gagpcf %X the behaviour of price discovery within a context of an agent based stock market, in which the twin assumptions, namely, rational expectations and the representative agents normally made in mainstream economics, are removed. In this model, traders stochastically update their forecasts by searching the business school whose evolution is driven by genetic programming. Via these agent based simulations, it is found that, except for some extreme cases, the mean prices generated from these artificial markets deviate from the homogeneous rational expectation equilibrium (HREE) prices no more than by 20per cent. This figure provides us a rough idea on how different we can possibly be when the twin assumptions are not taken. Furthermore, while the HREE price should be a deterministic constant in all of our simulations, the artificial price series generated exhibit quite wild fluctuation, which may be coined as the well-known excessive volatility in finance. %K genetic algorithms, genetic programming, Price Discovery, Homogeneous Rational Expectation Equilibrium, Agent-Based Computational Finance, Excessive Volatility %R 10.1007/978-1-4615-0835-9_16 %U http://www.aiecon.org/staff/shc/pdf/apga002.pdf %U http://dx.doi.org/10.1007/978-1-4615-0835-9_16 %P 335-356 %0 Book Section %T Individual Rationality as a Partial Impediment to Market Efficiency: Allocative Efficiency of Markets with Smart Traders %A Chen, Shu-Heng %A Tai, Chung-Ching %A Chie, Bin-Tzong %E Chen, Shu-Heng %B Genetic Algorithms and Genetic Programming in Computational Finance %D 2002 %I Kluwer Academic Press %@ 0-7923-7601-3 %F ChenTaiChie:2002:gagpcf %X we conduct two experiments within an agent-based double auction market. These two experiments allow us to see the effect of learning and smartness on price dynamics and allocative efficiency. Our results are largely consistent with the stylized facts observed in experimental economics with human subjects. From the amelioration of price deviation and allocative efficiency, the effect of learning is vividly seen. However, smartness does not enhance market performance. In fact, the experiment with smarter agents (agents without a quote limit) results in a less stable price dynamics and lower allocative efficiency. %K genetic algorithms, genetic programming, Agent-Based Double Auction Markets, Quote Limits, Alpha Value, Allocative Efficiency %R 10.1007/978-1-4615-0835-9_17 %U http://www.econ.iastate.edu/tesfatsi/shusmart.ps %U http://dx.doi.org/10.1007/978-1-4615-0835-9_17 %P 357-377 %0 Conference Proceedings %T Economic Models of Innovations: Why GP Can Be a Possible Way Out? %A Chen, Shu-heng %A Chie, Bin-tzong %Y Lipson, Hod %Y Antonsson, Erik K. %Y Koza, John R. %S AAAI Spring Symposium, Computational Synthesis: From Basic Building Blocks to High Level Functionality %S AAAI Technical Report %D 2003 %N SS-03-02 %I The AAAI Press %G en %F Chen:2003:AAAIs %X No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation. %K genetic algorithms, genetic programming %U https://aaai.org/Library/Symposia/Spring/2003/ss03-02-007.php %0 Conference Proceedings %T Overfitting or Poor Learning: A Critique of Current Financial Applications of GP %A Chen, Shu-Heng %A Kuo, Tzu-Wen %Y Ryan, Conor %Y Soule, Terence %Y Keijzer, Maarten %Y Tsang, Edward %Y Poli, Riccardo %Y Costa, Ernesto %S Genetic Programming, Proceedings of EuroGP’2003 %S LNCS %D 2003 %8 14 16 apr %V 2610 %I Springer-Verlag %C Essex %@ 3-540-00971-X %F chen03 %X Motivated by a measure of predictability, this paper uses the extracted signal ratio as a measure of the degree of overfitting. With this measure, we examine the performance of one type of overfitting-avoidance design frequently used in financial applications of GP. Based on the simulation results run with the software Simple GP, we find that this design is not effective in avoiding overfitting. Furthermore, within the range of search intensity typically considered by these applications, we find that underfitting, instead of overfitting, is the more prevalent problem. This problem becomes more serious when the data is generated by a process that has a high degree of algorithmic complexity. This paper, therefore, casts doubt on the conclusions made by those early applications regarding the poor performance of GP, and recommends that changes be made to ensure progress. %K genetic algorithms, genetic programming %R 10.1007/3-540-36599-0_4 %U http://dx.doi.org/10.1007/3-540-36599-0_4 %P 34-46 %0 Conference Proceedings %T Modeling International Short-Term Capital Flow with Genetic Programming %A Chen, Shu-Heng %A Kuo, Tzu-Wen %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 Shu-HengChen:2003:CINC %X In this paper, a non-deterministic (portfolio-based) finite-state automaton is proposed to generalise the current financial trading applications of genetic programming from single risky asset to multi risky assets. The GP-evolved trading rules are tested under various settings with respect to search intensity, genetic portfolios, and validating parameters. The rules are compared with performance of a buy and hold strategy in a context of international capital flow using data from Taiwan, the U.S., Hong Kong, Japan and the U.K. The GP are evaluated by using both the mean rule and the majority rule. However, by and large, it is found that GP was outperformed by the buy-and-hold strategy in both cases. %K genetic algorithms, genetic programming %U http://nccur.lib.nccu.edu.tw/handle/140.119/23210 %0 Generic %T A Functional Modularity Approach to Agent-based Modeling of the Evolution of Technology,’ (with B.-T %A Chen, Shu-heng %A Chie, Bin-tzong %D 2014 %8 dec 17 %I Springer %G en %F oai:CiteSeerX.psu:10.1.1.483.8279 %X No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet avail-able. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation. %K genetic algorithms, genetic programming, agent-based computational economics, innovation, functional modularity %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.483.8279 %0 Conference Proceedings %T Functional Modularity in the Test Bed of Economic Theory – Using Genetic Programming %A Chen, Shu-Heng %A Chie, Bin-Tzong %Y Keijzer, Maarten %S Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference %D 2004 %8 26 jul %C Seattle, Washington, USA %F chen:2004:lbp %X In this paper, we follow the model of Chen and Chie (2004), but start with the primeval setup. The implementation of computer simulations show mutation did play an important role in the technology evolution. In a well define simulation world, the producer will exert all of his effort to make the life get better. The parameter of mutation rate is just like the frequency of innovation in the real world. Different mutation rate will shift the model to the different path of history. The path of real world might be represented by one of the mutation rate, but it must be emergent from the different behaviours of the bottom actors. %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2004/LBP062.pdf %0 Journal Article %T Functional Modularity in the Fundamentals of Economic Theory: Toward an Agent-Based Economic Modeling of the Evolution of Technology %A Chen, Shu-Heng %A Chie, Bin-Tzong %J International Journal of Modern Physics B %D 2004 %8 jul 30 %V 18 %N 17-19 %F Shu-HengChen:2004:IJMPB %X No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation. %K genetic algorithms, genetic programming, Agent-based computational economics, innovation, functional modularity %9 journal article %R 10.1142/S0217979204025403 %U http://dx.doi.org/10.1142/S0217979204025403 %P 2376-2386 %0 Journal Article %T Agent-based computational modeling of the stock price-volume relation %A Chen, Shu-Heng %A Liao, Chung-Chih %J Information Sciences %D 2005 %8 18 feb %V 170 %N 1 %F Chen:2005:IS %X From the perspective of the agent-based model of stock markets, this paper examines the possible explanations for the presence of the causal relation between stock returns and trading volume. Using the agent-based approach, we find that the explanation for the presence of the stock price-volume relation may be more fundamental. Conventional devices such as information asymmetry, reaction asymmetry, noise traders or tax motives are not explicitly required. In fact, our simulation results show that the stock price-volume relation may be regarded as a generic property of a financial market, when it is correctly represented as an evolving decentralised system of autonomous interacting agents. One striking feature of agent-based models is the rich profile of agents’ behaviour. This paper makes use of the advantage and investigates the micro-macro relations within the market. In particular, we trace the evolution of agents’ beliefs and examine their consistency with the observed aggregate market behavior. We argue that a full understanding of the price-volume relation cannot be accomplished unless the feedback relation between individual behaviour at the bottom and aggregate phenomena at the top is well understood. %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.ins.2003.03.026 %U http://www.sciencedirect.com/science/article/B6V0C-4B3JHTS-6/2/9e023835b1c70f176d1903dd3a8b638e %U http://dx.doi.org/10.1016/j.ins.2003.03.026 %P 75-100 %0 Conference Proceedings %T On the Role of Risk Preference in Survivability %A Chen, Shu-Heng %A Huang, Ya-Chi %Y Wang, Lipo %Y Chen, Ke %Y Ong, Yew-Soon %S Advances in Natural Computation, Proceedings of First International Conference, ICNC 2005, Part III %S Lecture Notes in Computer Science %D 2005 %8 aug 27 29 %V 3612 %I Springer %C Changsha, China %@ 3-540-28320-X %F DBLP:conf/icnc/ChenH05a %X Using an agent-based multi-asset artificial stock market, we simulate the survival dynamics of investors with different risk preferences. It is found that the survivability of investors is closely related to their risk preferences. Among the eight types of investors considered in this paper, only the CRRA investors with RRA coefficients close to one can survive in the long run. Other types of agents are eventually driven out of the market, including the famous CARA agents and agents who base their decision on the capital asset pricing model. %K genetic algorithms %R 10.1007/11539902_74 %U http://www4.nccu.edu.tw/ezkm11/ezcatfiles/cust/img/img/29.pdf %U http://dx.doi.org/10.1007/11539902_74 %P 612-621 %0 Book Section %T A Functional Modularity Approach to Agent-based Modeling of the Evolution of Technology %A Chen, Shu-Heng %A Chie, Bin-Tzong %E Namatame, Akira %E Aruka, Yuuji %E Kaizouji, Taisei %B The Complex Networks of Economic Interactions: Essays in Agent-Based Economics and Econophysics %S Lecture Notes in Economics and Mathematical Systems %D 2006 %8 jan %V 567 %I Springer %@ 3-540-28726-4 %F Chen:2006:CNEI %X No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation. %K genetic algorithms, genetic programming, agent-based computational economics, innovation, functional modularity %R 10.1007/3-540-28727-2_11 %U http://dx.doi.org/10.1007/3-540-28727-2_11 %P 165-178 %0 Journal Article %T Computationally intelligent agents in economics and Finance %A Chen, Shu-Heng %J Information Sciences %D 2007 %8 January %V 177 %N 5 %F Chen:2006:IS %X This paper is an editorial guide for the second special issue on Computational Intelligence in economics and finance, which is a continuation of the special issue of Information Sciences, Vol. 170, No. 1. This second issue appears as a part of the outcome from the 3rd International Workshop on Computational Intelligence in Economics and Finance, which was held in Cary, North Carolina, September 26-30, 2003. This paper offers some main highlights of this event, with a particular emphasis on some of the observed progress made in this research field, and a brief introduction to the papers included in this special issue. %K genetic algorithms, genetic programming, Computational intelligence, Agent-based computational economics %9 journal article %R 10.1016/j.ins.2006.08.001 %U http://www.aiecon.org/staff/shc/pdf/INS_7416.pdf %U http://dx.doi.org/10.1016/j.ins.2006.08.001 %P 1153-1168 %0 Conference Proceedings %T Pretests for Genetic-Programming Evolved Trading Programs: zero-intelligence Strategies and Lottery Trading %A Chen, Shu-Heng %A Navet, Nicolas %Y King, Irwin %Y Wang, Jun %Y Chan, Laiwan %Y Wang, DeLiang L. %S Neural Information Processing, 13th International Conference, ICONIP 2006, Proceedings, Part III %S Lecture Notes in Computer Science %D 2006 %8 oct 3 6 %V 4234 %I Springer %C Hong Kong, China %@ 3-540-46484-0 %F conf/iconip/ChenN06 %X Over the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clear-cut answers on whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends. %K genetic algorithms, genetic programming %R 10.1007/11893295_50 %U http://dx.doi.org/10.1007/11893295_50 %P 450-460 %0 Conference Proceedings %T Modularity, Product Innovation, and Consumer Satisfaction: An Agent-Based Approach %A Chen, Shu-heng %A Chie, Bin-tzong %Y Yin, Hujun %Y Tino, Peter %Y Corchado, Emilio %Y Byrne, Will %Y Yao, Xin %S 8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007 %S LNCS %D 2007 %8 dec 16 19 %V 4881 %I Springer %C Birmingham, UK %G en %F Chen:2007:IDEAL %X The importance of modularity in product innovation is analysed in this paper. Through simulations with an agent-based modular economic model, we examine the significance of the use of a modular structure in new product designs in terms of its impacts upon customer satisfaction and firms’ competitiveness. To achieve the above purpose, the automatically defined terminal is proposed and is used to modify the simple genetic programming. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-77226-2_105 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.610.9050 %U http://dx.doi.org/10.1007/978-3-540-77226-2_105 %P 1053-1062 %0 Book Section %T Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms %A Chen, Shu-heng %A Navet, Nicolas %E Chen, Shu-Heng %E Wang, Paul P. %E Kuo, Tzu-Wen %B Computational Intelligence in Economics and Finance: Volume II %D 2007 %I Springer %G en %F Chen:2007:chen %X Over the last decade, numerous papers have investigated the use of Genetic Programming (GP) for creating financial trading strategies. Typically, in the literature, the results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests aimed at giving more clear-cut answers as to whether GP can be effective with the training data at hand. Precisely, pretesting allows us to distinguish between a failure due to the market being efficient or due to GP being inefficient. The basic idea here is to compare GP with several variants of random searches and random trading behaviors having well-defined characteristics. In particular, if the outcomes of the pretests reveal no statistical evidence that GP possesses a predictive ability superior to a random search or a random trading behavior, then this suggests to us that there is no point in investing further resources in GP. The analysis is illustrated with GP-evolved strategies for nine markets exhibiting various trends. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-72821-4_11 %U http://www.loria.fr/~nnavet/publi/SHC_NN_Springer2007.pdf %U http://dx.doi.org/10.1007/978-3-540-72821-4_11 %P 169-182 %0 Book Section %T Co-Evolving Trading Strategies to Analyze Bounded Rationality in Double Auction Markets %A Chen, Shu-Heng %A Zeng, Ren-Jie %A Yu, Tina %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 Chen:2008:GPTP %X We investigate double-auction (DA) market behaviour under traders with different degrees of rationality (intelligence or cognitive ability). The rationality of decision making is implemented using genetic programming (GP), where each trader evolves a population of strategies to conduct an auction. By assigning the GP traders different population sizes to differentiate their cognitive ability, through a series of simulations, we find that increasing the traders intelligence improves the markets efficiency. However, increasing the number of intelligent traders in the market leads to a decline in the markets efficiency. By analysing the individual GP traders strategies and their co-evolution dynamics, we provide explanations to these emerging market phenomena. While auction markets are gaining popularity on the Internet, the insights can help market designers devise robust and efficient auction e-markets. %K genetic algorithms, genetic programming, bounded rationality, zero-intelligence, agent-based modelling, human subject experiments, auction markets design, double-auction markets, macroeconomics, trading strategies, software agents, market simulation, market efficiency %R 10.1007/978-0-387-87623-8_13 %U http://www.cs.mun.ca/~tinayu/Publications_files/gptp2008.pdf %U http://dx.doi.org/10.1007/978-0-387-87623-8_13 %P 195-215 %0 Conference Proceedings %T Modeling intelligence of learning agents in an artificial double auction market %A Chen, Shu-Heng %A Tai, Chung-Ching %S IEEE Symposium on Computational Intelligence for Financial Engineering, CIFEr ’09 %D 2009 %8 30 mar apr 2 %F Chen:2009:CIFEr %X In psychological as well as socioeconomic studies, individual intelligence has been found decisive in many domains. In this paper, we employ genetic programming as the algorithm of our learning agents who compete with other designed strategies extracted from the literature.We then discuss the possibility of using population size as a proxy parameter of individual intelligence of software agents. By modeling individual intelligence in this way, we demonstrate not only a nearly positive relation between individual intelligence and performance, but more interestingly the effect of decreasing marginal contribution of IQ to performance found in psychological literature. %K genetic algorithms, genetic programming, artificial double auction market, individual intelligence modeling, learning agents, psychological, socioeconomic, software agents, commerce, psychology, socio-economic effects, software agents %R 10.1109/CIFER.2009.4937500 %U http://dx.doi.org/10.1109/CIFER.2009.4937500 %P 36-42 %0 Conference Proceedings %T Modeling Intelligence of Learning Agents in An Artificial Double Auction Market %A Chen, Shu-Heng %A Tai, Chung-Ching %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 Chen:2009:eurogp %X Individual differences in intellectual abilities can be observed across time and everywhere in the world, and this fact has been well studied by psychologists for a long time. To capture the innate heterogeneity of human intellectual abilities, this paper employs genetic programming as the algorithm of the learning agents, and then proposes the possibility of using population size as a proxy parameter of individual intelligence. By modeling individual intelligence in this way, we demonstrate not only a nearly positive relation between individual intelligence and performance, but more interestingly the effect of decreasing marginal contribution of IQ to performance found in psychological literature. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-01181-8_15 %U http://dx.doi.org/10.1007/978-3-642-01181-8_15 %P 171-182 %0 Conference Proceedings %T Analysis of micro-behavior and bounded rationality in double auction markets using co-evolutionary GP %A Chen, Shu-Heng %A Zeng, Ren-Jie %A Yu, Tina %Y Xu, Lihong %Y Goodman, Erik D. %Y Chen, Guoliang %Y Whitley, Darrell %Y Ding, Yongsheng %S GEC ’09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation %D 2009 %8 jun 12 14 %I ACM %C Shanghai, China %F ChenZY:2009:GEC %X We investigate the dynamics of trader behaviors using a co-evolutionary genetic programming system to simulate a double-auction market. The objective of this study is two-fold. First, we seek to evaluate how, if any, the difference in trader rationality/intelligence influences trading behavior. Second, besides rationality, we also analyze how, if any, the co-evolution between two learnable traders impacts their trading behaviors. We have found that traders with different degrees of rationality may exhibit different behavior depending on the type of market they are in. When the market has a profit zone to explore, the more intelligent trader demonstrate more intelligent behaviors. Also, when the market has two learnable buyers, their co-evolution produced more profitable transactions than when there was only one learnable buyer in the market. We have analyzed the learnable traders’ strategies and found their behavior are very similar to humans in decision making. We will conduct human subject experiments to validate these results in the near future. %K genetic algorithms, genetic programming, Poster %R 10.1145/1543834.1543948 %U http://www.cs.mun.ca/~tinayu/Publications_files/p807.pdf %U http://dx.doi.org/10.1145/1543834.1543948 %P 807-810 %0 Conference Proceedings %T Does Cognitive Capacity Matter When Learning Using Genetic Programming in Double Auction Markets? %A Chen, Shu-Heng %A Tai, Chung-Ching %A Wang, Shu G. %Y di Tosto, Gennaro %Y Van Dyke Parunak, H. %S Multi-Agent-Based Simulation X %S Lecture Notes in Computer Science %D 2009 %V 5683 %I Springer %F conf/mabs/ChenTW09 %X The relationship between human subjects’ cognitive capacity and their economic performances has been noticed in recent years due to the evidence found in a series of cognitive economic experiments. However, there are few agent-based models aiming to characterise such relationship. This paper attempts to bridge this gap and serve as an agent-based model with a focus on agents’ cognitive capacity. To capture the heterogeneity of human cognitive capacity, this paper employs genetic programming as the algorithm of the learning agents, and then uses population size as a proxy parameter of individual cognitive capacity. By modelling agents in this way, we demonstrate a nearly positive relationship between cognitive abilities and economic performance. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-13553-8_4 %U http://dx.doi.org/10.1007/978-3-642-13553-8_4 %P 37-48 %0 Conference Proceedings %T Genetic Programming and Agent-Based Computational Economics: From Autonomous Agents to Product Innovation %A Chen, Shu-Heng %S Agent-Based Approaches in Economic and Social Complex Systems V %D 2009 %I Springer %F chen:2009:AAESCS %K genetic algorithms, genetic programming %R 10.1007/978-4-431-87435-5_1 %U http://link.springer.com/chapter/10.1007/978-4-431-87435-5_1 %U http://dx.doi.org/10.1007/978-4-431-87435-5_1 %0 Book Section %T Bounded Rationality and Market Micro-Behaviors: Case Studies Based on Agent-Based Double Auction Markets %A Chen, Shu-Heng %A Zeng, Ren-Jie %A Yu, Tina %A Wang, Shu G. %E Chen, Shu-Heng %E Kambayashi, Yasushi %E Sato, Hiroshi %B Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization %D 2010 %I IGI Global %F Chen:2010:maaECbit %X We investigate the dynamics of trader behaviours using an agent-based genetic programming system to simulate double-auction markets. The objective of this study is two-fold. First, we seek to evaluate how, if any, the difference in trader rationality/intelligence influences trading behaviour. Second, besides rationality, we also analyse how, if any, the co-evolution between two learnable traders impacts their trading behaviors. We have found that traders with different degrees of rationality may exhibit different behavior depending on the type of market they are in. When the market has a profit zone to explore, the more intelligent trader demonstrates more intelligent behaviors. Also, when the market has two learnable buyers, their co-evolution produced more profitable transactions than when there was only one learnable buyer in the market. We have analyzed the trading strategies and found the learning behaviors are very similar to humans in decision-making. We plan to conduct human subject experiments to validate these results in the near future. %K genetic algorithms, genetic programming %R 10.4018/978-1-60566-898-7.ch005 %U http://dx.doi.org/10.4018/978-1-60566-898-7.ch005 %P 78-94 %0 Journal Article %T Agents learned, but do we? Knowledge discovery using the agent-based double auction markets %A Chen, Shu-Heng %A Yu, Tina %J Frontiers of Electrical and Electronic Engineering in China %D 2011 %8 mar %V 6 %N 1 %@ 1673-3584 %F Chen:2011:frontierEE %X This paper demonstrates the potential role of autonomous agents in economic theory. We first dispatch autonomous agents, built by genetic programming, to double auction markets. We then study the bargaining strategies, discovered by them, and from there, an autonomous-agent-inspired economic theory with regard to the optimal procrastination is derived. %K genetic algorithms, genetic programming, agent-based double auction markets, autonomous agents, bargaining strategies, monopsony, procrastination strategy %9 journal article %R 10.1007/s11460-011-0132-4 %U http://www.cs.mun.ca/~tinayu/Publications_files/frontierEE.pdf %U http://dx.doi.org/10.1007/s11460-011-0132-4 %P 159-170 %0 Generic %T Toward an Autonomous-Agents Inspired Economic Analysis %A Chen, Shu-Heng %A Yu, Tina %D 2011 %8 18 may %I Discussion paper %F DP_6_2011_II %X This paper demonstrates the potential role of autonomous agents in economic theory. We first dispatch autonomous agents, built by genetic programming, to double auction markets. We then study the bargaining strategies discovered by them, and from there an autonomous-agent-inspired economic theory with regard to the optimal procrastination is derived. %K genetic algorithms, genetic programming, agent-based double auction markets, autonomous agents, bargaining strategies, monopsony, procrastination strategy %U http://www.assru.economia.unitn.it/files/DP_6_2011_II.pdf %0 Conference Proceedings %T Is Genetic Programming “Human-Competitive”? The Case of Experimental Double Auction Markets %A Chen, Shu-Heng %A Shik, Kuo-Chuan %Y Yin, Hujun %Y Wang, Wenjia %Y Rayward-Smith, Victor J. %S Proceedings of the 12th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011 %S Lecture Notes in Computer Science %D 2011 %8 sep 7 9 %V 6936 %I Springer %C Norwich, UK %F conf/ideal/ChenS11 %X In this paper, the performance of human subjects is compared with genetic programming in trading. Within a kind of double auction market, we compare the learning performance between human subjects and autonomous agents whose trading behaviour is driven by genetic programming (GP). To this end, a learning index based upon the optimal solution to a double auction market problem, characterised as integer programming, is developed, and criteria tailor-made for humans are proposed to evaluate the performance of both human subjects and software agents. It is found that GP robots generally fail to discover the best strategy, which is a two-stage procrastination strategy, but some human subjects are able to do so. An analysis from the point of view of cognitive psychology further shows that the minority who were able to find this best strategy tend to have higher working memory capacities than the majority who failed to do so. Therefore, even though GP can outperform most human subjects, it is not human-competitive from a higher standard. %K genetic algorithms, genetic programming, experimental markets, double auctions, working memory capacity %R 10.1007/978-3-642-23878-9_15 %U http://dx.doi.org/10.1007/978-3-642-23878-9_15 %P 116-126 %0 Journal Article %T Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective %A Chen, Shu-Heng %J Journal of Economic Dynamics and Control %D 2012 %V 36 %N 1 %@ 0165-1889 %F Chen20121 %X In this paper, we trace four origins of agent-based computational economics (ACE), namely, the markets origin, the cellular-automata origin, the tournaments origin, and the experiments origin. Along with this trace, we examine how these origins have motivated different concepts and designs of agents in ACE, which starts from the early work on simple programmed agents, randomly behaving agents, zero-intelligence agents, human-written programmed agents, autonomous agents, and empirically calibrated agents, and extends to the newly developing cognitive agents, psychological agents, and culturally sensitive agents. The review also shows that the intellectual ideas underlying these varieties of agents cross several disciplines, which may be considered as a part of a general attempt to study humans (and their behaviour) with an integrated interdisciplinary foundation. %K genetic algorithms, genetic programming, Cellular automata, Autonomous agents, Tournaments, Cognitive capacity %9 journal article %R 10.1016/j.jedc.2011.09.003 %U http://www.sciencedirect.com/science/article/pii/S0165188911001692 %U http://dx.doi.org/10.1016/j.jedc.2011.09.003 %P 1-25 %0 Journal Article %T Predicting item exposure parameters in computerized adaptive testing %A Chen, Shu-Ying %A Doong, Shing-Hwang %J British Journal of Mathematical and Statistical Psychology %D 2008 %8 may %V 61 %N 1 %I Blackwell Publishing Ltd %@ 2044-8317 %G en %F BMSP:BMSP255 %X The purpose of this study is to find a formula that describes the relationship between item exposure parameters and item parameters in computerized adaptive tests by using genetic programming (GP) - a biologically inspired artificial intelligence technique. Based on the formula, item exposure parameters for new parallel item pools can be predicted without conducting additional iterative simulations. Results show that an interesting formula between item exposure parameters and item parameters in a pool can be found by using GP. The item exposure parameters predicted based on the found formula were close to those observed from the Sympson and Hetter (1985) procedure and performed well in controlling item exposure rates. Similar results were observed for the Stocking and Lewis (1998) multinomial model for item selection and the Sympson and Hetter procedure with content balancing. The proposed GP approach has provided a knowledge-based solution for finding item exposure parameters. %K genetic algorithms, genetic programming %9 journal article %R 10.1348/000711006X129553 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.624.6855 %U http://dx.doi.org/10.1348/000711006X129553 %P 75-91 %0 Conference Proceedings %T Experiments on Commonality in Sequencing Operators %A Chen, Stephen %A Smith, Stephen F. %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 %F chen:1998:ecso %K genetic algorithms %P 471-478 %0 Conference Proceedings %T Improving Genetic Algorithms by Search Space Reductions (with Applications to Flow Shop Scheduling) %A Chen, Stephen %A Smith, Stephen F. %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 chen:1999:IGASSRAFSS %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-829.pdf %P 135-140 %0 Conference Proceedings %T Introducing a New Advantage of Crossover: Commonality-Based Selection %A Chen, Stephen %A Smith, Stephen F. %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 chen:1999:INACCS %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-827.pdf %P 122-128 %0 Conference Proceedings %T Non-Standard Crossover for a Standard Representation – Commonality-Based Feature Subset Selection %A Chen, Stephen %A Guerra-Salcedo, Cesar %A Smith, Stephen F. %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 chen:1999:NCSRCFSS %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-828.ps %P 129-134 %0 Conference Proceedings %T Orientation Design and Research of Heavy Bamboo Substrate Considering Genetic Programming and Artificial Intelligence Algorithm %A Chen2, Wei %Y Hung, Jason C. %Y Yen, Neil Y. %Y Chang, Jia-Wei %S Frontier Computing %S LNEE %D 2022 %V 827 %I Springer %F chen:2022:FC %X As a kind of cheap, easily available and renewable natural material, bamboo is an advantageous resource for sustainable design in today’s circular economy era. a kind of genetic artificial intelligence algorithm is studied for the development of heavy bamboo substrate oriented products. Genetic algorithm is a widely used intelligent optimization algorithm. The selection optimization problem in heavy bamboo product development system is studied. The technology platform for manufacturing bamboo based fiber composites has been built, and a variety of bamboo based fiber composites used in landscape, building structural materials, packaging and transportation materials, interior high-end decoration and other fields have been successfully developed and industrialized. %K genetic algorithms, genetic programming %R 10.1007/978-981-16-8052-6_241 %U http://link.springer.com/chapter/10.1007/978-981-16-8052-6_241 %U http://dx.doi.org/10.1007/978-981-16-8052-6_241 %P 1643-1648 %0 Conference Proceedings %T Optimization of Creep Constitutive Modeling of Lead-Free Solder Using Gene Expression Programming Methods %A Chen3, Wei %A He, Xu %A Xu, Xiaowei %A Liu, Lu %S 2024 25th International Conference on Electronic Packaging Technology (ICEPT) %D 2024 %8 aug %F Chen:2024:ICEPT %X In this paper, the GEP method is used to derive an ontological model for creep data, focusing on the mechanical properties and structural deformation mechanisms of solder at different scales. By analysing the effect of micro-and meso-structural morphology on the material, experimental validation is provided for the construction of an ontological model that depicts the viscoplasticity of Pb- Free solder in detail. Iterative optimisation of a larger population of experimental samples will drive the development of a fine-grained intrinsic model that will be supported by solid theoretical and empirical evidence, advancing the field towards deeper understanding and innovation. In this study, the GEP algorithm is used to fit the intrinsic model of solder creep and is shown to have good fitting results. The advantages of GEP in equation model fitting are the simplicity of its heuristic algorithm, the ease of its implementation and its good optimisation results. In addition, GEP is highly flexible, adaptable and robust to noisy data. After comparing the experimental data simulations, it is found that GEP is able to accurately analyse and predict in the establishment and optimisation of creep model, which brings an important contribution to the field of solder research. %K genetic algorithms, genetic programming, Adaptation models, Analytical models, Creep, Lead, Predictive models, Prediction algorithms, creep constitutive modelling, gene expression programming methods, machine learning, gene expression programming %R 10.1109/ICEPT63120.2024.10668800 %U http://dx.doi.org/10.1109/ICEPT63120.2024.10668800 %0 Journal Article %T Data-driven analysis on ultimate axial strain of FRP-confined concrete cylinders based on explicit and implicit algorithms %A Chen, Wenguang %A Xu, Jinjun %A Dong, Minhao %A Yu, Yong %A Elchalakani, Mohamed %A Zhang, Fengliang %J Composite Structures %D 2021 %V 268 %@ 0263-8223 %F CHEN:2021:CS %X The existing models for predicting the ultimate axial strain of FRP-confined concrete cylinders are mainly derived from the regression analyses on small datasets. Such models usually targeted more specific use cases and could give inaccurate outcomes when generalized. To this end, this paper presents the data-driven Bayesian probabilistic and machine learning prediction models (i.e., back-propagation artificial neural network, multi-gene genetic programming and support vector machine) with high accuracy. First, a comprehensive database containing 471 test results on the ultimate conditions of FRP-confined concrete cylinders was elaborately compiled from the open literature, and the quality of the database was examined and evaluated in detail. Then, an updating procedure characterized by the Bayesian parameter estimation technique was developed to evaluate the critical parameters in the existing models and refine the selected existing models accordingly. The database was also employed for deriving machine learning models. The computational efficiency, transferability and precision of the proposed models are verified. Results show that the proposed Bayesian posterior models, back-propagation artificial neural network, multi-gene genetic programming and support vector machine models achieved outstanding predictive performance, with the support vector machine yielding the highest prediction accuracy. The superior accuracy of the proposed models should assist various stakeholders in optimal use of FRP-confined concrete columns in diverse construction applications %K genetic algorithms, genetic programming, FRP-confined concrete, Ultimate axial strain, Bayesian theory, Machine learning, Back-propagation artificial neural network, Multi-gene genetic programming, Support vector machine %9 journal article %R 10.1016/j.compstruct.2021.113904 %U https://research-repository.uwa.edu.au/en/publications/data-driven-analysis-on-ultimate-axial-strain-of-frp-confined-con %U http://dx.doi.org/10.1016/j.compstruct.2021.113904 %P 113904 %0 Journal Article %T Artificial intelligence high-throughput prediction building dataset to enhance the interpretability of hybrid halide perovskite bandgap %A Chen, Wenning %A Yun, Jungchul %A Im, Doyun %A Li, Sijia %A Mularso, Kelvian T. %A Nam, Jihun %A Jo, Bonghyun %A Lee, Sangwook %A Jung, Hyun Suk %J Journal of Energy Chemistry %D 2025 %@ 2095-4956 %F Chen:2025:jechem %X The bandgap is a key parameter for understanding and designing hybrid perovskite material properties, as well as developing photovoltaic devices. Traditional bandgap calculation methods like ultraviolet-visible spectroscopy and first-principles calculations are time- and power-consuming, not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space. In the present work, an artificial intelligence ensemble comprising two classifiers (with F1 scores of 0.9125 and 0.925) and a regressor (with mean squared error of 0.0014 eV) is constructed to achieve high-precision prediction of the bandgap. The bandgap perovskite dataset is established through high-throughput prediction of bandgaps by the ensemble. Based on the self-built dataset, partial dependence analysis (PDA) is developed to interpret the bandgap influential mechanism. Meanwhile, an interpretable mathematical model with an R2 of 0.8417 is generated using the genetic programming symbolic regression (GPSR) technique. The constructed PDA maps agree well with the Shapley Additive exPlanations, the GPSR model, and experiment verification. Through PDA, we reveal the boundary effect, the bowing effect, and their evolution trends with key descriptors %K genetic algorithms, genetic programming, Artificial intelligence, High-throughput, Perovskite bandgap, Partial dependence analysis, Model interpretability %9 journal article %R 10.1016/j.jechem.2025.05.059 %U https://www.sciencedirect.com/science/article/pii/S2095495625004632 %U http://dx.doi.org/10.1016/j.jechem.2025.05.059 %0 Book Section %T Macroeconomic Forecasting Using Genetic Programming Based Vector Error Correction Model %A Chen, Xi %A Pang, Ye %A Zheng, Guihuan %E Wang, Jue %E Wang, Shouyang %B Buisness Intelligence in Economic Forcasting %D 2010 %I IGI Global %F Chen:2010:BIEF %X Vector autoregressions are widely used in macroeconomic forecasting since they became known in the 1970s. Extensions including vector error correction models, co-integration and dynamic factor models are all rooted in the framework of vector autoregression. The three important extensions are demonstrated to have formal equivalence between each other. Above all, they all emphasise the importance of common trends or common factors. Many researches, including a series of work of Stock and Watson, find that common factor models significantly improve accuracy in forecasting macroeconomic time series. This study follows the work of Stock and Watson. The authors propose a hybrid framework called genetic programming based vector error correction model (GPVECM), introducing genetic programming to traditional econometric models. This new method could construct common factors directly from nonstationary data set, avoiding differencing the original data and thus preserving more information. The authors’ model guarantees that the constructed common factors satisfy the requirements of econometric models such as co-integration, in contrast to the traditional approach. Finally but not trivially, their model could save lots of time and energy from repeated work of unit root tests and differencing, which they believe is convenient for practitioners. An empirical study of forecasting US import from China is reported. The results of the new method dominates those of the plain vector error correction model and the ARIMA model. %K genetic algorithms, genetic programming %R 10.4018/978-1-61520-629-2 %U http://dx.doi.org/10.4018/978-1-61520-629-2 %P 1-15 %0 Generic %T Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems %A Chen, Xian %A Qu, Rong %A Dong, Jing %A Bai, Ruibin %A Jin, Yaochu %D 2025 %I arXiv %F DBLP:journals/corr/abs-2504-07779 %K genetic algorithms, genetic programming %R 10.48550/ARXIV.2504.07779 %U https://doi.org/10.48550/arXiv.2504.07779 %U http://dx.doi.org/10.48550/ARXIV.2504.07779 %0 Journal Article %T Multi-step optimal control of complex process: a genetic programming strategy and its application %A Chen, Xiaofang %A Gui, Weihua %A Wang, Yalin %A Cen, Lihui %J Engineering Applications of Artificial Intelligence %D 2004 %V 17 %N 5 %@ 0952-1976 %F Chen:2004:EAAI %X In many industrial processes, especially chemistry and metallurgy industry, the plant is slow for feedback and data test because of complex and varying factors. Considering the multi-objective feature and the complex problem of production stability in optimal control, this paper proposed an optimal control strategy based on genetic programming (GP), used as a multi-step state transferring procedure. The fitness function is computed by multi-step comprehensive evaluation algorithm, which provides a synthetic evaluation of multi-objective in process state based on single objective models. The punishment to process state variance is also introduced for the balance between optimal performance and stability of production. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution in GP with compact individuals. The optimal solution gained by evolution is a multi-step command program of process control, which not only ensures the optimisation tendency but also avoids violent process variation by adjusting control parameters step by step. An optimal control system for operation direction is developed based on this strategy for imperial smelting process in Shaoguan. The simulation and application results showed its effectiveness for production objects optimisation in complex process control. %K genetic algorithms, genetic programming, Multi-step comprehensive evaluation, Fitness function, Process optimal control %9 journal article %R 10.1016/j.engappai.2004.04.018 %U http://www.sciencedirect.com/science/article/B6V2M-4CMHSNB-1/2/5c02b126719099d090f4dba0eaaa5cea %U http://dx.doi.org/10.1016/j.engappai.2004.04.018 %P 491-500 %0 Book Section %T Engineering Optimization Approaches of Nonferrous Metallurgical Processes %A Chen, Xiaofang %A Xu, Honglei %E Xu, Honglei %E Wang, Xiangyu %B Optimization and Control Methods in Industrial Engineering and Construction %S Intelligent Systems, Control and Automation: Science and Engineering %D 2014 %V 72 %I Springer %G English %F Chen:2014:OCMIEC %X The engineering optimisation approaches arising in nonferrous metallurgical processes are developed to deal with the challenges in current nonferrous metallurgical industry including resource shortage, energy crisis and environmental pollution. The great difficulties in engineering optimisation for nonferrous metallurgical process operation lie in variety of mineral resources, complexity of reactions, strong coupling and measurement disadvantages. Some engineering optimisation approaches are discussed, including operational-pattern optimisation, satisfactory optimisation with soft constraints adjustment and multi-objective intelligent satisfactory optimisation. As an engineering optimisation case, an intelligent sequential operating method for a practical Imperial Smelting Process is illustrated. Considering the complex operating optimisation for the Imperial Smelting Process, with the operating stability concerned, an intelligent sequential operating strategy is proposed on the basis of genetic programming (GP) adaptively designed, implemented as a multi-step state transferring procedure. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution with compact individuals. The optimal solution gained by evolution is a sequential operating program of process control, which not only ensures the tendency to optimisation but also avoids violent variation by operating the parameters in ordered sequences. Industrial application data are given as verifications. %K genetic algorithms, genetic programming, engineering optimisation, nonferrous metallurgical processes, sequential operating, imperial smelting furnace %R 10.1007/978-94-017-8044-5_7 %U http://dx.doi.org/10.1007/978-94-017-8044-5_7 %P 107-124 %0 Journal Article %T A genetic programming based cooperative evolutionary algorithm for flexible job shop with crane transportation and setup times %A Chen, Xiaolong %A Li, Junqing %A Wang, Zunxun %A Li, Jiake %A Gao, Kaizhou %J Applied Soft Computing %D 2025 %V 169 %@ 1568-4946 %F Chen:2025:asoc %X Confronted with increasingly complex industrial scenarios, limited transportation resources and complicated time constraints introduce significant challenges to production efficiency, requiring more robust and adaptive scheduling heuristics. In this study, a flexible job shop scheduling problem with single crane transportation and sequence-dependent setup time is considered. To address the problem, a mixed integer linear programming model is established, where two objectives, including the maximum completion time and total energy consumption, are determined simultaneously. Additionally, a genetic programming (GP) based cooperative evolutionary algorithm is developed to address the problem, in which GP is investigated as a hyper-heuristic to construct a set of problem-specific dispatching rules (DRs). The GP-based hyper-heuristic (GPHH) first evolves a set of DRs during iterations and then applies these DRs to the initialization of the population. Next, four critical path-based neighbourhood structures combined with an adaptive local search mechanism are used to enhance the exploitation capability of the algorithm. The simulation results demonstrate that the GPHH used for initialization significantly outperforms other classical heuristics in convergence capability, while the proposed GP-CEA algorithm also surpasses six state-of-the-art algorithms in exploration and exploitation, achieving superior performance on the HV, IGD, and SC metrics in approximately 59.4percent, 46.9percent, and 50.0percent of instances, respectively %K genetic algorithms, genetic programming, Genetic programming hyper heuristic, Dispatching rules, Cooperative evolutionary algorithm, Flexible job shop scheduling problem, Crane transportation %9 journal article %R 10.1016/j.asoc.2024.112614 %U https://www.sciencedirect.com/science/article/pii/S1568494624013887 %U http://dx.doi.org/10.1016/j.asoc.2024.112614 %P 112614 %0 Journal Article %T Optimizing Dynamic Flexible Job Shop Scheduling Using an Evolutionary Multi-Task Optimization Framework and Genetic Programming %A Chen, Xiaolong %A Li, Junqing %A Wang, Zunxun %A Chen, Qingda %A Gao, Kaizhou %A Pan, Quanke %J IEEE Transactions on Evolutionary Computation %D 2025 %8 oct %V 29 %N 5 %@ 1941-0026 %F Xiaolong_Chen:TEVC %X Driven by the evolution of smart and sustainable manufacturing paradigms under Industry 5.0, which emphasize adaptability, connectivity, and data-driven decision-making, the dynamic flexible job shop scheduling problem (DFJSSP) has emerged as a critical area of research. The DFJSSP involves scheduling jobs in a highly dynamic and uncertain manufacturing environment where new tasks are continually introduced, further complicating the scheduling process. In this study, the DFJSSP is extended to incorporate single crane transportation and sequence-dependent setup times, reflecting real-world manufacturing constraints. To tackle this multifaceted problem, we introduce a novel approach, i.e., a multipopulation-based evolutionary multi-task optimisation(EMTO) framework. Additionally, the genetic programming algorithm is employed as a generative hyperheuristic to deal with the dynamic uncertainties in the shop floor. Two components are collaborated to optimise two objectives, i.e., minimizing the maximum completion time and the total tardiness. Furthermore, a dynamic transfer ratio is proposed, allowing the proportion of knowledge transfer to adapt throughout the iteration process, balancing convergence speed with population diversity. The results demonstrate that both the EMTO framework and the dynamic transfer ratio significantly enhance the performance of the algorithm. Compared to well-known constructive heuristics and reinforcement learning algorithm, the proposed approach enables parallel resolution of multiple optimisation objectives, leading to enhanced scheduling efficiency and adaptability in dynamic manufacturing environments. %K genetic algorithms, genetic programming, Dynamic scheduling, Job shop scheduling, Heuristic algorithms, Optimisation, Real-time systems, Multitasking, Production, Evolutionary computation, Vehicle dynamics, Cranes, dynamic flexible job shop scheduling problem, evolutionary multi-task optimisation, hyperheuristics %9 journal article %R 10.1109/TEVC.2025.3543770 %U http://dx.doi.org/10.1109/TEVC.2025.3543770 %P 1502-1516 %0 Conference Proceedings %T Model of Water Production Function with Genetic Programming %A Chen, Xiao-nan %A Chen, Hai-tao %A Qiu, Lin %A Duan, Chun-qing %S Fourth International Conference on Natural Computation, ICNC ’08 %D 2008 %8 oct %V 6 %F Chen:2008:ICNC %X A new model for analyzing the relation between production and water stress is proposed. A model of genetic programming is established to describe water production function with evolution calculation, which can find the optimal model structure by samples. Simulation and experimental results indicated that water production function based on genetic programming is good at searching optimal structure automatically, and intelligent, accurate. %K genetic algorithms, genetic programming, evolution calculation, optimal structure searching, water production function, water stress, irrigation, search problems %R 10.1109/ICNC.2008.118 %U http://dx.doi.org/10.1109/ICNC.2008.118 %P 311-314 %0 Conference Proceedings %T Transformer Surrogate Genetic Programming for Dynamic Container Port Truck Dispatching %A Chen, Xinan %A Dong, Jing %A Qu, Rong %A Bai, Ruibin %S Bio-Inspired Computing: Theories and Applications %D 2023 %8 15 17 dec %I Springer %C Changsha, China %F chen:2023:BIC-TA %K genetic algorithms, genetic programming %R 10.1007/978-981-97-2272-3_21 %U http://link.springer.com/chapter/10.1007/978-981-97-2272-3_21 %U http://dx.doi.org/10.1007/978-981-97-2272-3_21 %P 276-290 %0 Journal Article %T Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments %A Chen, Xinan %A Qu, Rong %A Dong, Jing %A Dong, Haibo %A Bai, Ruibin %J Applied Soft Computing %D 2024 %V 166 %@ 1568-4946 %F Chen:2024:asoc %X Efficient truck dispatching strategies are paramount in container terminal operations. The quality of these strategies heavily relies on accurate and expedient simulations, which provide a crucial platform for training and evaluating dispatching algorithms. In this study, we introduce data-driven machine learning methods to enhance container port truck dispatching simulation accuracy. These methods effectively surrogate the intersections within the simulation, thereby increasing the accuracy of simulated outcomes without imposing significant computational overhead in sparse data environments. We incorporate three data-driven learning methods: genetic programming (GP), reinforcement learning (RL), and a GP and RL hybrid heuristic (GPRL-H) approach. The GPRL-H method proved the most efficacious through a detailed comparative study, striking an effective balance between simulation accuracy and computational efficiency. It reduced the error rate of simulation from approximately 35percent to about 7percent, while also halving the simulation time compared to the RL-based method. Our proposed method also does not rely on precise Global Positioning System (GPS) data to simulate truck operations within a port accurately. Demonstrating robustness and adaptability, this approach holds promise for extending beyond port operations to improve the simulation accuracy of vehicle operations in various scenarios characterised by sparse data %K genetic algorithms, genetic programming, Intelligent intersection, Transport simulation, Reinforcement learning, Port optimization %9 journal article %R 10.1016/j.asoc.2024.112190 %U https://www.sciencedirect.com/science/article/pii/S1568494624009645 %U http://dx.doi.org/10.1016/j.asoc.2024.112190 %P 112190 %0 Conference Proceedings %T TEA-MAC: Traffic Estimation Adaptive MAC Protocol for Underwater Acoustic Networks %A Chen, Xianyi %A Lin, Guolan %S 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS) %D 2018 %8 nov %F Chen:2018:ICSESS %X Underwater acoustic sensor networks (UASNs) have been applied dramatically in many activities, such as ocean exploration and tsunami warning. However, due to the characteristics of underwater acoustic channel is quite difference from the radio and optical channel, media access control (MAC) is a crucial issue in underwater acoustic sensor networks. In this paper, we propose a Traffic Estimation Adaptive :MAC Protocol (TEA-MAC) based on a changeable duty cycle according to the traffic load. As the better the duty cycle matches the traffic of UASNs, the less energy and delay the nodes consume for data transmission, it is very import to sense the network load correctly. To address this issue, a traffic estimation algorithm based on nodes clustering and Genetic Programming is proposed in TEA-MAC, which can predict the network load successfully and set the duty cycle desirably. The Simulation results show that TEA-MAC performs better than the existing representative MAC protocols in terms of network throughput, end-to-end delay and energy efficiency. %K genetic algorithms, genetic programming %R 10.1109/ICSESS.2018.8663928 %U http://dx.doi.org/10.1109/ICSESS.2018.8663928 %0 Conference Proceedings %T Genetic Network Programming with Sarsa Learning and Its Application to Creating Stock Trading Rules %A Chen, Yan %A Mabu, Shingo %A Hirasawa, Kotaro %A Hu, Jinglu %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 Chen:2007:cec %X In this paper, trading rules on stock market using the Genetic Network Programming (GNP) with Sarsa learning is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the timing of the buying and selling stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalisation ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed. %K genetic algorithms, genetic programming %R 10.1109/CEC.2007.4424475 %U 1636.pdf %U http://dx.doi.org/10.1109/CEC.2007.4424475 %P 220-227 %0 Conference Proceedings %T Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices %A Chen, Yan %A Mabu, Shingo %A Shimada, Kaoru %A Hirasawa, Kotaro %Y Wang, Jun %S 2008 IEEE World Congress on Computational Intelligence %D 2008 %8 January 6 jun %I IEEE Press %C Hong Kong %F Chen2:2008:cec %X The key in stock trading model is to take the right actions for trading at the right time, primarily based on accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to create a stock trading model. In this paper, we present a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are two important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the change of stock prices according to the real time updating. Second, we combine RTU-GNP with a reinforcement learning algorithm to create the programs efficiently. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without time updating method. It yielded significantly higher profits than the traditional trading model without time updating. We also compare the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method. %K genetic algorithms, genetic programming %R 10.1109/CEC.2008.4630824 %U EC0109.pdf %U http://dx.doi.org/10.1109/CEC.2008.4630824 %P 370-377 %0 Conference Proceedings %T Construction of portfolio optimization system using genetic network programming with control nodes %A Chen, Yan %A Mabu, Shingo %A Shimada, Kaoru %A Hirasawa, Kotaro %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 Chen:2008:gecco %K genetic algorithms, genetic programming, control node, genetic network programming, portfolio optimisation, reinforcement learning, Real-World application: Poster %R 10.1145/1389095.1389413 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1693.pdf %U http://dx.doi.org/10.1145/1389095.1389413 %P 1693-1694 %0 Conference Proceedings %T Constructing Portfolio Investment Strategy Based on Time Adapting Genetic Network Programming %A Chen, Yan %A Mabu, Shingo %A Ohkawa, Etsushi %A Hirasawa, Kotaro %Y Tyrrell, Andy %S 2009 IEEE Congress on Evolutionary Computation %D 2009 %8 18 21 may %I IEEE Press %C Trondheim, Norway %F Chen2:2009:cec %X The classical portfolio problem is a problem of distributing capital to a set of stocks. By adapting to the change of stock prices, this study proposes an portfolio investment strategy based on an evolutionary method named ’Genetic Network Programming’ (GNP). This method makes use of the information from Technical Indices and Candlestick Chart. The proposed portfolio model, consisting of technical analysis rules, are trained to generate investment advice. Experimental results on the Japanese stock market show that the proposed investment strategy using Time Adapting GNP (TA-GNP) method outperforms other traditional models in terms of both accuracy and efficiency. We also compared the experimental results of the proposed model with the conventional GNP based methods, GA and Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed investment strategy is effective on the portfolio optimization problem. %K genetic algorithms, genetic programming, genetic network programming, Japanese stock market, candlestick chart, evolutionary method, investment advice, portfolio investment strategy, portfolio model, portfolio optimisation problem, portfolio problem, stock prices, technical analysis rules, technical indices, time adapting genetic network programming, investment, stock markets %R 10.1109/CEC.2009.4983238 %U P026.pdf %U http://dx.doi.org/10.1109/CEC.2009.4983238 %P 2379-2386 %0 Conference Proceedings %T A portfolio selection model using genetic relation algorithm and genetic network programming %A Chen, Yan %A Hirasawa, Kotaro %A Mabu, Shingo %S IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 %D 2009 %8 November 14 oct %F Chen:2009:ieeeSMC %X In this paper, a new evolutionary method named genetic relation algorithm (GRA) has been proposed and applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up a fixed number of the most efficient portfolio, the proposed model considers the correlation coefficient between stocks as strength, which indicates the relationship between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. The efficiency of GRA method is confirmed by the stock trading model using genetic network programming (GNP) that has been proposed in the previous study. We present the experimental results obtained by GRA and compare them with those obtained by traditional method, and it is clarified that the proposed model can obtain much higher profits than the traditional one. %K genetic algorithms, genetic programming, genetic network programming, correlation coefficient, evolutionary method, genetic relation algorithm, portfolio selection model, stock market, stock markets %R 10.1109/ICSMC.2009.5346940 %U http://dx.doi.org/10.1109/ICSMC.2009.5346940 %P 4378-4383 %0 Journal Article %T A portfolio optimization model using Genetic Network Programming with control nodes %A Chen, Yan %A Ohkawa, Etsushi %A Mabu, Shingo %A Shimada, Kaoru %A Hirasawa, Kotaro %J Expert Systems with Applications %D 2009 %V 36 %N 7 %@ 0957-4174 %F Chen200910735 %X Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named ’Genetic Network Programming’ (GNP) has been developed and applied to the stock market recently. The efficient trading rules created by GNP has been confirmed in our previous research. In this paper a multi-brands portfolio optimisation model based on Genetic Network Programming with control nodes is presented. This method makes use of the information from technical indices and candlestick chart. The proposed optimization model, consisting of technical analysis rules, are trained to generate trading advice. The experimental results on the Japanese stock market show that the proposed optimization system using GNP with control nodes method outperforms other traditional models in terms of both accuracy and efficiency. We also compared the experimental results of the proposed model with the conventional GNP based methods, GA and Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than these methods. %K genetic algorithms, genetic programming, Portfolio optimization, Genetic Network Programming, Control node, Reinforcement learning %9 journal article %R 10.1016/j.eswa.2009.02.049 %U http://www.sciencedirect.com/science/article/B6V03-4VPD6KS-2/2/3cf6750a5518ab6e7d6cf817197d96bd %U http://dx.doi.org/10.1016/j.eswa.2009.02.049 %P 10735-10745 %0 Journal Article %T A genetic network programming with learning approach for enhanced stock trading model %A Chen, Yan %A Mabu, Shingo %A Shimada, Kaoru %A Hirasawa, Kotaro %J Expert Systems with Applications %D 2009 %V 36 %N 10 %@ 0957-4174 %F Chen200912537 %X In this paper, an enhancement of stock trading model using Genetic Network Programming (GNP) with Sarsa Learning is described. There are three important points in this paper: First, we use GNP with Sarsa Learning as the basic algorithm while both Technical Indices and Candlestick Charts are introduced for efficient stock trading decision-making. In order to create more efficient judgement functions to judge the current stock price appropriately, Importance Index (IMX) has been proposed to tell GNP the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we proposed a new method that can learn the appropriate function describing the relation between the value of each technical index and the value of the IMX. This is an important point that devotes to the enhancement of the GNP-Sarsa algorithm. The third point is that in order to create more efficient judgment functions, sub-nodes are introduced in each node to select appropriate stock price information depending on the situations and to determine appropriate actions (buying/selling). To confirm the effectiveness of the proposed method, we carried out the simulation and compared the results of GNP-Sarsa with other methods like GNP with Actor Critic, GNP with Candlestick Chart, GA and Buy&Hold method. The results shows that the stock trading model using GNP-Sarsa outperforms all the other methods. %K genetic algorithms, genetic programming, Genetic Network Programming, Sarsa Learning, Stock trading model, Technical Index, Candlestick Chart %9 journal article %R 10.1016/j.eswa.2009.05.054 %U http://www.sciencedirect.com/science/article/B6V03-4WC113D-2/2/a6c6277183e3b22cc3cc50ba71d7062f %U http://dx.doi.org/10.1016/j.eswa.2009.05.054 %P 12537-12546 %0 Journal Article %T A model of portfolio optimization using time adapting genetic network programming %A Chen, Yan %A Mabu, Shingo %A Hirasawa, Kotaro %J Computers & Operations Research %D 2010 %8 oct %V 37 %N 10 %@ 0305-0548 %F Chen2009 %X This paper describes a decision-making model of dynamic portfolio optimisation for adapting to the change of stock prices based on an evolutionary computation method named genetic network programming (GNP). The proposed model, making use of the information from technical indices and candlestick chart, is trained to generate portfolio investment advice. Experimental results on the Japanese stock market show that the decision-making model using time adapting genetic network programming (TA-GNP) method outperforms other traditional models in terms of both accuracy and efficiency. A comprehensive analysis of the results is provided, and it is clarified that the TA-GNP method is effective on the portfolio optimization problem. %K genetic algorithms, genetic programming, Genetic network programming, Portfolio optimisation, Reinforcement learning, Technical indices, Candlestick chart %9 journal article %R 10.1016/j.cor.2009.12.003 %U http://www.sciencedirect.com/science/article/B6VC5-4Y0D6CX-1/2/2b2154c00eb0c11cef64666b20be06e1 %U http://dx.doi.org/10.1016/j.cor.2009.12.003 %P 1697-1707 %0 Conference Proceedings %T A portfolio selection strategy using Genetic Relation Algorithm %A Chen, Yan %A Mabu, Shingo %A Hirasawa, Kotaro %S IEEE Congress on Evolutionary Computation (CEC 2010) %D 2010 %8 18 23 jul %I IEEE Press %C Barcelona, Spain %F Chen:2010:cec %X This paper proposes a new strategy #x03B2;-GRA for portfolio selection in which the return and risk are considered as measures of strength in Genetic Relation Algorithm (GRA). Since the portfolio beta #x03B2; efficiently measures the volatility relative to the benchmark index or the capital market, #x03B2; is usually employed for portfolio evaluation or prediction, but scarcely for portfolio construction process. The main objective of this paper is to propose an integrated portfolio selection strategy, which selects stocks based on #x03B2; using GRA. GRA is a new evolutionary algorithm designed to solve the optimisation problem due to its special structure. We illustrate the proposed strategy by experiments and compare the results with those derived from the traditional models. %K genetic algorithms, genetic programming %R 10.1109/CEC.2010.5586430 %U http://dx.doi.org/10.1109/CEC.2010.5586430 %0 Conference Proceedings %T Parameter Identification Inverse Problems of Partial Differential Equations Based on the Improved Gene Expression Programming %A Chen, Yan %A Li, Kangshun %A Chen, Zhangxin %Y Xie, Jiang %Y Chen, Zhangxin %Y Douglas, Craig C. %Y Zhang, Wu %Y Chen, Yan %S High Performance Computing and Applications: Third International Conference, HPCA 2015 %S Lecture Notes in Computer Science %D 2015 %8 jul 26 30 %V 9576 %I Springer %C Shanghai, China %F conf/cnhpca/ChenLC15 %O Revised Selected Papers %X Traditionally, solving the parameter identification inverse problems of partial differential equations encountered many difficulties and insufficiency. In this paper, we propose an improved GEP (Gene Expression Programming) to identify the parameters in the reverse problems of partial differential equations based on the self-adaptation, self-organization and self-learning characters of GEP. This algorithm simulates a parametric function itself of a partial differential equation directly through the observed values by fully taking into account inverse results caused by noises of a measured value. Modelling is unnecessary to add regularization in the modeling process aiming at special problems again. The experiment results show that the algorithm has good noise-immunity. In case there is no noise or noise is very low, the identified parametric function is almost the same as the original accurate value; when noise is very high, good results can still be obtained, which successfully realizes automation of the parameter modeling process for partial differential equations. %K genetic algorithms, genetic programming, gene expression programming, partial differential equation, inverse problems, thomas algorithm %R 10.1007/978-3-319-32557-6_24 %U http://dx.doi.org/10.1007/978-3-319-32557-6_24 %P 218-227 %0 Journal Article %T Generating Trading Rules for Stock Markets Using Robust Genetic Network Programming and Portfolio Beta %A Chen, Yan %A Shi, Zhihui %J Journal of Advanced Computational Intelligence and Intelligent Informatics %D 2016 %8 may %V 20 %N 3 %@ 1343-0130 %F DBLP:journals/jaciii/ChenS16 %X Robust Genetic Network Programming (R-GNP) for generating trading rules for stocks is described. R-GNP is a new evolutionary algorithm, where solutions are represented using graph structures. It has been clarified that R-GNP works well especially in dynamic environments. In the proposed hybrid model, R-GNP is applied to generating stock trading rules with variance of fitness values. The unique point is that the generalization ability of R-GNP is improved by using the robust fitness function, which consists of the fitness functions with the original data and a good number of correlated data. Generally speaking, the hybrid intelligent system consists of three steps: priority selection by the portfolio beta, optimization by the Genetic Relation Algorithm (GRA), and stock trading by R-GNP. In the simulations, the trading model is trained using the stock prices of 10 brands on the Tokyo Stock Exchange, and then the generalization ability is tested. From the simulation results, it is clarified that the trading rules created by the proposed R-GNP model obtain much higher profits than the traditional methods even in the world-wide financial crisis of 2007. Hence, its effectiveness has been confirmed. %K genetic algorithms, genetic programming, portfolio beta, genetic relation algorithm, robust genetic network programming, stock trading %9 journal article %R 10.20965/jaciii.2016.p0484 %U https://doi.org/10.20965/jaciii.2016.p0484 %U http://dx.doi.org/10.20965/jaciii.2016.p0484 %P 484-491 %0 Journal Article %T Restricted gene expression programming: a new approach for parameter identification inverse problems of partial differential equation %A Chen, Yan %A Li, Kangshun %A Chen, Zhangxing %A Wang, Jinfeng %J Soft Computing %D 2017 %V 21 %N 10 %F journals/soco/ChenLCW17 %K genetic algorithms, genetic programming, gene expression programming %9 journal article %R 10.1007/s00500-015-1965-1 %U http://dx.doi.org/10.1007/s00500-015-1965-1 %P 2651-2663 %0 Conference Proceedings %T Comprehensive Learning Gene Expression Programming for Automatic Implicit Equation Discovery %A Chen, Yongliang %A Zhong, Jinghui %A Tan, Mingkui %Y Shi, Yong %Y Fu, Haohuan %Y Tian, Yingjie %Y Krzhizhanovskaya, Valeria V. %Y Lees, Michael Harold %Y Dongarra, Jack J. %Y Sloot, Peter M. A. %S Computational Science - ICCS 2018 - 18th International Conference, Wuxi, China, June 11-13, 2018, Proceedings, Part I %S Lecture Notes in Computer Science %D 2018 %V 10860 %I Springer %F Chen:2018:ICCS %K genetic algorithms, genetic programming, gene expression programming %R 10.1007/978-3-319-93698-7_9 %U http://dx.doi.org/10.1007/978-3-319-93698-7_9 %P 114-128 %0 Thesis %T Hybrid Soft Computing Approach to Identification and Control of Nonlinear Systems %A Chen, Yuehui %D 2001 %8 mar %C Japan %C Department of Computer Science, Kumamoto University %F YuehuiChen:thesis %X Recently, complex industrial plants such as mobile robots, flexible manufacturing system etc., are often required to perform complex tasks with high precision under ill-defined conditions, and conventional control techniques may not be quite effective in these systems. Soft computing approaches are some computational models inspired by the simulated human and/or natural intelligence, and includes fuzzy logic, artificial neural networks, genetic and evolutionary algorithms. There have been many successful researches for the identification and control of nonlinear systems by using various soft computing techniques with different computational architectures. The experiences gained over the past decade indicate that it can be more effective to use the various soft computing approaches in a combined manner. But there is no common recognition about how to combine them in an effective way, and a unified framework of hybrid soft computing models in which various soft computing models can be developed, evolved and evaluated has not been established. %K genetic algorithms, genetic programming, PIPE Algorithm %9 Ph.D. thesis %U http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.001 %0 Conference Proceedings %T Optimal design of hierarchical wavelet networks for time-series forecasting %A Chen, Yuehui %A Yang, Bo %A Abraham, Ajith %S 14th European Symposium on Artificial Neural Networks (ESANN 2006) %D 2006 %8 apr 26 28 %C Bruges, Belgium %G en %F Chen:2006:ESANN %X The purpose of this study is to identify the Hierarchical Wavelet Neural Networks (HWNN) and select important input features for each sub-wavelet neural network automatically. Based on the predefined instruction/operator sets, a HWNN is created and evolved using tree-structure based Extended Compact Genetic Programming (ECGP), and the parameters are optimised by Differential Evolution (DE) algorithm. This framework also allows input variables selection. Empirical results on benchmark time-series approximation problems indicate that the proposed method is effective and efficient. %K genetic algorithms, genetic programming, ECGP %U http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-57.pdf %P 155-160 %0 Conference Proceedings %T Face Recognition Using DCT and Hierarchical RBF Model %A Chen, Yuehui %A Zhao, Yaou %Y Corchado, Emilio %Y Yin, Hujun %Y Botti, Vicente %Y Fyfe, Colin %S Intelligent Data Engineering and Automated Learning, IDEAL 2006 %S Lecture Notes in Computer Science %D 2009 %8 sep 20 23 %V 4224 %I Springer %C Burgos, Spain %G en %F Chen:2006:IDEAL %X This paper proposes a new face recognition approach by using the Discrete Cosine Transform (DCT) and Hierarchical Radial Basis Function Network (HRBF) classification model. The DCT is employed to extract the input features to build a face recognition system, and the HRBF is used to identify the faces. Based on the pre-defined instruction/operator sets, a HRBF model can be created and evolved. This framework allows input features selection. The HRBF structure is developed using Extended Compact Genetic Programming (ECGP) and the parameters are optimised by Differential Evolution (DE). Empirical results indicate that the proposed framework is efficient for face recognition. %K genetic algorithms, genetic programming, DE, ECGP %R 10.1007/11875581_43 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.482.9685 %U http://dx.doi.org/10.1007/11875581_43 %P 355-362 %0 Journal Article %T Feature selection and classification using flexible neural tree %A Chen, Yuehui %A Abraham, Ajith %A Yang, Bo %J Neurocomputing %D 2006 %8 dec %V 70 %N 1-3 %@ 0925-2312 %G en %F Chen:2006:N %O Selected Papers from the 7th Brazilian Symposium on Neural Networks (SBRN ’04), 7th Brazilian Symposium on Neural Networks %X The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimised by a memetic algorithm (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate. %K genetic algorithms, genetic programming, Flexible neural tree model, Memetic algorithm, Intrusion detection system, Breast cancer classification %9 journal article %R 10.1016/j.neucom.2006.01.022 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1041.7313 %U http://dx.doi.org/10.1016/j.neucom.2006.01.022 %P 305-313 %0 Conference Proceedings %T An IP and GEP Based Dynamic Decision Model for Stock Market Forecasting %A Chen, Yuehui %A Wu, Qiang %A Chen2, Feng %Y Liu, Derong %Y Fei, Shumin %Y Hou, Zeng-Guang %Y Zhang, Huaguang %Y Sun, Changyin %S 4th International Symposium on Neural Networks Advances in Neural Networks, ISNN 2007, Part I %S LNCS %D 2007 %8 jun 3 7 %V 4491 %I Springer %C Nanjing, China %G en %F Chen:2007:ISNN %X The forecasting models for stock market index using computational intelligence such as Artificial Neural networks (ANNs) and Genetic programming(GP), especially hybrid Immune Programming (IP) Algorithm and Gene Expression Programming (GEP) have achieved favourable results. However, these studies, have assumed a static environment. This study investigates the development of a new dynamic decision forecasting model. Application results prove the higher precision and generalisation capacity of the predicting model obtained by the new method than static models. %K genetic algorithms, genetic programming, gene expression programming, stock market forecasting, dynamic decision model, application result, forecasting model, favourable result, new method, static model, hybrid immune programming, generalisation capacity, artificial neural network, computational intelligence, static environment, stock market index, new dynamic decision forecasting model %R 10.1007/978-3-540-72383-7_56 %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.626.3509 %U http://dx.doi.org/10.1007/978-3-540-72383-7_56 %P 473-479 %0 Journal Article %T Flexible neural trees ensemble for stock index modeling %A Chen, Yuehui %A Yang, Bo %A Abraham, Ajith %J Neurocomputing %D 2007 %8 jan %V 70 %N 4-6 %F Chen:2007:N %O Advanced Neurocomputing Theory and Methodology - Selected papers from the International Conference on Intelligent Computing 2005 (ICIC 2005), International Conference on Intelligent Computing 2005 %X The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behaviour of stock markets could be well represented using flexible neural tree (FNT) ensemble technique. We considered the Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index. We analysed 7-year Nasdaq-100 main index values and 4-year NIFTY index values. This paper investigates the development of novel reliable and efficient techniques to model the seemingly chaotic behaviour of stock markets. The structure and parameters of FNT are optimised using genetic programming (GP) like tree structure-based evolutionary algorithm and particle swarm optimization (PSO) algorithms, respectively. A good ensemble model is formulated by the local weighted polynomial regression (LWPR). This paper investigates whether the proposed method can provide the required level of performance, which is sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experimental results show that the model considered could represent the stock indexes behaviour very accurately. %K genetic algorithms, genetic programming, Flexible neural tree, GP-like tree structure-based evolutionary algorithm, Particle swarm optimisation, Ensemble learning, Stock index %9 journal article %R 10.1016/j.neucom.2006.10.005 %U http://dx.doi.org/10.1016/j.neucom.2006.10.005 %P 697-703 %0 Book %T Tree-Structure based Hybrid Computational Intelligence %A Chen, Yuehui %A Abraham, Ajith %S Intelligent Systems Reference Library %D 2010 %V 2 %I Springer %F Chen:2010:book %X Research in computational intelligence is directed toward building thinking machines and improving our understanding of intelligence. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. In this book, the authors illustrate an hybrid computational intelligence framework and it applications for various problem solving tasks. Based on tree-structure based encoding and the specific function operators, the models can be flexibly constructed and evolved by using simple computational intelligence techniques. The main idea behind this model is the flexible neural tree, which is very adaptive, accurate and efficient. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This volume comprises of 6 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques and data mining will find the comprehensive coverage of this book invaluable. %K genetic algorithms, genetic programming, Computational Intelligence, flexible neural trees, flexible neural trees networks, neural networks %R 10.1007/978-3-642-04739-8 %U http://www.springer.com/engineering/book/978-3-642-04738-1 %U http://dx.doi.org/10.1007/978-3-642-04739-8 %0 Journal Article %T Time-series forecasting using a system of ordinary differential equations %A Chen, Yuehui %A Yang, Bin %A Meng, Qingfang %A Zhao, Yaou %A Abraham, Ajith %J Information Sciences %D 2011 %V 181 %N 1 %@ 0020-0255 %F Chen2011106 %X This paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations (ODEs) to predict the small-time scale traffic measurements data. We used the tree-structure based evolutionary algorithm to evolve the architecture and a particle swarm optimization (PSO) algorithm to fine tune the parameters of the additive tree models for the system of ordinary differential equations. We also illustrate some experimental comparisons with genetic programming, gene expression programming and a feedforward neural network optimised using PSO algorithm. Experimental results reveal that the proposed method is feasible and efficient for forecasting the small-scale traffic measurements data. %K genetic algorithms, genetic programming, PSO, Hybrid evolutionary method, Network traffic, Small-time scale, The additive tree models, Ordinary differential equations, Particle swarm optimisation %9 journal article %R 10.1016/j.ins.2010.09.006 %U http://www.sciencedirect.com/science/article/B6V0C-5100HS4-3/2/c9722759c9e35e7dba49e35c559ae617 %U http://dx.doi.org/10.1016/j.ins.2010.09.006 %P 106-114 %0 Journal Article %T Small-time scale network traffic prediction based on flexible neural tree %A Chen, Yuehui %A Yang, Bin %A Meng, Qingfang %J Applied Soft Computing %D 2012 %V 12 %N 1 %@ 1568-4946 %F Chen2012274 %X In this paper, the flexible neural tree (FNT) model is employed to predict the small-time scale traffic measurements data. Based on the pre-defined instruction/operator sets, the FNT model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using the Genetic Programming (GP) and the parameters are optimised by the Particle Swarm Optimisation algorithm (PSO). The experimental results indicate that the proposed method is efficient for forecasting small-time scale traffic measurements and can reproduce the statistical features of real traffic measurements. We also compare the performance of the FNT model with the feed-forward neural network optimised by PSO for the same problem. %K genetic algorithms, genetic programming, Flexible neural tree model, Particle Swarm Optimization, Network traffic, Small-time scale %9 journal article %R 10.1016/j.asoc.2011.08.045 %U http://www.sciencedirect.com/science/article/pii/S1568494611003280 %U http://dx.doi.org/10.1016/j.asoc.2011.08.045 %P 274-279 %0 Conference Proceedings %T Multi-objective Location-Aware Service Brokering in Multi-cloud - A GPHH Approach with Transfer Learning %A Chen, Yuheng %A Shi, Tao %A Ma, Hui %A Chen2, Gang %Y Correia, Joao %Y Smith, Stephen %Y Qaddoura, Raneem %S 26th International Conference, EvoApplications 2023 %S LNCS %D 2023 %8 apr 12 14 %V 13989 %I Springer Verlag %C Brno, Czech Republic %F Chen:2023:evoapplications %K genetic algorithms, genetic programming, Multi-objective optimisation, Multi-cloud, Service brokering, GPHH, Transfer learning %R 10.1007/978-3-031-30229-9_37 %U http://dx.doi.org/10.1007/978-3-031-30229-9_37 %P 573-587 %0 Journal Article %T Solving symbolic regression problems with uniform design-aided gene expression programming %A Chen, Yunliang %A Chen, Dan %A Khan, Samee Ullah %A Huang, Jianzhong %A Xie, Changsheng %J The Journal of Supercomputing %D 2013 %V 66 %N 3 %F journals/tjs/ChenCKHX13 %X Gene Expression Programming (GEP) significantly surpasses traditional evolutionary approaches to solving symbolic regression problems. However, existing GEP algorithms still suffer from premature convergence and slow evolution in anaphase. Aiming at these pitfalls, we designed a novel evolutionary algorithm, namely Uniform Design-Aided Gene Expression Programming (UGEP). UGEP uses (1) a mixed-level uniform table for generating initial population and (2) multiparent crossover operators by taking advantages of the dispersibility of uniform design. In addition to a theoretic analysis, we compared UGEP to existing GEP variants via a number of experiments in dealing with symbolic regression problems including function fitting and chaotic time series prediction. Experimental results indicate that UGEP excels in terms of both the capability of achieving the global optimum and the convergence speed in solving symbolic regression problems. %K genetic algorithms, genetic programming, gene expression programming, GEP %9 journal article %U http://dx.doi.org/10.1007/s11227-013-0943-6 %P 1553-1575 %0 Thesis %T A Novel Hybrid Focused Crawling Algorithm to Build Domain-Specific Collections %A Chen, Yuxin %D 2007 %8 feb 5 %C Blacksburg, Virginia, USA %C Virginia Polytechnic Institute and State University %F Yuxin_Chen:thesis %X The Web, containing a large amount of useful information and resources, is expanding rapidly. Collecting domain-specific documents/information from the Web is one of the most important methods to build digital libraries for the scientific community. Focused Crawlers can selectively retrieve Web documents relevant to a specific domain to build collections for domain-specific search engines or digital libraries. Traditional focused crawlers normally adopting the simple Vector Space Model and local Web search algorithms typically only find relevant Web pages with low precision. Recall also often is low, since they explore a limited sub-graph of the Web that surrounds the starting URL set, and will ignore relevant pages outside this sub-graph. In this work, we investigated how to apply an inductive machine learning algorithm and meta-search technique, to the traditional focused crawling process, to overcome the above mentioned problems and to improve performance. We proposed a novel hybrid focused crawling framework based on Genetic Programming (GP) and meta-search. We showed that our novel hybrid framework can be applied to traditional focused crawlers to accurately find more relevant Web documents for the use of digital libraries and domain-specific search engines. The framework is validated through experiments performed on test documents from the Open Directory Project. Our studies have shown that improvement can be achieved relative to the traditional focused crawler if genetic programming and meta-search methods are introduced into the focused crawling process. %K genetic algorithms, genetic programming, digital libraries, focused crawler, classification, meta-search %9 Ph.D. thesis %U http://scholar.lib.vt.edu/theses/available/etd-02162007-005107/ %0 Conference Proceedings %T A Genetic Programming Approach for Classification of Textures Based on Wavelet Analysis %A Chen, Zheng %A Lu, Siwei %S IEEE International Symposium on Intelligent Signal Processing, WISP 2007 %D 2007 %8 oct %F Chen:2007:WISP %X In this paper, we propose a method for classifying textures using Genetic Programming (GP). Texture features are extracted from the energy of subimages of the wavelet decomposition. The GP is then used to evolve rules, which are arithmetic combinations of energy features, to identify whether a texture image belongs to certain class. Instead of using only one rule to discriminate the samples, a set of rules are used to perform the prediction by applying the majority voting technique. In our experiment results based on Brodatz dataset, the proposed method has achieved 99.6percent test accuracy on an average. In addition, the experiment results also show that classification rules generated by this approach are robust to some noises on textures. %K genetic algorithms, genetic programming, feature extraction, texture classification, wavelet analysis, wavelet decomposition, feature extraction, image classification, image texture, wavelet transforms %R 10.1109/WISP.2007.4447575 %U http://dx.doi.org/10.1109/WISP.2007.4447575 %P 1-6 %0 Conference Proceedings %T A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching %A Chen, Xinan %A Bai, Ruibin %A Qu, Rong %A Dong, Haibo %A Chen, Jianjun %Y Jin, Yaochu %S 2020 IEEE Congress on Evolutionary Computation, CEC 2020 %D 2020 %8 19 24 jul %I IEEE Press %C internet %F Chen:2020:CEC %X International and domestic maritime trade has been expanding dramatically in last few decades, seaborne container transportation has become an indispensable part of maritime trade efficient and easy-to-use containers. As an important hub of container transport, container terminals use a range of metrics to measure their efficiency, among which the hourly container throughput (i.e., the number of twenty-foot equivalent unit containers, or TEUs) is the most important objective to improve. We propose a genetic programming approach to build adynamic truck dispatching system trained on real-world stochastic operations data. The experimental results demonstrated the superiority of this dynamic approach and the potential for practical applications. %K genetic algorithms, genetic programming, container terminal, lorry, truck dispatching, dynamic %R 10.1109/CEC48606.2020.9185659 %U http://www.cs.nott.ac.uk/~pszrq/files/CEC2020HGP.pdf %U http://dx.doi.org/10.1109/CEC48606.2020.9185659 %P paperid24651 %0 Journal Article %T Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching %A Chen, Xinan %A Bai, Ruibin %A Qu, Rong %A Dong, Haibo %J IEEE Transactions on Evolutionary Computation %D 2023 %8 oct %V 27 %N 5 %@ 1089-778X %F Xinan_Chen:ieeeTEC %X In a marine container terminal, truck dispatching is a crucial problem that impacts on the operation efficiency of the whole port. Traditionally, this problem is formulated as an offline optimisation problem, whose solutions are, however, impractical for most real-world scenarios primarily because of the uncertainties of dynamic events in both yard operations and seaside loading-unloading operations. These solutions are either unattractive or infeasible to execute. Herein, for more intelligent handling of these uncertainties and dynamics, a novel cooperative double-layer genetic programming hyper-heuristic (CD-GPHH) is proposed to tackle this challenging online optimisation problem. In this new CD-GPHH, a novel scenario genetic programming (GP) approach is added on top of a traditional GP method that chooses among different GP heuristics for different scenarios to facilitate optimised truck dispatching. In contrast to traditional arithmetic GP (AGP) and GP with logic operators (LGP) which only evolve on one population, our CD-GPHH method separates the scenario and the calculation into two populations, which improved the quality of solutions in multi-scenario problems while reducing the search space. Experimental results show that our CD-GPHH dominates AGP and LGP in solving a multi-scenario function fitting problem as well as a truck dispatching problem in container terminal. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TEVC.2022.3209985 %U http://dx.doi.org/10.1109/TEVC.2022.3209985 %P 1220-1234 %0 Journal Article %T Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks %A Chen, Xinan %A Bai, Ruibin %A Qu, Rong %A Dong, Jing %A Jin, Yaochu %J IEEE Transactions on Evolutionary Computation %D 2025 %8 aug %V 29 %N 4 %@ 1941-0026 %F Chen:TEVC %X Efficient truck dispatching is crucial for optimising container terminal operations within dynamic and complex scenarios. Despite good progress being made recently with more advanced uncertainty-handling techniques, existing approaches still have generalisation issues and require considerable expertise and manual interventions in algorithm design. In this work, we present deep reinforcement learning-assisted genetic programming hyper-heuristics (DRL-GPHH) and their ensemble variant (DRL-GPEHH). These frameworks use a reinforcement learning agent to orchestrate a set of auto-generated genetic programming (GP) low-level heuristics, leveraging the collective intelligence, ensuring advanced robustness and an increased level of automation of the algorithm development. DRL-GPEHH, notably, excels through its concurrent integration of a GP heuristic ensemble, achieving enhanced adaptability and performance in complex, dynamic optimisation tasks. This method effectively navigates traditional convergence issues of deep reinforcement learning (DRL) in sparse reward and vast action spaces, while avoiding the reliance on expert-designed heuristics. It also addresses the inadequate performance of the single GP individual in varying and complex environments and preserves the inherent interpretability of the GP approach. Evaluations across various real port operational instances highlight the adaptability and efficacy of our frameworks. Essentially, innovations in DRL-GPHH and DRL-GPEHH reveal the synergistic potential of reinforcement learning and GP in dynamic truck dispatching, yielding transformative impacts on algorithm design and significantly advancing solutions to complex real-world optimisation problems. %K genetic algorithms, genetic programming, Containers, Dispatching, Seaports, Optimisation, Heuristic algorithms, Reinforcement learning, Marine vehicles, automatic truck dispatching, dynamic task scheduling, reinforcement learning %9 journal article %R 10.1109/TEVC.2024.3381042 %U http://dx.doi.org/10.1109/TEVC.2024.3381042 %P 1371-1385 %0 Conference Proceedings %T Energy Efficient NFV Resource Allocation in Edge Computing Environment %A Chen, Xiao %S 2020 International Conference on Computing, Networking and Communications (ICNC) %D 2020 %8 feb %F Chen:2020:ICNC %X With the development of IoT and 5G communication, a recent trend is to shift the Network Function Virtualisation (NFV) from the centralized cloud computing to edge computing. In this paper, we study the energy efficient NFV-Resource Allocation problem in the edge computing environment. We define two problems. In the first problem, we assume that the physical resources (PRs) on the edge do not have energy constraint. Our objective is to find an optimal deployment so that the maximum energy consumption on the PRs is minimized. In the second problem, we assume that the PRs have energy constraint and aim to find an optimal deployment to reduce the number of PRs. We prove both problems NP-complete and propose heuristic algorithms to solve them. We also design baseline algorithms using genetic programming to find approximate optimal solutions to these problems. We conduct simulations to evaluate the performance of our proposed algorithms. Simulation results show that our algorithms produce results very close to those of the baseline algorithms in a much shorter time. %K genetic algorithms, genetic programming %R 10.1109/ICNC47757.2020.9049765 %U http://dx.doi.org/10.1109/ICNC47757.2020.9049765 %P 477-481 %0 Journal Article %T A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED %A Chen, Xiaowu %A Jiang, Guozhang %A Xiao, Yongmao %A Li, Gongfa %A Xiang, Feng %J Mathematics %D 2021 %V 9 %N 18 %@ 2227-7390 %F chen:2021:Mathematics %X Intelligent manufacturing is the trend of the steel industry. A cyber-physical system oriented steel production scheduling system framework is proposed. To make up for the difficulty of dynamic scheduling of steel production in a complex environment and provide an idea for developing steel production to intelligent manufacturing. The dynamic steel production scheduling model characteristics are studied, and an ontology-based steel cyber-physical system production scheduling knowledge model and its ontology attribute knowledge representation method are proposed. For the dynamic scheduling, the heuristic scheduling rules were established. With the method, a hyper-heuristic algorithm based on genetic programming is presented. The learning-based high-level selection strategy method was adopted to manage the low-level heuristic. An automatic scheduling rule generation framework based on genetic programming is designed to manage and generate excellent heuristic rules and solve scheduling problems based on different production disturbances. Finally, the performance of the algorithm is verified by a simulation case. %K genetic algorithms, genetic programming, teel production scheduling, cyber-physical system, hyper-heuristic algorithm, heuristic scheduling rule %9 journal article %R 10.3390/math9182256 %U https://www.mdpi.com/2227-7390/9/18/2256 %U http://dx.doi.org/10.3390/math9182256 %0 Conference Proceedings %T Deep Neural Network Guided Evolution of L-System Trees %A Chen, Xuhao Eric %A Ross, Brian J. %Y Ong, Yew-Soon %S 2021 IEEE Congress on Evolutionary Computation (CEC) %D 2021 %8 28 jun 1 jul %C Krakow, Poland %F Chen:2021:CEC %X Lindenmayer systems (L-systems) are mathematical formalisms used for generating recursive structures. They are particularly effective for defining realistic tree and plant models. It takes experience to use L-systems effectively, however, as the final rendered results are often difficult to predict. This research explores the use of genetic programming (GP) and deep learning towards the automatic evolution of L-system expressions that render 2D tree designs. As done before by other researchers, the L-system language is easily defined and manipulated by the GP system. It is challenging, however, to determine a fitness function to evaluate the suitability of evolved expressions. We train a deep convolutional neural network (CNN) to recognize suitable trees rendered in the style of the L-system language. Experiments explore a number of deep CNN strategies. Results in some experiments are very promising, as images conforming to specified styles of tree species were often produced. We found that underspecifying or over-complicating the training requirements can arise, and the results become unsatisfactory in such cases. Our results also confirm that of other researchers, in that deep learning can be fooled by evolutionary algorithms, and the criteria for success learned by deep neural networks might not conform with those of human users. %K genetic algorithms, genetic programming, Deep learning, Training, Solid modeling, Three-dimensional displays, Architecture, Vegetation, Evolutionary computation, convolutional neural networks, L-systems %R 10.1109/CEC45853.2021.9504827 %U http://dx.doi.org/10.1109/CEC45853.2021.9504827 %P 2507-2514 %0 Journal Article %T Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning %A Chen, Yimian %A Wang, Shuize %A Xiong, Jie %A Wu, Guilin %A Gao, Junheng %A Wu, Yuan %A Ma, Guoqiang %A Wu, Hong-Hui %A Mao, Xinping %J Journal of Materials Science & Technology %D 2023 %V 132 %@ 1005-0302 %F CHEN:2023:jmst %X High toughness is highly desired for low-alloy steel in engineering structure applications, wherein Charpy impact toughness (CIT) is a critical factor determining the toughness performance. In the current work, CIT data of low-alloy steel were collected, and then CIT prediction models based on machine learning (ML) algorithms were established. Three feature construction strategies were proposed. One is solely based on alloy composition, another is based on alloy composition and heat treatment parameters, and the last one is based on alloy composition, heat treatment parameters, and physical features. A series of ML methods were used to effectively select models and material descriptors from a large number of alternatives. Compared with the strategy solely based on the alloy composition, the strategy based on alloy composition, heat treatment parameters together with physical features perform much better. Finally, a genetic programming (GP) based symbolic regression (SR) approach was developed to establish a physical meaningful formula between the selected features and targeted CIT data %K genetic algorithms, genetic programming, Machine learning, Symbolic regression, Low-alloy steel, Charpy impact toughness %9 journal article %R 10.1016/j.jmst.2022.05.051 %U https://www.sciencedirect.com/science/article/pii/S100503022200545X %U http://dx.doi.org/10.1016/j.jmst.2022.05.051 %P 213-222 %0 Conference Proceedings %T Automatically Design Heuristics for Multi-Objective Location-Aware Service Brokering in Multi-Cloud %A Chen, Yuheng %A Shi, Tao %A Ma, Hui %A Chen2, Gang %S 2022 IEEE International Conference on Services Computing (SCC) %D 2022 %8 October 16 jul %C Barcelona, Spain %F Chen:2022:SCC %X Multi-cloud provides cloud services at distributed locations. As the number of cloud services from multi-cloud providers growing, how to select proper cloud services to optimize multiple potentially conflicting objectives simultaneously has become a challenging task. Multi-objective location-aware service brokering (MOLSB) aims to provide a set of trade-off solutions to minimize cost and latency. To handle dynamic resource requirements, various heuristics have been proposed to efficiently find suitable cloud services. However, these heuristics cannot achieve consistently good performance on a wide range of problem instances. Additionally, instead of replying on a single heuristic, it is desirable to design a set of effective heuristics that can balance different objectives with varied trade-offs. Genetic Programming hyper-heuristics (GPHH) have been applied to automatically design heuristics for many multi-objective dynamic optimization problems, e.g., workflow scheduling. In this pa-per, we propose a new GPHH-based approach, named GPHH-MOLSB, to automatically generate a group of Pareto-optimal heuristics that can be used to satisfy varied QoS preferences. GPHH-MOLSB can significantly outperform several existing approaches based on evaluation on real-world datasets. %K genetic algorithms, genetic programming, Costs, Service computing, Quality of service, QoS, Dynamic scheduling, Dynamic programming, Task analysis, Multi-objective optimization, multi-cloud, location-aware, service brokering, GPHH %R 10.1109/SCC55611.2022.00039 %U http://dx.doi.org/10.1109/SCC55611.2022.00039 %P 206-214 %0 Conference Proceedings %T Neural Network Assisted Genetic Programming in Dynamic Container Port Truck Dispatching %A Chen, Xinan %A Bao, Feiyang %A Qu, Rong %A Dong, Jing %A Bai, Ruibin %S 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) %D 2023 %8 sep %F Chen:2023:ITSC %X Efficient truck dispatching is crucial for container port operations. Dynamic container port truck dispatching, a complex online optimisation problem, poses significant challenges due to its uncertain and non-linear nature. This paper presents a novel neural network assisted genetic programming (NN-GP) approach, which combines the global search of genetic programming (GP) and the local search of recurrent neural network (RNN). In this framework, the RNN further refines GP individuals after genetic operations (crossovers and mutations), enhancing solution adaptability and precision in response to dynamic and uncertain scenarios. The proposed method leverages RNN’s understanding of temporal dynamics and GP’s robust exploration of the solution space, effectively addressing the dynamic container truck dispatching problem. Experiments using real-world container port data demonstrate that the RNN-GP model outperforms traditional heuristic methods and standalone GP algorithms, reducing dispatching time and increasing port efficiency. This research highlights the potential of hybridizing machine learning techniques with GP in solving complex real-world optimisation problems. %K genetic algorithms, genetic programming, Adaptation models, Recurrent neural networks, Artificial neural networks, ANN, Containers, Dispatching, Optimisation %R 10.1109/ITSC57777.2023.10422513 %U http://dx.doi.org/10.1109/ITSC57777.2023.10422513 %P 2246-2251 %0 Conference Proceedings %T Heuristic Navigation Model Based on Genetic Programming for Multi-UAV Power Inspection Problem with Charging Stations %A Chen, Xiang-Ling %A Liao, Xiao-Cheng %A Chen, Wei-Neng %S 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) %D 2023 %8 January 4 oct %C Honolulu, Oahu, HI, USA %F Chen:2023:SMC %X Efficient power inspection is crucial for maintaining a stable power system. During an inspection, unmanned aerial vehicles (UAVs) usually need to be recharged due to the wide geographical range of inspection and the limited battery capacity of UAVs. This limitation makes the problem more challenging that requires not only optimising the task execution order, but also taking the chargings of UAVs into consideration. In order to address this complex problem, this work first formulates the UAV power inspection planning problem with charging stations. After that, we propose a new heuristic navigation model, in which UAVs can follow a heuristic rule to decide where to go next based on both its own information and task-related information. To obtain the heuristic rule, we design a set of features to describe the status of the UAVs and task completion. Then a genetic programming (GP) algorithm is introduced to evolve and get the heuristic rule. Finally, by applying heuristic navigation rule, the UAV navigation model can automatically prioritize task and charging order, and generate UAV flight routes that satisfy all constraints. The experiment results show that our method significantly outperforms the state-of-the-art algorithms. %K genetic algorithms, genetic programming, gene expression programming, Adaptation models, Navigation, Inspection, Charging stations, Autonomous aerial vehicles, Mathematical models, Task analysis, unmanned aerial vehicles (UAVs), task assignment problem, charging problem, heuristic navigation model %R 10.1109/SMC53992.2023.10394169 %U https://human-competitive.org/sites/default/files/entryform_6.txt %U http://dx.doi.org/10.1109/SMC53992.2023.10394169 %P 363-370 %0 Conference Proceedings %T A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression %A Chen, Xinan %A Yi, Wenjie %A Bai, Ruibin %A Qu, Rong %A Jin, Yaochu %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F chen:2024:CEC %X In regression analysis, methodologies range from black-box approaches like artificial neural networks to white-box techniques like symbolic regression. Renowned for its trans-parency and interpretability, symbolic regression has become increasingly prominent in elucidating complex data relationships. Nevertheless, its effectiveness in managing complex piecewise symbolic regression tasks poses significant challenges. This paper introduces a novel Hierarchical Cooperative Genetic Program-ming (HCGP) framework to address this issue. The HCGP model uses a unique hierarchical structure, incorporating dual cooperative genetic programming (GP) populations. This innovative design significantly enhances the capability to solve complex piecewise symbolic regression problems. Implementing a scenario-based GP is central to the HCGP framework, which strategically selects the appropriate underlying calculation GP. This feature enables the system to autonomously learn and adapt to complex scenarios, selecting the most suitable calculation GPs for each case. Our HCGP approach distinguishes itself from traditional and state-of-the-art methods. It demonstrates particular proficiency in modelling piecewise expressions within complex scenarios. The empirical evaluation of our model, conducted using benchmark datasets, has exhibited its superior accuracy and computational efficiency. This progress emphasizes the potential of HCGP in sophisticated data modelling and marks a substantial advancement in a hierarchical structure in complex piecewise symbolic regression. %K genetic algorithms, genetic programming, Adaptation models, Computational modeling, Sociology, Evolutionary computation, Data models, Regression analysis, symbolic regression, hierarchical structure, evolutionary algorithm %R 10.1109/CEC60901.2024.10611754 %U http://dx.doi.org/10.1109/CEC60901.2024.10611754 %0 Journal Article %T Experimental Investigation of Water Vapor Concentration on Fracture Properties of Asphalt Concrete %A Chen, Yu %A Huang, Tingting %A Wen, Xuqing %A Zhang, Kai %A Li, Zhengang %J Materials %D 2024 %V 17 %N 13 %@ 1996-1944 %F chen:2024:Materials %X The effect of moisture on the fracture resistance of asphalt concrete is a significant concern in pavement engineering. To investigate the effect of the water vapor concentration on the fracture properties of asphalt concrete, this study first designed a humidity conditioning program at the relative humidity (RH) levels of 2percent, 50percent, 80percent, and 100percent for the three types of asphalt concrete mixtures (AC-13C, AC-20C, and AC-25C).The finite element model was developed to simulate the water vapor diffusion and determine the duration of the conditioning period. The semi-circular bending (SCB) test was then performed at varying temperatures of 5 ?C, 15 ?C, and 25 ?C to evaluate the fracture energy and tensile strength of the humidity-conditioned specimens. The test results showed that the increasing temperature and the RH levels resulted in a lower peak load but greater displacement of the mixtures. Both the fracture energy and tensile strength tended to diminish with the rising temperature. It was also found that moisture had a significant effect on the tensile strength and fracture energy of asphalt concrete. Specifically, as the RH level increased from 2percent to 100percent (i.e., the water vapor concentration rose from 0.35 g/m3 to 17.27 g/m3), the tensile strength of the three types of mixtures was reduced by 34.84percent on average, which revealed that the water vapor led to the loss of adhesion and cohesion within the mixture. The genetic expression programming (GEP) model was developed to quantify the effect of water vapor concentrations and temperature on the fracture indices. %K genetic algorithms, genetic programming, gene expression programming %9 journal article %R 10.3390/ma17133289 %U https://www.mdpi.com/1996-1944/17/13/3289 %U http://dx.doi.org/10.3390/ma17133289 %P ArticleNo.3289 %0 Conference Proceedings %T Investigating Combined Algorithm Selection and Hyperparameter Optimization for Fairness %A Chen, Zhiang %A Connor, Mark %A Pant, Sudarshan %A O’Neill, Michael %Y Urbanowicz, Ryan %Y Browne, Will N. %S Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion %S GECCO ’25 Companion %D 2025 %8 14 18 jul %I Association for Computing Machinery %C Malaga, Spain %F chen:2025:GECCOcomp %X As machine learning algorithms are increasingly employed for critical decision-making, ensuring algorithmic fairness has become imperative. Fairness-aware Automated Machine Learning has emerged as a flexible and effective method for enhancing both model accuracy and fairness. However, most existing studies focus on hyperparameter optimization for a single model. In this study, we frame the problem as a multi-objective Combined Algorithm Selection and Hyperparameter Optimization (CASH) problem, aiming to jointly optimize both accuracy and fairness across a diverse set of machine learning algorithms and their corresponding hyperparameters. To address this challenge, we apply Multi-Objective Grammatical Evolution (MOGE). The results demonstrate that MOGE not only effectively identifies models that achieve higher fairness and accuracy, while also exploring the trade-offs between accuracy and fairness efficiently. %K genetic algorithms, genetic programming, grammatical evolution, AutoML, algorithmic fairness, multi-objective optimization, Evolutionary Machine Learning: Poster %R 10.1145/3712255.3726608 %U https://doi.org/10.1145/3712255.3726608 %U http://dx.doi.org/10.1145/3712255.3726608 %P 255-258 %0 Journal Article %T Development of genetic programming-based model for predicting oyster norovirus outbreak risks %A Chenar, Shima Shamkhali %A Deng, Zhiqiang %J Water Research %D 2018 %V 128 %@ 0043-1354 %F CHENAR:2018:WR %X Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide but exact causes of the outbreaks are rarely identified, making it highly unlikely to reduce the risks. This paper presents a genetic programming (GP) based approach to identifying the primary cause of oyster norovirus outbreaks and predicting oyster norovirus outbreaks in order to reduce the risks. In terms of the primary cause, it was found that oyster norovirus outbreaks were controlled by cumulative effects of antecedent environmental conditions characterized by low solar radiation, low water temperature, low gage height (the height of water above a gage datum), low salinity, heavy rainfall, and strong offshore wind. The six environmental variables were determined by using Random Forest (RF) and Binary Logistic Regression (BLR) methods within the framework of the GP approach. In terms of predicting norovirus outbreaks, a risk-based GP model was developed using the six environmental variables and various combinations of the variables with different time lags. The results of local and global sensitivity analyses showed that gage height, temperature, and solar radiation were by far the three most important environmental predictors for oyster norovirus outbreaks, though other variables were also important. Specifically, very low temperature and gage height significantly increased the risk of norovirus outbreaks while high solar radiation markedly reduced the risk, suggesting that low temperature and gage height were associated with the norovirus source while solar radiation was the primary sink of norovirus. The GP model was used to hindcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast. The daily hindcasting results indicated that the GP model was capable of hindcasting all historical oyster norovirus outbreaks from January 2002 to June 2014 in the Gulf of Mexico with only two false positive outbreaks for the 12.5-year period. The performance of the GP model was characterized with the area under the Receiver Operating Characteristic curve of 0.86, the true positive rate (sensitivity) of 78.53percent and the true negative rate (specificity) of 88.82percent, respectively, demonstrating the efficacy of the GP model. The findings and results offered new insights into the oyster norovirus outbreaks in terms of source, sink, cause, and predictors. The GP model provided an efficient and effective tool for predicting potential oyster norovirus outbreaks and implementing management interventions to prevent or at least reduce norovirus risks to both the human health and the seafood industry %K genetic algorithms, genetic programming, Oyster norovirus outbreaks, Predictive model, Sensitivity analysis %9 journal article %R 10.1016/j.watres.2017.10.032 %U http://www.sciencedirect.com/science/article/pii/S0043135417308692 %U http://dx.doi.org/10.1016/j.watres.2017.10.032 %P 20-37 %0 Conference Proceedings %T Application of Machine-Learning Methods to Understand Gene Expression Regulation %A Cheng, Chao %A Worzel, William P. %Y Riolo, Rick %Y Worzel, William P. %Y Kotanchek, Mark %S Genetic Programming Theory and Practice XII %S Genetic and Evolutionary Computation %D 2014 %8 August 10 may %I Springer %C Ann Arbor, USA %F Cheng:2014:GPTP %X With the development and application of high-throughput technologies, an enormous amount of biological data has been produced in the past few years. These large-scale datasets make it possible and necessary to implement machine learning techniques for mining biological insights. In this chapter, we describe several examples to show how machine learning approaches are used to elucidate the mechanism of transcriptional regulation mediated by transcription factors and histone modifications. We demonstrate that machine learning provides powerful tools to quantitatively relate gene expression with transcription factor binding and histone modifications, to identify novel regulatory DNA elements in the genomes, and to predict gene functions. We also discuss the advantages and limitations of genetic programming in analysing and processing biological data. %K genetic algorithms, genetic programming, Support Vector Machine, SVM, Random forest, GP, ENCODE, modENCODE, Transcription Factor (TF), Histone modification, ChIP-Chip, ChIP-seq, RNA-seq %R 10.1007/978-3-319-16030-6_1 %U http://dx.doi.org/10.1007/978-3-319-16030-6_1 %P 1-15 %0 Book Section %T Recognizing Poker Hands with Genetic Programming and Restricted Iteration %A Cheng, Cleve %E Koza, John R. %B Genetic Algorithms and Genetic Programming at Stanford 1997 %D 1997 %8 17 mar %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-205981-2 %F Cheng:1997:rphGPri %K genetic algorithms, genetic programming %P 18-27 %0 Journal Article %T A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress %A Cheng, Ching-Hsue %A Chan, Chia-Pang %A Yang, Jun-He %J Computational Intelligence and Neuroscience %D 2018 %V 2018 %F Cheng:2018:cin %K genetic algorithms, genetic programming, gene expression programming %9 journal article %R 10.1155/2018/1067350 %U http://dx.doi.org/10.1155/2018/1067350 %P 1067350:1-1067350:14 %0 Conference Proceedings %T Evaluation and Design of Non-cryptographic Hash Functions for Network Data Stream Algorithms %A Cheng, Guang %A Yan, Yang %S 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM) %D 2017 %8 aug %F Cheng:2017:BIGCOM %X Non-cryptographic hash function is the core algorithm in network data stream technologies, its performance plays a crucial role in data stream algorithms. In this paper, two new quality criteria active flow metric and homology hash value correlation metric are firstly proposed for evaluating hash functions used in data stream algorithms. Experiments towards the metrics defined on 15 representative hash functions are performed using the real IPv6 network data captured from CERNET backbone. Bitwise operators are common candidates for implementing hash functions. We experimentally prove that XOR can introduce the most entropy to hash values compared with other 3 operators. On the basis of operator analysis, we design a novel hash function using Genetic Programming for data stream algorithm and network measurement. It can compete with the state of the art hash functions. %K genetic algorithms, genetic programming %R 10.1109/BIGCOM.2017.38 %U http://dx.doi.org/10.1109/BIGCOM.2017.38 %P 239-244 %0 Conference Proceedings %T Improved Genetic Programming Model for Software Reliability %A Cheng, Huifang %A Zhang, Yongqiang %A Zhao, Jing %S International Asia Symposium on Intelligent Interaction and Affective Computing, ASIA ’09 %D 2009 %8 dec %F Cheng:2009:ASIA %X Many existing software reliability models are based on some subjective assumptions those could be easily impractical in reality. Genetic Programming(GP for short) does not need some subjective assumption due to the basic characteristic of the data. Also, this method doesn’t require to understand the inherent processes for failures, but to create models based on the given data for a ’true’ process during the specific modeling course, which can describe the software failure mechanisms more effectually and predict for the next failure times more exactly. This paper adopts improved GP(IGP for short) algorithm to hunting model, which can possibly reflect system behaviors, in the function spaces are compoundly constituted by the authorized function operators. Meanwhile, we have proved that IGP can obtain the best solution for failure behavior’s variation rules from the convergence character of itself. Moreover, this paper makes use of Orthogonal experimental to adjust the parameters. %K genetic algorithms, genetic programming, SBSE, IGP algorithm, improved genetic programming model, software failure mechanism, software reliability, software reliability %R 10.1109/ASIA.2009.38 %U http://dx.doi.org/10.1109/ASIA.2009.38 %P 164-167 %0 Conference Proceedings %T Improved Genetic Programming Algorithm %A Cheng, Huifang %A Zhang, Yongqiang %A Li, Fangping %S International Asia Symposium on Intelligent Interaction and Affective Computing, ASIA ’09 %D 2009 %8 dec %F Cheng:2009:ASIA2 %X The present study aims at improving the problem solving ability of the canonical genetic programming algorithm. The proposed method can be described as follows. The first investigates initialising population, the second investigates reproduction operator, the third investigates crossover operator, the fourth investigates mutation operation. This approach is examined on two experiments about symbolic regression. The results suggest that the new approach is more effective and more efficient than the canonical one. %K genetic algorithms, genetic programming, canonical genetic programming algorithm, crossover operator, mutation operation, problem solving, reproduction operator, symbolic regression, regression analysis %R 10.1109/ASIA.2009.39 %U http://dx.doi.org/10.1109/ASIA.2009.39 %P 168-171 %0 Journal Article %T Data mining for fast and accurate makespan estimation in machining workshops %A Cheng, Lixin %A Tang, Qiuhua %A Zhang, Zikai %A Wu, Shiqian %J Journal of Intelligent Manufacturing %D 2021 %V 32 %I springer %F Cheng:2021:JIM %X The fast and accurate estimation of makespan is essential for the determination of the delivery date and the sustainable development of the enterprise. In this paper, a high-quality training dataset is constructed and an adaptive ensemble model is proposed to achieve fast and accurate makespan estimation. First, both the logistics features extracted by the Pearson correlation coefficient and the new meaningful nonlinear combination features dug out by gene expression programming are first involved in this paper for constructing a high-quality dataset. Secondly, an improved clustering with elbow criterion and a resampling operation are applied simultaneously to generate representative subsets; and correspondingly, several back propagation neural network (BPNN) with the architecture optimised by genetic algorithm are trained by these subsets respectively to generate effective diverse learners; and then, a K-nearest neighbour based dynamic weight combination strategy which is sensitive to current testing sample is proposed to make full use of the learners positive effects and avoid its negative effects. Finally, the results of effective experiments prove that both the newly involved features and the improvements in the proposed ensemble are effective. In addition, comparison experiments confirm that the proposed enhanced ensemble of BPNNs outperforms significantly the prevailing approaches, including single, ensemble and hybrid models. And hence, the proposed model can be used as a convenient and reliable tool to support customer order acceptance. %K genetic algorithms, genetic programming, gene expression programming, makespan estimation, ensemble of bpnn, clustering %9 journal article %R 10.1007/s10845-020-01585-y %U http://link.springer.com/10.1007/s10845-020-01585-y %U http://dx.doi.org/10.1007/s10845-020-01585-y %P 483-500 %0 Conference Proceedings %T An Efficient Cooperative Co-Evolutionary Gene Expression Programming %A Cheng, Tiantian %A Zhong, Jinghui %S 2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) %D 2018 %8 oct %F Cheng:2018:SmartWorld %X Gene Expression Programming (GEP) is a popular and powerful evolutionary optimization technique for automatic generation of computer programs. In this paper, a Cooperative Co-evolutionary framework is proposed to improve the performance of GEP. The proposed framework consists of three components to find high-quality computer programs. One component focusing on searches for both structures and coefficients of computer programs, while the other two components focus on optimizing the structures and coefficients, respectively. The three components are working cooperatively during the evolution process. The proposed framework is tested on twelve symbolic regression problems and two real-world regression problems. Experimental results demonstrated that the proposed method can offer enhanced performances over two state-of-the-art algorithms in terms of solution accuracy and search efficiency. %K genetic algorithms, genetic programming, Gene Expression Programming, GEP, high-quality computer programs, coevolutionary gene expression, coevolutionary framework, evolutionary optimization technique, Cooperative Co-evolution %R 10.1109/SmartWorld.2018.00246 %U http://dx.doi.org/10.1109/SmartWorld.2018.00246 %P 1422-1427 %0 Journal Article %T An efficient memetic genetic programming framework for symbolic regression %A Cheng, Tiantian %A Zhong, Jinghui %J Memetic Comput. %D 2020 %V 12 %N 4 %F DBLP:journals/memetic/ChengZ20 %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s12293-020-00311-8 %U https://doi.org/10.1007/s12293-020-00311-8 %U http://dx.doi.org/10.1007/s12293-020-00311-8 %P 299-315 %0 Conference Proceedings %T Air Traffic Control Using Genetic Search Techniques %A Cheng, V. H. L. %A Crawford, L. S. %A Menon, P. K. %S 1999 IEEE International Conference on Control Applications %D 1999 %8 aug 22 27 %V 1 %I IEEE %C Hawai’i, HA, USA %@ 0-7803-5446-X %G en %F oai:CiteSeerPSU:521419 %X Genetic search techniques constitute an optimisation methodology effective for solving discontinuous, non-convex, nonlinear, or non-analytic problems. This paper explores the application of such techniques to a non-analytic event-related air traffic control problem, that of runway assignment, sequencing, and scheduling of arrival flights at an airport with multiple runways. Several genetic search formulations are developed and evaluated with a representative arrival traffic scenario. The results exemplify the importance of the selection of the chromosomal representation for a genetic-search problem. %K genetic algorithms, genetic programming %R 10.1109/CCA.1999.806209 %U http://www.optisyn.com/research/papers/papers/1999/traffic_99.pdf %U http://dx.doi.org/10.1109/CCA.1999.806209 %P 249-254 %0 Journal Article %T An intelligent supplier evaluation model based on data-driven support vector regression in global supply chain %A Cheng, Yijun %A Peng, Jun %A Gu, Xin %A Zhang, Xiaoyong %A Liu, Weirong %A Zhou, Zhuofu %A Yang, Yingze %A Huang, Zhiwu %J Computer & Industrial Engineering %D 2020 %V 139 %@ 0360-8352 %F CHENG:2020:CIE %X Supplier evaluation is an important issue in supply chain management. Most existing studies rely on expert experience to evaluate supplier performance. In order to alleviate the pressure on experts in global supply chain, an intelligent supplier evaluation model based on data-driven support vector regression (SVR) is proposed in this paper. Two methods are used in the construction process of the proposed intelligent model for supplier evaluation. The integrated multiple criteria decision making (MCDM) is employed to obtain the label of each supplier instead of the manual label. Then the obtained labels are used to train the SVR. Genetic programming (GP) is adopted to set three critical parameters of SVR without prior knowledge, which are kernel function k(.), the penalty parameter C, and the tolerable deviation epsilon. The performance of the proposed intelligent model is evaluated with the commercially available ARCIC data set. Simulation results show that the accuracy and robustness of proposed intelligent model are superior when compared with existing models %K genetic algorithms, genetic programming, SVM, Supply chain management, Supplier evaluation, Support vector regression, Multiple criteria decision making %9 journal article %R 10.1016/j.cie.2019.04.047 %U http://www.sciencedirect.com/science/article/pii/S036083521930258X %U http://dx.doi.org/10.1016/j.cie.2019.04.047 %P 105834 %0 Journal Article %T Genetic programming model for estimating soil suction in shallow soil layers in the vicinity of a tree %A Cheng, Zhi-Liang %A Zhou, Wan-Huan %A Garg, Ankit %J Engineering Geology %D 2020 %V 268 %@ 0013-7952 %F CHENG:2020:EG %X Soil suction, an important parameter in the safety and risk assessment of geotechnical and green infrastructures, is greatly affected by plants and weather in the shallow soil layers of urban landscapes/green infrastructure. In this study, a computational model consisting of a drying-cycle model and wetting-cycle model was developed by means of a genetic programming method to depict variations in soil suction using select influential parameters. The input data in the model development were measured in a field monitoring test on the campus of the University of Macau. Soil suction was quantified by field monitoring at different distances (0.5 m, 1.5 m, and 3.0 m) from a tree, at a constant depth of 20 cm, with selected influential parameters including initial soil suction, air humidity, rainfall amount, cycle duration, and ratio of distance from tree to tree canopy. Based on the performance analysis, the efficiency and reliability of the proposed computational model are validated. The importance of each input and the coupled effect of each two input variables on the output were investigated using global sensitivity analysis. It can be concluded that the proposed computational model based on the artificial intelligence simulation method describes the relationship between field soil suction in drying-wetting cycles and select input variables within an acceptable degree of error. Accordingly, it can serve as a tool for supporting geotechnical construction design and for assessing the safety and risk of geotechnical green infrastructures %K genetic algorithms, genetic programming, Drying cycle, Global sensitivity analysis, Performance analysis, Soil suction, Wetting cycle %9 journal article %R 10.1016/j.enggeo.2020.105506 %U http://www.sciencedirect.com/science/article/pii/S0013795219308154 %U http://dx.doi.org/10.1016/j.enggeo.2020.105506 %P 105506 %0 Journal Article %T Physics-guided genetic programming for predicting field-monitored suction variation with effects of vegetation and atmosphere %A Cheng, Zhi-Liang %A Kannangara, K. K. Pabodha M. %A Su, Li-Jun %A Zhou, Wan-Huan %A Tian, Chen %J Engineering Geology %D 2023 %V 315 %@ 0013-7952 %F CHENG:2023:enggeo %X The complicated interactions among shallow soil, vegetation, and atmospheric parameters make the precise prediction of field-monitored soil suction under natural conditions challenging. This study integrated an analytical solution with a genetic programming (GP) model in proposing a physics-guided GP method for better calculation and prediction of field-monitored matric suction in a shallow soil layer. Model development and analysis involved 3987 collected data values for soil suction as well as atmospheric and tree-related parameters from a field monitoring site. Natural algorithm values of transpiration rates obtained by back-calculation were simulated with GP using easily obtained parameters. Global sensitivity analysis demonstrated that the tree canopy-related parameter was the most important for transpiration rate. It was indicated that the proposed physics-guided GP method greatly improved calculation accuracy and, as a result, demonstrated a better performance and was more reliable than the individual GP method in calculating field-monitored suction. The proposed physics-guided GP method was also validated as more stable and reliable due to its smaller uncertainty and higher confidence level compared to the individual GP method based on quantile regression uncertainty analysis %K genetic algorithms, genetic programming, Field-monitored soil suction, Physics-guided genetic programming, Performance evaluation, Global sensitivity analysis, Uncertainty analysis %9 journal article %R 10.1016/j.enggeo.2023.107031 %U https://www.sciencedirect.com/science/article/pii/S0013795223000480 %U http://dx.doi.org/10.1016/j.enggeo.2023.107031 %P 107031 %0 Journal Article %T Multi-perspective analysis on rainfall-induced spatial response of soil suction in a vegetated soil %A Cheng, Zhiliang %A Zhou, Wanhuan %A Tian, Chen %J Journal of Rock Mechanics and Geotechnical Engineering %D 2022 %V 14 %N 4 %@ 1674-7755 %F CHENG:2022:jrmge %X In this study, an intelligent monitoring platform is established for continuous quantification of soil, vegetation, and atmosphere parameters (e.g. soil suction, rainfall, tree canopy, air temperature, and wind speed) to provide an efficient dataset for modeling suction response through machine learning. Two characteristic parameters representing suction response during wetting processes, i.e. response time and mean reduction rate of suction, are formulated through multi-gene genetic programming (MGGP) using eight selected influential parameters including depth, initial soil suction, vegetation- and atmosphere-related parameters. An error standard-based performance evaluation indicated that MGGP has appreciable potential for model development when working with even fewer than 100 data. Global sensitivity analysis revealed the importance of tree canopy and mean wind speed to estimation of response time and indicated that initial soil suction and rainfall amount have an important effect on the estimated suction reduction rate during a wetting process. Uncertainty assessment indicated that the two MGGP models describing suction response after rainfall are reliable and robust under uncertain conditions. In-depth analysis of spatial variations in suction response validated the robustness of two obtained MGGP models in prediction of suction variation characteristics under natural conditions %K genetic algorithms, genetic programming, Global sensitivity analysis (GSA), Multi-gene genetic programming (MGGP), Soil suction response, Spatial variation of suction response, Uncertainty assessment %9 journal article %R 10.1016/j.jrmge.2022.02.009 %U https://www.sciencedirect.com/science/article/pii/S1674775522000622 %U http://dx.doi.org/10.1016/j.jrmge.2022.02.009 %P 1280-1291 %0 Journal Article %T Mathematical model for approximating shield tunneling-induced surface settlement via multi-gene genetic programming %A Cheng, Zhi-Liang %A Kannangara, K. K. Pabodha M. %A Su, Li-Jun %A Zhou, Wan-Huan %J Acta Geotechnica %D 2023 %V 18 %N 9 %F cheng:2023:AG %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s11440-023-01847-y %U http://link.springer.com/article/10.1007/s11440-023-01847-y %U http://dx.doi.org/10.1007/s11440-023-01847-y %0 Conference Proceedings %T An Empirical Analysis Through the Time Complexity of GE Problems %A Chennupati, Gopinath %A Ryan, Conor %A Azad, Raja Muhammad Atif %Y Matousek, Radomil %S 19th International Conference on Soft Computing, MENDEL 2013 %D 2013 %8 jun 26 28 Brno %C Brno, Czech Republic %F Chennupati:2013:mendel %X Computational complexity analysis on Evolutionary Algorithms can provide crucial insight into how they work. While relatively straight forward for fixed length structures, it is less so for variable length structures, although initial work has already been conducted on tree based Genetic Programming (GP) algorithms. Grammatical Evolution (GE) is a variable length string based Evolutionary Algorithm (EA) that evolves arbitrarily complex programs through complex gene interactions, but thus far, no such analysis has been conducted. We empirically analyse the time complexity of GE on two well known GP problems: the Santa Fe Ant Trail and a Symbolic Regression problem. Using a power law, we analyse the time complexity of GE in terms of population size. As a result of this, several observations are made estimating the average terminating generation, actual length and effective lengths of individuals based on the quality of the solution. We show that even with the extra layer of complexity of GE, time increases linearly for GE on Santa Fe Ant Trail problem and quadratic in nature on a symbolic regression problem as the size of simulations (i.e. population size) increase. To the best of our knowledge, this is the first attempt measuring the run-time complexity of GE. This analysis provides a way to produce a reasonably good prediction system of how a particular run will perform, and we provide details of how one can leverage this data to predict the success or otherwise of a GE run in the early generations. with the amount of data collected. %K genetic algorithms, genetic programming, grammatical evolution %U https://www.researchgate.net/publication/264464282_An_empirical_analysis_through_the_time_complexity_of_GE_problems %P 37-44 %0 Conference Proceedings %T On The Efficiency of Multi-core Grammatical Evolution (MCGE) Evolving Multi-Core Parallel Programs %A Chennupati, Gopinath %A Fitzgerald, Jeannie %A Ryan, Conor %Y Madureira, Ana Maria %Y Abraham, Ajith %Y Corchado, Emilio %Y Varela, Leonilde %Y Muda, Azah Kamilah %Y yun Huoy, Choo %S Sixth World Congress on Nature and Biologically Inspired Computing %D 2014 %8 30 jul 1 jul %I IEEE %C Porto, Portugal %F Chennupati:2014:NaBIC %X In this paper we investigate a novel technique that optimises the execution time of Grammatical Evolution through the usage of on-chip multiple processors. This technique, Multi-core Grammatical Evolution (MCGE) evolves natively parallel programs with the help of OpenMP primitives through the grammars, such that not only can we exploit parallelism while evolving individuals, but the final individuals produced can also be executed on parallel architectures even outside the evolutionary system. We test MCGE on two difficult benchmark GP problems and show its efficiency in exploiting the power of the multi-core architectures. We further discuss that, on these problems, the system evolves longer individuals while they are evaluated quicker than their serial implementation. %K genetic algorithms, genetic programming, Grammatical Evolution, OpenMP, Parallel programming, GPU %R 10.1109/NaBIC.2014.6921885 %U http://dx.doi.org/10.1109/NaBIC.2014.6921885 %P 238-243 %0 Conference Proceedings %T Predict the success or failure of an evolutionary algorithm run %A Chennupati, Gopinath %A Ryan, Conor %A Azad, R. Muhammad Atif %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 Chennupati:2014:GECCOcomp %X The quality of candidate solutions in evolutionary computation (EC) depend on multiple independent runs and a large number of them fail to guarantee optimal result. These runs consume more or less equal or sometimes higher amount of computational resources on par the runs that produce desirable results. This research work addresses these two issues (run quality, execution time), Run Prediction Model (RPM), in which undesirable quality evolutionary runs are identified to discontinue from their execution. An Ant Colony Optimisation (ACO) based classifier that learns to discover a prediction model from the early generations of an EC run. We consider Grammatical Evolution (GE) as our EC technique to apply RPM that is evaluated on four symbolic regression problems. We establish that the RPM applied GE produces a significant improvement in the success rate while reducing the execution time. %K genetic algorithms, genetic programming, grammatical evolution: Poster %R 10.1145/2598394.2598471 %U http://doi.acm.org/10.1145/2598394.2598471 %U http://dx.doi.org/10.1145/2598394.2598471 %P 131-132 %0 Conference Proceedings %T Multi-core GE: automatic evolution of CPU based multi-core parallel programs %A Chennupati, Gopinath %A Azad, R. Muhammad Atif %A Ryan, Conor %Y Tusar, Tea %Y Naujoks, Boris %S GECCO 2014 student workshop %D 2014 %8 December 16 jul %I ACM %C Vancouver, BC, Canada %F Chennupati:2014:GECCOcompa %X We describe the use of on-chip multiple CPU architectures to automatically evolve parallel computer programs. These programs have the capability of exploiting the computational efficiency of the modern multi-core machines. This is significantly different from other parallel EC approaches because not only do we produce individuals that, in their final form, can exploit parallel architectures, we can also exploit the same parallel architecture during evolution to reduce evolution time. We use Grammatical Evolution along with OpenMP specific grammars to produce natively parallel code, and demonstrate that not only do we enjoy the benefit of final individuals that can run in parallel, but that our system scales effectively with the number of cores. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1145/2598394.2605670 %U http://doi.acm.org/10.1145/2598394.2605670 %U http://dx.doi.org/10.1145/2598394.2605670 %P 1041-1044 %0 Conference Proceedings %T Predict the performance of GE with an ACO based machine learning algorithm %A Chennupati, Gopinath %A Azad, R. Muhammad Atif %A Ryan, Conor %Y Gustafson, Steven %Y Vladislavleva, Ekaterina %S GECCO 2014 Workshop on Symbolic Regression and Modelling %D 2014 %8 December 16 jul %I ACM %C Vancouver, BC, Canada %F Chennupati:2014:GECCOcompb %X The quality of the evolved solutions of an evolutionary algorithm (EA) varies across different runs and a significant percentage of runs can produce solutions of undesirable quality. These runs are a waste of computational resources, particularly in difficult problems where practitioners have time bound limitations in repeating runs. This paper proposes a completely novel approach, that of a Run Prediction Model (RPM) in which we identify and terminate evolutionary runs that are likely to produce low-quality solutions. This is justified with an Ant Colony Optimization (ACO) based classifier that learns from the early generations of a run and decides whether to continue or not. We apply RPM to Grammatical Evolution (GE) applied to four benchmark symbolic regression problems and consider several contemporary machine learning algorithms to train the predictive models and find that ACO produces the best results and acceptable predictive accuracy for this first investigation. The ACO discovered prediction models are in the form of a list of simple rules. We further analyse that list manually to tune them in order to predict poor GE runs. We then apply the analysed model to GE runs on the regression problems and terminate the runs identified by the model likely to be poor, thus increasing the rate of production of successful runs while reducing the computational effort required. We demonstrate that, although there is a high bootstrapping cost for RPM, further investigation is warranted as the mean success rate and the total execution time enjoys a statistically significant boost on all the four benchmark problems. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1145/2598394.2609860 %U http://doi.acm.org/10.1145/2598394.2609860 %U http://dx.doi.org/10.1145/2598394.2609860 %P 1353-1360 %0 Conference Proceedings %T Automatic Evolution of Parallel Recursive Programs %A Chennupati, Gopinath %A Azad, R. Muhammad Atif %A Ryan, Conor %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 Chennupati:2015:EuroGP %X Writing recursive programs for fine-grained task-level execution on parallel architectures, such as the current generation of multi-core machines, often require the application of skilled parallelization knowledge to fully realize the potential of the hardware. This paper automates the process by using Grammatical Evolution (GE) to exploit the multi-cores through the evolution of natively parallel programs. We present Multi-core Grammatical Evolution (MCGE-II), which employs GE and OpenMP specific pragmatic information to automatically evolve task-level parallel recursive programs. MCGE-II is evaluated on six recursive C programs, and we show that it solves each of them using parallel code. We further show that MCGE-II significantly decreases the parallel computational effort as the number of cores increase, when tested on an Intel processor. %K genetic algorithms, genetic programming, Grammatical Evolution, GPU, Automatic Parallelization, Recursion, Program Synthesis, OpenMP, Evolutionary Parallelization: Poster %R 10.1007/978-3-319-16501-1_14 %U http://dx.doi.org/10.1007/978-3-319-16501-1_14 %P 167-178 %0 Conference Proceedings %T Automatic Evolution of Parallel Sorting Programs on Multi-cores %A Chennupati, Gopinath %A Azad, R. Muhammad Atif %A Ryan, Conor %Y Mora, Antonio M. %Y Squillero, Giovanni %S 18th European Conference on the Applications of Evolutionary Computation %S LNCS %D 2015 %8 August 10 apr %V 9028 %I Springer %C Copenhagen %F Chennupati:2015:evoApplications %X Sorting algorithms that offer the potential for data-parallel execution on parallel architectures are an excellent tool for the current generation of multi-core processors that often require skilled parallelisation knowledge to fully realize the potential of the hardware. We propose to automate the evolution of natively parallel programs using the Grammatical Evolution (GE) approach to use the computational potential of multi-cores. The proposed system, Multi-core Grammatical Evolution for Parallel Sorting (MCGE-PS), applies GE mapping along with explicit OpenMP #pragma compiler directives to automatically evolve data-level parallel iterative sorting algorithms. MCGE-PS is assessed on the generation of four non-recursive sorting programs in C. We show that it generated programs that can solve the problem that are also parallel. On a high performance Intel processor, MCGE-PS significantly reduced the execution time of the evolved programs for all the benchmark problems. %K genetic algorithms, genetic programming, Grammatical evolution, Automatic parallelisation, Recursion, Program synthesis, OpenMP, Evolutionary parallelization: Poster %R 10.1007/978-3-319-16549-3_57 %U http://dx.doi.org/10.1007/978-3-319-16549-3_57 %P 706-717 %0 Conference Proceedings %T Performance Optimization of Multi-Core Grammatical Evolution Generated Parallel Recursive Programs %A Chennupati, Gopinath %A Azad, R. Muhammad Atif %A Ryan, Conor %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 Chennupati:2015:GECCO %X Although Evolutionary Computation (EC) has been used with considerable success to evolve computer programs, the majority of this work has targeted the production of serial code. Recent work with Grammatical Evolution (GE) produced Multi-core Grammatical Evolution (MCGE-II), a system that natively produces parallel code, including the ability to execute recursive calls in parallel. This paper extends this work by including practical constraints into the grammars and fitness functions, such as increased control over the level of parallelism for each individual. These changes execute the best-of-generation programs faster than the original MCGE-II with an average factor of 8.13 across a selection of hard problems from the literature. We analyze the time complexity of these programs and identify avoiding excessive parallelism as a key for further performance scaling. We amend the grammars to evolve a mix of serial and parallel code, which spawns only as many threads as is efficient given the underlying OS and hardware; this speeds up execution by a factor of 9.97. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1145/2739480.2754746 %U http://doi.acm.org/10.1145/2739480.2754746 %U http://dx.doi.org/10.1145/2739480.2754746 %P 1007-1014 %0 Conference Proceedings %T Synthesis of Parallel Iterative Sorts with Multi-Core Grammatical Evolution %A Chennupati, Gopinath %A Azad, R. Muhammad Atif %A Ryan, Conor %Y Woodward, John %Y Tauritz, Daniel %Y Lopez-Ibanez, Manuel %S GECCO 2015 5th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA’15) %D 2015 %8 November 15 jul %I ACM %C Madrid, Spain %F Chennupati:2015:GECCOcomp %X Writing parallel programs is a challenging but unavoidable proposition to take true advantage of multi-core processors. In this paper, we extend Multi-core Grammatical Evolution for Parallel Sorting (MCGE-PS) to evolve parallel iterative sorting algorithms while also optimizing their degree of parallelism. We use evolution to optimize the performance of these parallel programs in terms of their execution time, and our results demonstrate a significant optimization of 11.03 in performance when compared with various MCGE-PS variations as well as the GNU GCC compiler optimizations that reduce the execution time through code minimization. We then analyse the evolutionary (code growth) and non-evolutionary (thread scheduling) factors that cause performance implications. We address them to further optimize the performance and report it as 12.52. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1145/2739482.2768458 %U http://doi.acm.org/10.1145/2739482.2768458 %U http://dx.doi.org/10.1145/2739482.2768458 %P 1059-1066 %0 Conference Proceedings %T On the Automatic Generation of Efficient Parallel Iterative Sorting Algorithms %A Chennupati, Gopinath %A Azad, R. Muhammad Atif %A Ryan, Conor %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 Chennupati:2015:GECCOcompa %X Increasing availability of multiple processing elements on the recent desktop and personal computers poses unavoidable challenges in realizing their processing power. The challenges include programming these high processing elements. Parallel programming is an apt solution for such a realization of the computational capacity. However, it has many difficulties in developing the parallel programs. We present Multi-core Grammatical Evolution for Parallel Sorting (MCGE-PS) that automatically produces native parallel sorting programs. These programs are of iterative nature that also exploit the processing power of the multi-core processors efficiently. The performance of the resultant programs is measured in terms of the execution time. The results indicate a significant improvement over the state-of-the-art implementations. Finally, we conduct an empirical analysis on computational complexity of the evolving parallel programs. The results are competitive with that of the state-of-the-art evolutionary attempts. %K genetic algorithms, genetic programming, grammatical evolution: Poster %R 10.1145/2739482.2764695 %U http://doi.acm.org/10.1145/2739482.2764695 %U http://dx.doi.org/10.1145/2739482.2764695 %P 1369-1370 %0 Thesis %T Grammatical Evolution + Multi-Cores = Automatic Parallel Programming! %A Chennupati, Gopinath %D 2015 %8 sep %C Ireland %C CSIS Department, University of Limerick %F Chennupati:thesis %X Multi-core processors are shared memory multiprocessors integrated on a single chip which offer significantly higher processing power than traditional, single core processors. However, as the number of cores available on a single processor increases, efficiently programming them becomes increasingly more complex, often to the point where the limiting factor in speeding up tasks is the software. This thesis presents Grammatical Automatic Parallel Programming (GAPP) which uses Grammatical Evolution to automatically generate natively parallel code on multi-core processors by directly embedding GAPP OpenMP parallelization directives in problem-specific Context Free Grammars. As a result, it obviates the need for programmers to think in a parallel manner while still letting them produce parallel code. We first perform a thorough analysis on the computational complexity of Grammatical Evolution using standard benchmark problems. This analysis results in an interesting experiment which produces a system capable of predicting on-the-fly the likelihood of a particular GE run being successful. A number of difficult proof of concept problems are examined in evaluating GAPP. The performance of the system on these informs the further optimization of both the design of grammars and fitness function to extract further parallelism. We demonstrate a surprising side effect of uncontrolled parallelism, which leads to the under-use of the cores. This is addressed through the automatic generation of programs with controlled degree of parallelism. In this case, the automatically generated programs adapt to the number of cores on which they are scheduled to execute. Finally, GAPP is applied to Automatic Lockless Programming, an enormously difficult design problem, resulting in parallel code guaranteed to avoid locks on shared resources, thereby further optimizing the execution time. We then draw conclusions and make future recommendations on the use of evolutionary systems in the generation of highly constrained parallel code. %K genetic algorithms, genetic programming, Grammatical Evolution, grammatical automatic parallel programming, GAPP %9 Ph.D. thesis %U https://hdl.handle.net/10344/4828 %0 Conference Proceedings %T Automatic Lock-free Parallel Programming on Multi-core Processors %A Chennupati, Gopinath %A Azad, R. Muhammad Atif %A Ryan, Conor %Y Ong, Yew Soon %S CEC 2016 %D 2016 %8 25 29 jul %I IEEE %C Vancouver %F Chennupati:2016:CEC %X Writing correct and efficient parallel programs is an unavoidable challenge; the challenge becomes arduous with lock-free programming. This paper presents an automated approach, Automatic Lock-free Programming (ALP) that avoids the programming difficulties via locks for an average programmer. ALP synthesizes parallel lock-free recursive programs that are directly compilable on multi-core processors. ALP attains the dual objective of evolving parallel lock-free programs and optimizing their performance. These programs perform (in terms of execution time) significantly better than that of the parallel programs with locks, while they are competitive with the human developed programs. %K genetic algorithms, genetic programming, Multi-cores, Lock-Free Programming, Evolutionary Computation, Program Synthesis, OpenMP %R 10.1109/CEC.2016.7744316 %U http://dx.doi.org/10.1109/CEC.2016.7744316 %P 4143-4150 %0 Book Section %T Synthesis of Parallel Programs on Multi-Cores %A Chennupati, Gopinath %A Azad, R. Muhammad Atif %A Ryan, Conor %A Eidenbenz, Stephan %A Santhi, Nandakishore %E Ryan, Conor %E O’Neill, Michael %E Collins, J. J. %B Handbook of Grammatical Evolution %D 2018 %I Springer %F Chennupati:2018:hbge %X Multi-cores offer higher processing power than single core processors. However, as the number of cores available on a single processor increases, efficiently programming them becomes increasingly more complex, often to the point where the limiting factor in speeding up tasks is the software. We present Grammatical Automatic Parallel Programming (GAPP), a system that synthesizes parallel code on multi-cores using OpenMP parallelization primitives in problem-specific grammars. As a result, GAPP obviates the need for programmers to think parallel while still letting them produce parallel code. The performance of GAPP on a number of difficult proof of concept benchmarks informs further optimization of both the design of grammars and fitness function to extract further parallelism. We demonstrate an improved performance of evolving programs with controlled degree of parallelism. These programs adapt to the number of cores on which they are scheduled to execute. %K genetic algorithms, genetic programming, Grammatical Evolution %R 10.1007/978-3-319-78717-6_12 %U http://dx.doi.org/10.1007/978-3-319-78717-6_12 %P 289-315 %0 Conference Proceedings %T Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics %A Cherrier, Noelie %A Poli, Jean-Philippe %A Defurne, Maxime %A Sabatie, Franck %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 Cherrier:2019:CEC %X A good feature representation is a determinant factor to achieve high performance for many machine learning algorithms in terms of classification. This is especially true for techniques that do not build complex internal representations of data (e.g. decision trees, in contrast to deep neural networks). To transform the feature space, feature construction techniques build new high-level features from the original ones. Among these techniques, Genetic Programming is a good candidate to provide interpretable features required for data analysis in high energy physics. Classically, original features or higher-level features based on physics first principles are used as inputs for training. However, physicists would benefit from an automatic and interpretable feature construction for the classification of particle collision events.Our main contribution consists in combining different aspects of Genetic Programming and applying them to feature construction for experimental physics. In parti %K genetic algorithms, genetic programming, construction, grammar-guided genetic programming, high-energy physics, interpretability %R 10.1109/CEC.2019.8789937 %U http://dx.doi.org/10.1109/CEC.2019.8789937 %P 1650-1658 %0 Journal Article %T RIGSS - Inverse Generative Social Science using R %A Chesney, Thomas %A Pasley, Robert %A Jaffer, Muhammad Asif %J Software Impacts %D 2024 %V 21 %@ 2665-9638 %F Chesney:2024:simpa %X We present RIGSS, software that can be used to run an Inverse Generative Social Science study in R. We give a brief overview of this research method and explain how our software can be used. We implement a Hawk Dove game as an executable example. We then discuss the potential that Inverse Generative Social Science has %K genetic algorithms, genetic programming, Agent-based modelling, Social theory %9 journal article %R 10.1016/j.simpa.2024.100689 %U https://www.sciencedirect.com/science/article/pii/S2665963824000770 %U http://dx.doi.org/10.1016/j.simpa.2024.100689 %P 100689 %0 Conference Proceedings %T Use of evolutionary computation techniques for exploration and prediction of helicopter loads %A Cheung, Catherine %A Valdes, Julio J. %A Li, Matthew %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 Cheung:2012:CEC %X The development of accurate load spectra for helicopters is necessary for life cycle management and life extension efforts. This paper explores continued efforts to use evolutionary computation (EC) methods and machine learning techniques to estimate several helicopter dynamic loads. Estimates for the main rotor normal bending (MRNBX) on the Australian Black Hawk helicopter were generated from an input set that included thirty standard flight state and control system parameters under several flight conditions (full speed forward level flight, rolling left pullout at 1.5g, and steady 45deg left turn at full speed). Multiobjective genetic algorithms (MOGA) used in combination with the Gamma test found reduced subsets of predictor variables with Madelin potential. These subsets were used to estimate MRNBX using Cartesian genetic programming and neural network models trained by deterministic and evolutionary computation techniques, including particle swarm optimization (PSO), differential evolution (DE), and MOGA. PSO and DE were used alone or in combination with deterministic methods. Different error measures were explored including a fuzzy-based asymmetric error function. EC techniques played an important role in both the exploratory and Madelin phase of the investigation. The results of this work show that the addition of EC techniques in the modelling stage generated more accurate and correlated models than could be obtained using only deterministic optimization. %K genetic algorithms, genetic programming, Ensemble Methods in Computational Intelligence (IEEE-CEC), Defence and cyber security, Classification, clustering, data analysis and data mining %R 10.1109/CEC.2012.6252905 %U http://dx.doi.org/10.1109/CEC.2012.6252905 %P 1130-1137 %0 Conference Proceedings %T Designing Card Game Strategies with Genetic Programming and Monte-Carlo Tree Search: A Case Study of Hearthstone %A Chia, Hao-Cheng %A Yeh, Tsung-Su %A Chiang, Tsung-Che %S 2020 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2020 %8 dec %F Chia:2020:SSCI %X This paper addresses an agent design problem of a digital collectible card game, Hearthstone, which is a two-player turn-based game. The agent has to play cards based on the game state, the hand cards, and the deck of cards to defeat the opponent. First, we design a rule-based agent by searching for the board evaluation criterion through genetic programming (GP). Then, we integrate the rule-based agent into the Monte-Carlo tree search (MCTS) framework to generate an advanced agent. Performance of the proposed agents are verified by playing against three participants in two recent Hearthstone competitions. Experimental results showed that the GP-agent can beat a simple MCTS agent and the mid-level agent in the competition. The MCTS-GP agent showed competitive performance against the best agents in the competition. We also examine the rule found by GP and observed that GP is able to identify key attributes of game states and to combine them into a useful rule automatically. %K genetic algorithms, genetic programming, collectible card games, Hearthstone: Heroes of Warcraft, Monte-Carlo tree search %R 10.1109/SSCI47803.2020.9308459 %U https://scholar.lib.ntnu.edu.tw/en/publications/designing-card-game-strategies-with-genetic-programming-and-monte-2 %U http://dx.doi.org/10.1109/SSCI47803.2020.9308459 %P 2351-2358 %0 Journal Article %T Neural Logic Network Learning Using Genetic Programming %A Chia, Henry Wai Kit %A Tan, Chew Lim %J International Journal of Computational Intelligence and Applications %D 2001 %V 1 %N 4 %F DBLP:journals/ijcia/ChiaT01 %X Neural Logic Networks or Neulonets are hybrids of neural networks and expert systems capable of representing complex human logic in decision making. Each neulonet is composed of rudimentary net rules which themselves depict a wide variety of fundamental human logic rules. An early methodology employed in neulonet learning for pattern classification involved weight adjustments during back-propagation training which ultimately rendered the net rules incomprehensible. A new technique is now developed that allows the neulonet to learn by composing the net rules using genetic programming without the need to impose weight modifications, thereby maintaining the inherent logic of the net rules. Experimental results are presented to illustrate this new and exciting capability in capturing human decision logic from examples. The extraction and analysis of human logic net rules from an evolved neulonet will be discussed. These extracted net rules will be shown to provide an alternate perspective to the greater extent of knowledge that can be expressed and discovered. Comparisons will also be made to demonstrate the added advantage of using net rules, against the use of standard boolean logic of negation, disjunction and conjunction, in the realm of evolutionary computation. %K genetic algorithms, genetic programming, Neural network, rule-based learning, data mining %9 journal article %R 10.1142/S1469026801000299 %U http://dx.doi.org/10.1142/S1469026801000299 %P 357-368 %0 Conference Proceedings %T Association-Based Evolution of Comprehensible Neural Logic Networks %A Chia, Henry Wai-Kit %A Tan, Chew-Lim %Y Keijzer, Maarten %S Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference %D 2004 %8 26 jul %C Seattle, Washington, USA %F chia:2004:lbp %X Neural Logic Network (Neulonet) learning has been successfully used in emulating complex human reasoning processes. One recent implementation generates a single large neulonet via genetic programming using an accuracy-based fitness measure. However, in terms of human comprehensibility and amenability during logic inference, evolving multiple compact neulonets are preferred. The present work realizes this by adopting associative-classification measures of confidence and support as part of the fitness computation. The evolved neulonets are combined together to form an eventual macro-classier. Empirical study shows that associative classification integrated with neulonet learning performs better than general association-based classifiers in terms of higher accuracies and smaller rule sets. This is primarily due to the richness in logic expression inherent in the neulonet learning paradigm. %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2004/LBP061.pdf %0 Conference Proceedings %T Confidence and Support Classification Using Genetically Programmed Neural Logic Networks %A Chia, Henry Wai-Kit %A Tan, Chew-Lim %Y Deb, Kalyanmoy %Y Poli, Riccardo %Y Banzhaf, Wolfgang %Y Beyer, Hans-Georg %Y Burke, Edmund %Y Darwen, Paul %Y Dasgupta, Dipankar %Y Floreano, Dario %Y Foster, James %Y Harman, Mark %Y Holland, Owen %Y Lanzi, Pier Luca %Y Spector, Lee %Y Tettamanzi, Andrea %Y Thierens, Dirk %Y Tyrrell, Andy %S Genetic and Evolutionary Computation – GECCO-2004, Part II %S Lecture Notes in Computer Science %D 2004 %8 26 30 jun %V 3103 %I Springer-Verlag %C Seattle, WA, USA %@ 3-540-22343-6 %F chia:cas:gecco2004 %X Typical learning classifier systems employ conjunctive logic rules for representing domain knowledge. The classifier XCS is an extension of LCS with the ability to learn boolean logic functions for data mining. However, most data mining problems cannot be expressed simply with boolean logic. Neural Logic Network (Neulonet) learning is a technique that emulates the complex human reasoning processes through the use of net rules. Each neulonet is analogous to a learning classifier that is rewarded using support and confidence measures which are often used in association-based classification. Empirical results shows promise in terms of generalisation ability and the comprehensibility of rules. %K genetic algorithms, genetic programming, Poster %R 10.1007/b98645 %U http://www.comp.nus.edu.sg/~tancl/Papers/GECCO2004/gecco04post.pdf %U http://dx.doi.org/10.1007/b98645 %P 836-837 %0 Journal Article %T Enhancing Knowledge Discovery via Association-Based Evolution of Neural Logic Networks %A Chia, Henry W. K. %A Tan, Chew Lim %A Sung, Sam Y. %J IEEE Transactions on Knowledge and Data Engineering %D 2006 %V 18 %N 7 %I IEEE Computer Society %C Los Alamitos, CA, USA %@ 1041-4347 %F 10.1109/TKDE.2006.111 %X The comprehensibility aspect of rule discovery is of emerging interest in the realm of knowledge discovery in databases. Of the many cognitive and psychological factors relating the comprehensibility of knowledge, we focus on the use of human amenable concepts as a representation language in expressing classification rules. Existing work in neural logic networks (or neulonets) provides impetus for our research; its strength lies in its ability to learn and represent complex human logic in decision-making using symbolic-interpretable net rules. A novel technique is developed for neulonet learning by composing net rules using genetic programming. Coupled with a sequential covering approach for generating a list of neulonets, the straightforward extraction of human-like logic rules from each neulonet provides an alternate perspective to the greater extent of knowledge that can potentially be expressed and discovered, while the entire list of neulonets together constitute an effective classifier. We show how the sequential covering approach is analogous to association-based classification, leading to the development of an association-based neulonet classifier. Empirical study shows that associative classification integrated with the genetic construction of neulonets performs better than general association-based classifiers in terms of higher accuracies and smaller rule sets. This is due to the richness in logic expression inherent in the neulonet learning paradigm. %K genetic algorithms, genetic programming, Data mining, knowledge acquisition, connectionism and neural nets, rule-based knowledge representation %9 journal article %R 10.1109/TKDE.2006.111 %U http://dx.doi.org/10.1109/TKDE.2006.111 %P 889-901 %0 Conference Proceedings %T The Differences between Social and Individual Learning on the Time Series Properties: The Approach Based on Genetic Programming %A Yeh, Chia-Hsuan %A Chen, Shu-Heng %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 chia-hsuanyeh:2001:gecco %K genetic algorithms, genetic programming: Poster, Social Learning, Individual Learning, Artificial Stock Market, Agent-Based Modeling %U http://gpbib.cs.ucl.ac.uk/gecco2001/d02.pdf %P 191 %0 Conference Proceedings %T A genetic programming based rule generation approach for intelligent control systems %A Chiang, Cheng-Hsiung %S 2010 International Symposium on Computer Communication Control and Automation (3CA) %D 2010 %8 may %V 1 %F Chiang:2010:3CA %X This paper presents an intelligent control system (namely GPICS). The GPICS consists of a Symbolic Rule Controller, a Percepter and a rAdaptor. The Percepter judges whether the control system can adapt the environment. If the system is inadaptable, the rAdaptor will be activated to search the new rule to adapt the environment; otherwise, the controller will keeps on its controlling assignments. Once the rAdaptor is activated, the flexible genetic programming will be employed for searching the new rule. Simulation results of the robotic path planning showed that the GPICS method can successfully find a satisfactory path. %K genetic algorithms, genetic programming, genetic programming intelligent control system, percepter, radaptor, rule generation approach, symbolic rule controller, intelligent control, learning (artificial intelligence), path planning %R 10.1109/3CA.2010.5533882 %U http://dx.doi.org/10.1109/3CA.2010.5533882 %P 104-107 %0 Conference Proceedings %T A Novel Symbolic Regressor Enhancer Using Genetic Programming %A Chiang, Tu-Chin %A Chang, Chi-Hsien %A Yu, Tian-Li %Y Xue, Bing %S 2024 IEEE Congress on Evolutionary Computation (CEC) %D 2024 %8 30 jun 5 jul %I IEEE %C Yokohama, Japan %F chiang:2024:CEC %X This paper proposes a framework combining genetic programming (GP) with other symbolic regression (SR) methods, called the symbolic regressor enhancer (SRE). The basic idea is to use the syntax tree of the expression obtained from other SR methods to improve both the efficiency and the quality of the evolutionary procedure. Specifically, this paper investigates on the different ways of hybridization, selection, and crossover to assemble the proposed SRE. The effectiveness of SRE is demonstrated with the Taylor polynomial, the fast function extraction, and the GP-based SR methods, including Operon, the GP variant of gene-pool optimal mixing evolutionary algorithm, the epsilon-Iexicase selection, and gplearn. Out of 28 benchmarks from the SR benchmark and the Feynman SR database, the statistical test indicates that SRE applied to each selected SR method significantly outperforms the respective SR method in at least 8 and at most 24 benchmarks. %K genetic algorithms, genetic programming, Databases, Evolutionary computation, Benchmark testing, Syntactics, Programming, Polynomials, Symbolic regression %R 10.1109/CEC60901.2024.10612124 %U http://dx.doi.org/10.1109/CEC60901.2024.10612124 %0 Conference Proceedings %T Image Registration of Very Large Images via Genetic Programming %A Chicotay, Sarit %A David, Omid E. %A Netanyahu, Nathan S. %S IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2014) %D 2014 %8 jun %F Chicotay:2014:CVPRW %X Image registration (IR) is a fundamental task in image processing for matching two or more images of the same scene taken at different times, from different viewpoints and/or by different sensors. Due to the enormous diversity of IR applications, automatic IR remains a challenging problem to this day. A wide range of techniques has been developed for various data types and problems. These techniques might not handle effectively very large images, which give rise usually to more complex transformations, e.g., deformations and various other distortions. In this paper we present a genetic programming (GP) based approach for IR, which could offer a significant advantage in dealing with very large images, as it does not make any prior assumptions about the transformation model. Thus, by incorporating certain generic building blocks into the proposed GP framework, we hope to realize a large set of specialised transformations that should yield accurate registration of very large images. %K genetic algorithms, genetic programming %R 10.1109/CVPRW.2014.56 %U http://dx.doi.org/10.1109/CVPRW.2014.56 %P 329-334 %0 Report %T An Adaptive Evolutionary Approach to Option Pricing via Genetic Programming %A Chidambaran, N. K. %A Jevons Lee, Chi-Wen %A Trigueros, Joaquin R. %D 1998 %8 nov %N FIN-98-086 %I Leonard N. Stern School of Buisness, New York University %F wpa98086 %X We propose a methodology of Genetic Programming to approximate the relationship between the option price, its contract terms and the properties of the underlying stock price. An important advantage of the Genetic Programming approach is that we can incorporate currently known formulas, such as the Black-Scholes model, in the search for the best approximation to the true pricing formula. Using Monte Carlo simulations, we show that the Genetic Programming model approximates the true solution better than the Black-Scholes model when stock prices folow a jump-diffusion process. We also show that the Genetic Programming model outperforms various other models in many different settings. Other advantages of the Genetic Programming approach include its robustness to changing environment, its low demand for data, and its computational speed. Since genetic programs are flexible, self-learning and sefl-improving, they are an ideal tool for practitioners. %K genetic algorithms, genetic programming %9 Working paper %U http://www.stern.nyu.edu/fin/workpapers/wpa98086.pdf %0 Conference Proceedings %T An Adaptive Evolutionary Approach to Option Pricing via Genetic Programming %A Chidambaran, N. K. %A Lee, C. H. Jevons %A Trigueros, Joaquin R. %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 chidambaran:1998:aeaopGP %K genetic algorithms, genetic programming %P 38-41 %0 Book Section %T Option Pricing via Genetic Programming %A Chidambaran, N. K. %A Triqueros, Joaquin %A Lee, Chi-Wen Jevons %E Chen, Shu-Heng %B Evolutionary Computation in Economics and Finance %S Studies in Fuzziness and Soft Computing %D 2002 %8 2002 %V 100 %I Physica Verlag %@ 3-7908-1476-8 %F chidambaran:2002:ECEF %X We propose a methodology of Genetic Programming to approximate the relationship between the option price, its contract terms and the properties of the underlying stock price. An important advantage of the Genetic Programming approach is that we can incorporate currently known formulas, such as the Black-Scholes model, in the search for the best approximation to the true pricing formula. Using Monte Carlo simulations, we show that the Genetic Programming model approximates the true solution better than the Black-Scholes model when stock prices follow a jump-diffusion process. We also show that the Genetic Programming model outperforms various other models when pricing options in the real world. Other advantages of the Genetic Programming approach include its low demand for data, and its computational speed. Published previously in: Computational Finance Proceedings of the Sixth International Conference, Leonard N. Stern School of Business, January 1999. MIT Press, Cambridge, MA %K genetic algorithms, genetic programming %R 10.1007/978-3-7908-1784-3_20 %U http://dx.doi.org/10.1007/978-3-7908-1784-3_20 %P 383-397 %0 Conference Proceedings %T Genetic programming with Monte Carlo simulation for option pricing %A Chidambaran, N. K. %Y Chick, S. %Y Sanchez, P. J. %Y Ferrin, D. %Y Morrice, D. J. %S Proceedings of the 2003 Winter Simulation Conference %D 2003 %8 July 10 dec %V 1 %I IEEE %C New Orleans, USA %@ 0-7803-8132-7 %F Chidambaran:2003:WSC %X I examine the role of programming parameters in determining the accuracy of genetic programming for option pricing. I use Monte Carlo simulations to generate stock and option price data needed to develop a genetic option pricing program. I simulate data for two different stock price processes - a geometric Brownian process and a jump-diffusion process. In the jump-diffusion setting, I seed the genetic program with the Black-Scholes equation as a starting approximation. I find that population size, fitness criteria, and the ability to seed the program with known analytical equations, are important determinants of the efficiency of genetic programming. %K genetic algorithms, genetic programming %U http://www.informs-sim.org/wsc03papers/035.pdf %P 285-292 %0 Conference Proceedings %T Model for Evolutionary Technology - An Automatically Defined Terminal Approach %A Chie, Bin-Tzong %A Wang, Chih-Chien %Y Grahl, Jörn %S Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO’2006) %D 2006 %8 August 12 jul %C Seattle, WA, USA %F Chie:gecco06lbp %X automatically defined terminal (ADT) to keep ready and stable building blocks growing into complex structure. The idea is originated from the functional modularity approach. ADT is tested in an agent-based innovation model to see how it works and whether there is any improvement in searching new commodities for commercialising in the market; hence the market represents an environment for nourishing the development during innovative process. This paper will not only show how the capable producers with ADT work, but also how market selection plays an important role in the evolution of innovation. In other word, the agent-based modelling approach will present the evolutionary dynamic of interaction between producers and consumers in a commodity market. %K genetic algorithms, genetic programming, Automatically Defined Terminal, Agent-Based Modeling %U http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/lbp129.pdf %0 Thesis %T Innovation in Economics: Agent-Based Computational Modelling %A Chie, Bin-Tzong %D 2007 %C Taiwan %C National Chengchi University %F Bin-Tzong_Chie:thesis %O in Chinese %K genetic algorithms, genetic programming, innovation, software agent, learning, quality-oriented, quantity-oriented %9 Ph.D. thesis %U http://www.aiecon.org/whoweare/~btc/ %0 Journal Article %T Competition in a New Industrial Economy: Toward an Agent-Based Economic Model of Modularity %A Chie, Bin-Tzong %A Chen, Shu-Heng %J Administrative Sciences %D 2014 %V 4 %N 3 %@ 2076-3387 %F chie:2014:AS %X When firms (conglomerates) are competing, not only for the present, with a given population of customers and a fixed set of commodities or service, but also for the future, in which products are constantly evolving, what will be their competitive strategies and what will be the emerging ecology of the market? In this paper, we use the agent-based modelling of a modular economy to study the markup rate dynamics in a duopolistic setting. We find that there are multiple equilibria in the market, characterised by either a fixed point or a limit cycle. In the former case, both firms compete with the same markup rate, which is a situation similar to the familiar classic Bertrand model, except that the rate is not necessarily zero. In the latter case, both firms survive by maintaining different markup rates and different market shares. %K genetic algorithms, genetic programming, modularity, modular economy, hierarchy, markups %9 journal article %R 10.3390/admsci4030192 %U https://www.mdpi.com/2076-3387/4/3/192 %U http://dx.doi.org/10.3390/admsci4030192 %0 Journal Article %T Learning discriminant functions with fuzzy attributes for classification using genetic programming %A Chien, Been-Chian %A Lin, Jung Yi %A Hong, Tzung-Pei %J Expert Systems with Applications %D 2002 %V 23 %N 1 %@ 0957-4174 %F Chien:2002:ESA %X Classification is one of the important tasks in developing expert systems. Most of the previous approaches for classification problem are based on classification rules generated by decision trees. we propose a new learning approach based on genetic programming to generate discriminant functions for classifying data. An adaptable incremental learning strategy and a distance-based fitness function are developed to improve the efficiency of genetic programming-based learning process. We first transform attributes of objects into fuzzy attributes and then a set of discriminant functions is generated based on the proposed learning procedure. The set of derived functions with fuzzy attributes gives high accuracy of classification and presents a linear form. Hence, the functions can be transformed into inference rules easily and we can use the rules to provide the building of rule base in an expert system. %K genetic algorithms, genetic programming, Classification, Knowledge discovery, Fuzzy sets %9 journal article %R 10.1016/S0957-4174(02)00025-8 %U http://www.sciencedirect.com/science/article/B6V03-45C00T2-1/2/e7d49cc18dd12961ac2e5c114c41f667 %U http://dx.doi.org/10.1016/S0957-4174(02)00025-8 %P 31-37 %0 Conference Proceedings %T A Classifier with the Function-based Decision Tree %A Chien, Been-Chian %A Lin, Jung-Yi %Y Damiani, E. %Y Jain, L. C. %Y Howlett, R. J. %Y Ichalkaranje, N. %S Proceedings of KES’2002 the Sixth International Conference on Knowledge-Based Intelligent Information Engineering Systems %S Frontiers in Artificial Intelligence and Applications %D 2002 %8 19 19 sep %V 82 %I IOS Press %C Podere d’Ombriano, Crema, Italy %@ 1-58603-280-1 %F Chien:2002:KES %X Classification is one of the important problems in the research area of knowledge discovery and machine learning. In this paper, an accurate classifier with multi-category based on the genetic programming is proposed. The classifier consists of the discriminant functions that are generated by genetic programming. We propose the function-based decision tree (FDT) to resolve the problem of ambiguity between discriminant functions, and the experiments show that the proposed methods are accurate. %K genetic algorithms, genetic programming, classification, decision tree, Knowledge discovery, Machine learning %U http://myweb.nutn.edu.tw/~bcchien/Papers/C_KES2002.pdf %P 648-652 %0 Conference Proceedings %T Generating Effective Classifiers with Supervised Learning of Genetic Programming %A Chien, Been-Chian %A Yang, Jui-Hsiang %A Lin, Wen-Yang %S Data Warehousing and Knowledge Discovery: 5th International Conference, DaWaK 2003 %S Lecture Notes in Computer Science %D 2003 %8 March 5 sep %V 2737 %I Springer-Verlag %C Prague, Czech Republic %@ 3-540-40807-X %F Chien:2003:DaWaK %X A new approach of learning classifiers using genetic programming has been developed recently. Most of the previous researches generate classification rules to classify data. However, the generation of rules is time consuming and the recognition accuracy is limited. In this paper, an approach of learning classification functions by genetic programming is proposed for classification. Since a classification function deals with numerical attributes only, the proposed scheme first transforms the nominal data into numerical values by rough membership functions. Then, the learning technique of genetic programming is used to generate classification functions. For the purpose of improving the accuracy of classification, we proposed an adaptive interval fitness function. Combining the learned classification functions with training samples, an effective classification method is presented. Numbers of data sets selected from UCI Machine Learning repository are used to show the effectiveness of the proposed method and compare with other classifiers. %K genetic algorithms, genetic programming %R 10.1007/b11825 %U http://dx.doi.org/10.1007/b11825 %P 192-201 %0 Journal Article %T Learning effective classifiers with Z-value measure based on genetic programming %A Chien, Been-Chian %A Lin, Jung-Yi %A Yang, Wei-Pang %J Pattern Recognition %D 2004 %8 oct %V 37 %N 10 %F Chien:2004:PR %X This paper presents a learning scheme for data classification based on genetic programming. The proposed learning approach consists of an adaptive incremental learning strategy and distance-based fitness functions for generating the discriminant functions using genetic programming. To classify data using the discriminant functions effectively, the mechanism called Z-value measure is developed. Based on the Z-value measure, we give two classification algorithms to resolve ambiguity among the discriminant functions. The experiments show that the proposed approach has less training time than previous GP learning methods. The learned classifiers also have high accuracy of classification in comparison with the previous classifiers. %K genetic algorithms, genetic programming %9 journal article %R 10.1016/j.patcog.2004.03.016 %U http://www.sciencedirect.com/science/article/B6V14-4CPVJFT-3/2/51f0ecbd7d198da15f4ae094e378c5d0 %U http://dx.doi.org/10.1016/j.patcog.2004.03.016 %P 1957-1972 %0 Conference Proceedings %T Features Selection based on Rough Membership and Genetic Programming %A Chien, Been-Chian %A Yang, Jui-Hsiang %S IEEE International Conference on Systems, Man and Cybernetics, ICSMC ’06 %D 2006 %8 August 11 oct %V 5 %I IEEE %C Taipei, Taiwan %@ 1-4244-0100-3 %F Chien:2006:ICSMC %X This paper discusses the feature selection problem upon supervised learning. A learning method based on rough sets and genetic programming is proposed to select significant features and classify numerical data. The proposed method uses rough membership to transform nominal data into numerical values, then selects important features and learns classification functions using genetic programming. We use several UCI data sets to show the performance of the proposed scheme and make comparisons with three different features selection approaches: distance measure, information measure and dependence measure. The results demonstrate that the proposed method is effective both in features selection and classification. %K genetic algorithms, genetic programming %R 10.1109/ICSMC.2006.384780 %U http://dx.doi.org/10.1109/ICSMC.2006.384780 %P 4124-4129 %0 Journal Article %T Learning Discriminant Functions based on Genetic Programming and Rough Sets %A Chien, Been-Chian %A Yang, Jui-Hsiang %A Hong, Tzung-Pei %J Multiple-Valued Logic and Soft Computing %D 2011 %V 17 %N 2-3 %@ 1542-3980 %F DBLP:journals/mvl/ChienYH11 %X Supervised learning based on genetic programming can find different classification models including decision trees, classification rules and discriminant functions. The previous researches have shown that the classifiers learnt by GP have high precision in many application domains. However, nominal data cannot be handled and calculated by the model of using discriminant functions. In this paper, we present a scheme based on rough set theory and genetic programming to learn discriminant functions from general data containing both nominal and numerical attributes. The proposed scheme first transforms the nominal data into numerical values by applying the technique of rough sets. Then, genetic programming is used to learn discriminant functions. The conflict problem among discriminant functions is solved by an effective conflict resolution method based on the distance-based fitness function. The experimental results show that the classifiers generated by the proposed scheme using GP are effective on nominal data in comparison with C4.5, CBA, and NB-based classifiers. %K genetic algorithms, genetic programming, Machine learning, discriminant function, classification, rough sets. %9 journal article %U http://www.oldcitypublishing.com/journals/mvlsc-home/mvlsc-issue-contents/mvlsc-volume-17-number-2-3-2011/mvlsc-17-2-3-p-135-155/ %P 135-155 %0 Book Section %T Grid-Based Trace Routing Using Evolutionary Methods %A Chien, Edward K. %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 chien:2000:GTRUEM %K genetic algorithms %P 90-97 %0 Conference Proceedings %T Collaborative Learning Agents with Structural Classifier Systems %A Chikara, Maezawa %A Masayasu, Atsumi %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 chikara:1999:CLASCS %K genetic algorithms and classifier systems, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-859.pdf %P 777 %0 Journal Article %T Triple Bottomline Many-Objective-Based Decision Making for a Land Use Management Problem %A Chikumbo, Oliver %A Goodman, Erik %A Deb, Kalyanmoy %J Journal of Multi-Criteria Decision Analysis %D 2015 %V 22 %N 3-4 %F Chikumbo:2015:JMCDA %X A land use many-objective optimization problem for a 1500-ha farm with 315 paddocks was formulated with 14 objectives (maximizing sawlog production, pulpwood production, milk solids, beef, sheep meat, wool, carbon sequestration, water production, income and Earnings Before Interest and Tax; and minimizing costs, nitrate leaching, phosphorus loss and sedimentation). This was solved using a modified Reference-point-based Non-dominated Sorting Genetic Algorithm II augmented by simulated epigenetic operations. The search space had complex variable interactions and was based on economic data and several interoperating simulation models. The solution was an approximation of a Hyperspace Pareto Frontier (HPF), where each non-dominated trade-off point represented a set of land-use management actions taken within a 10-year period and their related management options, spanning a planning period of 50 years. A trade-off analysis was achieved using Hyper-Radial Visualization (HRV) by collapsing the HPF into a 2-D visualization capability through an interactive virtual reality (VR)-based method, thereby facilitating intuitive selection of a sound compromise solution dictated by the decision makers preferences under uncertainty conditions. Four scenarios of the HRV were considered emphasizing economic, sedimentation and nitrate leaching aspects, giving rise to a triple bottomline (i.e. the economic, environmental and social complex, where the social aspect is represented by the preferences of the various stakeholders). Highlights of the proposed approach are the development of an innovative epigenetics-based multi-objective optimizer, uncertainty incorporation in the search space data and decision making on a multi-dimensional space through a VR-simulation-based visual steering process controlled at its core by a multi-criterion decision making-based process. This approach has widespread applicability to many other wicked societal problem-solving tasks %K genetic algorithms, genetic programming, GPTIPS, evolutionary algorithms, epigenetics, Reference-Point-Based Non-dominated Sorting Genetic Algorithm II (R-NSGA II), Hyperspace Pareto Frontier (HPF), triple bottomline, Hyper-radial visualization (HRV), visual steering, multiplicative analytic hierarchy process (MAHP) %9 journal article %R 10.1002/mcda.1536 %U https://onlinelibrary.wiley.com/doi/abs/10.1002/mcda.1536 %U http://dx.doi.org/10.1002/mcda.1536 %P 133-159 %0 Conference Proceedings %T Category Partition Method and Satisfiability Modulo Theories for test case generation %A Chimisliu, Valentin %A Wotawa, Franz %S 7th International Workshop on Automation of Software Test (AST 2012) %D 2012 %8 jun %C Zurich %F Chimisliu:2012:AST %X In this paper we focus on test case generation for large database applications in the telecommunication industry domain. In particular, we present an approach that is based on the Category Partition Method and uses the SMT solver Z3 for automatically generating input test data values for the obtained test cases. For the generation process, we make use of different test case generation strategies. First initial results show that the one based on genetic programming delivers the fewest number of test cases while retaining choice coverage. Moreover, the obtained results indicate that the presented approach is feasible for the intended application domain. %K genetic algorithms, genetic programming, SBSE, SMT solver Z3, automatic test data values generation, category partition method, intended application domain, large database applications, satisfiability modulo theories, telecommunication industry domain, test case generation strategies, automatic test pattern generation, computability, database management systems, telecommunication computing, telecommunication industry %R 10.1109/IWAST.2012.6228992 %U http://dx.doi.org/10.1109/IWAST.2012.6228992 %P 64-70 %0 Report %T Object Detection using Neural Networks and Genetic Programming %A Chin, Barret %A Zhang, Mengjie %D 2007 %8 nov %N CS-TR-07-3 %I Computer Science, Victoria University of Wellington %C New Zealand %F CS-TR-07-3 %X This paper describes a domain independent approach to the use of neural networks (NNs) and genetic programming (GP) for object detection problems. Instead of using high level features for a particular task, this approach uses domain independent pixel statistics for object detection. The paper first compares an NN method and a GP method on four image data sets providing object detection problems of increasing difficulty. The results show that the GP method performs better than the NN method on these problems but still produces a large number of false alarms on the difficult problem and computation cost is still high. To deal with these problems, we develop a new method called GP-refine that uses a two stage learning process. The results suggest that the new GP method further improves object detection performance on the difficult object detection task. %K genetic algorithms, genetic programming, object detection, neural networks, region refinement, feature selection %U http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-07/CS-TR-07-3.pdf %0 Conference Proceedings %T Object Detection Using Neural Networks and Genetic Programming %A Chin, Barret %A Zhang, Mengjie %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/ChinZ08 %K genetic algorithms, genetic programming %R 10.1007/978-3-540-78761-7_34 %U http://dx.doi.org/10.1007/978-3-540-78761-7_34 %P 335-340 %0 Thesis %T Probabilistic Learning and Optimization Applied to Quantitative Finance %A Chinthalapati, Venkata Lakshmipathi Raju %D 2011 %8 sep %C UK %C Dept. of Mathematics, London School of Economics and Political Science %F Chinthalapati:thesis %X his thesis concerns probabilistic learning theory and stochastic optimisation and investigates applications to a variety of problems arising in finance. In many sequential decision tasks, the consequences of an action emerge at a multitude of times after the action is taken. A key problem is to find good strategies for selecting actions based on both their short and long term consequences. We develop a simulation-based, two-timescale actor-critic algorithm for infinite horizon Markov decision processes with finite state and action spaces, with a discounted reward criterion. The algorithm is of the gradient descent type, searching the space of stationary randomised policies and using certain simultaneous deterministic perturbation stochastic approximation (SDPSA) gradient estimates for enhanced performance. We apply our algorithm to a mortgage refinancing problem and find that it obtains the optimal refinancing strategies in a computationally efficient manner. The problem of identifying pairs of similar time series is an important one with several applications in finance, especially to directional trading, where traders try to spot arbitrage opportunities. We use a variant of the Optimal Thermal Causal Path method (obtained by adding a curvature term and by using an approximation technique to increase the efficiency) to determine the lead-lag structure between a given pair of time-series. We apply the method to various market sectors of NYSE data and extract highly correlated pairs of time series. Because Genetic Programming (GP) is known for its ability to detect patterns such as the conditional mean and conditional variance of a time series, it is potentially well-suited to volatility forecasting. We introduce a technique for forecasting 5-day annualised volatility in exchange rates. The technique employs a series of standard methods (such as MA, EWMA, GARCH and its variants) alongside Genetic Programming forecasting methods, dynamically opting for the most appropriate technique at a given time, determined through out-of-sample tests. A particular challenge with volatility forecasting using GP is that, during learning, the GP is presented with training data generated by a noisy Markovian process, not something that is modelled in the standard probabilistic learning frameworks. We analyse, in a probabilistic model of learning, how much such training data should be presented to the GP in the learning phase for the learning to be successful. %K genetic algorithms, genetic programming, computational, information-theoretic learning with statistics, learning/statistics and optimisation, theory and algorithms, Markov processes %9 Ph.D. thesis %U http://www.lse.ac.uk/Mathematics/Research-Students/PhD-Roll-of-Honour %0 Conference Proceedings %T Volatility Forecast in FX Markets using Evolutionary Computing and Heuristic Technique %A Chinthalapati, V. L. Raju %Y Golan, Robert %S IEEE Computational Intelligence for Financial Engineering and Economic (CIFEr 2012) %D 2012 %8 29 30 mar %I IEEE %C New York, USA %F Chinthalapati:2012:CIFEr %X A financial asset’s volatility exhibits key characteristics, such as mean-reversion and high autocorrelation [1], [2]. Empirical evidence suggests that this volatility autocorrelation exponentially decays (or exhibits long-range memory) [3]. We employ Genetic Programming (GP) for volatility forecasting because of its ability to detect patterns such as the conditional mean and conditional variance of a time-series. Genetic Programming is typically applied to optimisation, searching, and machine learning applications like classification, prediction etc. From our experiments, we see that Genetic Programming is a good competitor to the standard forecasting techniques like GARCH(1,1), Moving Average (MA), Exponentially Weighted Moving Average (EWMA). However it is not a silver bullet: we observe that different forecasting methods would perform better in different market conditions. In addition to Genetic Programming, we consider a heuristic technique that employs a series of standard forecasting methods and dynamically opts for the most appropriate technique at a given time. Using a heuristic technique, we try to identify the best forecasting method that would perform better than the rest of the methods in the near out-of-sample horizon. Our work introduces a preliminary framework for forecasting 5-day annualised volatility in GBP/USD, USD/JPY, and EUR/USD. %K genetic algorithms, genetic programming, autoregressive processes, economic forecasting, foreign exchange trading, learning (artificial intelligence), time series, 5-day annualised volatility forecasting, EUR-USD, EWMA, FX markets, GARCH(1,1), GBP-USD, GP, USD-JPY, evolutionary computing, exponentially weighted moving average, financial asset volatility, heuristic techniques, machine learning applications, mean-reversion, optimisation, time-series, volatility autocorrelation, volatility forecast, Biological system modelling, Correlation, Forecasting, Sociology, Standards %9 Conference or Workshop Item; PeerReviewed %R 10.1109/CIFEr.2012.6327813 %U http://dx.doi.org/10.1109/CIFEr.2012.6327813 %0 Conference Proceedings %T Genetic programming for agricultural purposes %A Chion, Clement %A Da Costa, Luis E. %A Landry, Jacques-Andre %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 1144138 %K genetic algorithms, genetic programming, crop nitrogen content, GP, hyperspectral imagery, management, precision farming, remote sensing, site-specific management, spectral vegetation indices (SVI), vegetation indices %R 10.1145/1143997.1144138 %U http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p783.pdf %U http://dx.doi.org/10.1145/1143997.1144138 %P 783-790 %0 Journal Article %T A Genetic-Programming-Based Method for Hyperspectral Data Information Extraction: Agricultural Applications %A Chion, Clement %A Landry, Jacques-Andre %A Da Costa, Luis %J IEEE Transactions on Geoscience and Remote Sensing %D 2008 %8 aug %V 46 %N 8 %@ 0196-2892 %F Chion:2008:ieeeTGRS %X A new method, called genetic programming-spectral vegetation index (GP-SVI), for the extraction of information from hyperspectral data is presented. This method is introduced in the context of precision farming. GP-SVI derives a regression model describing a specific crop biophysical variable from hyperspectral images (verified with in situ observations). GP-SVI performed better than other methods [multiple regression, tree-based modeling, and genetic algorithm-partial least squares (GA-PLS)] on the task of correlating canopy nitrogen content in a cornfield with pixel reflectance. It is also shown that the band selection performed by GP-SVI is comparable with the selection performed by GA-PLS, a method that is specifically designed to deal with hyperspectral data. %K genetic algorithms, genetic programming, CASI sensor, agricultural application, band selection, canopy nitrogen content, crop biophysical variable, feature selection, genetic programming-spectral vegetation index, hyperspectral data information extraction, hyperspectral remote sensing, pixel reflectance, precision farming, crops, farming, feature extraction, geophysical signal processing, vegetation mapping %9 journal article %R 10.1109/TGRS.2008.922061 %U http://dx.doi.org/10.1109/TGRS.2008.922061 %P 2446-2457 %0 Conference Proceedings %T A Comparative Analysis of Optimizing Medical Insurance Prediction Using Genetic Algorithm and Other Machine Learning Algorithms %A Chittilappilly, Rose Mary %A Suresh, Sanjana %A Shanmugam, Shanthini %S 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) %D 2023 %8 may %F Chittilappilly:2023:ACCAI %X an analysis is conducted on different machine learning strategies to predict medical insurance charges using demographic and health-related information on individuals. Each algorithm was trained and tested on a preprocessed dataset, and the performance of various models is compared, including linear regression, random forest, gradient boosting, LassoLarsCV, and a model created with automated machine learning using TPOT, which makes use of genetic programming for optimisation. Conclusively, the results bring to light that the TPOT-generated model, which is a combination of LassoLarsCV and GradientBoostingRegressor, performed better than the other models, attaining a root mean square error of 0.0686 and an accuracy of 87.45percent on the test set. These findings suggest that automated machine learning techniques and metaheuristic optimisation, as was used in TPOT, can bring improvement to the performance of existing medical insurance cost prediction models. %K genetic algorithms, genetic programming, TPOT, Machine learning algorithms, Metaheuristics, Linear regression, Insurance, Manuals, Predictive models, Prediction algorithms, machine learning, medical insurance, random forest, gradient boosting, LassoLarsCV, Linear Regression, Metaheuristic Algorithm %R 10.1109/ACCAI58221.2023.10199979 %U http://dx.doi.org/10.1109/ACCAI58221.2023.10199979 %0 Conference Proceedings %T A data parallel approach to genetic programming using programmable graphics hardware %A Chitty, Darren M. %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 1277274 %X In recent years the computing power of graphics cards has increased significantly. Indeed, the growth in the computing power of these graphics cards is now several orders of magnitude greater than the growth in the power of computer processor units. Thus these graphics cards are now beginning to be used by the scientific community as low cost, high performance computing platforms. Traditional genetic programming is a highly computer intensive algorithm but due to its parallel nature it can be distributed over multiple processors to increase the speed of the algorithm considerably. This is not applicable for single processor architectures but graphics cards provide a mechanism for developing a data parallel implementation of genetic programming. In this paper we will describe the technique of general purpose computing using graphics cards and how to extend this technique to genetic programming. We will demonstrate the improvement in the performance of genetic programming on single processor architectures which can be achieved by harnessing the computing power of these next generation graphics cards. %K genetic algorithms, genetic programming, data parallelism, GPU, graphics cards, OpenGL, Cg %R 10.1145/1276958.1277274 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1566.pdf %U http://dx.doi.org/10.1145/1276958.1277274 %P 1566-1573 %0 Journal Article %T Fast parallel genetic programming: multi-core CPU versus many-core GPU %A Chitty, Darren M. %J Soft Computing %D 2012 %8 oct %V 16 %N 10 %I Springer-Verlag %@ 1432-7643 %G English %F Chitty:2012:SC %X Genetic Programming (GP) is a computationally intensive technique which is also highly parallel in nature. In recent years, significant performance improvements have been achieved over a standard GP CPU-based approach by harnessing the parallel computational power of many-core graphics cards which have hundreds of processing cores. This enables both fitness cases and candidate solutions to be evaluated in parallel. However, this paper will demonstrate that by fully exploiting a multi-core CPU, similar performance gains can also be achieved. This paper will present a new GP model which demonstrates greater efficiency whilst also exploiting the cache memory. Furthermore, the model presented in this paper will use Streaming SIMD Extensions to gain further performance improvements. A parallel version of the GP model is also presented which optimises multiple thread execution and cache memory. The results presented will demonstrate that a multi-core CPU implementation of GP can yield performance levels that match and exceed those of the latest graphics card implementations of GP. Indeed, a performance gain of up to 420-fold over standard GP is demonstrated and a threefold gain over a graphics card implementation. %K genetic algorithms, genetic programming, GPU %9 journal article %R 10.1007/s00500-012-0862-0 %U http://www.cs.bris.ac.uk/Publications/Papers/2001629.pdf %U http://dx.doi.org/10.1007/s00500-012-0862-0 %P 1795-1814 %0 Thesis %T Improving the computational speed of genetic programming %A Chitty, Darren M. %D 2015 %C UK %C University of Bristol %F Chitty:thesis %X Genetic Programming (GP) is well known as a computationally intensive technique especially when considering regression or classification tasks with large datasets. Consequently, there has been considerable work conducted into improving the computational speed of GP. Recently, this has concentrated on exploiting highly parallel architectures in the form of Graphics Processing Units (GPUs). However, the reported speeds fall considerably short of the computational capabilities of these GPUs. This thesis investigates this issue, seeking to considerably improve the computational speed of GP. Indeed, this thesis will demonstrate that considerable improvements in the speed of GP can be achieved when fully exploiting a parallel Central Processing Unit (CPU) exceeding the performance of the latest GPU implementations. This is achieved by recognising that GP is as much a memory bound technique as a compute bound technique. By adopting a two dimensional stack approach, better exploitation of memory resources is achieved in addition to reducing interpreter overheads. This approach is applied to CPU and GPU implementations and compares favourably with compiled versions of GP. The second aspect of this thesis demonstrates that although considerable performance gains can be achieved using parallel hardware, the role of efficiency within GP should not be forgotten. Efficiency saving can boost the computational speed of parallel GP significantly. Two methods are considered, parsimony pressure measures and efficient tournament selection. The second efficiency technique enables a CPU implementation of GP to outperform a GPU implementation for classification type tasks even though the CPU has only a tenth of the computational power. Finally both CPU and GPU are combined for ultimate performance. Speedups of more than a thousand fold over a basic sequential version of GP are achieved and three fold over the best GPU implementation from the literature. Consequently, this speedup increases the usefulness of GP as a machine learning technique. %K genetic algorithms, genetic programming, GPU %9 Ph.D. thesis %U https://books.google.co.uk/books?id=-IqF0AEACAAJ %0 Generic %T Faster GPU Based Genetic Programming Using A Two Dimensional Stack %A Chitty, Darren M. %D 2016 %I ArXiv %F Chitty:2016:ArXiv %X Genetic Programming (GP) is a computationally intensive technique which also has a high degree of natural parallelism. Parallel computing architectures have become commonplace especially with regards Graphics Processing Units (GPU). Hence, versions of GP have been implemented that use these highly parallel computing platforms enabling significant gains in the computational speed of GP to be achieved. However, recently a two dimensional stack approach to GP using a multi-core CPU also demonstrated considerable performance gains. Indeed, performances equivalent to or exceeding that achieved by a GPU were demonstrated. This paper will demonstrate that a similar two dimensional stack approach can also be applied to a GPU based approach to GP to better exploit the underlying technology. Performance gains are achieved over a standard single dimensional stack approach when using a GPU. Overall, a peak computational speed of over 55 billion Genetic Programming Operations per Second are observed, a two fold improvement over the best GPU based single dimensional stack approach from the literature %K genetic algorithms, genetic programming %U http://arxiv.org/abs/1601.00221 %0 Journal Article %T Improving the performance of GPU-based genetic programming through exploitation of on-chip memory %A Chitty, Darren M. %J Soft Computing %D 2016 %8 feb %V 20 %N 2 %@ 1432-7643 %F journals/soco/Chitty16 %X Genetic Programming (GP) (Koza, Genetic programming, MIT Press, Cambridge, 1992) is well-known as a computationally intensive technique. Subsequently, faster parallel versions have been implemented that harness the highly parallel hardware provided by graphics cards enabling significant gains in the performance of GP to be achieved. However, extracting the maximum performance from a graphics card for the purposes of GP is difficult. A key reason for this is that in addition to the processor resources, the fast on-chip memory of graphics cards needs to be fully exploited. Techniques will be presented that will improve the performance of a graphics card implementation of tree-based GP by better exploiting this faster memory. It will be demonstrated that both L1 cache and shared memory need to be considered for extracting the maximum performance. Better GP program representation and use of the register file is also explored to further boost performance. Using an NVidia Kepler 670GTX GPU, a maximum performance of 36 billion Genetic Programming Operations per Second is demonstrated. %K genetic algorithms, genetic programming, GPU, GPGPU, Many-core GPU, Parallel programming %9 journal article %R 10.1007/s00500-014-1530-3 %U http://dx.doi.org/10.1007/s00500-014-1530-3 %P 661-680 %0 Conference Proceedings %T Experiments with High Performance Genetic Programming for Classification Problems %A Chitty, Darren M. %Y Bramer, Max %Y Petridis, Miltos %S Proceedings of AI-2016, The Thirty-Sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence %D 2016 %8 dec %I Springer %C Cambridge %F Chitty:2016:SGAI %X In recent years there have been many papers concerned with significantly improving the computational speed of Genetic Programming (GP) through exploitation of parallel hardware. The benefits of timeliness or being able to consider larger datasets are obvious. However, a question remains in whether there are wider benefits of this high performance GP approach. Consequently, this paper will investigate leveraging this performance by using a higher degree of evolution and ensemble approaches in order to discern if any improvement in classification accuracies can be achieved from high performance GP thereby advancing the technique itself. %K genetic algorithms, genetic programming, GPU, Classification Parallel Processing %R 10.1007/978-3-319-47175-4_15 %U https://link.springer.com/chapter/10.1007/978-3-319-47175-4_15 %U http://dx.doi.org/10.1007/978-3-319-47175-4_15 %P 221-227 %0 Journal Article %T Faster GPU-based genetic programming using a two-dimensional stack %A Chitty, Darren M. %J Soft Computing %D 2017 %8 jul %V 21 %N 14 %I Springer %F chitty2017faster %X Genetic programming (GP) is a computationally intensive technique which also has a high degree of natural parallelism. Parallel computing architectures have become commonplace especially with regards to Graphics Processing Units (GPU). Hence, versions of GP have been implemented that use these highly parallel computing platforms enabling significant gains in the computational speed of GP to be achieved. However, recently a two-dimensional stack approach to GP using a multi-core CPU also demonstrated considerable performance gains. Indeed, performances equivalent to or exceeding that achieved by a GPU were demonstrated. This paper will demonstrate that a similar two-dimensional stack approach can also be applied to a GPU-based approach to GP to better exploit the underlying technology. Performance gains are achieved over a standard single-dimensional stack approach when using a GPU. Overall, a peak computational speed of over 55 billion Genetic Programming Operations per Second are observed, a twofold improvement over the best GPU-based single-dimensional stack approach from the literature. %K genetic algorithms, genetic programming, GPU, Many-core GPU Parallel programming %9 journal article %R 10.1007/s00500-016-2034-0 %U https://link.springer.com/article/10.1007/s00500-016-2034-0 %U http://dx.doi.org/10.1007/s00500-016-2034-0 %P 3859-3878 %0 Conference Proceedings %T Exploiting Tournament Selection for Efficient Parallel Genetic Programming %A Chitty, Darren Michael %Y Lotfi, Ahmad %Y Bouchachia, Hamid %Y Gegov, Alexander %Y Langensiepen, Caroline %Y McGinnity, Martin %S 18th Annual UK Workshop on Computational Intelligence, UKCI 2018 %S AISC %D 2018 %8 May 7 sep 2018 %V 840 %I Springer %C Nottingham Trent University, UK %F chitty:2018:ukci %X Genetic Programming (GP) is a computationally intensive technique which is naturally parallel in nature. Consequently, many attempts have been made to improve its run-time from exploiting highly parallel hardware such as GPUs. However, a second methodology of improving the speed of GP is through efficiency techniques such as subtree caching. However achieving parallel performance and efficiency is a difficult task. This paper will demonstrate an efficiency saving for GP compatible with the harnessing of parallel CPU hardware by exploiting tournament selection. Significant efficiency savings are demonstrated whilst retaining the capability of a high performance parallel implementation of GP. Indeed, a 74percent improvement in the speed of GP is achieved with a peak rate of 96 billion GPop/s for classification type problems. %K genetic algorithms, genetic programming, HPC, Computational Efficiency %R 10.1007/978-3-319-97982-3_4 %U http://dx.doi.org/10.1007/978-3-319-97982-3_4 %P 41-53 %0 Conference Proceedings %T Phased Genetic Programming for Application to the Traveling Salesman Problem %A Chitty, Darren %A Keedwell, Ed %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F chitty:2023:GECCOcomp %X The Traveling Salesman Problem (TSP) is a difficult permutation-based optimisation problem typically solved using heuristics or meta-heuristics which search the solution problem space. An alternative is to find sets of manipulations to a solution which lead to optimality. Hyper-heuristics search this space applying heuristics sequentially, similar to a program. Genetic Programming (GP) evolves programs typically for classification or regression problems. This paper hypothesizes that GP can be used to evolve heuristic programs to directly solve the TSP. However, evolving a full program to solve the TSP is likely difficult due to required length and complexity. Consequently, a phased GP method is proposed whereby after a phase of generations the best program is saved and executed. The subsequent generation phase restarts operating on this saved program output. A full program is evolved piecemeal. Experiments demonstrate that whilst pure GP cannot solve TSP instances when using simple operators, Phased-GP can obtain solutions within 4% of optimal for TSPs of several hundred cities. Moreover, Phased-GP operates up to nine times faster than pure GP. %K genetic algorithms, genetic programming, traveling salesman problem: Poster %R 10.1145/3583133.3590673 %U http://dx.doi.org/10.1145/3583133.3590673 %P 547-550 %0 Conference Proceedings %T Strategies to Apply Genetic Programming Directly to the Traveling Salesman Problem %A Chitty, Darren M. %S UK Workshop on Computational Intelligence %D 2023 %8 June 8 sep %I Springer %C Birmingham %F chitty:2023:UKCI %K genetic algorithms, genetic programming %R 10.1007/978-3-031-47508-5_25 %U http://link.springer.com/chapter/10.1007/978-3-031-47508-5_25 %U http://dx.doi.org/10.1007/978-3-031-47508-5_25 %0 Conference Proceedings %T Greedy Strategies to Improve Phased Genetic Programming When Applied Directly to the Traveling Salesman Problem %A Chitty, Darren M. %A Keedwell, Ed %Y Hu, Ting %Y Ekart, Aniko %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F chitty:2024:GECCOcomp %X Genetic Programming (GP) can be applied directly to combinatorial optimisation problems such as the Traveling Salesman Problem (TSP) using a phased approach. Similar to hyper-heuristics, Phased-GP evolves a program of simple operators to apply to a solution to improve it whilst operating in phases to facilitate hill-climbing. However, as optimality is approached, evolving a program of multiple operations that are not detrimental to solution quality is unlikely. Although, it can be hypothesized that if Phased-GP operates in a greedy manner, the probability of improving a near optimal solution is much greater. Two greedy Phased-GP strategies are proposed. First, using greedy GP operators which can only improve current solution quality. Second, a greedy program strategy whereby only the aspect of a GP program that provides best solution quality is retained. Combining both strategies reduced relative errors by up to a further 6% obtaining solutions within 7% of optimal when applied to TSPs of several thousand cities. %K genetic algorithms, genetic programming, optimisation, greedy methods: Poster %R 10.1145/3638530.3654358 %U http://dx.doi.org/10.1145/3638530.3654358 %P 491-494 %0 Conference Proceedings %T Improving the Efficiency Of Genetic Programming for Classification Tasks Using a Phased Approach %A Chitty, Darren %Y Alzueta, Silvino Fernandez %Y Stuetzle, Thomas %S 9th Workshop on Industrial Applications of Metaheuristics (IAM 2024) %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F chitty:2024:GECCOcomp2 %X Genetic Programming (GP) uses Darwinian evolution to generate algorithms for tasks such as classification and symbolic regression. However, a drawback is the interpreter used to evaluate candidate programs adding significant computational cost. Hence, many studies have sought to improve the speed of GP primarily via parallelism. However, efficiency can also offer considerable performance gains. GP has recently been applied to combinatorial optimisation using a phased approach (Phased-GP) whereby programs are evolved piecemeal avoiding reinterpretation of sub-programs. This method was found to be highly effective and efficient compared to standard GP. This paper investigates if a similar effect is observed when using phased GP to incrementally build classifiers. Tested upon known real-world classification problems, an efficiency saving of up to 98% can be achieved with a speedup of 70 fold and no significant loss of classification accuracy. Moreover, the method can be easily used within any existing high performance parallel GP models. %K genetic algorithms, genetic programming, high performance computing, efficiency %R 10.1145/3638530.3664184 %U http://dx.doi.org/10.1145/3638530.3664184 %P 1702-1705 %0 Conference Proceedings %T Ensemble Phased Genetic Programming for Roundabout Turn Restriction Prediction %A Chitty, Darren %A Helal, Ayah %A Rowlands, Sareh %A Willis, Craig %A Underwood, Christopher %A Keedwell, Ed %Y Kalkreuth, Roman %Y Brownlee, Alexander %S Proceedings of the 2025 Genetic and Evolutionary Computation Conference %S GECCO ’25 %D 2025 %8 14 18 jul %I Association for Computing Machinery %C Malaga, Spain %F chitty:2025:GECCO %X Ensemble methods are among the best performing in the machine learning literature, often outperforming single methods in training accuracy and the prevention of overfitting. This work builds on the previously successful phased genetic programming (GP) approach to build ensembles of GP trees to create ensemble phased GP (EPGP). The method is tested in a real-world transportation modelling problem, the roundabout (traffic circle, rotary) turn restriction problem using data from OpenStreetMap, an important and time-consuming element of the traffic modelling process. EPGP is compared with standard and phased GP formulations and representative algorithms from the machine learning literature and is found to outperform them on this task. %K genetic algorithms, genetic programming, Real World Applications, GIS %R 10.1145/3712256.3726424 %U https://doi.org/10.1145/3712256.3726424 %U http://dx.doi.org/10.1145/3712256.3726424 %P 1345-1353 %0 Conference Proceedings %T The Application of Genetic Programming in Milk Yield Prediction for Dairy Cows %A Chiu, Chaochang %A Hsu, Jih-Tay %A Lin, Chih-Yung %Y Ziarko, W. %Y Yao, Y. %S Rough Sets and Current Trends in Computing : Second International Conference, RSCTC 2000. Revised Papers %S Lecture Notes in Computer Science %D 2001 %8 oct 16 19 %V 2005 %I Springer-Verlag %C Banff, Canada %F Chiu:2001:AGP %X Milk yield forecasting can help dairy farmers to deal with the continuously changing condition all year round and to reduce the unnecessary overheads. Several variables (somatic cell count, pariety, day in milk, milk protein content, milk fat content, season) related to milk yield are collected as the parameters of the forecasting model. The use of an improved Genetic Programming (GP) technique with dynamic learning operators is proposed and achieved with acceptable prediction results. %K genetic algorithms, genetic programming, dynamic mutation, milk yield prediction %R 10.1007/3-540-45554-X_75 %U http://dx.doi.org/10.1007/3-540-45554-X_75 %P 598-602 %0 Conference Proceedings %T MuACOsm: a new mutation-based ant colony optimization algorithm for learning finite-state machines %A Chivilikhin, Daniil %A Ulyantsev, Vladimir %Y Blum, Christian %Y Alba, Enrique %Y Auger, Anne %Y Bacardit, Jaume %Y Bongard, Josh %Y Branke, Juergen %Y Bredeche, Nicolas %Y Brockhoff, Dimo %Y Chicano, Francisco %Y Dorin, Alan %Y Doursat, Rene %Y Ekart, Aniko %Y Friedrich, Tobias %Y Giacobini, Mario %Y Harman, Mark %Y Iba, Hitoshi %Y Igel, Christian %Y Jansen, Thomas %Y Kovacs, Tim %Y Kowaliw, Taras %Y Lopez-Ibanez, Manuel %Y Lozano, Jose A. %Y Luque, Gabriel %Y McCall, John %Y Moraglio, Alberto %Y Motsinger-Reif, Alison %Y Neumann, Frank %Y Ochoa, Gabriela %Y Olague, Gustavo %Y Ong, Yew-Soon %Y Palmer, Michael E. %Y Pappa, Gisele Lobo %Y Parsopoulos, Konstantinos E. %Y Schmickl, Thomas %Y Smith, Stephen L. %Y Solnon, Christine %Y Stuetzle, Thomas %Y Talbi, El-Ghazali %Y Tauritz, Daniel %Y Vanneschi, Leonardo %S GECCO ’13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference %D 2013 %8 June 10 jul %I ACM %C Amsterdam, The Netherlands %F Chivilikhin:2013:GECCO %X In this paper we present MuACOsm, a new method of learning Finite-State Machines (FSM) based on Ant Colony Optimisation (ACO) and a graph representation of the search space. The input data is a set of events, a set of actions and the number of states in the target FSM. The goal is to maximise the given fitness function, which is defined on the set of all FSMs with given parameters. The new algorithm is compared with evolutionary algorithms and a genetic programming related approach on the well-known Artificial Ant problem. %K genetic algorithms, genetic programming %R 10.1145/2463372.2463440 %U http://dx.doi.org/10.1145/2463372.2463440 %P 511-518 %0 Conference Proceedings %T Inferring Temporal Properties of Finite-State Machine Models with Genetic Programming %A Chivilikhin, Daniil %A Ivanov, Ilya %A Shalyto, Anatoly %Y Tusar, Tea %Y Naujoks, Boris %S GECCO’15 Student Workshop %D 2015 %8 November 15 jul %I ACM %C Madrid, Spain %F Chivilikhin:2015:GECCOcomp %X The paper presents a genetic programming based approach for inferring general form Linear Temporal Logic properties of finite-state machine models. Candidate properties are evaluated using several fitness functions, therefore multiobjective evolutionary algorithms are used. The feasibility of the approach is demonstrated by two examples. %K genetic algorithms, genetic programming %R 10.1145/2739482.2768475 %U http://doi.acm.org/10.1145/2739482.2768475 %U http://dx.doi.org/10.1145/2739482.2768475 %P 1185-1188 %0 Journal Article %T Solving Five Instances of the Artificial Ant Problem with Ant Colony Optimization %A Chivilikhin, Daniil S. %A Ulyantsev, Vladimir I. %A Shalyto, Anatoly A. %J IFAC Proceedings Volumes %D 2013 %V 46 %N 9 %@ 1474-6670 %F Chivilikhin:2013:PV %O 7th IFAC Conference on Manufacturing Modelling, Management, and Control %X The Artificial Ant problem is a common benchmark problem often used for metaheuristic algorithm performance evaluation. The problem is to find a strategy controlling an agent (called an Artificial Ant) in a game performed on a square toroidal field. Some cells of the field contain ’food’ pellets, which are distributed along a certain trail. In this paper we use Finite-State Machines (FSM) for strategy representation and present a new algorithm -MuACOsm - for learning finite-state machines. The new algorithm is based on an Ant Colony Optimization algorithm (ACO) and a graph representation of the search space. We compare the new algorithm with a genetic algorithm (GA), evolutionary strategies (ES), a genetic programming related approach and reinforcement learning on five instances of the Artificial Ant Problem. %K genetic algorithms, genetic programming, ant colony optimization, automata-based programming, finite-state machine, learning, induction, artificial ant problem %9 journal article %R 10.3182/20130619-3-RU-3018.00436 %U http://www.sciencedirect.com/science/article/pii/S1474667016344275 %U http://dx.doi.org/10.3182/20130619-3-RU-3018.00436 %P 1043-1048 %0 Conference Proceedings %T Evolutionary Optimization of a Focused Ultrasound Propagation Predictor Neural Network %A Chlebik, Jakub %A Jaros, Jiri %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F chlebik:2023:GECCOcomp %X The search for the optimal treatment plan of a focused ultrasound-based procedure is a complex multi-modal problem, trying to deliver the solution in clinically relevant time while not sacrificing the precision below a critical threshold. To test a solution, many computationally expensive simulations must be evaluated, often thousands of times. The recent renaissance of machine learning could provide an answer to this. Indeed, a state-of-the-art neural predictor of Acoustic Propagation through a human skull was published recently, speeding up the simulation significantly. The architecture, however, could use some improvements in precision. To explore the design more deeply, we made an attempt to improve the solver by use of an evolutionary algorithm, challenging the importance of different building blocks. Using Genetic Programming, we improved their solution significantly, resulting in a solver with approximately an order of magnitude better RMSE of the predictor, while still delivering solutions in a reasonable time frame. Furthermore, a second study was conducted to gauge the effects of the multi-resolution encoding on the precision of the network, providing interesting topics for further research on the effects of the memory blocks and convolution kernel sizes for PDE RCNN solvers. %K genetic algorithms, genetic programming, cartesian genetic programming, ultrasound propagation predictor, evolutionary design, evolutionary optimisation: Poster %R 10.1145/3583133.3590661 %U http://dx.doi.org/10.1145/3583133.3590661 %P 635-638 %0 Conference Proceedings %T Modular Neural Networks Evolved by Genetic Programming %A Cho, Sung-Bae %A Shimohara, Katsunori %S Proceedings of the 1996 IEEE International Conference on Evolutionary Computation %D 1996 %8 20 22 may %V 1 %C Nagoya, Japan %@ 0-7803-2902-3 %F cho:1996:mNNeGP %X In this paper we present an evolvable model of modular neural networks which are rich in autonomy and creativity. In order to build an artificial neural network which is rich in autonomy and creativity, we have adopted the ideas and methodologies of Artificial Life. This paper describes the concepts and methodologies for the evolvable model of modular neural networks, which will be able not only to develop new functionality spontaneously but also to grow and evolve its own structure autonomously. Although the ultimate goal of this model is to design the control system for such behaviour-based robots as Khepera, we have attempted to apply the mechanism to a visual categorisation task with handwritten digits. The evolutionary mechanism has shown a strong possibility to generate useful network architectures from an initial set of randomly-connected networks. %K genetic algorithms, genetic programming, Khepera, artificial life, artificial neural network, behavior based robots, control system design, evolutionary mechanism, evolvable model, genetic programming, handwritten digits, modular neural networks, network architectures, randomly connected networks, visual categorization task, intelligent control, neural net architecture, neurocontrollers, systems analysis %R 10.1109/ICEC.1996.542683 %U http://dx.doi.org/10.1109/ICEC.1996.542683 %P 681-684 %0 Journal Article %T Evolutionary Learning of Modular Neural Networks with Genetic Programming %A Cho, Sung-Bae %A Shimohara, Katsunori %J Applied Intelligence %D 1998 %8 nov / dec %V 9 %N 3 %@ 0924-669X %F cho:1998:mNNeGP %X Evolutionary design of neural networks has shown a great potential as a powerful optimisation tool. However, most evolutionary neural networks have not taken advantage of the fact that they can evolve from modules. This paper presents a hybrid method of modular neural networks and genetic programming as a promising model for evolutionary learning. This paper describes the concepts and methodologies for the evolvable model of modular neural networks, which might not only develop new functionality spontaneously, but also grow and evolve its own structure autonomously. We show the potential of the method by applying an evolved modular network to a visual categorisation task with handwritten digits. Sophisticated network architectures as well as functional subsystems emerge from an initial set of randomly-connected networks. Moreover, the evolved neural network has reproduced some of the characteristics of natural visual system, such as the organisation of coarse and fine processing of stimuli in separate pathways. %K genetic algorithms, genetic programming, neural networks, evolutionary computation, modules, emergence, handwritten digits, OCR %9 journal article %R 10.1023/A:1008388118869 %U http://dx.doi.org/10.1023/A:1008388118869 %P 191-200 %0 Conference Proceedings %T Proceedings of The First Asian-Pacific Workshop on Genetic Programming %E Cho, Sung-Bae %E Hoai, Nguyen Xuan %E Shan, Yin %D 2003 %8 August %C Rydges (lakeside) Hotel, Canberra, Australia %@ 0-9751724-0-9 %F aspgp03 %K genetic algorithms, genetic programming %U http://sc.snu.ac.kr/~aspgp/aspgp03/aspgp03.html %0 Conference Proceedings %T Genetic programming of multi-agent cooperation strategies for table transport %A Cho, Dong-Yeon %A Zhang, Byoung-Tak %Y Min, K. C. %S The Third Asian Fuzzy Systems Symposium %D 1998 %8 18 21 jun %C Kyungnam University, Masan, Korea %F D.Y.Cho:1998:GPmacstt %X transporting a large table using multiple... %K genetic algorithms, genetic programming, multiagent learning, artificial life, alfife, fitness switching %U http://bi.snu.ac.kr/Publications/Conferences/International/AFSS98_ChoDY.pdf %P 170-175 %0 Conference Proceedings %T Genetic programming-based Alife techniques for evolving collective robotic intelligence %A Cho, D. Y. %A Zhang, B. T. %Y Sugisaka, M. %S Proceedings 4th International Symposium on Artificial Life and Robotics %D 1999 %8 19 22 jan %C B-Con Plaza, Beppu, Oita, Japan %F cho:1999:GPalecri %X Control strategies for a multiple robot system should be adaptive and decentralized like those of social insects. To evolve this kind of control programs, we use genetic programming (GP). However, conventional GP methods are difficult to evolve complex coordinated behaviors and not powerful enough to solve the class of problems which require some emergent behaviors to be achieved in sequence. In a previous work, we presented a novel method called fitness switching. Here we extend the fitness switching method by introducing the concept of active data selection to further accelerate evolution speed of GP. Experimental results are reported on a table transport problem in which multiple autonomous mobile robots should cooperate to transport a large and heavy table. %K genetic algorithms, genetic programming, artificial life, multiagent learning, fitness switching, training data selection %U http://bi.snu.ac.kr/Publications/Conferences/International/AROB99.ps %P 236-239 %0 Conference Proceedings %T Bayesian Evolutionary Algorithms for Evolving Neural Tree Models of Time Series Data %A Cho, Dong-Yeon %A Zhang, Byoung-Tak %S Proceedings of the 2000 Congress on Evolutionary Computation CEC00 %D 2000 %8 June 9 jul %V 2 %I IEEE Press %C La Jolla Marriott Hotel La Jolla, California, USA %@ 0-7803-6375-2 %F cho:2000:BEAENTMTSD %X Model induction plays an important role in many fields of science and engineering to analyse data. Specifically, the performance of time series prediction whose objectives are to find out the dynamics of the underlying process in given data is greatly affected by the model. Bayesian evolutionary algorithms have been proposed as a method for automatic model induction from data. We apply Bayesian evolutionary algorithms (BEAs) to evolving neural tree models of time series data. The performances of various BEAs are compared on two time series prediction problems by varying the population size and the type of variation operations. Our experimental results support that population based BEAs with unlimited crossover find good models more efficiently than single individual BEAs, parallelised individual based BEAs, and population based BEAs with limited crossover. %K Bayesian evolutionary algorithms, automatic model induction, evolving neural tree models, model induction, parallelised individual based BEAs, population based BEAs, population size, time series data, time series prediction, time series prediction problems, unlimited crossover, variation operations, Bayes methods, data analysis, evolutionary computation, neural nets, time series, trees (mathematics) %R 10.1109/CEC.2000.870825 %U http://dx.doi.org/10.1109/CEC.2000.870825 %P 1451-1458 %0 Journal Article %T Identification of biochemical networks by S-tree based genetic programming %A Cho, Dong-Yeon %A Cho, Kwang-Hyun %A Zhang, Byoung-Tak %J Bioinformatics %D 2006 %8 jul %V 22 %N 13 %@ 1367-4803 %F Cho:2006:B %X Motivation: Most previous approaches to model biochemical networks have focused either on the characterisation of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviours of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system. Results: We propose a new representation named S-trees for both the structural and dynamical modelling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are less than 5percent regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within 10percent noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10**2 (??). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia coli. We confirm that the proposed algorithm can successfully identify the true structure except only one relation. Availability: The executable program and data are available from the authors upon request. %K genetic algorithms, genetic programming %9 journal article %R 10.1093/bioinformatics/btl122 %U http://dx.doi.org/10.1093/bioinformatics/btl122 %P 1631-1640 %0 Journal Article %T Solving quay wall allocation problems based on deep reinforcement learning %A Cho, Young-in %A Oh, Seung-heon %A Choi, Jae-ho %A Woo, Jong Hun %J Engineering Applications of Artificial Intelligence %D 2025 %V 150 %@ 0952-1976 %F Cho:2025:engappai %X Quay walls and graving docks are critical production resources in shipyards. Traditionally, quay walls have not been a bottleneck resource for constructing conventional vessels, such as oil carriers and container ships. However, the growing demand for high value-added vessels requiring more complex post-stage outfitting operations has increased workloads at quay walls. Accordingly, the importance of efficient quay wall allocation has grown significantly to improve overall production efficiency and ensure timely vessel delivery. In this study, the quay wall allocation problem is modelled as a flexible job shop scheduling problem, incorporating machine preferences and preemption conditions. Notably, the uncertainty in vessel launching dates caused by delays in the erection process at graving docks is considered in the scheduling problems. To address the dynamic quay wall allocation problems, this study develops a dynamic quay wall allocation algorithm based on deep reinforcement learning, which adaptively allocates vessels to quay walls based on the working status of quay walls and the progress of outfitting operations. For this purpose, a novel Markov decision process is proposed, where a compound state representation composed of heterogeneous graphs and auxiliary matrices is devised to capture the complex relationships between quay walls and outfitting operations. In addition, an extended scheduling action space incorporating operation interruptions is defined, which can effectively use preemption conditions to enhance the scheduling performance. The performance of the proposed algorithm is evaluated through extensive numerical experiments based on test instances generated from real-world shipyard data under various environmental conditions. Experimental results demonstrate that the proposed algorithm consistently outperforms traditional rule-based heuristics and exhibits superior scalability compared to genetic programming, making it a promising solution for large-scale quay wall allocation problems %K genetic algorithms, genetic programming, Deep reinforcement learning, Heterogeneous graph, Flexible job-shop scheduling problem, Post-stage outfitting process, Shipbuilding %9 journal article %R 10.1016/j.engappai.2025.110598 %U https://www.sciencedirect.com/science/article/pii/S0952197625005986 %U http://dx.doi.org/10.1016/j.engappai.2025.110598 %P 110598 %0 Conference Proceedings %T On the Suitability of Genetic-Based Algorithms for Data Mining %A Choenni, Sunil %Y Kambayashi, Yahiko %Y Lee, Dik Lun %Y Lim, Ee-Peng %Y Mohania, Mukesh Kumar %Y Masunaga, Yoshifumi %S Advances in Database Technologies %S LNCS %D 1999 %8 19 20 nov 1998 %V 1552 %I Springer-Verlag %C Singapore %@ 3-540-65690-1 %F Choenni:1999:SGB %X Data mining has as goal to extract knowledge from large databases. A database may be considered as a search space consisting of an enormous number of elements, and a mining algorithm as a search strategy. In general, an exhaustive search of the space is infeasible. Therefore, efficient search strategies are of vital importance. Search strategies on genetic-based algorithms have been applied successfully in a wide range of applications. We focus on the suitability of genetic-based algorithms for data mining. We discuss the design and implementation of a genetic-based algorithm for data mining and illustrate its potentials. %K genetic algorithms, genetic programming, ADT, conceptual modelling, database technologies, mobile data access, spatio-temporal data management %R 10.1007/978-3-540-49121-7_5 %U http://dx.doi.org/10.1007/978-3-540-49121-7_5 %P 55-67 %0 Report %T On the Suitability of Genetic-Based Algorithms for Data Mining %A Choenni, Sunil %D 1998 %8 nov %N NLR-TP-98484 %I National Aerospace Laboratory %C Amsterdam %F choenni:1998:SGADM %X Data mining has as goal to extract knowledge from large databases. To extract this knowledge, a database may be considered as a large search space, and a mining algorithm as a search strategy. In general, a search space consists of an enormous number of elements, making an exhaustive search infeasible. Therefore, efficient search strategies are of vital importance. Search strategies based on genetic-based algorithms have been applied successfully in a wide range of applications. In this paper, we discuss the suitability of genetic-based algorithms for data mining. We discuss the design and implementation of a genetic-based algorithm for data mining and illustrate its potentials. %K genetic algorithms, genetic programming %U http://www.nlr.nl/NLR-TP-98484.pdf %0 Report %T Implementation and Evaluation of a Genetic-Based Data Mining Algorithm %A Choenni, Sunil %D 1999 %8 jul %N NLR-TR-99281 %I National Aerospace Laboratory %C Amsterdam %F choenni:1999:ieGDMa %X GA can be rapidly implemented for DM yielding reasonable results. However, building an operational tool requires more effort %K genetic algorithms, genetic programming %0 Conference Proceedings %T Optimizing Local Area Networks Using Genetic Algorithms %A Choi, Andy %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 choi:1996:LANGA %K Genetic Algorithms %U http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap77.pdf %P 467-472 %0 Book Section %T Optimizing Local Area Networks Using Genetic Algorithms %A Choi, Andy %E Koza, John R. %B Genetic Algorithms and Genetic Programming at Stanford 1995 %D 1995 %8 November %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-195720-5 %F choi:1995:OLANUGA %K genetic algorithms, genetic programming %P 49-58 %0 Journal Article %T Physical habitat simulations of the Dal River in Korea using the GEP Model %A Choi, Byungwoong %A Choi, Sung-Uk %J Ecological Engineering %D 2015 %V 83 %@ 0925-8574 %F Choi:2015:EE %X The GEP model, a recently-developed robust artificial intelligence technique, captures the benefits of both genetic algorithm and genetic programming by using chromosomes and expression trees. This paper presents a physical habitat simulation using the GEP model. The study area is a 2.5 km long reach of a stream, located downstream from a dam in the Dal River in Korea. Field monitoring revealed that Zacco platypus is the dominant species in the study area. The CCHE2D model and the GEP model were used for hydraulic and habitat simulations, respectively. Since the GEP model belongs to the data-driven approach, the model directly predicts the composite suitability index using the monitoring data. The GEP model is capable of considering correlations between all physical habitat variables, which is a clear advantage over knowledge-based models, such as the habitat suitability index model. The model was first validated using measured data. Distributions of the composite suitability index were then predicted using the GEP model for various flows. The predicted results were compared with those obtained using the habitat suitability index model. A sensitivity study of the GEP model was also carried out. Finally, the GEP model was used to construct habitat suitability curves for each physical habitat variable. The resulting habitat suitability curves were found to be very similar to those constructed by the method of Gosse (1982). The findings indicate that the conventional multiplicative aggregation method consistently underestimates the composite suitability index. Thus, the geometric mean method is proposed for use with calibrated coefficients. %K genetic algorithms, genetic programming, Physical habitat simulation, GEP model, Habitat suitability index, Composite suitability index, The Dal River %9 journal article %R 10.1016/j.ecoleng.2015.06.042 %U http://www.sciencedirect.com/science/article/pii/S0925857415301038 %U http://dx.doi.org/10.1016/j.ecoleng.2015.06.042 %P 456-465 %0 Journal Article %T Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics %A Choi, Ji Whae %A Hu, Rong %A Zhao, Yijun %A Purkayastha, Subhanik %A Wu, Jing %A McGirr, Aidan J. %A Stavropoulos, S. William %A Silva, Alvin C. %A Soulen, Michael C. %A Palmer, Matthew B. %A Zhang, Paul J. L. %A Zhu, Chengzhang %A Ahn, Sun Ho %A Bai, Harrison X. %J Abdominal Radiology %D 2021 %8 jun %V 46 %N 6 %@ 2366-004X %F Choi:2021:AbdomRadiol %O Special Section on Ovarian Cancer %X Purpose Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics. Methods A multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [ge 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT). Results The manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95percent CI 0.816-0.937), specificity of 0.95 (95percent CI 0.875-0.984), and sensitivity of 0.72 (95percent CI 0.537-0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95percent CI 0.816-0.937), specificity of 0.95 (95percent CI 0.875-0.984), and sensitivity of 0.72 (95percent CI 0.537-0.852) on the test set. Conclusion Preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers. %K genetic algorithms, genetic programming, TPOT, Renal cancer, Neoplasm progression, Imaging analysis, Medical imaging, Kidneys, Ureters, Bladder, Retroperitoneum %9 journal article %R 10.1007/s00261-020-02876-x %U http://dx.doi.org/10.1007/s00261-020-02876-x %P 2656-2664 %0 Conference Proceedings %T Learning Fault Localisation for Both Humans and Machines using Multi-Objective GP %A Choi, Kabdo %A Sohn, Jeongju %A Yoo, Shin %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 Choi:2018:SSBSE %X Genetic Programming has been successfully applied to fault localisation to learn ranking models that place the faulty program element as near the top as possible. However, it is also known that, when localisation results are used by Automatic Program Repair (APR) techniques, higher rankings of faulty program elements do not necessarily result in better repair effectiveness. Since APR techniques tend to use localisation scores as weights for program mutation, lower scores for non-faulty program elements are as important as high scores for faulty program elements. We formulate a multi-objective version of GP based fault localisation to learn ranking models that not only aim to place the faulty program element higher in the ranking, but also aim to assign as low scores as possible to non-faulty program elements. The results show minor improvements in the suspiciousness score distribution. However, surprisingly, the multi-objective formulation also results in more accurate fault localisation ranking-wise, placing 155 out of 386 faulty methods at the top, compared to 135 placed at the top by the single objective formulation. %K genetic algorithms, genetic programming, SBSE, MOGP, Fault localisation, FLUCCS, Multi-objective evolutionary algorithm, NSGA-II %R 10.1007/978-3-319-99241-9_20 %U https://coinse.kaist.ac.kr/publications/pdfs/Choi2018aa.pdf %U http://dx.doi.org/10.1007/978-3-319-99241-9_20 %P 349-355 %0 Book Section %T Speedups for Efficient Genetic Algorithms: Design Optimization of Low-Boom Supersonic Jet Using Parallel GA and Micro-GA with External Memory %A Choi, Seongim %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 choi:2003:SEGADOLSJUPGMEM %K genetic algorithms %U http://www.genetic-programming.org/sp2003/Choi.pdf %P 21-30 %0 Conference Proceedings %T Polynomial Approximation of Survival Probabilities Under Multi-point Crossover %A Choi, Sung-Soon %A Moon, Byung-Ro %Y Deb, Kalyanmoy %Y Poli, Riccardo %Y Banzhaf, Wolfgang %Y Beyer, Hans-Georg %Y Burke, Edmund %Y Darwen, Paul %Y Dasgupta, Dipankar %Y Floreano, Dario %Y Foster, James %Y Harman, Mark %Y Holland, Owen %Y Lanzi, Pier Luca %Y Spector, Lee %Y Tettamanzi, Andrea %Y Thierens, Dirk %Y Tyrrell, Andy %S Genetic and Evolutionary Computation – GECCO-2004, Part I %S Lecture Notes in Computer Science %D 2004 %8 26 30 jun %V 3102 %I Springer-Verlag %C Seattle, WA, USA %@ 3-540-22344-4 %F choi:pao:gecco2004 %K genetic algorithms, genetic programming %R 10.1007/b98643 %U http://dx.doi.org/10.1007/b98643 %P 994-1005 %0 Journal Article %T Artificial life based on boids model and evolutionary chaotic neural networks for creating artworks %A Choi, Tae Jong %A Ahn, Chang Wook %J Swarm and Evolutionary Computation %D 2017 %@ 2210-6502 %F CHOI:2017:SEC %X In this paper, we propose a multi-agent based art production framework. In existing artwork creation systems, images were generated using artificial life and evolutionary computation approaches. In artificial life, swarm intelligence or Boids model, and in evolutionary computation, genetic algorithm or genetic programming are commonly used to create images. These automated artwork creation systems make it easy to create artistic images even if the users are not professional artists. Despite the high possibility of these creation systems, however, much research has not been done so far. In this paper, we propose an art production framework that generates images using multi-agents with chaotic dynamics features. Agents act on the canvas following the three rules of Boids model. In addition, each agent possesses a chaotic neural network which trained by differential evolution algorithm, so that colors can be evolved to represent a better style. As a result, we propose an art production framework for generating processing artworks that contain highly complex dynamics. Finally, we created the glitch artworks using the proposed framework, which shows a new glitch style %K genetic algorithms, genetic programming, Artificial life, Boids model, Chaotic neural networks, ANN, Differential evolution, Glitch art %9 journal article %R 10.1016/j.swevo.2017.09.003 %U http://www.sciencedirect.com/science/article/pii/S2210650217301700 %U http://dx.doi.org/10.1016/j.swevo.2017.09.003 %0 Conference Proceedings %T Computer-aided detection of pulmonary nodules using genetic programming %A Choi, Wook-Jin %A Choi, Tae-Sun %S 17th IEEE International Conference on Image Processing (ICIP 2010 ) %D 2010 %8 26 29 sep %F Choi:2010:ICIP %X This paper describes a novel nodule detection method that enhances false positive reduction. Lung region is extracted from CT image sequence using adaptive thresholding and 18-connectedness voxel labelling. In the extracted lung region, nodule candidates are detected using adaptive multiple thresholding and rule based classifier. After that, we extract the 3D and 2D features from nodule candidates. The nodule candidates are then classified using genetic programming (GP) based classifier. In this work, a new fitness function is proposed to generate optimal adaptive classifier. We tested the proposed algorithm by using Lung Imaging Database Consortium (LIDC) database of National Cancer Institute (NCI). The classifier was trained and evaluated using two independent dataset and whole dataset. The proposed method reduced the false positives in nodule candidates and achieved 92percent detection rate with 6.5 false positives per scan. %K genetic algorithms, genetic programming, CT image sequence, adaptive thresholding, computer-aided detection, false positive reduction, feature extraction, fitness function, lung imaging database consortium, lung region, nodule detection, pulmonary nodules, rule based classifier, voxel labelling, computerised tomography, feature extraction, image classification, image segmentation, image sequences, lung, medical image processing %R 10.1109/ICIP.2010.5652369 %U http://dx.doi.org/10.1109/ICIP.2010.5652369 %P 4353-4356 %0 Journal Article %T Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images %A Choi, Wook-Jin %A Choi, Tae-Sun %J Information Sciences %D 2012 %8 January %V 212 %@ 0020-0255 %F Choi201257 %X An effective automated pulmonary nodule detection system can assist radiologists in detecting lung abnormalities at an early stage. In this paper, we propose a novel pulmonary nodule detection system based on a genetic programming (GP)-based classifier. The proposed system consists of three steps. In the first step, the lung volume is segmented using thresholding and 3D-connected component labelling. In the second step, optimal multiple thresholding and rule-based pruning are applied to detect and segment nodule candidates. In this step, a set of features is extracted from the detected nodule candidates, and essential 3D and 2D features are subsequently selected. In the final step, a GP-based classifier (GPC) is trained and used to classify nodules and non-nodules. GP is suitable for detecting nodules because it is a flexible and powerful technique; as such, the GPC can optimally combine the selected features, mathematical functions, and random constants. Performance of the proposed system is then evaluated using the Lung Image Database Consortium (LIDC) database. As a result, it was found that the proposed method could significantly reduce the number of false positives in the nodule candidates, ultimately achieving a 94.1percent sensitivity at 5.45 false positives per scan. %K genetic algorithms, genetic programming, CT, Pulmonary nodule detection, CAD %9 journal article %R 10.1016/j.ins.2012.05.008 %U http://www.sciencedirect.com/science/article/pii/S0020025512003362 %U http://dx.doi.org/10.1016/j.ins.2012.05.008 %P 57-78 %0 Conference Proceedings %T Collaborative Analytics with Genetic Programming for Workflow Recommendation %A Chong, Chee Seng %A Zhang, Tianyou %A Lee, Kee Khoon %A Hung, Gih Guang %A Lee, Bu-Sung %S IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013) %D 2013 %8 oct %F Chong:2013:SMC %X Formulation of appropriate data analytics workflows requires intricate knowledge and rich experiences of data analytics experts. This problem is further compounded by continuous advancement and improvement in analytical algorithms. In this paper, a generic non-domain specific solution for the creation of appropriate work-flows targeted at supervised learning problems is proposed. Our adaptive work flow recommendation engine based on collaborative analytics matches analytics needs with relevant work flows in repository. It is capable of picking workflows with better performance as compared to randomly selected work-flows. The recommendation engine is now augmented by a work-flow optimiser that applies genetic programming to further improve the recommended workflows through iterative evolution, leading to better alternative workflows. This unique Collaborative Analytics Recommender System is tested on seven UCI benchmark datasets. It is shown that the final workflows produced by the system could closely approximate, in terms of accuracy, the best workflows that analytics experts could possibly design. %K genetic algorithms, genetic programming, Work flow recommendation, collaborative analytics %R 10.1109/SMC.2013.117 %U http://dx.doi.org/10.1109/SMC.2013.117 %P 657-662 %0 Conference Proceedings %T Using Genetic Programming to Achieve High Broadband Absorptivity Metamaterial in Compact Radar Band (1-11 GHz) without Lossy Materials %A Chong, Edmond %A Clemens, Scott %A Iskander, Magdy F. %A Yun, Zhengqing %A Brown, Joseph J. %A Nakamura, Matthew %S 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI) %D 2022 %8 jul %F Chong:2022:AP-S %X In the modern age of metamaterial absorbers (MA), many implementations are generally in the C and X microwave bands. Genetic programming (GP) software is used to generate new 3D designs for metamaterial absorbers with multilayer dielectrics to achieve high broadband absorptivity in the compact radar band (CRB) (1-11 GHz) without lossy materials. GP was previously used to create an artificial magnetic conductor (AMC) with broadband capabilities in 225-450 MHz without magnetic or absorbing materials. Using the capabilities of GP to create broadband structures, the GP software is modified to create broadband MAs. Two 2D patterned structures with multilayer dielectrics were generated with above 9percent absorptivity peaks around 3 and 5 GHz. The preliminary use of GP to create broadband MA structures shows excellent potential for creating a structure that broadens the whole CRB. %K genetic algorithms, genetic programming, Three-dimensional displays, Magnetic multilayers, Radar, Nonhomogeneous media, Software, Metamaterials %R 10.1109/AP-S/USNC-URSI47032.2022.9886220 %U http://dx.doi.org/10.1109/AP-S/USNC-URSI47032.2022.9886220 %P 1364-1365 %0 Conference Proceedings %T Hybrid Genetic Programming-Based Comparative Design of Broadband Metamaterial Absorbers Using Graphene, Resistive Sheets, and Carbon Fiber %A Chong, Edmond %A Zhang, Sunny %A Iskander, Magdy F. %A Yun, Zhengqing %S 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI) %D 2023 %8 jul %F Chong:2023:USNC-URSI %X Hybrid genetic programming (HGP) is proposed to create new design topologies in the lower gigahertz frequency with new materials such as graphene, resistive sheet, and carbon fiber. HGP can create new topologies optimised per input parameters, such as low frequency and high broadband absorptivity. These designs are built and simulated in Ansys High-Frequency Simulation Software (HFSS) and evaluated by HGP. Graphene, resistive sheet, and carbon fiber patterning are explored and implemented with HGP to create low-gigahertz frequency and high-absorptivity MMAs. The graphene, resistive sheet, and carbon fiber-based patterned designs achieved 80percent bandwidth above 80percent absorptivity from 4.6 to 11 GHz, up to 15 GHz, from 3.83 to 9.13 GHz, and from 3.77 to 10.28 GHz, respectively. %K genetic algorithms, genetic programming, Conferences, Graphene, Bandwidth, Software, Metamaterials, Broadband communication %R 10.1109/USNC-URSI52151.2023.10237681 %U http://dx.doi.org/10.1109/USNC-URSI52151.2023.10237681 %P 1249-1250 %0 Thesis %T A Java based Distributed Approach to Genetic Programming on the Internet %A Chong, Fuey Sian %D 1998 %C Computer Science, University of Birmingham %F p.chong:mastersthesis %X This paper presents a distributed approach to parallelise Genetic Programming on the Internet. The motivation for the approach is to harness the wealth of computing resources available on the Internet to provide the computing power required for solving difficult problems. A distributed genetic programming system termed DGP is developed in the Java programming language to demonstrate the feasibility of distributing genetic programming on the Internet. Unique features of the DGP system include the use of Java Servlets to handle the communication between DGP clients, the use of a population pool to neutralise differences in speeds of hosts, the interactive user interface and graphical displays of the evolution process. The DGP system has been implemented over the Internet and the results are favourable. Experiments were conducted to determine the performance of the DGP system. Results showed that the DGP system has a much higher probability of finding solutions as compared to the distributed approaches taken in our previous studies and the single population Genetic Programming. %K genetic algorithms, genetic programming %9 Masters thesis %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/p.chong.msc.25-sep-98.ps.gz %0 Report %T Java based Distributed Genetic Programming on the Internet %A Chong, Fuey Sian %D 1999 %8 apr %N CSRP-99-7 %I University of Birmingham, School of Computer Science %F chong:1999:jDGPiTR %X We proposed a distributed approach for parallelising Genetic Programming on the Internet. The approach harnesses the wealth of computing resources available on the Internet to provide the computing power required by Genetic Programming to solve hard problems. A distributed genetic programming system termed DGP is developed in the Java progamming language to demonstrate the feasibility of our approach. Features of the DGP system include the use of Java Servlets to handle communication between distributed machines and the use of a population pool to facilitate migrations. In addition, the DGP system has an interactive user interface for controlling the run and graphical displays of the evolution process. The DGP system has been implemented live over the Internet and the results prove that the approach is feasible. An experiment was conducted to determine the performance of the DGP system and results showed that the DGP system has a much higher probability of finding solutions than the distributed approaches taken in our previous work and the conventional single population Genetic Programming approach. %K genetic algorithms, genetic programming, DGP %U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1999/CSRP-99-07.ps.gz %0 Conference Proceedings %T Java based Distributed Genetic Programming on the Internet %A Chong, Fuey Sian %A Langdon, W. B. %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 chong:1999:jDGPi %O Full text in technical report CSRP-99-7 %X A distributed approach for parallelising Genetic Programming (GP) on the Internet is proposed and its feasibility demonstrated with a distributed GP system termed DGP developed in Java. DGP is run successfully across the world over the Internet on heterogeneous platforms without any central co-ordination. The run results and the outcome of an experiment to determine DGP’s performance are reported together with a description of DGP. %K genetic algorithms, genetic programming, DGP, Distributed Computing, Java Applet / Application, World Wide Computing, Internet, Servlets, poster %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/DGPposter.pdf %P 1229 %0 Conference Proceedings %T Java based Distributed Genetic Programming on the Internet %A Chong, Fuey Sian %Y Cantu-Paz, Erick %Y Punch, Bill %S Evolutionary computation and parallel processing %D 1999 %8 13 jul %C Orlando, Florida, USA %F chong:1999:parGA %K genetic algorithms, genetic programming %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/GeccoWkShop.ps.gz %P 163-166 %0 Generic %T Java based Distributed Genetic Programming on the Internet %A Chong, Fuey Sian %A Langdon, W. B. %E O’Reilly, Una-May %D 1999 %8 13 jul %C Orlando, Florida, USA %F chong:1999:jDGPis %X GECCO’99 graduate WKSHOP Phyllis Chong %K genetic algorithms, genetic programming, distributed, evolutionary programming, Internet, java, parallel %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/DGPposter.ps.gz %P 345 %0 Book Section %T Genetic Algorithms Applied to Computational Genomics %A Chong, Sanders %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 chong:2002:GAACG %K genetic algorithms %U http://www.genetic-programming.org/sp2002/Chong.pdf %P 58-64 %0 Conference Proceedings %T Hardware Evolution Platform Reasearch Based on Matrix Coding CGP %A Li, Chong-cun %A Xu, Li-zhi %A Song, Xue-jun %A Guo, Zhen-xing %A Liu, Xiong %S 2018 10th International Conference on Communications, Circuits and Systems (ICCCAS) %D 2018 %8 dec %F Chong-cun:2018:ICCCAS %X Evolvable hardware is a combination of evolutionary algorithms and reconfigurable hardware, which can change itself structure to adapt to the living environment. Evolvable hardware possessed the characteristics of self-organization, self-adaptation and self-repair. The off-line evolution of digital circuits is similar to a simulation process, which lacks real-time capability and cannot generate actual circuits for every evolutionary digital circuit. The hardware online evolution platform is designed for evolution digital circuit based on Field Programmer Gate Array. Compared with off-line evolution, the platform can monitor the status of the designed circuit in real time, and it is easily to evolve a digital circuit for practical products directly. The multiplier circuit has obtained using the on online evolution platform combining based on matrix coded Cartesian Genetic Programming. %K genetic algorithms, genetic programming %R 10.1109/ICCCAS.2018.8768964 %U http://dx.doi.org/10.1109/ICCCAS.2018.8768964 %P 466-470 %0 Journal Article %T Using Perturbation To Improve Robustness Of Solutions Generated By Genetic Programming For Robot Learning %A Chongstitvatana, Prabhas %J Journal of Circuits, Systems and Computers %D 1999 %V 9 %N 1-2 %I World Scientific Publishing Company %G en %F oai:CiteSeerPSU:421006 %X This paper proposes a method to improve robustness of the robot programs generated by genetic programming. The main idea is to inject perturbation into the simulation during the evolution of the solutions. The resulting robot programs are more robust because they have evolved to tolerate the changes in their environment. We set out to test this idea using the problem of navigating a mobile robot from a starting point to a target in an unknown cluttered environment. The result of the experiments shows the effectiveness of this scheme. The analysis of the result shows that the robustness depends on the ’experience’ that a robot program acquired during evolution. To improve robustness, the size of the set of ’experience’ should be increased and/or the amount of reusing the ’experience’ should be increased. %K genetic algorithms, genetic programming %9 journal article %R 10.1142/S0218126699000128 %U http://www.worldscinet.com/123/09/0901n02/S0218126699000128.html %U http://dx.doi.org/10.1142/S0218126699000128 %P 133-143 %0 Book Section %T Emergence of a Division of Labor in a Bee Colony %A Choo, Shou-yen %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 choo:2000:EDLBC %K genetic algorithms, genetic programming %P 98-107 %0 Journal Article %T Wave Propagation and Optimal Antenna Layout using a Genetic Algorithm %A Chopard, B. %A Baggi, Y. %A Luthi, P. %A Wagen, J. F. %J Speedup %D 1997 %8 nov %V 11 %N 2 %F BC-telepar:97 %O TelePar Conference, EPFL, 1997 %9 journal article %P 42-47 %0 Journal Article %T Parallel and distributed evolutionary computation for financial applications %A Chopard, Bastien %A Pictet, Olivier %A Tomassini, Marco %J Parallel Algorithms and Applications %D 2000 %V 15 %@ 1063-7192 %F chopard2000 %X A survey of two parallel evolutionary computation techniques is presented: the genetic algorithms and genetic programming methods. An application of this approach to the induction of trading models is presented for financial assets, which is known as a hard problem. This study analyses the potential of this approach and the benefit of parallelisation. %K genetic algorithms, genetic programming, Evolutionary algorithms, Parallel computing, Financial application, Trading model induction %9 journal article %R 10.1080/01495730008947348 %U http://dx.doi.org/10.1080/01495730008947348 %P 15-36 %0 Generic %T Implementesi Algoritma Grammatical Evolution Menggunakan Steady State Untuk Prediksi Ketinggian Gelombang Laut %A Chorisma, Bondan %A Nikentari, Nerfita %A Rathomi, Muhamad Radzi %D 2017 %I Fakultas Teknik %F BONDAN-CHORISMA-120155201062 %X Riau islands have small islands of which is the Bintan island. The majority of people in Bintan island fishermen, addition to his activities in sea as means of transport to cross the nearby islands including the state directly neighbour to Bintan island specifically Malaysia and Singapore. The weather conditions were very important factors influencing the smooth running of activities at sea one of them is the height of sea waves. The wave height can be predicted based on the data time series that have been obtained which will serve as a pattern to determine the height of a wave of the future. Based on the results of observations made by using two methods of survivor selection obtained the average percentage error steady state method of 4.243percent while the generational replacement of 4.897percent.The combination with the methods of steady state with 30 generations, the population size of 100, crossover probability (Pc) 0.8,Probability of mutation (Pm) 0.2,whereas the method of generational replacement using 50 generations, the population size of 100, crossover probability (Pc) 0.7 The probability of mutation (Pm) 0.2. %K genetic algorithms, genetic programming, grammatical evolution, Prediksi, Probabilitas, Crossover, Mutasi, Prediction, Probability, Crossover, Mutation, Steady State, Generational Replacement %U http://jurnal.umrah.ac.id/wp-content/uploads/gravity_forms/1-ec61c9cb232a03a96d0947c6478e525e/2017/08/BONDAN-CHORISMA-120155201062.pdf %0 Journal Article %T Recent developments in parameter estimation and structure identification of biochemical and genomic systems %A Chou, I-Chun %A Voit, Eberhard O. %J Mathematical Biosciences %D 2009 %8 jun %V 219 %N 2 %@ 0025-5564 %F Chou200957 %X The organisation, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterise the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using top-down or inverse approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorised into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimisation and support algorithms. Many recent articles have addressed inverse problems within the modelling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational work-flow that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions. %K genetic algorithms, genetic programming, Parameter estimation, Network identification, Inverse modelling, Biochemical Systems Theory %9 journal article %R 10.1016/j.mbs.2009.03.002 %U http://dx.doi.org/10.1016/j.mbs.2009.03.002 %P 57-83 %0 Conference Proceedings %T Channel assignment using genetic programming in wireless networks %A Chou, Li-Der %A Wang, Shao-Chi %S Global Telecommunications Conference, 1998. GLOBECOM 98. The Bridge to Global Integration. IEEE %D 1998 %8 August 12 nov %V 5 %I IEEE %C Sydney, NSW, Australia %@ 0-7803-4984-9 %F Chou:1998:GC %X It has become an important issue to design a control scheme to assign efficiently channel resources, according to the changes in network environment, in wireless networks. In the paper, a control scheme based on genetic programming is proposed and applied to assign channels in wireless networks. Compared to traditional schemes, simulation results demonstrate the superiority of the proposed control scheme %K genetic algorithms, genetic programming %R 10.1109/GLOCOM.1998.776469 %U http://dx.doi.org/10.1109/GLOCOM.1998.776469 %P 2664-2668 %0 Conference Proceedings %T A dynamic stock trading system based on a Multi-objective Quantum-Inspired Tabu Search algorithm %A Chou, Yao-Hsin %A Kuo, Shu-Yu %A Kuo, Chun %S 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC) %D 2014 %8 oct %F Chou:2014:ieeeSMC %X Recently evolutionary algorithms, such as the Genetic Algorithm (GA), Genetic Programming (GP) and Particle Swarm Optimisation (PSO), have become common approaches used in financial applications to address stock trading problems. In this paper, we propose a novel method called the Multi-objective Quantum-inspired Tabu Search (MOQTS) algorithm, which can be applied in a stock trading system. Determining the best time to buy and sell in the stock market and maximizing profits while incurring fewer risks are important issues in financial research. In order to identify ideal trading points, the proposed trading system uses various kinds of technical indicators as trading rules in order to cope with different stock situations. The proposed algorithm is used to identify the optimal combination of trading rules as our trading strategy. Moreover, it makes use of a sliding window in order to avoid the major problem of over-fitting. In the experiment, the algorithm uses both profit earned and other aspects, such as successful transaction rate and standard deviation, to analyse this system. The experimental results, in relation to profit earned and successful transaction rates in the U.S.A stock market, outperform both the traditional method and the Buy & Hold method which are common benchmarks in the field. %K genetic algorithms, genetic programming %R 10.1109/SMC.2014.6973893 %U http://dx.doi.org/10.1109/SMC.2014.6973893 %P 112-119 %0 Conference Proceedings %T Learning to Predict Code Review Rounds in Modern Code Review Using Multi-Objective Genetic Programming %A Chouchen, Moataz %A Oukhay, Issam %A Ouni, Ali %Y Ekart, Aniko %Y Pillay, Nelishia %S Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion %S GECCO ’25 Companion %D 2025 %8 14 18 jul %I Association for Computing Machinery %C Malaga, Spain %F chouchen:2025:GECCOcomp %X Code review is an essential practice for software quality assurance. However, code review can be cumbersome as patches often undergo multiple rounds to fix bugs, enforce coding standards, and improve structure before merging or abandonment. Predicting the number of review rounds can help developers prioritize tasks and streamline the process. Existing machine learning models for review round prediction suffer from key limitations. Their black-box nature makes them difficult to interpret, reducing trust and adoption. Additionally, they rely on data re-balancing techniques that introduce artificial points, causing concept shifts and reducing reliability. To address these issues, we propose MORRP, a novel Multi-Objective Review Rounds Prediction approach. MORRP is based on Multi-Objective Genetic Programming (MOGP) to predict review rounds. Our method evolves interpretable models while optimizing precision, recall, and specificity without relying on data re-balancing. We evaluate our approach on three large open-source projects: Eclipse, OpenDaylight, and OpenStack. Results show that MORRP achieves competitive performance, with a micro F1 score between 0.65 and 0.75, outperforming complex ML models like Random Forest and LightGBM. %K genetic algorithms, genetic programming, SBSE, code review, review rounds: Poster %R 10.1145/3712255.3726730 %U https://doi.org/10.1145/3712255.3726730 %U http://dx.doi.org/10.1145/3712255.3726730 %P 599-602 %0 Conference Proceedings %T Passive Analog Filter Design Using GP Population Control Strategies %A Chouza, Mariano %A Rancan, Claudio %A Clua, Osvaldo %A Garcia-Martinez, Ramon %Y Chien, Been-Chian %Y Hong, Tzung-Pei %S Opportunities and Challenges for Next-Generation Applied Intelligence: Proceedings of the International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE) 2009 %S Studies in Computational Intelligence %D 2009 %V 214 %I Springer-Verlag %F chouza09:_passiv_analog_filter_desig_using %X This paper presents the use of two different strategies for genetic programming (GP) population growth control: decreasing the computational effort by plagues and dynamic adjustment of fitness; applied to passive analog filters design based on general topologies. Obtained experimental results show that proposed strategies improve the design process performance. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-92814-0_24 %U http://www.iidia.com.ar/rgm/articulos/CIS-214-153-158.pdf %U http://dx.doi.org/10.1007/978-3-540-92814-0_24 %P 153-158 %0 Journal Article %T Machine learning control for experimental shear flows targeting the reduction of a recirculation bubble %A Chovet, C. %A Keirsbulck, L. %A Noack, B. R. %A Lippert, M. %A Foucaut, J.-M. %J IFAC-PapersOnLine %D 2017 %V 50 %N 1 %@ 2405-8963 %F CHOVET:2017:IFAC-PapersOnLine %O 20th IFAC World Congress %X The goal is to experimentally reduce the recirculation zone of a turbulent flow (ReH = 31500). The flow is manipulated by a row of micro-blowers (pulsed jets) that are able to generate unsteady jets proportional to any variable DC. Already, periodic jet injection at a forcing frequency of StH = 0.226 can effectively reduce the reattachment length and thus the recirculation zone. A model-free machine learning control (MLC) is used to improve performance. MLC optimizes a control law with respect to a cost function and applies genetic programming as regression technique. The cost function is based on the recirculation length and penalizes actuation. MLC is shown to outperform periodic forcing. The current study demonstrates the efficacy of MLC to reduce the recirculation zone in a turbulent flow regime. Given current and past successes, we anticipate numerous experimental MLC applications %K genetic algorithms, genetic programming, Machine learning control, experimental flow control, recirculation zone %9 journal article %R 10.1016/j.ifacol.2017.08.2157 %U http://www.sciencedirect.com/science/article/pii/S2405896317328264 %U http://dx.doi.org/10.1016/j.ifacol.2017.08.2157 %P 12307-12311 %0 Thesis %T A Study of the Impact of Interaction Mechanisms and Population Diversity in Evolutionary Multiagent Systems %A Chowdhury, Sadat U. %D 2016 %8 sep %C USA %C City University of New York %F Chowdhury:thesis %X In the Evolutionary Computation (EC) research community, a major concern is maintaining optimal levels of population diversity. In the Multiagent Systems (MAS) research community, a major concern is implementing effective agent coordination through various interaction mechanisms. These two concerns coincide when one is faced with Evolutionary Multiagent Systems (EMAS). This thesis demonstrates a methodology to study the relationship between interaction mechanisms, population diversity, and performance of an evolving multiagent system in a dynamic, real-time, and asynchronous environment. An open sourced extensible experimentation platform is developed that allows plug-ins for evolutionary models, interaction mechanisms, and genotypical encoding schemes beyond the one used to run experiments. Moreover, the platform is designed to scale arbitrarily large number of parallel experiments in multi-core clustered environments. The main contribution of this thesis is better understanding of the role played by population diversity and interaction mechanisms in the evolution of multiagent systems. First, it is shown, through carefully planned experiments in three different evolutionary models, that both interaction mechanisms and population diversity have a statistically significant impact on performance in a system of evolutionary agents coordinating to achieve a shared goal of completing problems in sequential task domains. Second, it is experimentally verified that, in the sequential task domain, a larger heterogeneous population of limited-capability agents will evolve to perform better than a smaller homogeneous population of full-capability agents, and performance is influenced by the ways in which the agents interact. Finally, two novel trait-based population diversity levels are described and are shown to be effective in their applicability. %K genetic algorithms, genetic programming, Artificial Intelligence and Robotics, Computer Sciences, machine learning, artificial life, multiagent systems, robotics, evolutionary systems %9 Ph.D. thesis %U https://academicworks.cuny.edu/gc_etds/1607 %0 Conference Proceedings %T An Analysis of Koza’s Computational Effort Statistic for Genetic Programming %A Christensen, Steffen %A Oppacher, Franz %Y Foster, James A. %Y Lutton, Evelyne %Y Miller, Julian %Y Ryan, Conor %Y Tettamanzi, Andrea G. B. %S Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002 %S LNCS %D 2002 %8 March 5 apr %V 2278 %I Springer-Verlag %C Kinsale, Ireland %@ 3-540-43378-3 %F christensen:2002:EuroGP %X As research into the theory of genetic programming progresses, more effort is being placed on systematically comparing results to give an indication of the effectiveness of sundry modifications to traditional GP. The statistic that is commonly used to report the amount of computational effort to solve a particular problem with 99percent probability is Koza’s I(M, i, z) statistic. This paper analyzes this measure from a statistical perspective. In particular, Koza’s I tends to underestimate the true computational effort, by 25percent or more for commonly used GP parameters and run sizes. The magnitude of this underestimate is nonlinearly decreasing with increasing run count, leading to the possibility that published results based on few runs may in fact be unmatchable when replicated at higher resolution. Additional analysis shows that this statistic also under reports the generation at which optimal results are achieved. %K genetic algorithms, genetic programming %R 10.1007/3-540-45984-7_18 %U https://rdcu.be/eg43v %U http://dx.doi.org/10.1007/3-540-45984-7_18 %P 182-191 %0 Conference Proceedings %T The Y-Test: Fairly Comparing Experimental Setups with Unequal Effort %A Christensen, Steffen %A Oppacher, Franz %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 16 21 jul %I IEEE Press %C Vancouver %@ 0-7803-9487-9 %F Christensen:2006:CEC %X Evolutionary Computation has been dogged by a central statistical issue: how does one fairly compare the performance of two techniques which differ in the amount of work required? While Koza’s computational effort statistic attempts to answer this problem, it is a point statistic and has other statistical problems. We present the y-test, a statistical test which takes as input a set of outcomes from the observed runs of two processes A and B. The y-test synthetically performs a work-balanced comparison between k runs of A and l runs of B. We show that by choosing k and l appropriately, we can compensate for the fact that one of the processes is computationally more efficient than the other. %K genetic algorithms, genetic programming %R 10.1109/CEC.2006.1688330 %U http://dx.doi.org/10.1109/CEC.2006.1688330 %P 1060-1065 %0 Conference Proceedings %T Solving the artificial ant on the Santa Fe trail problem in 20,696 fitness evaluations %A Christensen, Steffen %A Oppacher, Franz %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 1277275 %X In this paper, we provide an algorithm that systematically considers all small trees in the search space of genetic programming. These small trees are used to generate useful subroutines for genetic programming. This algorithm is tested on the Artificial Ant on the Santa Fe Trail problem, a venerable problem for genetic programming systems. When four levels of iteration are used, the algorithm presented here generates better results than any known published result by a factor of 7. %K genetic algorithms, genetic programming, representation, runtime analysis, speedup technique %R 10.1145/1276958.1277275 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1574.pdf %U http://dx.doi.org/10.1145/1276958.1277275 %P 1574-1579 %0 Thesis %T Towards scalable genetic programming %A Christensen, Steffen Moffatt %D 2007 %8 14 nov %C Ottawa, Canada %C Carleton University %F Christensen:thesis %X Genetic programming (GP) is a technique for automatically solving optimisation problems where candidate solutions are expressible as trees with no human intervention. We propose an extension of GP, termed scalable genetic programming, which solves problems parametrised by a scalable difficulty parameter. We first define a taxonomy of evolutionary computation (EC) systems that identifies variability dimensions and levels for EC systems. We define an algorithm, the scientist algorithm, which uses genetic programming as a subroutine to reliably make progress on scalable problems. The scientist algorithm uses a toolkit of provided routines to progress, by carrying out experiments to determine the value of different methods. We define several of the tools for this toolkit. We define and implement an algorithm for systematically considering all small trees for a problem. We then use these small trees in an iterative algorithm to define subroutines that improve performance on a problem under study. Using this algorithm, we beat the best known performance on the artificial ant on the Santa Fe trail problem by a factor of 7. As science depends on accurate hypothesis testing to make progress, we perform a comparison and evaluation of statistical techniques used to evaluate evolutionary computation systems. Finding many of these wanting, with the exception of computational effort, we introduce two additional techniques, effective mean best fitness and the y-test. We also perform an extensive analysis of the computational effort, and identify some statistical cautions around the use of this key statistic. We provide an algorithm that carefully uses computational effort to determine the best values of population size and generation number for an EC treatment. Finally, we identify several components that are of use with the scientist algorithm. We treat the use of multiobjective algorithms in GP, principal components analysis, and their combination. We demonstrate this by providing and testing an algorithm that makes evolved trees parsimonious. We introduce the notion of incremental evolution, and use it to make useful subroutines automatically from successful solutions to easy problems. We then use this to demonstrate scalable genetic programming on an integer sorting problem. %K genetic algorithms, genetic programming %9 Ph.D. thesis %R 10.22215/etd/2007-06411 %U https://curve.carleton.ca/1ecedf3e-b559-41e6-aede-eac9b2209694 %U http://dx.doi.org/10.22215/etd/2007-06411 %0 Conference Proceedings %T Genetic C Programming with Probabilistic Evaluation %A Christmas, Jacqueline %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 Christmas:2015:GECCOcomp %X We introduce the concept of probabilistic program evaluation, whereby the order in which the statements of a proposed program are executed, and whether individual statements are executed at all, are controlled by probability distributions associated with each statement. The sufficient statistics of these probability distributions are mutated as part of the GP scheme. We demonstrate the method on the simple problems of swapping two array elements and identifying the maximum value in an array. %K genetic algorithms, genetic programming: Poster %R 10.1145/2739482.2764642 %U http://doi.acm.org/10.1145/2739482.2764642 %U http://dx.doi.org/10.1145/2739482.2764642 %P 1371-1372 %0 Conference Proceedings %T Using strongly typed genetic programming to combine technical and sentiment analysis for algorithmic trading %A Christodoulaki, Eva %A Kampouridis, Michael %Y Coello, Carlos A. Coello %Y Mostaghim, Sanaz %S 2022 IEEE Congress on Evolutionary Computation (CEC) %D 2022 %8 18 23 jul %C Padua, Italy %F Christodoulaki:2022:CEC %X Algorithmic trading has become an increasingly thriving research area and a lot of focus has been given on indicators from technical and sentiment analysis. In this paper, we examine the advantages of combining features from both analyses. To do this, we use two different genetic programming algorithms (GP). The first algorithm allows trees to contain technical and/or sentiment analysis indicators without any constraints. The second algorithm introduces technical and sentiment analysis types through a strongly typed GP, whereby one branch of a given tree contains only technical analysis indicators and another branch of the same tree contains only sentiment analysis features. This allows for better exploration and exploitation of the search space of the indicators. We perform experiments on 10 international stocks and compare the above two GPs performances. Our goal is to demonstrate that the combination of the indicators leads to improved financial performance. Our results show that the strongly typed GP is able to rank first in terms of Sharpe ratio and statistically outperform all other algorithms in terms of rate of return. %K genetic algorithms, genetic programming, Sentiment analysis, Evolutionary computation, Technical Analysis, Sentiment Analysis, Algorithmic Trading %R 10.1109/CEC55065.2022.9870240 %U http://dx.doi.org/10.1109/CEC55065.2022.9870240 %0 Conference Proceedings %T Technical and Sentiment Analysis in Financial Forecasting with Genetic Programming %A Christodoulaki, Eva %A Kampouridis, Michael %A Kanellopoulos, Panagiotis %S 2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) %D 2022 %8 may %F Christodoulaki:2022:CIFEr %X Financial Forecasting is a popular and thriving research area that relies on indicators derived from technical and sentiment analysis. In this paper, we investigate the advantages that sentiment analysis indicators provide, by comparing their performance to that of technical indicators, when both are used individually as features into a genetic programming algorithm focusing on the maximization of the Sharpe ratio. Moreover, while previous sentiment analysis research has focused mostly on the titles of articles, in this paper we use the text of the articles and their summaries. Our goal is to explore further on all possible sentiment features and identify which features contribute the most. We perform experiments on 26 different datasets and show that sentiment analysis produces better, and statistically significant, average results than technical analysis in terms of Sharpe ratio and risk. %K genetic algorithms, genetic programming, Economics, Sentiment analysis, Focusing, Forecasting, Computational intelligence, Technical Analysis, Sentiment Analysis, Financial Forecasting %R 10.1109/CIFEr52523.2022.9776186 %U http://dx.doi.org/10.1109/CIFEr52523.2022.9776186 %0 Conference Proceedings %T Enhanced Strongly Typed Genetic Programming for Algorithmic Trading %A Christodoulaki, Evangelia %A Kampouridis, Michael %A Kyropoulou, Maria %Y Silva, Sara %Y Paquete, Luis %Y Vanneschi, Leonardo %Y Lourenco, Nuno %Y Zamuda, Ales %Y Kheiri, Ahmed %Y Liefooghe, Arnaud %Y Xue, Bing %Y Bi, Ying %Y Pillay, Nelishia %Y Moser, Irene %Y Guijt, Arthur %Y Catarino, Jessica %Y Garcia-Sanchez, Pablo %Y Trujillo, Leonardo %Y Silva, Carla %Y Veerapen, Nadarajen %S Proceedings of the 2023 Genetic and Evolutionary Computation Conference %S GECCO ’23 %D 2023 %8 15 19 jul %I Association for Computing Machinery %C Lisbon, Portugal %F christodoulaki:2023:GECCO %X This paper proposes a novel strongly typed Genetic Programming (STGP) algorithm that combines Technical (TA) and Sentiment analysis (SA) indicators to produce trading strategies. While TA and SA have been successful when used individually, their combination has not been considered extensively. Our proposed STGP algorithm has a novel fitness function, which rewards not only a tree’s trading performance, but also the trading performance of its TA and SA subtrees. To achieve this, the fitness function is equal to the sum of three components: the fitness function for the complete tree, the fitness function of the TA subtree, and the fitness function of the SA subtree. In doing so, we ensure that the evolved trees contain profitable trading strategies that take full advantage of both technical and sentiment analysis. We run experiments on 35 international stocks and compare the STGP’s performance to four other GP algorithms, as well as multilayer perceptron, support vector machines, and buy and hold. Results show that the proposed GP algorithm statistically and significantly outperforms all benchmarks and it improves the financial performance of the trading strategies produced by other GP algorithms by up to a factor of two for the median rate of return. %K genetic algorithms, genetic programming, algorithmic trading, technical analysis, sentiment analysis %R 10.1145/3583131.3590359 %U http://dx.doi.org/10.1145/3583131.3590359 %P 1055-1063 %0 Conference Proceedings %T Fundamental, Technical and Sentiment Analysis for Algorithmic Trading with Genetic Programming %A Christodoulaki, Eva %A Kampouridis, Michael %S 2023 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2023 %8 dec %F Christodoulaki:2023:SSCI %X Algorithmic trading is a topic with major developments in the last years. Investors rely mostly on indicators derived from fundamental (FA) or technical analysis (TA), while sentiment analysis (SA) has also received attention in the last decade. This has led to great financial advantages with algorithms being the main tool to create pre-programmed trading strategies. Although the three analysis types have been mainly considered individually, their combination has not been studied as much. Given the ability of each individual analysis type in identifying profitable trading strategies, we are motivated to investigate if we can increase the profitability of such strategies by combining their indicators. we propose a novel Genetic Programming (GP) algorithm that combines the three analysis types and we showcase the advantages of their combination in terms of three financial metrics, namely Sharpe ratio, rate of return and risk. We conduct experiments on 30 companies and based on the results, the combination of the three analysis types statistically and significantly outperforms their individual results, as well as their pairwise combinations. More specifically, the proposed GP algorithm has the highest mean and median values for Sharpe ratio and rate of return, and the lowest (best) mean value for risk. Moreover, we benchmark our GP algorithm against multilayer perceptron and support vector machine, and show that it statistically outperforms both algorithms in terms of Sharpe ratio and risk. %K genetic algorithms, genetic programming, Support vector machines, Measurement, Sentiment analysis, Profitability, Companies, Algorithmic Trading %R 10.1109/SSCI52147.2023.10372070 %U http://dx.doi.org/10.1109/SSCI52147.2023.10372070 %P 83-89 %0 Thesis %T Fundamental, Sentiment and Technical analysis for Algorithmic Trading using Novel Genetic Programming algorithms %A Christodoulaki, Evangelia Paraskevi %D 2024 %8 jan %C UK %C School of Computer Science and Electronic Engineering, University of Essex %F Christodoulaki:thesis %X This thesis explores genetic programming (GP) applications in algorithmic trading, addressing significant advancements in the field. Investors typically rely on fundamental analysis (FA) or technical analysis (TA) indicators, with sentiment analysis (SA) gaining recent attention. Consequently, algorithms have become the primary method for developing pre-programmed trading strategies, leading to substantial financial benefits. While each analysis type has been studied individually, their combined exploration remains limited. Our motivation is to assess if integrating FA, SA, and TA indicators can improve financial profitability. Thus, in Chapter 5, we introduce a novel GP algorithm which combines the three analysis types within the same GP structure, wanting to understand the advantages of their combination. Chapter 6 presents a strongly-typed GP architecture, where each branch of the algorithm represents one analysis type, facilitating improved exploration and exploitation. Furthermore, we showcase a novel fitness function that rewards a tree trading performance and the performance of its FA, SA,and TA subtrees. Chapter 7 aims to enhance the GP algorithm performance and increase the individuals financial advantages. Therefore, we propose a novel GP operator that encourages active trading by injecting trees into the GP population that perform a high number of trades while achieving high profitability at low risk. To evaluate our GP variants performance, we conduct experiments on stocks of 42 international companies, comparing the novel algorithm with the GP variants introduced in the same chapter. Moreover, in Chapters 5 and 6, we compare the proposed GP algorithm against four machine learning benchmarks and a financial trading strategy, while Chapter 7 focuses on comparing the novel GP algorithm exclusively with GP benchmarks. The evaluation employs three financial metrics: Sharpe ratio, rate of return, and risk. Results consistently show that the proposed GP algorithms in each chapter enhance the financial performance of trading strategies, surpassing the benchmarks %K genetic algorithms, genetic programming, Financial Forcasting, strongly-typed genetic programming, FinTech %9 Ph.D. thesis %U http://kampouridis.net/papers/Eva_PhdThesis_with_template.pdf %0 Conference Proceedings %T Combining Technical and Sentiment Analysis Under a Genetic Programming Algorithm %A Christodoulaki, Eva %A Kampouridis, Michael %Y Panoutsos, George %Y Mahfouf, Mahdi %Y Mihaylova, Lyudmila S. %S 21st UK Workshop on Computational Intelligence %S Advances in Intelligent Systems and Computing %D 2022 %8 sep 7 9 %V 1454 %I Springer %C Sheffield, UK %F christodoulaki:2022:UKCI %K genetic algorithms, genetic programming %R 10.1007/978-3-031-55568-8_42 %U https://link.springer.com/chapter/10.1007/978-3-031-55568-8_42 %U http://dx.doi.org/10.1007/978-3-031-55568-8_42 %P 502-513 %0 Journal Article %T A novel strongly-typed Genetic Programming algorithm for combining sentiment and technical analysis for algorithmic trading %A Christodoulaki, Eva %A Kampouridis, Michael %A Kyropoulou, Maria %J Knowledge-Based Systems %D 2025 %V 311 %@ 0950-7051 %F Christodoulaki:2025:knosys %X The use of algorithms in finance and trading has become an increasingly thriving research area, with researchers creating automated and pre programmed trading instructions using indicators from technical and sentiment analysis. The indicators of the two analyses have been used mostly individually, despite evidence that their combination can be profitable and financially advantageous. In this paper, we examine the advantages of combining indicators from both technical and sentiment analysis through a novel genetic programming algorithm, named STGP-SATA. Our algorithm introduces technical and sentiment analysis types, through a strongly-typed architecture, whereby the associated tree contains one branch with only technical indicators and another branch with only sentiment analysis indicators. This approach allows for better exploration and exploitation of the search space of the indicators. To evaluate the performance of STGP-SATA we compare it with three other GP variants on three financial metrics, namely Sharpe ratio, rate of return and risk. We furthermore compare STGP-SATA against two financial and four algorithmic benchmarks, namely, multilayer perceptron, support vector machine, extreme gradient boosting, and long short term memory network. Our study shows that the combination of technical and sentiment analysis indicators through STGP-SATA improves the financial performance of the trading strategies and statistically and significantly outperforms the other benchmarks across the three financial metrics %K genetic algorithms, genetic programming, Sentiment analysis, Technical analysis, Algorithmic trading %9 journal article %R 10.1016/j.knosys.2025.113054 %U https://www.sciencedirect.com/science/article/pii/S0950705125001017 %U http://dx.doi.org/10.1016/j.knosys.2025.113054 %P 113054 %0 Thesis %T Application of genetic programming to text categorization %A Chrosny, Wojciech M. %D 2000 %8 jan %C USA? %C Computer Science, Polytechnic University %F Chrosny:thesis %X This dissertation uses genetic programming in text categorization problems. Genetic programming algorithms are applied to a set of news articles to evolve programs that determine whether the article belongs to a particular category. The programs are randomly generated from the set of initial functions and constants. Programs with the fewest amount of false assignments are favoured in the selection for recombination in the subsequent iterations of the genetic programming algorithm. The form of the solution is not determined a priori as in other text categorization methods. The basis set of functions and constants used by the genetic analysis program are specified in advance and may include the three basic logical functions and a set of vocabulary words. Other sets of basis functions can be supplied to the genetic algorithm to obtain different programs. The form in which these functions and constants are combined is determined randomly by the genetic algorithm. The results indicate that genetic programming methods are in the cases examined as good and slightly better than other decision tree or rule induction methods described by Apte et. al. [Apte 1994]. The Genetic Programming methods used a simpler set of features and functions: no word stemming no explicit stop word removal, local dictionary, Boolean functions. The F1-measure of categorization performance of 80.percent achieved by Genetic Programming compares favorably with 78.5percent break even performance of traditional Boolean rule induction methods. It is comparable with 80.5percent Breakeven performance of the rule induction methods with a more complex feature set such as word frequency [Apte 1994]. Characteristics of Genetic Programming text categorization were studied to understand the sensitivity of Genetic Programming methods to vocabulary size, population size, training and testing set selection methods. Temporal characteristics of the Reuters Article Corpus [Lewis-21578) were studied. The results are of interest to both Genetic Programming as well as Traditional categorization methods and may point to significant future performance improvements in both domains. In some cases these results were better than Apte’s. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U http://search.proquest.com/docview/85536142/ %0 Conference Proceedings %T Dynamic Degree Constrained Network Design: A Genetic Algorithm Approach %A Chu, Chao-Hsien %A Premkumar, G. %A Chou, Carey %A Sun, Jianzhong %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 chu:1999:DDCNDAGAA %K genetic algorithms and classifier systems %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-846.pdf %P 141-148 %0 Journal Article %T Application of Temperature and Process Duration as a Method for Predicting the Mechanical Properties of Thermally Modified Timber %A Chu, Demiao %A Hasanagic, Redzo %A Hodzic, Atif %A Krzisnik, Davor %A Hodzic, Damir %A Bahmani, Mohsen %A Petric, Marko %A Humar, Miha %J Forests %D 2022 %V 13 %N 2 %@ 1999-4907 %F chu:2022:Forests %X This study aims to investigate the influence of thermal modification (TM) on the physical and mechanical properties of wood. For this purpose, the experimental part focused on selected influential parameters, namely temperature, residence time, and density, while the four-point bending strength is obtained as the output parameter. The obtained experimental data are stochastically modelled and compared with the model created by genetic programming (GP). The classical mathematical analysis obtained treatment parameters in relation to the maximum bending strength (T = 187 °C, t = 125 min ρ = 0.780 g/cm3) and compared with the results obtained by genetic algorithm (GA) (T = 208 °C, t = 122 min, and ρ = 0.728 g/cm3). It is possible to obtain models that describe experimental results well with stochastic modelling and evolutionary algorithms. %K genetic algorithms, genetic programming, thermal modification, mathematical modeling, optimization %9 journal article %R 10.3390/f13020217 %U https://www.mdpi.com/1999-4907/13/2/217 %U http://dx.doi.org/10.3390/f13020217 %0 Conference Proceedings %T Crossover Operators to Control Size Growth in Linear GP and Variable Length GAs %A Chu, Dominique %A Rowe, Jonathan E. %Y Wang, Jun %S 2008 IEEE World Congress on Computational Intelligence %D 2008 %8 January 6 jun %I IEEE Press %C Hong Kong %F Chu:2008:cec %X In various nuances of evolutionary algorithms it has been observed that variable sized genomes exhibit large degrees of redundancy and corresponding undue growth. This phenomenon is commonly referred to as “bloat.” The present contribution investigates the role of crossover operators as the cause for length changes in variable length genetic algorithms and linear GP. Three crossover operators are defined; each is tested with three different fitness functions. The aim of this article is to indicate suitable designs of crossover operators that allow efficient exploration of designs of solutions of a wide variety of sizes, while at the same time avoiding bloat. %K genetic algorithms, genetic programming %R 10.1109/CEC.2008.4630819 %U EC0096.pdf %U http://dx.doi.org/10.1109/CEC.2008.4630819 %P 336-343 %0 Conference Proceedings %T A new implementation to speed up Genetic Programming %A Chu, Thi Huong %A Nguyen, Quang Uy %S IEEE RIVF International Conference on Computing Communication Technologies - Research, Innovation, and Vision for the Future (2015 RIVF) %D 2015 %8 jan %F Chu:2015:RIVF %X Genetic Programming (GP) is an evolutionary algorithm inspired by the evolutionary process in biology. Although, GP has successfully applied to various problems, its major weakness lies in the slowness of the evolutionary process. This drawback may limit GP applications particularly in complex problems where the computational time required by GP often grows excessively as the problem complexity increases. In this paper, we propose a novel method to speed up GP based on a new implementation that can be implemented on the normal hardware of personal computers. The experiments were conducted on numerous regression problems drawn from UCI machine learning data set. The results were compared with standard GP (the traditional implementation) and an implementation based on subtree caching showing that the proposed method significantly reduces the computational time compared to the previous approaches, reaching a speedup of up to nearly 200 times. %K genetic algorithms, genetic programming, Clustering algorithms, Hardware, Sociology, Standards, Statistics, Training data, Fitness Evaluation, Speed up %R 10.1109/RIVF.2015.7049871 %U http://dx.doi.org/10.1109/RIVF.2015.7049871 %P 35-40 %0 Conference Proceedings %T Tournament Selection based on Statistical Test in Genetic Programming %A Chu, Thi Huong %A Nguyen, Quang Uy %A O’Neill, Michael %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 Chu:2016:PPSN %X Selection plays a critical role in the performance of evolutionary algorithms. Tournament selection is often considered the most popular techniques among several selection methods. Standard tournament selection randomly selects several individuals from the population and the individual with the best fitness value is chosen as the winner. In the context of Genetic Programming, this approach ignores the error value on the fitness cases of the problem emphasising relative fitness quality rather than detailed quantitative comparison. Subsequently, potentially useful information from the error vector may be lost. In this paper, we introduce the use of a statistical test into selection that uses information from the individual’s error vector. Two variants of tournament selection are proposed, and tested on Genetic Programming for symbolic regression problems. On the benchmark problems examined we observe a benefit of the proposed methods in reducing code growth and generalisation error. %K genetic algorithms, genetic programming %R 10.1007/978-3-319-45823-6_28 %U http://dx.doi.org/10.1007/978-3-319-45823-6_28 %P 303-312 %0 Conference Proceedings %T Reducing code bloat in Genetic Programming based on subtree substituting technique %A Chu, Thi Huong %A Nguyen, Quang Uy %S 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES) %D 2017 %8 nov %F Chu:2017:APSIES %X Code bloat is a phenomenon in Genetic Programming (GP) that increases the size of individuals during the evolutionary process. Over the years, there has been a large number of research that attempted to address this problem. In this paper, we propose a new method to control code bloat and reduce the complexity of the solutions in GP. The proposed method is called Substituting a subtree with an Approximate Terminal (SAT-GP). The idea of SAT-GP is to select a portion of the largest individuals in each generation and then replace a random subtree in every individual in this portion by an approximate terminal of the similar semantics. SAT-GP is tested on twelve regression problems and its performance is compared to standard GP and the latest bloat control method (neat-GP). The experimental results are encouraging, SAT-GP achieved good performance on all tested problems regarding to the four popular performance metrics in GP research. %K genetic algorithms, genetic programming %R 10.1109/IESYS.2017.8233556 %U http://dx.doi.org/10.1109/IESYS.2017.8233556 %P 25-30 %0 Journal Article %T Semantic tournament selection for genetic programming based on statistical analysis of error vectors %A Chu, Thi Houng %A Nguyen, Quang Uy %A O’Neill, Michael %J Information Sciences %D 2018 %8 apr %V 436-437 %F Chu:2018:IS %X The selection mechanism plays a very important role in the performance of Genetic Programming (GP). Among several selection techniques, tournament selection is often considered the most popular. Standard tournament selection randomly selects a set of individuals from the population and the individual with the best fitness value is chosen as the winner. However, an opportunity exists to enhance tournament selection as the standard approach ignores finer-grained semantics which can be collected during GP program execution. In the case of symbolic regression problems, the error vectors on the training fitness cases can be used in a more detailed quantitative comparison. In this paper we introduce the use of a statistical test into GP tournament selection that uses information from the individual’s error vector, and three variants of the selection strategy are proposed. We tested these methods on twenty five regression problems and their noisy variants. The experimental results demonstrate the benefit of the proposed methods in reducing GP code growth and improving the generalisation behaviour of GP solutions when compared to standard tournament selection, a similar selection technique and a state of the art bloat control approach. %K genetic algorithms, genetic programming Tournament selection, Statistical test, Code bloat, Semantics %9 journal article %R 10.1016/j.ins.2018.01.030 %U http://dx.doi.org/10.1016/j.ins.2018.01.030 %P 352-366 %0 Conference Proceedings %T Network Anomaly Detection Using Genetic Programming with Semantic Approximation Techniques %A Chu, Thi Huong %A Uy Nguyen, Quang %S 2021 RIVF International Conference on Computing and Communication Technologies (RIVF) %D 2021 %8 aug %F Chu:2021:RIVF %X Network anomaly detection aims at detecting malicious behaviors to the network systems. This problem is of great importance in developing intrusion detection systems to protect networks from intrusive activities. Recently, machine learning-based methods for anomaly detection have become more popular in the research community thanks to their capability in discovering unknown attacks. In the paper, we propose an application of Genetic Programming (GP) with the semantics approximation technique to network anomaly detection. Specifically, two recently proposed techniques for reducing GP code bloat, i.e. Subtree Approximation (SA) and Desired Approximation (DA) are applied for detecting network anomalies. SA and DA are evaluated on 6 datasets in the field of anomaly detection and compared with standard GP and five common machine learning methods. Experimental results show that SA and DA have achieved better results than that of standard GP and the performance of GP is competitive with other machine learning algorithms. %K genetic algorithms, genetic programming, Learning systems, Machine learning algorithms, Semantics, Intrusion detection, Machine learning, Communications technology, Semantic Approximation, Network Anomaly Detection %R 10.1109/RIVF51545.2021.9642140 %U http://dx.doi.org/10.1109/RIVF51545.2021.9642140 %0 Conference Proceedings %T Semantics Based Substituting Technique for Reducing Code Bloat in Genetic Programming %A Chu, Thi Huong %A Nguyen, Quang Uy %A Cao, Van Loi %S Proceedings of the Ninth International Symposium on Information and Communication Technology, SoICT 2018 %D 2018 %8 dec 6 7 %I ACM %C Danang City, Viet Nam %F Chu:2018:SoICT %X Genetic Programming (GP) is a technique that allows computer programs encoded as a set of tree structures to be evolved using an evolutionary algorithm. In GP, code bloat is a common phenomenon characterised by the size of individuals gradually increasing during the evolution. This phenomenon has a negative impact on GP performance in solving problems. In order to address this problem, we have recently introduced a code bloat control method based on semantics: Substituting a subtree with an Approximate Terminal (SAT-GP). In this paper, we propose an extension of SAT-GP, namely Substituting a subtree with an Approximate Subprogram (SAS-GP). We tested this method with different GP parameter settings on a real-world time series forecasting problem. The experimental results demonstrate the benefit of the proposed method in reducing the code bloat phenomenon and improving GP performance. Particularly, SAS-GP often achieves the best performance compared to other tested GP systems using four popular performance metrics in GP. %K genetic algorithms, genetic programming, code growth, code bloat, semantics, time series %R 10.1145/3287921.3287948 %U http://doi.acm.org/10.1145/3287921.3287948 %U http://dx.doi.org/10.1145/3287921.3287948 %P 77-83 %0 Conference Proceedings %T A Genetic Programming Approach to Integrate Multilayer CNN Features for Image Classification %A Chu, Wei-Ta %A Chu, Hao-An %S MultiMedia Modeling %D 2019 %I Springer %F chu:2019:MMM %K genetic algorithms, genetic programming %R 10.1007/978-3-030-05710-7_53 %U http://link.springer.com/chapter/10.1007/978-3-030-05710-7_53 %U http://dx.doi.org/10.1007/978-3-030-05710-7_53 %0 Journal Article %T Predicting fetal birth weight by ultrasound with the use of genetic programming %A Chuang, Louise L. %A Hwang, Jeng-Yang %A Chien, Been Chian %A Lin, Jung Yi %A Chang, Chiung Hsin %A Yu, Chen Hsiang %A Chang, Fong Ming %J Ultrasound in Medicine & Biology %D 2003 %8 may %V 29 %N 5, Supplement 1 %@ 0301-5629 %F Chuang:2003:UMB %K genetic algorithms, genetic programming %9 journal article %R 10.1016/S0301-5629(03)00653-7 %U https://www.umbjournal.org/action/showCitFormats?pii=S0301-5629%2803%2900653-7 %U http://dx.doi.org/10.1016/S0301-5629(03)00653-7 %P S163-S163 %0 Conference Proceedings %T Opportunities for Genetic Improvement of Cryptographic Code %A Chuengsatiansup, Chitchanok %A Wagner, Markus %A Yarom, Yuval %Y Bruce, Bobby R. %Y Nowack, Vesna %Y Blot, Aymeric %Y Winter, Emily %Y Langdon, W. B. %Y Petke, Justyna %S GI @ GECCO 2022 %D 2022 %8 September %I Association for Computing Machinery %C Boston, USA %F Chuengsatiansup:2022:GI %X Cryptography is one of the main tools underlying the security of our connected world. Cryptographic code must meet not only high security requirements, but also exhibit excellent non-functional properties, such as high performance and unique security requirements. As both automatic code generation and genetic improvement of such code are under explored, we motivate here what makes cryptographic code a prime target for future research. %K genetic algorithms, genetic programming, genetic improvement, intermediate representation, LLVM IR, CryptOpt %R 10.1145/3520304.3534049 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Chuengsatiansup_2022_GI.pdf %U http://dx.doi.org/10.1145/3520304.3534049 %P 1928-1929 %0 Journal Article %T Three Metaheuristic Approaches for Tumor Phylogeny Inference: An Experimental Comparison %A Ciccolella, Simone %A Della Vedova, Gianluca %A Filipovic, Vladimir %A Soto Gomez, Mauricio %J Algorithms %D 2023 %V 16 %N 7 %@ 1999-4893 %F ciccolella:2023:Algorithms %X Being able to infer the clonal evolution and progression of cancer makes it possible to devise targeted therapies to treat the disease. As discussed in several studies, understanding the history of accumulation and the evolution of mutations during cancer progression is of key importance when devising treatment strategies. Given the importance of the task, many methods for phylogeny reconstructions have been developed over the years, mostly employing probabilistic frameworks. Our goal was to explore different methods to take on this phylogeny inference problem; therefore, we devised and implemented three different metaheuristic approaches–Particle Swarm Optimisation (PSO), Genetic Programming (GP) and Variable Neighbourhood Search (VNS)–under the Perfect Phylogeny and the Dollo-k evolutionary models. We adapted the algorithms to be applied to this specific context, specifically to a tree-based search space, and proposed six different experimental settings, in increasing order of difficulty, to test the novel methods amongst themselves and against a state-of-the-art method. Of the three, the PSO shows particularly promising results and is comparable to published tools, even at this exploratory stage. Thus, we foresee great improvements if alternative definitions of distance and velocity in a tree space, capable of better handling such non-Euclidean search spaces, are devised in future works. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/a16070333 %U https://www.mdpi.com/1999-4893/16/7/333 %U http://dx.doi.org/10.3390/a16070333 %P ArticleNo.333 %0 Conference Proceedings %T Developing a team of soccer playing robots by genetic programming %A Ciesielski, Victor %A Wilson, Peter %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 Ciesielski:1999:AJ %K genetic algorithms, genetic programming %U http://www.cs.rmit.edu.au/~vc/papers/aus-jap-ec99.ps.gz %P 101-108 %0 Conference Proceedings %T Prevention of Early Convergence in Genetic Programming by Replacement of Similar Programs %A Ciesielski, Vic %A Mawhinney, Dylan %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 ciesielski:2002:poecigpbrosp %X We have investigated an approach to preventing or minimising the occurrence of premature convergence by measuring the similarity between the programs in the population and replacing the most similar ones with randomly generated programs. On a problem with known premature convergence behaviour, the MAX problem, similarity replacement significantly decreased the rate of premature convergence over the best that could be achieved by manipulation of the mutation rate. The expected CPU time for a successful run was increased due to the additional cost of the similarity matching. On a problem which has a very expensive fitness function, the evolution of a team of soccer playing programs, the degree of premature convergence rate was also significantly reduced. However, in this case the expected time for a successful run was significantly decreased indicating that similarity replacement can be worthwhile for problems with expensive evaluation functions. A significant discovery from our experimental work is that a small change to the way mutation is carried out can result in significant reductions in premature convergence %K genetic algorithms, genetic programming, CPU time, MAX problem, early convergence prevention, experimental work, fitness function, mutation, mutation rate, premature convergence, randomly generated programs, similar program replacement, similarity matching, soccer playing programs, convergence, programming %R 10.1109/CEC.2002.1006211 %U http://dx.doi.org/10.1109/CEC.2002.1006211 %P 67-72 %0 Conference Proceedings %T Genetic Programming for Robot Soccer %A Ciesielski, Vic %A Mawhinney, Dylan %A Wilson, Peter %Y Birk, Andreas %Y Coradeschi, Silvia %Y Tadokoro, Satoshi %S RoboCup 2001: Robot Soccer World Cup V %S Lecture Notes in Computer Science %D 2002 %8 aug 2001 %V 2377 %I Springer %C Seattle, Washington, USA %@ 3-540-43912-9 %F Ciesielski:2002:GPR %X RoboCup is a complex simulated environment in which a team of players must cooperate to overcome their opposition in a game of soccer. This paper describes three experiments in the use of genetic programming to develop teams for RoboCup. The experiments used different combinations of low level and high level functions. The teams generated in experiment 2 were clearly better than the teams in experiment 1, and reached the level of ‘school boy soccer’ where the players follow the ball and try to kick it. The teams generated in experiment 3 were quite good, however they were not as good as the teams evolved in experiment 2. The results suggest that genetic programming could be used to develop viable teams for the competition, however, much more work is needed on the higher level functions, fitness measures and fitness evaluation. %K genetic algorithms, genetic programming %R 10.1007/3-540-45603-1_37 %U http://dx.doi.org/10.1007/3-540-45603-1_37 %P 319-324 %0 Conference Proceedings %T Pyramid search: Finding solutions for deceptive problems quickly in genetic programming %A Ciesielski, Vic %A Li, Xiang %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 ciesielski:2003:psfsfdpqigp %X In deceptive problems many runs lead to suboptimal solutions and it can be difficult to escape from these local optima and find the global best solution. We propose a pyramid search strategy for these kinds of problems. In the pyramid strategy a number of populations are initialised and independently evolved for a number of generations at which point the worst performing populations are discarded. This evolve/discard process is continued until the problem is solved or one population remains. We show that for a number of deceptive problems the pyramid strategy results in a higher probability of success with fewer evaluations and a lower standard deviation of the number evaluations to success than the conventional approach of running to a maximum number of generations and then restarting. %K genetic algorithms, genetic programming, Australia, Computer science, Information technology, Parallel processing, probability, search problems, deceptive problem, discard process, evolve process, probability, pyramid search strategy, standard deviation %R 10.1109/CEC.2003.1299767 %U http://dx.doi.org/10.1109/CEC.2003.1299767 %P 936-943 %0 Conference Proceedings %T Experiments with Explicit For-loops in Genetic Programming %A Ciesielski, Vic %A Li, Xiang %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 ciesielski:2004:ewefigp %X Evolving programs with explicit loops presents major difficulties, primarily due to the massive increase in the size of the search space. Fitness evaluation becomes computationally expensive. We have investigated ways of dealing with these poblems by the evolution of for-loops of increasing semantic complexity. We have chosen two problems – a modified Santa Fe ant problem and a sorting problem – which have natural looping constructs in their solution and a solution without loops is not possible unless the tree depth is very large. We have shown that by conrolling the complexity of the loop structures it is possible to evolve smaller and more understandable programs for these problems. %K genetic algorithms, genetic programming, Theory of evolutionary algorithms %R 10.1109/CEC.2004.1330897 %U http://dx.doi.org/10.1109/CEC.2004.1330897 %P 494-501 %0 Conference Proceedings %T Understanding Evolved Genetic Programs for a Real World Object Detection Problem %A Ciesielski, Victor %A Innes, Andrew %A John, Sabu %A Mamutil, John %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:CiesielskiIJM05 %X We describe an approach to understanding evolved programs for a real world object detection problem, that of finding orthodontic landmarks in cranio-facial X-Rays. The approach involves modifying the fitness function to encourage the evolution of small programs, limiting the function set to a minimal number of operators and limiting the number of terminals (features). When this was done for two landmarks, an easy one and a difficult one, the evolved programs implemented a linear function of the features. Analysis of these linear functions revealed that underlying regularities were being captured and that successful evolutionary runs usually terminated with the best programs implementing one of a small number of underlying algorithms. Analysis of these algorithms revealed that they are a realistic solution to the object detection problem, given the features and operators available. %K genetic algorithms, genetic programming: Poster %R 10.1007/978-3-540-31989-4_32 %U http://dx.doi.org/10.1007/978-3-540-31989-4_32 %P 351-360 %0 Conference Proceedings %T Analysis of the Superiority of Parameter Optimization over Genetic Programming for a Difficult Object Problem %A Ciesielski, Vic %A Wijesinghe, Gayan %A Innes, Andrew %A John, Sabu %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 16 21 jul %I IEEE Press %C Vancouver %@ 0-7803-9487-9 %F Ciesielski:2006:CEC %X We describe a progression of solutions to a difficult object detection problem, that of locating landmarks in X-Rays used in orthodontic treatment planning. In our first formulation an object detector was a genetic program whose inputs were a number of attributes computed from a scanning window. We used a rich function set comprising + - times divide min; max; ifthenelse. Experimentation with different function sets revealed that using the function set + - gave detectors that were almost as accurate. Such detectors are essentially a linear combination of attributes so we also implemented a parameter optimisation solution with a particle swarm optimiser. Contrary to expectation, the PSO detectors are more accurate and smaller than the GP ones. Our analysis of the reasons for this reveals that (1) the PSO approach involves a considerably smaller search space than the GP approach, (2) in the PSO approach there is a 1-1 mapping between genotype and phenotype while in the GP approach this mapping is many-1 and many semantically equivalent potential solutions are evaluated, (3) the fitness landscape for PSO is a good one for search in that solutions are distributed in areas of high fitness that are easy to locate while the GP landscape is much more difficult to characterise and areas of high fitness more difficult to find. %K genetic algorithms, genetic programming %R 10.1109/CEC.2006.1688454 %U http://dx.doi.org/10.1109/CEC.2006.1688454 %P 4407-4414 %0 Conference Proceedings %T Data Mining of Genetic Programming Run Logs %A Ciesielski, Vic %A Li, Xiang %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:ciesielski %X We have applied a range of data mining techniques to a data base of log file records created from genetic programming runs on twelve different problems. We have looked for unexpected patterns, or golden nuggets in the data. Six were found. The main discoveries were a surprising amount of evaluation of duplicate programs across the twelve problems and one case of pathological behaviour which suggested a review of the genetic programming configuration. For problems with expensive fitness evaluation, the results suggest that there would be considerable speedup by caching evolved programs and fitness values. A data mining analysis performed routinely in a GP application could identify problems early and lead to more effective genetic programming applications. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-71605-1_26 %U http://dx.doi.org/10.1007/978-3-540-71605-1_26 %P 281-290 %0 Journal Article %T Linear genetic programming, Springer Science+Business Media, Markus Brameier and Wolfgang Banzhaf, 2007, 315 pp, Book Series: Genetic Programming, Hard Cover, 62.95, ISBN 0-387-31029-0 %A Ciesielski, Vic %J Genetic Programming and Evolvable Machines %D 2008 %8 mar %V 9 %N 1 %@ 1389-2576 %F Ciesielski:2008:GPEM %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-007-9036-8 %U https://rdcu.be/dR8iD %U http://dx.doi.org/10.1007/s10710-007-9036-8 %P 105-106 %0 Journal Article %T Genetic programming approach to predict a model acidolysis system %A Ciftci, Ozan Nazim %A Fadiloglu, Sibel %A Gogus, Fahrettin %A Guven, Aytac %J Engineering Applications of Artificial Intelligence %D 2009 %V 22 %N 4-5 %@ 0952-1976 %F Ciftci:2009:EAEI %X This paper models acidolysis of triolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase. A gene-expression programming (GEP), which is an extension to genetic programming (GP)-based model was developed for the prediction of the concentration of major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO), 1,3-dipalmitoyl-2-oleoyl-glycerol (POP) and triolein (OOO). Substrate ratio (SR), reaction temperature (T) and reaction time (t) were used as input parameters. The predicted models were able to predict the progress of the reactions with a mean standard error (MSE) of less than 1.0 and R of 0.978. Explicit formulation of proposed GEP models was also presented. Considerable good performance was achieved in modeling acidolysis reaction by using GEP. The predictions of proposed GEP models were compared to those of neural network (NN) modeling, and strictly good agreement was observed between the two predictions. Statistics and scatter plots indicate that the new GEP formulations can be an alternative to experimental models. %K genetic algorithms, genetic programming, gene expression programming, Acidolysis %9 journal article %R 10.1016/j.engappai.2009.01.010 %U http://www.sciencedirect.com/science/article/B6V2M-4VTVJNC-2/2/5894a9c11ade2e94a1ff09a18b63a062 %U http://dx.doi.org/10.1016/j.engappai.2009.01.010 %P 759-766 %0 Journal Article %T Computer-aided derivation of the optimal mathematical models to study gear-pair dynamic by using genetic programming %A Ciglaric, I. %A Kidric, A. %J Structural and Multidisciplinary Optimization %D 2006 %V 32 %N 2 %F ciglaric:2006:SMO %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s00158-006-0004-3 %U http://link.springer.com/article/10.1007/s00158-006-0004-3 %U http://dx.doi.org/10.1007/s00158-006-0004-3 %0 Journal Article %T Algorithmic Clustering of Music Based on String Compression %A Cilibrasi, Rudi %A Vitanyi, Paul %A de Wolf, Ronald %J Computer Music Journal %D 2004 %8 Winter %V 28 %N 4 %F Cilibrasi:2004:CMJ %X All musical pieces are similar, but some are more similar than others. Apart from serving as an infinite source of discussion (”Haydn is just like Mozart No, he’s not!”), such similarities are also crucial for the design of efficient music information retrieval systems. The amount of digitised music available on the Internet has grown dramatically in recent years, both in the public domain and on commercial sites; Napster and its clones are prime examples. %K genetic algorithms, genetic programming, complearn %9 journal article %U http://homepages.cwi.nl/~paulv/papers/music.pdf %P 49-67 %0 Generic %T Automatic Meaning Discovery Using Google %A Cilibrasi, Rudi %A Vitanyi, Paul M. B. %D 2005 %8 15 mar %F cs.CL/0412098 %O v2 %X We have found a method to automatically extract the meaning of words and phrases from the world-wide-web using Google page counts. The approach is novel in its unrestricted problem domain, simplicity of implementation, and manifestly ontological underpinnings. The world-wide-web is the largest database on earth, and the latent semantic context information entered by millions of independent users averages out to provide automatic meaning of useful quality. We demonstrate positive correlations, evidencing an underlying semantic structure, in both numerical symbol notations and number-name words in a variety of natural languages and contexts. Next, we demonstrate the ability to distinguish between colours and numbers, and to distinguish between 17th century Dutch painters; the ability to understand electrical terms, religious terms, and emergency incidents; we conduct a massive experiment in understanding WordNet categories; and finally we demonstrate the ability to do a simple automatic English-Spanish translation. %K genetic algorithms, genetic programming, randomised hill-climbing, SVM, support vector machines, complearn, Computation and Language, Artificial Intelligence, Databases, Information Retrieval, Learning %U http://www.arxiv.org/abs/cs.CL/0412098 %0 Journal Article %T Clustering by Compression %A Cilibrasi, Rudi %A Vitanyi, Paul M. B. %J IEEE Transactions on Information Theory %D 2005 %8 apr %V 51 %N 4 %@ 0018-9448 %F Cilibrasi:2005:ITIT %X We present a new method for clustering based on compression. The method doesn’t use subject-specific features or background knowledge, and works as follows: First, we determine a parameter-free, universal, similarity distance, the normalized compression distance or NCD , computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalised information distance, co-developed by one of the authors, is provably optimal. However, the optimality comes at the price of using the non-computable notion of Kolmogorov complexity. We propose axioms to capture the real-world setting, and show that the NCD approximates optimality. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (binary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors. In genomics we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis. %K genetic algorithms, genetic programming, complearn, universal dissimilarity distance, normalised compression distance, hierarchical unsupervised clustering, quartet tree method, parameter-free data-mining, heterogenous data analysis, Kolmogorov complexity %9 journal article %R 10.1109/TIT.2005.844059 %U http://homepages.cwi.nl/~paulv/papers/cluster.pdf %U http://dx.doi.org/10.1109/TIT.2005.844059 %P 1523-1545 %0 Conference Proceedings %T A New Quartet Tree Heuristic for Hierarchical Clustering %A Cilibrasi, Rudi %A Vitanyi, Paul %S Principled methods of trading exploration and exploitation Workshop %D 2005 %8 June 7 jul %C London %F Cilibrasi:2005:pascal %X We consider the problem of constructing an an optimal-weight tree from the 3Chose(n,4) weighted quartet topologies on n objects, where optimality means that the summed weight of the embedded quartet topologies is optimal (so it can be the case that the optimal tree embeds all quartets as non-optimal topologies). We present a heuristic for reconstructing the optimal-weight tree, and a canonical manner to derive the quartet-topology weights from a given distance matrix. The method repeatedly transforms a bifurcating tree, with all objects involved as leaves, achieving a monotonic approximation to the exact single globally optimal tree. This contrasts to other heuristic search methods from biological phylogeny, like DNAML or quartet puzzling, which, repeatedly, incrementally construct a solution from a random order of objects, and subsequently add agreement values. We do not assume that there exists a true bifurcating supertree that embeds each quartet in the optimal topology, or represents the distance matrix faithfully|not even under the assumption that the weights or distances are corrupted by a measuring process. Our aim is to hierarchically cluster the input data as faithfully as possible, both phylogenetic data and data of completely different types. In our experiments with natural data, like genomic data, texts or music, the global optimum appears to be reached. Our method is capable of handling over 100 objects, possibly up to 1000 objects, while no existing quartet heuristic can computionally approximate the exact optimal solution of a quartet tree of more than about 20-30 objects without running for years. The method is implemented and available as public software. %K genetic algorithms, genetic programming, Computational, Information-Theoretic Learning with Statistics, Learning/Statistics, Optimisation, Theory, Algorithms %U http://www.cwi.nl/~paulv/papers/quartet.pdf %0 Conference Proceedings %T A New Quartet Tree Heuristic for Hierarchical Clustering %A Cilibrasi, Rudi %A Vitanyi, Paul M. B. %Y Arnold, Dirk V. %Y Jansen, Thomas %Y Vose, Michael D. %Y Rowe, Jonathan E. %S Theory of Evolutionary Algorithms %S Dagstuhl Seminar Proceedings %D 2006 %8 May 10 feb %N 06061 %I Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany %C Dagstuhl, Germany %F cilibrasi_et_al:DSP:2006:598 %O $<$http://drops.dagstuhl.de/opus/volltexte/2006/598$>$ [date of citation: 2006-01-01] %X We present a new quartet heuristic for hierarchical clustering from a given distance matrix. We determine a dendrogram (ternary tree) by a new quartet method and a fast heuristic to implement it. We do not assume that there is a true ternary tree that generated the distances and which we with to recover as closely as possible. Our aim is to model the distance matrix as faithfully as possible by the dendrogram. Our algorithm is essentially randomised hill-climbing, using parallelised Genetic Programming, where undirected trees evolve in a random walk driven by a prescribed fitness function. Our method is capable of handling up to 60–80 objects in a matter of hours, while no existing quartet heuristic can directly compute a quartet tree of more than about 20–30 objects without running for years. The method is implemented and available as public software at www.complearn.org. We present applications in many areas like music, literature, bird-flu (H5N1) virus clustering, and automatic meaning discovery using Google. %K genetic algorithms, genetic programming, hierarchical clustering, quartet tree method %U http://drops.dagstuhl.de/opus/volltexte/2006/598/pdf/06061.VitanyiPaulB.Paper.598.pdf %0 Thesis %T Statistical Inference Through Data Compression %A Cilibrasi, Rudi Langston %D 2007 %8 23 feb %C Plantage Muidergracht 24, 1018 TV, Amsterdam, Holland %C Institute for Logic, Language and Computation, Universiteit van Amsterdam %F Cilibrasi:thesis %X This thesis provides a breadth-first tour of artificial intelligence techniques using ordinary data compression programs like zip. Using mathematical theory such as Kolmogorov Complexity and Shannon’s Coding Theory, we arrive at a unique and generic perspective on universal learning with a plethora of real examples. Included are results from literature, astronomy, animal and virus evolution, linguistics, semantics, and music. An open source software package, CompLearn, is available for download so that interested readers may continue the research themselves in their own applications. %K genetic algorithms, genetic programming %9 Ph.D. thesis %U https://eprints.illc.uva.nl/id/eprint/2056/ %0 Journal Article %T The Google Similarity Distance %A Cilibrasi, Rudi L. %A Vitanyi, Paul M. B. %J IEEE Transactions on Knowledge and Data Engineering %D 2007 %8 mar %V 19 %N 3 %@ 1041-4347 %F Cilibrasi:2007:ieeeTKDE %X Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers, the equivalent of ’society’ is ’database,’ and the equivalent of ’use’ is ’a way to search the database’. We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts, we use the World Wide Web (WWW) as the database, and Google as the search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the WWW using Google page counts. The WWW is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colours and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87 percent with the expert crafted WordNet categories %K genetic algorithms, genetic programming, Kolmogorov complexity, wordnet, artificial common sense, Accuracy comparison with WordNet categories, automatic classification and clustering, automatic meaning discovery using Google, automatic relative semantics, automatic translation, dissimilarity semantic distance, Google search, Google distribution via page hit counts, Google code, Kolmogorov complexity, normalized compression distance (NCD ), normalized information distance (NID), normalized Google distance (NGD), meaning of words and phrases extracted from the Web, parameter-free data mining, universal similarity metric %9 journal article %R 10.1109/TKDE.2007.48 %U http://dx.doi.org/10.1109/TKDE.2007.48 %P 370-383 %0 Thesis %T Maintaining population diversity in evolutionary algorithms via epigenetics and speciation %A Cilliers, Michael %D 2022 %8 nov %C South Africa %C Academy Computer Science and Software Engineering, University of Johannesburg %G eng %F cilliers:thesis %X When working with evolutionary algorithms, a balance between exploration and exploitation has to be maintained. The main driver of exploration is the reproduction operation, which is dependent on population diversity to enable effective exploration. The lack of population diversity can thus become a problem if exploration is required during the later stages of the algorithm. One example where this is relevant is when working with dynamic environments. If a change occurs in the environment, the algorithm might require some exploration to adapt to the change. This research aims to demonstrate that if an algorithm is able to maintain population diversity during the latter of its execution, it will be able to adapt to changes more effectively. This research produced a set of novel algorithms to explore different methods of maintaining population diversity. The first group of developed algorithms draws inspiration from gene methylation by adding non-coding genes to the chromosome. The non-coding genes can then act as a reservoir of genetic diversity after the algorithm converges and the diversity in the expressed genes diminishes. The second algorithm implemented parapatric speciation in an evolutionary algorithm. The algorithm attempts to divide the population into separate species, which then populate different areas of the search space. Testing of the developed algorithm showed that it is possible to maintain more diversity in the latter generations of the algorithm. The developed algorithms were also able to adapt more effectively to change in the environment, indicating that the algorithms were able to use the increased diversity when required. Finally, the research showed that the maintaining of diversity is not the only option for allowing exploration in the latter stages of an algorithm. Algorithms that are less effective at maintaining diversity, but are able to rapidly produce diversity when required are also able to shift focus to exploration when adapting to change in the environment. %K genetic algorithms, genetic programming, Evolutionary computation, Species %9 Ph.D. thesis %U https://hdl.handle.net/10210/504017 %0 Thesis %T Enterprise information integration: on discovering links using genetic programming %A Cimmino Arriaga, Andrea Jesus %D 2019 %8 sep %C Sevilla, Spain %C Departamento de Lenguajes y Sistemas Informaticos, Universidad de Sevilla %F Cimmino:thesis %X Both established and emergent business rely heavily on data, chiefly those that wish to become game changers. The current biggest source of data is the Web, where there is a large amount of sparse data. The Web of Data aims at providing a unified view of these islands of data. To realise this vision, it is required that the resources in different data sources that refer to the same real-world entities must be linked, which is they key factor for such a unified view. Link discovery is a trending task that aims at finding link rules that specify whether these links must be established or not. Currently there are many proposals in the literature to produce these links, especially based on meta-heuristics. Unfortunately, creating proposals based on meta-heuristics is not a trivial task, which has led to a lack of comparison between some well-established proposals. On the other hand, it has been proved that these link rules fall short in cases in which resources that refer to different real-world entities are very similar or vice versa. In this dissertation, we introduce several proposals to address the previous lacks in the literature. On the one hand we, introduce Eva4LD, which is a generic framework to build genetic programming proposals for link discovery; which are a kind of meta-heuristics proposals. Furthermore, our framework allows to implement many proposals in the literature and compare their results fairly. On the other hand, we introduce Teide, which applies effectively the link rules increasing significantly their precision without dropping their recall significantly. Unfortunately, Teide does not learn link rules, and applying all the provided link rules is computationally expensive. Due to this reason we introduce Sorbas, which learns what we call contextual link rules. %K genetic algorithms, genetic programming, Eva4LD, Web of Data, Sorbas, Teide %9 Ph.D. thesis %U https://idus.us.es/handle/11441/92456 %0 Book %T Enterprise Information Integration: On Discovering Links Using Genetic Programming %A Cimmino, Andrea %A Corchuelo, Rafael %D 2020 %7 1 %I Dykinson, S.L. %C Madrid, Spain %F Cimmino:book %X Both established and emergent business rely heavily on data, chiefly those that wish to become game changers. The current biggest source of data is the Web, where there is a large amount of sparse data. The Web, where there is a large amount of sparse data. To realise this vision, it is required that the resources in different data sources that refer to the same real-world entities must be linked which is the key factor for such a unified view. Link discovery is a trending task that aims at finding link rules that specify whether these links must be established or not. Currently there are many proposals in the literature to produce these links, especially based on meta-heuristics. Unfortunately creating proposals based on meta-heuristics is not a trivial task, which has led to a lack of comparison between some well-established proposals. On the other hand, it has been proved that these link rules fall short in cases in which resources that refer to different real-world entities are very similar or vice versa. In this dissertation, we introduce several proposals to address the previous lacks in the literature. On the one hand we, introduce Eva4LD, , which is a generic framework to build generic programming proposals for link discovery; which are a kind of meta-heuristics proposals. Furthermore, our framework allows to implement many proposals in the literature and compare their results fairly. On the other hand, we introduce Teide, which applies effectively the link rules increasing significantly their precision without dropping their recall significantly. Unfortunately, Teide does not learn link rules, and applying all the provided link rules is computationally expensive. Due to this reason we introduce Sorbas, which learns what we call contextual link rules. %K genetic algorithms, genetic programming, Eva4LD, Teide, Sorbas %U https://www.dykinson.com/libros/enterprise-information-integration/9788413247748/ %0 Journal Article %T Genetic programming approach to evaluate complexity of texture images %A Ciocca, Gianluigi %A Corchs, Silvia %A Gasparini, Francesca %J J. Electronic Imaging %D 2016 %V 25 %N 6 %F DBLP:journals/jei/CioccaCG16 %K genetic algorithms, genetic programming %9 journal article %R 10.1117/1.JEI.25.6.061408 %U https://doi.org/10.1117/1.JEI.25.6.061408 %U http://dx.doi.org/10.1117/1.JEI.25.6.061408 %P 061408 %0 Journal Article %T Methodology of an Energy Efficient-Embedded Self-Adaptive Software Design for Multi-Cores and Frequency-Scaling Processors Used in Real-Time Systems %A Ciopinski, Leszek %J Electronics %D 2025 %V 14 %N 3 %@ 2079-9292 %F ciopinski:2025:Electronics %X In a kind of system, where strong time constraints exist, very often, worst-case design is applied. It could drive to the suboptimal usage of resources. In previous work, the mechanism of self-adaptive software that is able to reduce this was presented. This paper introduces a novel extension of the method for self-adaptive software synthesis applicable for real-time multicore embedded systems with dynamic voltage and frequency scaling (DVFS). It is based on a multi-criteria approach to task scheduling, optimising both energy consumption and proof against time delays. The method can be applied to a wide range of embedded systems, such as multimedia systems or Industrial Internet of Things (IIoT). The main aim of this research is to find the method of automatic construction of the task scheduler that is able to minimise energy consumption during the varying execution times of each task. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/electronics14030556 %U https://www.mdpi.com/2079-9292/14/3/556 %U http://dx.doi.org/10.3390/electronics14030556 %P ArticleNo.556 %0 Conference Proceedings %T A scalable symbolic expression tree interpreter for the heuristiclab optimization framework %A Cirillo, Simone %A Lloyd, Stefan %Y Wagner, Stefan %Y Affenzeller, Michael %S GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft) %D 2014 %8 December 16 jul %I ACM %C Vancouver, BC, Canada %F Cirillo:2014:GECCOcomp %X In this paper we describe a novel implementation of the Interpreter class for the metaheuristic optimisation framework HeuristicLab, comparing it with the three existing interpreters provided with the framework. The Interpreter class is an internal software component used by HeuristicLab for the evaluation of the symbolic expression trees on which its Genetic Programming (GP) implementation relies. The proposed implementation is based on the creation and compilation of a .NET Expression Tree. We also analyse the Interpreters’ performance, evaluating the algorithm execution times on GP Symbolic Regression problems for different run settings. Our implementation results to be the fastest on all evaluations, with comparatively better performance the larger the run population size, dataset length and tree size are, increasing HeuristicLab’s computational efficiency for large problem setups. %K genetic algorithms, genetic programming %R 10.1145/2598394.2605692 %U http://doi.acm.org/10.1145/2598394.2605692 %U http://dx.doi.org/10.1145/2598394.2605692 %P 1141-1148 %0 Generic %T Evolving intraday foreign exchange trading strategies utilizing multiple instruments price series %A Cirillo, Simone %A Lloyd, Stefan %A Nordin, Peter %D 2014 %8 August %I ArXiv %F DBLP:journals/corr/CirilloLN14 %X We propose a Genetic Programming architecture for the generation of foreign exchange trading strategies. The system’s principal features are the evolution of free-form strategies which do not rely on any prior models and the use of price series from multiple instruments as input data. This latter feature constitutes an innovation with respect to previous works documented in literature. In this article we use Open, High, Low, Close bar data at a 5 minutes frequency for the AUD.USD, EUR.USD, GBP.USD and USD.JPY currency pairs. We will test the implementation analysing the in-sample and out-of-sample performance of strategies for trading the USD.JPY obtained across multiple algorithm runs. We will also evaluate the differences between strategies selected according to two different criteria: one relies on the fitness obtained on the training set only, the second one makes use of an additional validation dataset. Strategy activity and trade accuracy are remarkably stable between in and out of sample results. From a profitability aspect, the two criteria both result in strategies successful on out-of-sample data but exhibiting different characteristics. The overall best performing out-of-sample strategy achieves a yearly return of 19percent. %K genetic algorithms, genetic programming %U http://arxiv.org/abs/1411.2153 %0 Journal Article %T Solar radiation prediction using multi-gene genetic programming approach %A Citakoglu, Hatice %A Babayigit, Bilal %A Haktanir, Nese Acanal %J Theoretical and Applied Climatology %D 2020 %V 142 %F Citakoglu:2020:TAC %X Accurate estimation of solar radiation both spatially and temporally is important for engineering studies related to climate and energy. The multi-gene genetic programming (MGGP) is proposed as a new compact method for this purpose, which is verified to yield more accurate solar radiation estimations in Turkey. Meteorological data such as extraterrestrial solar radiation, sunshine duration, mean of monthly maximum sunny hours, long-term mean of monthly maximum air temperatures, long-term mean of monthly minimum air temperatures, monthly mean air temperature, and monthly mean moisture data are selected as the MGGP model inputs. In the prediction models, the meteorological data measured from 163 stations in seven climate areas of Turkey over the period 1975 to 2015 are used. The MGGP model results for solar radiation prediction are found to be more accurate than the values given by some conventional empirical equations such as Abdalla, Angstrom, and Hargreaves-Samani. The performance of MGGP is also assessed for Turkey by single-data and multi-data models. The multi-data models of MGGP and the calibrated empirical equations are found to be more successful than the single-data models for solar radiation prediction. %K genetic algorithms, genetic programming, Multi-gene genetic programming, Solar radiation, Empirical equations, Turkey %9 journal article %R 10.1007/s00704-020-03356-4 %U https://avesis.erciyes.edu.tr/publication/details/2c40546c-794d-4d59-98b3-87a478e63ac9/solar-radiation-prediction-using-multi-gene-genetic-programming-approach %U http://dx.doi.org/10.1007/s00704-020-03356-4 %P 885-897 %0 Journal Article %T Developing numerical equality to regional intensity-duration-frequency curves using evolutionary algorithms and multi-gene genetic programming %A Citakoglu, Hatice %A Demir, Vahdettin %J Acta Geophysica %D 2023 %V 71 %N 1 %F citakoglu:2023:AG %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s11600-022-00883-8 %U http://link.springer.com/article/10.1007/s11600-022-00883-8 %U http://dx.doi.org/10.1007/s11600-022-00883-8 %0 Book Section %T High-significance Averages of Event-Related Potential via Genetic Programming %A Citi, Luca %A Poli, Riccardo %A Cinel, Caterina %E Riolo, Rick L. %E O’Reilly, Una-May %E McConaghy, Trent %B Genetic Programming Theory and Practice VII %S Genetic and Evolutionary Computation %D 2009 %8 14 16 may %I Springer %C Ann Arbor %F Citi:2009:GPTP %X In this paper we use register-based genetic programming with memory-with memory to discover probabilistic membership functions that are used to divide up data-sets of event-related potentials recorded via EEG in psycho-physiological experiments based on the corresponding response times. The objective is to evolve membership functions which lead to maximising the statistical significance with which true brain waves can be reconstructed when averaging the trials in each bin. Results show that GP can significantly improve the fidelity with which ERP components can be recovered. %K genetic algorithms, genetic programming, Event-related potentials, Register-based GP, Memory-with-Memory %R 10.1007/978-1-4419-1626-6_9 %U http://dx.doi.org/10.1007/978-1-4419-1626-6_9 %P 135-157 %0 Report %T An Adaptive Document Classification Agent %A Clack, Chris %A Farringdon, Jonny %A Lidwell, Peter %A Yu, Tina %D 1996 %8 21 jun %N RN/96/45 %I University College London %C Computer Science, Gower Street, London, WC1E 6BT, UK %F clack:1996:adca %O Submitted to BCS-ES96 %X The development of an intelligent text classification application is discussed which uses genetic programming methods. Learning capabilities are used to effect a adaptive system in order to meet the needs of dynamic-information users. Deriving structure and priority from text, target environments are discussed where large volumes of (on-line) textual documents are manipulated. %K genetic algorithms, genetic programming %9 Research Note %U https://web.archive.org/web/19970817124736/http://www.cs.ucl.ac.uk:80/staff/J.Farringdon/GP/Papers-es96/paper02.html %0 Report %T Advanced Technology Support for Information Management at Friends of the Earth %A Clack, Chris D. %A Gould, S. J. %A Lidwell, Peter R. %A McDonnell, Janet T. %D 1996 %N RN/96/48 %I University College London %C Computer Science, Gower Street, London, WC1E 6BT, UK %F clack:1996:rn38 %X INTRODUCTION We report early results from a project to study the application of advanced technology to enhance information management in a medium sized enterprise where the collection, analysis and dissemination of information are key business processes. Our two-year TCD-funded project is a collaboration between University College London (UCL) and Friends of the Earth (FOE), a research and campaigning organisation with 65 full time employees and a turnover of about 3.5 million pounds. We explain our strategy for re-engineering information management at Foe and present three example projects which demonstrate the application of innovative IT solutions to problems associated with fundamental working practices. %K genetic algorithms, genetic programming %9 Research Note %U ftp://bells.cs.ucl.ac.uk/functional/papers/Published/rn_96_38pagenums.pdf.gz %0 Report %T Autonomous Document Classification for Business %A Clack, Chris %A Farringdon, Jonny %A Lidwell, Peter %A Yu, Tina %D 1996 %8 jun %N RN/96/48 %I University College London %C Computer Science, Gower Street, London, WC1E 6BT, UK %F clack:1996:adcb %O Appears in Autonomous Agents ’97 %X With the continuing exponential growth of the Internet and the more recent growth of business Intranets, the commercial world is becoming increasingly aware of the problem of electronic information overload. This has encouraged interest in developing agents/softbots that can act as electronic personal assistants and can develop and adapt representations of users information needs, commonly known as profiles. As the result of collaborative research with Friends of the Earth, an environmental issues campaigning organisation, we have developed a general purpose information classification agent architecture and have applied it to the problem of document classification and routing. Collaboration with Friends of the Earth allows us to test our ideas in a non-academic context involving high volumes of documents. We use the technique of genetic programming (GP), (Koza and Rice 1992), to evolve classifying agents. This is a novel approach for document classification, where each agent evolves a parse-tree representation of a user’s particular information need. The other unusual feature of our research is the longevity of our agents and the fact that they undergo a continual training process; feedback from the user enables the agent to adapt to the user’s long-term information requirements. %K genetic algorithms, genetic programming, Softbot, agent architecture, pattern recognition, long term adaptation and learning %9 Research Note %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/clack_1997_adcb.pdf %0 Conference Proceedings %T Autonomous Document Classification for Business %A Clack, Chris %A Farringdon, Jonny %A Lidwell, Peter %A Yu, Tina %Y Johnson, W. Lewis %S The First International Conference on Autonomous Agents (Agents ’97) %D 1997 %8 feb 5 8 %I ACM Press %C Marina del Rey, California, USA %@ 0-89791-877-0 %F clack:1997:adcb %X With the continuing exponential growth of the Internet and the more recent growth of business Intranets, the commercial world is becoming increasingly aware of the problem of electronic information overload. This has encouraged interest in developing agents/softbots that can act as electronic personal assistants and can develop and adapt representations of users information needs, commonly known as profiles. As the result of collaborative research with Friends of the Earth, an environmental issues campaigning organisation, we have developed a general purpose information classification agent architecture and have applied it to the problem of document classification and routing. Collaboration with Friends of the Earth allows us to test our ideas in a non-academic context involving high volumes of documents. We use the technique of genetic programming (GP), (Koza and Rice 1992), to evolve classifying agents. This is a novel approach for document classification, where each agent evolves a parse-tree representation of a user’s particular information need. The other unusual features of our research are the longevity of our agents and the fact that they undergo a continual training process; feedback from the user enables the agent to adapt to the user’s long-term information requirements. %K genetic algorithms, genetic programming %R 10.1145/267658.267716 %U ftp://ftp.cs.ucl.ac.uk/functional/papers/Published/AA97.pdf.gz %U http://dx.doi.org/10.1145/267658.267716 %P 201-208 %0 Report %T Software – The Next Generation: Evolving Document Classification %A Clack, Chris %D 1997 %8 apr %I UCL, Andersen Consulting %C University College London, Gower Street, London %F clack:1997:edc %K genetic algorithms, genetic programming %9 white paper %0 Book Section %T Predator-Prey Interactions in a Simulated World %A Clark, Adam %E Koza, John R. %B Genetic Algorithms and Genetic Programming at Stanford 1995 %D 1995 %8 November %I Stanford Bookstore %C Stanford, California, 94305-3079 USA %@ 0-18-195720-5 %F clark:1995:PISW %K genetic algorithms, genetic programming %P 59-64 %0 Journal Article %T Evolution of Algebraic Terms 1: Term to Term Operation Continuity %A Clark, David M. %J International Journal of Algebra and Computation %D 2013 %8 aug %V 23 %N 05 %I World Scientific Publishing Company %@ 0218-1967 %F doi:10.1142/S0218196713500227 %X This study was inspired by recent successful applications of evolutionary computation to the problem of finding terms to represent arbitrarily given operations on a primal groupoid. Evolution requires that small changes in a term result in small changes in the associated term operation. We prove a theorem giving two readily testable conditions under which a groupoid must have this continuity property, and offer evidence that most primal groupoids satisfy these conditions. %K genetic algorithms, genetic programming, nand, theory, Evolutionary computation, term generation, term operation, primal algebras %9 journal article %R 10.1142/S0218196713500227 %U http://www.worldscientific.com/doi/abs/10.1142/S0218196713500227 %U http://dx.doi.org/10.1142/S0218196713500227 %P 1175-1205 %0 Journal Article %T Evolution of algebraic terms 2: Deep drilling algorithm %A Clark, David M. %A Keijzer, Maarten %A Spector, Lee %J International Journal of Algebra and Computation %D 2016 %V 26 %N 6 %I World Scientific Publishing Company %@ 0218-1967 %F Evolution_Algebraic_Terrms_2 %X The Deep Drilling Algorithm (DDA) is an efficient non-evolutionary algorithm, extracted from previous work with evolutionary algorithms, that takes as input a finite groupoid and an operation over its universe, and searches for a term representing that operation. We give theoretical and experimental evidence that this algorithm is successful for all idemprimal term continuous groupoids, which appear to be almost all finite groupoids, and that the DDA is seriously compromised or fails for most finite groupoids not meeting both of these conditions. See our online version of the DDA at http://hampshire.edu/lspector/dda %K genetic algorithms, genetic programming, theory, Evolutionary computation, term operation, idemprimality, primal algebras %9 journal article %R 10.1142/S021819671650048X %U http://dx.doi.org/10.1142/S021819671650048X %P 1141-1176 %0 Journal Article %T Evolution of algebraic terms 3: Term continuity and beam algorithms %A Clark, David M. %A Spector, Lee %J International Journal of Algebra and Computation %D 2018 %V 5 %N 28 %I World Scientific Publishing Company %@ 0218-1967 %F Evolution_Algebraic_Terrms_3 %X The first paper in this series introduced the notion of term to term operation continuity for finite groupoids, and proved that two testable conditions on a finite groupoid imply that it is term continuous (TC). The second presented an evolution inspired algorithm for finding terms for operations, and gave experimental evidence that, in general, it was successful exactly when the groupoid was both idemprimal and TC. Here we describe a new class of algorithms for finding terms which brings these results together. Theorems about idemprimality and term continuity show how each of these two properties impact our algorithms. They lead to a final explanation for the success of our algorithms when the groupoid is both idemprimal and TC. %K genetic algorithms, genetic programming, theory, Evolutionary computation, term operation, idemprimality, term continuity, randomizing algorithms %9 journal article %R 10.1142/S0218196718500352 %U http://dx.doi.org/10.1142/S0218196718500352 %P 759-790 %0 Book Section %T Cartesian Genetic Programming for Control Engineering %A Clarke, Tim %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 Clarke:2017:miller %X Genetic programming has a proven ability to discover novel solutions to engineering problems. The author has worked with Julian F. Miller, together with some undergraduate and postgraduate students, over the last ten or so years in exploring innovation through evolution, using Cartesian Genetic Programming (CGP). Our co-supervisions and private meetings stimulated many discussions about its application to a specific problem domain: control engineering. Initially, we explored the design of a flight control system for a single rotor helicopter, where the author has considerable theoretical and practical experience. The challenge of taming helicopter dynamics (which are non-linear, highly cross-coupled and unstable) seemed ideally suited to the application of CGP. However, our combined energies drew us towards the more fundamental issues of how best to generalise the problem with the objective of freeing up the innovation process from constrictions imposed by conventional engineering thinking. This chapter provides an outline of our thoughts and hopefully may motivate a reader out there to progress this still embryonic research. The scene is set by considering a simple class of problems: the single-input, single-output, linear, time-invariant system. %K genetic algorithms, genetic programming, Cartesian Genetic Programming %R 10.1007/978-3-319-67997-6_7 %U http://dx.doi.org/10.1007/978-3-319-67997-6_7 %P 157-173 %0 Journal Article %T Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies %A Claveria, Oscar %A Monte, Enric %A Torra, Salvador %J Eastern European Economics %D 2016 %V 54 %N 2 %@ 0012-8775 %F Claveria:2016:EEE %X Tendency surveys are the main source of agents expectations. This study has a twofold aim. First, it proposes a new method to quantify survey-based expectations by means of symbolic regression (SR) via genetic programming. Second, it combines the main SR-generated indicators to estimate the evolution of GDP, obtaining the best results for the Czech Republic and Hungary. Finally, it assesses the impact of the 2008 financial crisis, finding that the capacity of agents expectations to anticipate economic growth in most Central and Eastern European economies improved after the crisis. %K genetic algorithms, genetic programming, Economic Climate Indicators, evolutionary algorithms, forecasting, symbolic regression, survey-based expectations, tendency surveys %9 journal article %U https://doi.org/10.1080/00128775.2015.1136564 %P 171-189 %0 Journal Article %T Using survey data to forecast real activity with evolutionary algorithms. A cross-country analysis %A Claveria, Oscar %A Monte, Enric %A Torra, Salvador %J Journal of Applied Economics %D 2017 %V 20 %N 2 %@ 1514-0326 %F CLAVERIA:2017:JAE %X In this study we use survey expectations about a wide range of economic variables to forecast real activity. We propose an empirical approach to derive mathematical functional forms that link survey expectations to economic growth. Combining symbolic regression with genetic programming we generate two survey-based indicators: a perceptions index, using agents’ assessments about the present, and an expectations index with their expectations about the future. In order to find the optimal combination of both indexes that best replicates the evolution of economic activity in each country we use a portfolio management procedure known as index tracking. By means of a generalized reduced gradient algorithm we derive the relative weights of both indexes. In most economies, the survey-based predictions generated with the composite indicator outperform the benchmark model for one-quarter ahead forecasts, although these improvements are only significant in Austria, Belgium and Portugal %K genetic algorithms, genetic programming, C51, C55, C63, C83, C93, business and consumer surveys, forecasting, economic growth, symbolic regression, evolutionary algorithms %9 journal article %R 10.1016/S1514-0326(17)30015-6 %U http://www.sciencedirect.com/science/article/pii/S1514032617300156 %U http://dx.doi.org/10.1016/S1514-0326(17)30015-6 %P 329-349 %0 Journal Article %T Assessment of the effect of the financial crisis on agents expectations through symbolic regression %A Claveria, Oscar %A Monte, Enric %A Torra, Salvador %J Applied Economics Letters %D 2017 %V 24 %N 9 %@ 1350-4851 %F Claveria:2017:AEL %X Agents perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents expectations. The main objective of this study is to assess the impact of the 2008 financial crisis on agents expectations. With this aim, we evaluate the capacity of survey-based expectations to anticipate economic growth in the United States, Japan, Germany and the United Kingdom. We propose a symbolic regression (SR) via genetic programming approach to derive mathematical functional forms that link survey-based expectations to GDP growth. By combining the main SR-generated indicators, we generate estimates of the evolution of GDP. Finally, we analyse the effect of the crisis on the formation of expectations, and we find an improvement in the capacity of agents expectations to anticipate economic growth after the crisis in all countries except Germany. %K genetic algorithms, genetic programming, Symbolic regression, evolutionary algorithms, tendency surveys, expectations, forecasting %9 journal article %U https://doi.org/10.1080/13504851.2016.1218419 %P 648-652 %0 Journal Article %T A new approach for the quantification of qualitative measures of economic expectations %A Claveria, Oscar %A Monte, Enric %A Torra, Salvador %J Quality & Quantity %D 2017 %8 nov %V 51 %N 6 %@ 0033-5177 %F Claveria:2017:QQ %X In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents expectations. The research focuses on experts expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance. %K genetic algorithms, genetic programming, Economic growth, Qualitative survey data, Expectations, Symbolic regression, Evolutionary algorithms %9 journal article %R 10.1007/s11135-016-0416-0 %U http://dx.doi.org/10.1007/s11135-016-0416-0 %P 2685-2706 %0 Journal Article %T A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms %A Claveria, Oscar %A Monte, Enric %A Torra, Salvador %J Social Indicators Research %D 2018 %8 jan %V 135 %N 1 %@ 0303-8300 %F Claveria:2018:SIR %X we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency survey conducted by the Ifo Institute for Economic Research. By means of genetic programming we estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. We use the evolution of GDP as a target. This set of empirically-generated indicators of economic growth, are used as building blocks to construct an economic indicator. We compare the proposed indicator to the Economic Climate Index, and we evaluate its predictive performance to track the evolution of the GDP in ten European economies. We find that in most countries the proposed indicator outperforms forecasts generated by a benchmark model. %K genetic algorithms, genetic programming, Economic indicators, Survey-based indicators, Tendency surveys, Symbolic regression, Evolutionary algorithms, %9 journal article %R 10.1007/s11205-016-1490-3 %U http://dx.doi.org/10.1007/s11205-016-1490-3 %P 1-14 %0 Report %T Tracking economic growth by evolving expectations via genetic programming: A two-step approach %A Claveria, Oscar %A Monte, Enric %A Torra, Salvador %D 2018 %N 2018/01 %I Research Institute of Applied Economics, Universitat de Barcelona %C Av. Diagonal, 690, 08034 Barcelona, Spain %F RePEc:xrp:wpaper:xreap2018-4 %X The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by combining survey data on expectations about different economic variables. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick (economic growth). In a second step, this set of empirically-generated proxies of economic growth are linearly combined to track the evolution of GDP. To evaluate the forecasting performance of the generated estimates of GDP, we use them to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in 28 countries of the OECD. While in most economies we find an improvement in the capacity of agents’ to anticipate the evolution of GDP after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden, Austria and Finland. %K genetic algorithms, genetic programming, Evolutionary algorithms, Symbolic regression, Business and consumer surveys, Expectations, Forecasting %9 Working Paper %U http://www.ub.edu/irea/working_papers/2018/201801.pdf %0 Journal Article %T Evolutionary Computation for Macroeconomic Forecasting %A Claveria, Oscar %A Monte, Enric %A Torra, Salvador %J Computational Economics %D 2019 %8 feb %V 53 %N 2 %@ 0927-7099 %F Claveria:2019:CE %X The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden. %K genetic algorithms, genetic programming, Evolutionary algorithms, Symbolic regression, Business and consumer surveys, Expectations, Forecasting %9 journal article %R 10.1007/s10614-017-9767-4 %U http://dx.doi.org/10.1007/s10614-017-9767-4 %P 833-849 %0 Journal Article %T Empirical modelling of survey-based expectations for the design of economic indicators in five European regions %A Claveria, Oscar %A Monte, Enric %A Torra, Salvador %J Empirica %D 2019 %8 may %V 46 %N 2 %@ 0340-8744 %F Claveria:2019:Empirica %X we use agents expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive a formula using survey variables that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents perception about the overall economy compared to last year is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth %K genetic algorithms, genetic programming, Economic indicators, Qualitative survey data, Expectations, Symbolic regression, Evolutionary algorithms, %9 journal article %R 10.1007/s10663-017-9395-1 %U http://dx.doi.org/10.1007/s10663-017-9395-1 %P 205-227 %0 Journal Article %T Economic forecasting with evolved confidence indicators %A Claveria, Oscar %A Monte, Enric %A Torra, Salvador %J Economic Modelling %D 2020 %@ 0264-9993 %F CLAVERIA:2020:EM %X We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations that yield estimates of the expected rate of economic growth. Firms’ production expectations and consumers’ expectations to spend on home improvements are the most frequently selected variables - both lagged and contemporaneous. To assess the performance of the proposed approach, we have designed an out-of-sample iterative predictive experiment. We found that forecasts generated with the evolved indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, both from demand and supply sides. %K genetic algorithms, genetic programming, forecasting, economic growth, qualitative survey data, business and consumer expectations, symbolic regression, evolutionary algorithms %9 journal article %R 10.1016/j.econmod.2020.09.015 %U http://www.sciencedirect.com/science/article/pii/S0264999320311998 %U http://dx.doi.org/10.1016/j.econmod.2020.09.015 %0 Report %T Economic determinants of employment sentiment: A socio-demographic analysis for the euro area %A Claveria, Oscar %A Lolic, Ivana %A Monte, Enric %A Torra, Salvador %A Soric, Petar %D 2020 %8 jan 28 %N 2020/01 %I Research Institute of Applied Economics, University of Barcelona %C Spain %F IREA202001 %X In this study we construct quarterly consumer confidence indicators of unemployment for the euro area using as input the consumer expectations for sixteen socio-demographic groups elicited from the Joint Harmonised EU Consumer Survey. First, we use symbolic regressions to link unemployment rates to qualitative expectations about a wide range of economic variables. By means of genetic programming we obtain the combination of expectations that best tracks the evolution of unemployment for each group of consumers. Second, we test the out-of-sample forecasting performance of the evolved expressions. Third, we use a state-space model with time-varying parameters to identify the main macroeconomic drivers of unemployment confidence and to evaluate whether the strength of the interplay between variables varies across the economic cycle. We analyse the differences across groups, obtaining better forecasts for respondents comprised in the first quartile with regards to the income of the household and respondents with at least secondary education. We also find that the questions regarding expected major purchases over the next 12 months and savings at present are by far, the variables that most frequently appear in the evolved expressions, hinting at their predictive potential to track the evolution of unemployment. For the economically deprived consumers, the confidence indicator seems to evolve independently of the macroeconomy. This finding is rather consistent throughout the economic cycle, with the exception of stock market returns, which governed unemployment confidence in the pre-crisis period. %K genetic algorithms, genetic programming, unemployment, expectations, consumer behaviour, forecasting, state-space models yield %9 Working Paper %R 10.2139/ssrn.3526768 %U https://ideas.repec.org/p/ira/wpaper/202001.html %U http://dx.doi.org/10.2139/ssrn.3526768 %0 Journal Article %T A Genetic Programming Approach for Economic Forecasting with Survey Expectations %A Claveria, Oscar %A Monte, Enric %A Torra, Salvador %J Applied Sciences %D 2022 %V 12 %N 13 %@ 2076-3417 %F Claveria:2022:AS %X We apply a soft computing method to generate country-specific economic sentiment indicators that provide estimates of year-on-year GDP growth rates for 19 European economies. First, genetic programming is used to evolve business and consumer economic expectations to derive sentiment indicators for each country. To assess the performance of the proposed indicators, we first design a now-casting experiment in which we recursively generate estimates of GDP at the end of each quarter, using the latest business and consumer survey data available. Second, we design a forecasting exercise in which we iteratively re-compute the sentiment indicators in each out-of-sample period. When evaluating the accuracy of the predictions obtained for different forecast horizons, we find that the evolved sentiment indicators outperform the time-series models used as a benchmark. These results show the potential of the proposed approach for prediction purposes. %K genetic algorithms, genetic programming, forecasting, economic growth, expectations, business and consumer surveys, symbolic regression, evolutionary algorithms %9 journal article %R 10.3390/app12136661 %U https://www.mdpi.com/2076-3417/12/13/6661 %U http://dx.doi.org/10.3390/app12136661 %P articleno6661 %0 Thesis %T Extending Grammatical Evolution with Attribute Grammars: An Application to Knapsack Problems %A Cleary, Robert %D 2005 %C University of Limerick, Ireland %C University of Limerick %G en %F cleary:2005:EGEWAGAATKP %X Research extending the capabilities of the well-known evolutionary-algorithm (EA) of Grammatical Evolution (GE) is presented. GE essentially describes a software component for (potentially) any search algorithm (more prominently an EA) - whereby it serves to facilitate the generation of viable solutions to the problem at hand. In this way, GE can be thought of as a generally applicable, robust and pluggable component to any search-algorithm. Facilitating this plug- ability - is the ability to hand-describe the structure of solutions to a particular problem; this, under the guise of the concise and effective notation of a grammar definition. This grammar may be thought of, as the rules for the generation of solutions to a problem. Recent research has shown, that for static-problems - (problems whose optimum-solution resides within a finitely-describable set, for the set of all possible solutions), the ability to focus the search (for the optimum) on the more promising regions of this set, has provided the best-performing approaches to-date. As such, it is suggested that search be biased toward more promising areas of the set of all possible solutions. In it’s use of a grammar, GE provides such a bias (as a language-bias), yet remains unable, to effectively bias the search for problems of constrained optimisation. As such, and as detailed in this thesis - the mechanism of an attribute grammar is proposed to maintain GE as a pluggable component for problems of this type also; thus extending it’s robustness and general applicability. A family of academically recognised (hard) knapsack problems, are used as a testing-ground for the extended-system and the results presented are encouraging. As a side-effect of this study (and possibly more importantly) we observe some interesting behavioural findings about the GE system itself. The standard GE one-point crossover operator, emerges as exhibiting a mid evolutionary change-of-role from a constructive to destructive operator; GE’s ripple-crossover is found to be heavily dependent on the presence of a GE-tail (of residual-introns) in order to function effectively; and the propagation of individuals - characterised by large-proportions of such residual-introns - is found to be an evolutionary self- adaptive response to the destructive change of role found in the one-point crossover: all of these findings are found with respect to the problems examined. %K genetic algorithms, genetic programming, grammatical evolution, grammatical swarm, attribute grammars %9 Master of Science in Computer Science %9 Masters thesis %U http://ncra.ucd.ie/Site/evoilp.html %0 Conference Proceedings %T An Attribute Grammar Decoder for the 01 MultiConstrained Knapsack Problem %A Cleary, Robert %A O’Neill, Michael %Y Raidl, Günther R. %Y Gottlieb, Jens %S Evolutionary Computation in Combinatorial Optimization – EvoCOP 2005 %S LNCS %D 2005 %8 30 mar 1 apr %V 3448 %I Springer Verlag %C Lausanne, Switzerland %F cleary:2005:AAGDFR0MKP %X We describe how the standard genotype-phenotype mapping process of Grammatical Evolution (GE) can be enhanced with an attribute grammar to allow GE to operate as a decoder-based Evolutionary Algorithm (EA). Use of an attribute grammar allows GE to maintain context-sensitive and semantic information pertinent to the capacity constraints of the 01 Multi-constrained Knapsack Problem (MKP). An attribute grammar specification is used to perform decoding similar to a first-fit heuristic. The results presented are encouraging, demonstrating that GE in conjunction with attribute grammars can provide an improvement over the standard context-free mapping process for problems in this domain. %K genetic algorithms, genetic programming, grammatical evolution, evolutionary computation, attribute grammar %R 10.1007/978-3-540-31996-2_4 %U http://dx.doi.org/10.1007/978-3-540-31996-2_4 %P 34-45 %0 Conference Proceedings %T A new crossover technique for Cartesian genetic programming %A Clegg, Janet %A Walker, James Alfred %A Miller, Julian Francis %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 1277276 %X Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents’ trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree structures originally introduced by Koza. Cartesian Genetic Programming has been shown to perform better than the traditional Genetic Programming; but it does not use crossover to create offspring, it is implemented using mutation only. In this paper a new crossover method in Genetic Programming is introduced. The new technique is based on an adaptation of the Cartesian Genetic Programming representation and is tested on two simple regression problems. It is shown that by implementing the new crossover technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method. %K genetic algorithms, genetic programming, Cartesian Genetic Programming, crossover techniques, optimisation %R 10.1145/1276958.1277276 %U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1580.pdf %U http://dx.doi.org/10.1145/1276958.1277276 %P 1580-1587 %0 Conference Proceedings %T Combining cartesian genetic programming with an estimation of distribution algorithm %A Clegg, Janet %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 Clegg:2008:gecco %K genetic algorithms, genetic programming, cartesian genetic programming, crossover techniques, optimisation: Poster %R 10.1145/1389095.1389350 %U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1333.pdf %U http://dx.doi.org/10.1145/1389095.1389350 %P 1333-1334 %0 Conference Proceedings %T Analogue Circuit Control through Gene Expression %A Clegg, Kester %A Stepney, Susan %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/CleggS08 %X Software configurable analogue arrays offer an intriguing platform for automated design by evolutionary algorithms. Like previous evolvable hardware experiments, these platforms are subject to noise during physical interaction with their environment. We report preliminary results of an evolutionary system that uses concepts from gene expression to both discover and decide when to deploy analogue circuits. The output of a circuit is used to trigger its reconfiguration to meet changing conditions. We examine the issues of noise during our evolutionary runs, show how this was overcome and illustrate our system with a simple proof-of-concept task that shows how the same mechanism of control works for progressive developmental stages (canalisation) or adaptable control (homoeostasis). %K genetic algorithms, genetic programming, Cartesian genetic programming %R 10.1007/978-3-540-78761-7_16 %U http://dx.doi.org/10.1007/978-3-540-78761-7_16 %P 154-163 %0 Thesis %T Evolving gene expression to reconfigure analogue devices %A Clegg, Kester Dean %D 2008 %8 May %C UK %C University of York %F Clegg:thesis %X Repeated, morphological functionality, from limbs to leaves, is widespread in nature. Pattern formation in early embryo development has shed light on how and why the same genes are expressed in different locations or at different times. Practitioners working in evolutionary computation have long regarded nature’s reuse of modular functionality with admiration. But repeating nature’s trick has proven difficult. To date, no one has managed to evolve the design for a car, a house or a plane. Or indeed anything where the number of interdependent parts exposed to random mutation is large. It seems that while we can use evolutionary algorithms for search-based optimisation with great success, we cannot use them to tackle large, complex designs where functional reuse is essential. This thesis argues that the modular functionality provided by gene reuse could play an important part in evolutionary computation being able to scale, and that by expressing subsets of genes in specific contexts, successive stages of phenotype configuration can be controlled by evolutionary search. We present a conceptual model of context-specific gene expression and show how a genome representation can hold many genes, only a few of which need be expressed in a solution. As genes are expressed in different contexts, their functional role in a solution changes. By allowing gene expression to discover phenotype solutions, evolutionary search can guide itself across multiple search domains. The work here describes the design and implementation of a prototype system to demonstrates the above features and evolve genomes that are able to use gene expression to find and deploy solutions, permitting mechanisms of dynamic control to be discovered by evolutionary computation. %K genetic algorithms, genetic programming, Cartesian genetic programming %9 Ph.D. thesis %U http://www-users.cs.york.ac.uk/susan/teach/theses/clegg.htm %0 Conference Proceedings %T Travelling Salesman Problem solved ’in materio’ by evolved carbon nanotube device %A Clegg, Kester %A Miller, Julian %A Massey, Kieran %A Petty, Mike %Y Bartz-Beielstein, Thomas %Y Branke, Juergen %Y Filipic, Bogdan %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 Clegg:2014:PPSN %X We report for the first time on finding shortest path solutions for the travelling salesman problem (TSP) using hybrid in materio computation: a technique that uses search algorithms to configure materials for computation. A single-walled carbon nanotube (SWCNT) / polymer composite material deposited on a micro-electrode array is configured using static voltages so that voltage output readings determine the path order in which to visit cities in a TSP. Our initial results suggest that the hybrid computation with the SWCNT material is able to solve small instances of the TSP as efficiently as a comparable evolutionary search algorithm performing the same computation in software. Interestingly the results indicate that the hybrid system’s search performance on TSPs scales linearly rather than exponentially on these smaller instances. This exploratory work represents the first step towards building SWCNT-based electrode arrays in parallel so that they can solve much larger problems. %K genetic algorithms, genetic programming, Cartesian genetic programming %R 10.1007/978-3-319-10762-2_68 %U http://dx.doi.org/10.1007/978-3-319-10762-2_68 %P 692-701 %0 Journal Article %T Hybrid Genetic Programming for the Development of Metamaterials Designs With Improved Characteristics %A Clemens, Scott %A Iskander, Magdy F. %A Yun, Zhengqing %A Rayno, Jennifer %J IEEE Antennas and Wireless Propagation Letters %D 2018 %8 mar %V 17 %N 3 %@ 1536-1225 %F Clemens:2018:ieeeAWPL %X The expansion of a hybrid genetic program’s (HGP) functionality to allow for the development of broadband two-dimensional metamaterials designs with advanced characteristics is presented. Artificial magnetic conductor (AMC) ground planes and a tunable terahertz absorber generated by the HGP are compared with designs available in the literature. The HGP is shown to produce human-competitive results. The AMC ground planes synthesised by the HGP were found to produce improved results including wider bandwidths and larger reflection coefficient magnitudes than that of the human designs. For one of the designed AMCs, the HGP’s bandwidth is 73.3percent larger and the minimum reflection magnitude is 1.0percent larger than the reference AMC. Similarly, the absorber synthesised by the HGP has larger bandwidths than that of a recently published absorber optimised by random hill climbing. Three bias voltages were tested with the tunable absorber. The bandwidths of the HGP absorber are 23.1percent, 37.6percent, and 400percent larger than the reference absorber, for biases of 4, 2, and 0.5 V/nm, respectively. Four example designs are discussed together with comparative results to illustrate the advantages of the developed HGP-enabled design method. %K genetic algorithms, genetic programming, Artificial magnetic conductor (AMC), terahertz (THz) absorber %9 journal article %R 10.1109/LAWP.2018.2800057 %U http://dx.doi.org/10.1109/LAWP.2018.2800057 %P 513-516 %0 Conference Proceedings %T Optimization of Rural Cellular Coverage on the Islands of Hawaii %A Clemens, Scott %A Yun, Zhengqing %A Iskander, Magdy F. %S 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting %D 2019 %8 jul %F Clemens:2019:Meeting %X In this communication genetic programming (GP) is applied to optimize the coverage of wireless networks in rural areas. Three locations in the Hawaiian Islands were studied: Kohala area on Hawaii, the North Shore on Oahu, and the populated area on Maui between West Maui Mountains and Haleakala. Omnidirectional and directional base station scenarios were optimized and simulated. For all three locations studied directional base stations provided better coverage than omnidirectional base stations. For the Maui and Oahu locations directional base stations provided significantly more coverage. %K genetic algorithms, genetic programming %R 10.1109/APUSNCURSINRSM.2019.8888613 %U http://dx.doi.org/10.1109/APUSNCURSINRSM.2019.8888613 %P 2111-2112 %0 Conference Proceedings %T Genetic Programming for Coplanar Waveguide Continuous Transverse Stub Antenna Array Design %A Clemens, Scott %A Iskander, Magdy F. %A Yun, Zhengqing %A Chao, Gui %S 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting %D 2020 %8 jul %F Clemens:2020:Meeting %X Genetic programming (GP) is developed to design an antenna array with new topologies resulting in improved performance. Three elements of a coplanar waveguide continuous transverse stub (CPW-CTS) antenna are synthesized and optimized by GP. These antenna elements are placed in series to create a 3-element frequency scanning linear array. The fitness function used to design the CPW-CTS elements accounts for impedance bandwidth, radiation pattern, cross polarization, and transmission coefficient. Simulation results are verified experimentally. The GP designed CPW-CTS antenna array was able to achieve nearly 4 times the bandwidth of a conventional CPW-CTS array design. The GP designed array maintains the array’s gain when compared with the published CPW-CTS antenna array design. %K genetic algorithms, genetic programming, Weapons, Simulation, Bandwidth, Coplanar waveguides, Topology, Linear antenna arrays, Continuous transverse stub %R 10.1109/IEEECONF35879.2020.9329544 %U http://dx.doi.org/10.1109/IEEECONF35879.2020.9329544 %P 1949-1950 %0 Conference Proceedings %T Hybrid Genetic Programming Designed Laser-Induced Graphene Based Absorber %A Clemens, Scott %A Chong, Edmond %A Iskander, Magdy F. %A Yun, Zhengqing %A Brown, Joseph %A Ray, Tyler %A Nakamura, Matthew %A Nekoba, Deylen %S 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI) %D 2022 %8 October 15 jul %C Denver, CO, USA %F Clemens:2022:AP-S %X Hybrid genetic programming (HGP) is applied to the design and optimization of a laser-induced graphene (LIG) based metasurface (MS) electromagnetic absorber. The HGP designed absorber has bandwidths of 115.percent and 56.percent for absorptivity above 7percent and 8percent, respectively. It is 5.1 mm thick, with a unit cell (UC) periodicity of 8.7 mm. The LIG is generated on a polyimide substrate. The MS absorber has copper ground plane backing. %K genetic algorithms, genetic programming, Fabrication, Graphene, Polyimides, Bandwidth, Metasurfaces, Electromagnetic absorbers %R 10.1109/AP-S/USNC-URSI47032.2022.9887152 %U https://2022apsursi.org/view_paper.php?PaperNum=2459 %U http://dx.doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887152 %P 1084-1085 %0 Conference Proceedings %T Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming %A Clemente, Eddie %A Olague, Gustavo %A Dozal, Leon %A Mancilla, Martin %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 Clemente:evoapps12 %X Computational neuroscience is a discipline devoted to the study of brain function from an information processing standpoint. The ventral stream, also known as the ’what’ pathway, is widely accepted as the model for processing the visual information related to object identification. This paper proposes to evolve a mathematical description of the ventral stream where key features are identified in order to simplify the whole information processing. The idea is to create an artificial ventral stream by evolving the structure through an evolutionary computing approach. In previous research, the what pathway is described as being composed of two main stages: the interest region detection and feature description. For both stages a set of operations were identified with the aim of simplifying the total computational cost by avoiding a number of costly operations that are normally executed in the template matching and bag of feature approaches. Therefore, instead of applying a set of previously learnt patches, product of an off-line training process, the idea is to enforce a functional approach. Experiments were carried out with a standard database and the results show that instead of 1200 operations, the new model needs about 200 operations. %K genetic algorithms, genetic programming, Heterogeneous and Hierarchical Genetic Programming, Evolutionary Artificial Ventral Stream, Complex Designing System %R 10.1007/978-3-642-29178-4_32 %U https://rdcu.be/eyaR4 %U http://dx.doi.org/10.1007/978-3-642-29178-4_32 %P 315-325 %0 Conference Proceedings %T Self-adjusting focus of attention by means of GP for improving a laser point detection system %A Clemente, Eddie %A Chavez, Francisco %A Dozal, Leon %A Fernandez de Vega, Francisco %A Olague, Gustavo %Y Blum, Christian %Y Alba, Enrique %Y Auger, Anne %Y Bacardit, Jaume %Y Bongard, Josh %Y Branke, Juergen %Y Bredeche, Nicolas %Y Brockhoff, Dimo %Y Chicano, Francisco %Y Dorin, Alan %Y Doursat, Rene %Y Ekart, Aniko %Y Friedrich, Tobias %Y Giacobini, Mario %Y Harman, Mark %Y Iba, Hitoshi %Y Igel, Christian %Y Jansen, Thomas %Y Kovacs, Tim %Y Kowaliw, Taras %Y Lopez-Ibanez, Manuel %Y Lozano, Jose A. %Y Luque, Gabriel %Y McCall, John %Y Moraglio, Alberto %Y Motsinger-Reif, Alison %Y Neumann, Frank %Y Ochoa, Gabriela %Y Olague, Gustavo %Y Ong, Yew-Soon %Y Palmer, Michael E. %Y Pappa, Gisele Lobo %Y Parsopoulos, Konstantinos E. %Y Schmickl, Thomas %Y Smith, Stephen L. %Y Solnon, Christine %Y Stuetzle, Thomas %Y Talbi, El-Ghazali %Y Tauritz, Daniel %Y Vanneschi, Leonardo %S GECCO ’13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference %D 2013 %8 June 10 jul %I ACM %C Amsterdam, The Netherlands %F Clemente:2013:GECCO %X This paper introduces the application of a new GP based Focus of Attention technique capable of improving the accuracy level when using a Laser Pointer as an interactive device. Laser Pointers have been previously employed in combination with environment control systems as interaction devices, allowing users to send orders to devices. Accurate detection of laser spots is required for sending correct orders; moreover, false offs must be eradicated, thus preventing devices to autonomously activate/deactivate when orders have not been sent by users. The idea here is to apply a self-adjusting process to a GP based algorithm capable of focusing the attention of a visual recognition system on a narrow area of an image, where laser spots will be then located. Images are taken by video cameras working on users’ environment. The results show that the new approach improves significantly the accuracy level when laser spots are present, users sending orders while maintains the extremely low values of false offs provided by previous techniques. %K genetic algorithms, genetic programming, Self-Adjusting, Focus of Attention, Laser Pointer, Environment Control Systems %R 10.1145/2463372.2463530 %U http://dx.doi.org/10.1145/2463372.2463530 %P 1237-1244 %0 Journal Article %T Self-adjusting focus of attention in combination with a genetic fuzzy system for improving a laser environment control device system %A Clemente, Eddie %A Chavez, Francisco %A Fernandez de Vega, Francisco %A Olague, Gustavo %J Applied Soft Computing %D 2015 %8 jul %V 32 %@ 1568-4946 %F Clemente:2015:ASC %X This paper presents a new algorithm capable of improving the accuracy level of a laser pointer detector used within an interactive control device system. A genetic programming based approach has been employed to develop a focus of attention algorithm, which works cooperatively with a genetic fuzzy system. The idea is to improve the detection of laser-spots depicted on images captured by video cameras working on home environments. The new and more accurate detection system, in combination with an environment control system, allows to send correct orders to home devices. The algorithm is capable of eradicating false offs, thus preventing devices to autonomously activate/deactivate appliances when orders have not been really signalled by users. Moreover, by adding self-adjusting capabilities with a genetic fuzzy system the computer vision algorithm focuses its attention on a narrower area of the image. Extensive experimental results show that the combination of the focus of attention technique with dynamic thresholding and genetic fuzzy systems improves significantly the accuracy of the laser-spot detection system while maintaining extremely low false off rates in comparison with previous approaches. %K genetic algorithms, genetic programming, Self-adjusting, Focus of attention, Laser pointer, Environment control systems, Genetic fuzzy systems %9 journal article %R 10.1016/j.asoc.2015.03.011 %U http://www.sciencedirect.com/science/article/pii/S1568494615001647 %U http://dx.doi.org/10.1016/j.asoc.2015.03.011 %P 250-265 %0 Journal Article %T Adaptive Behaviors in Autonomous Navigation with Collision Avoidance and Bounded Velocity of an Omnidirectional Mobile Robot: A Control Theory with Genetic Programming Approach %A Clemente, Eddie %A Meza-Sanchez, Marlen %A Bugarin, Eusebio %A Aguilar-Bustos, Ana Yaveni %J Journal of Intelligent and Robotic Systems %D 2018 %8 oct %V 92 %N 2 %@ 0921-0296 %F Clemente:2018:JIRS %X Integration of Control Theory and Genetic Programming paradigm toward development a family of controllers is addressed in this paper. These controllers are applied for autonomous navigation with collision avoidance and bounded velocity of an omnidirectional mobile robot. We introduce the concepts of natural and adaptive behaviours to relate each control objective with a desired behaviour for the mobile robot. Natural behaviours lead the system to fulfil a task inherently. In this work, the motion of the mobile robot to achieve desired position, ensured by applying a Control-Theory-based controller, defines the natural behaviour. The adaptive behaviour, learnt through Genetic-Programming, fits the robot motion in order to avoid collision with an obstacle while fulfilling velocity constraints. Hence, the behaviour of the mobile robot is the addition of the natural and the adaptive behaviours. Our proposed methodology achieves the discovery of 9402 behaviours without collisions where asymptotic convergence to desired goal position is demonstrated by Lyapunov stability theory. Effectiveness of proposed framework is illustrated through a comparison between experiments and numerical simulations for a real mobile robot. %K genetic algorithms, genetic programming, evolutionary robotics, behaviours, collision avoidance, autonomous navigation, wheeled mobile robots, velocity constraint, lyapunov stability %9 journal article %R 10.1007/s10846-017-0751-y %U http://dx.doi.org/10.1007/s10846-017-0751-y %P 359-380 %0 Conference Proceedings %T Fitness Distance Correlation And Problem Difficulty For Genetic Programming %A Clergue, Manuel %A Collard, Philippe %A Tomassini, Marco %A Vanneschi, Leonardo %Y Langdon, W. B. %Y Cantú-Paz, E. %Y Mathias, K. %Y Roy, R. %Y Davis, D. %Y Poli, R. %Y Balakrishnan, K. %Y Honavar, V. %Y Rudolph, G. %Y Wegener, J. %Y Bull, L. %Y Potter, M. A. %Y Schultz, A. C. %Y Miller, J. F. %Y Burke, E. %Y Jonoska, N. %S GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %D 2002 %8 September 13 jul %I Morgan Kaufmann Publishers %C New York %@ 1-55860-878-8 %F clergue:2002:gecco %K genetic algorithms, genetic programming, distance between genotypes, fitness distance correlation, problem difficulty, royal trees, trap functions %U http://gpbib.cs.ucl.ac.uk/gecco2002/GP072.ps %P 724-732 %0 Journal Article %T The evolutionary origins of modularity %A Clune, Jeff %A Mouret, Jean-Baptiste %A Lipson, Hod %J Proceedings of the Royal Society B %D 2013 %8 22 mar %V 280 %N 1755 %@ 1471-2954 %F Clune:2013:PRSB %X A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks, their organisation as functional, sparsely connected subunits, but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximise network performance and minimise connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes. %K genetic algorithms, NSGA-II, modularity, evolution, networks, evolvability, systems biology %9 journal article %R 10.1098/rspb.2012.2863 %U http://dx.doi.org/10.1098/rspb.2012.2863 %P 20122863 %0 Conference Proceedings %T The use of Genetic programing in Exploring 3D Design Worlds %A Broughton, T. %A Tan, A. %A Coates, Paul S. %Y Junge, Richard %S CAAD Futures 97 %D 1997 %8 April 6 aug %I Kluwer Academic Publishers %C Technical University Munich, Germany %@ 0-7923-4726-9 %F coates:1997:GPx3dw %X Genetic algorithms are used to evolve rule systems for a generative process, in one case a shape grammar,which uses the ’Dawkins Biomorph’ paradigm of user driven choices to perform artificial selection, in the other a CA/Lindenmeyer system using the Hausdorff dimension of the resultant configuration to drive natural selection. 1) Using Genetic Programming in an interactive 3d shape grammar (Amy Tan and P S Coates) A report of a generative system combining genetic programming(GP) and 3D shape grammars. The reasoning that backs up the basis for this work depends on the interpretation of design as search In this system, a 3D form is a computer program made up of functions (transformations and terminals (building blocks). Each program evaluates into a structure. Hence, in this instance a program is synonymous with form. Building blocks of form are platonic solids (box, cylinder....etc.). A Variety of combinations of the simple affine transformations of translation, scaling, rotation together with Boolean operations of union, subtraction and intersection performed on the building blocks generate different configurations of 3D forms. Using to the methodology of genetic programming, an initial population of such programs are randomly generated,subjected to a test for fitness (the eyeball test). Individual programs that have passed the test are selected to be parents for reproducing the next generation of programs via the process of recombination. 2) Using a GA to evolve rule sets to achieve a goal configuration (T.Broughton and P.Coates). The aim of these experiments was to build a framework in which a structure’s form could be defined by a set of instructions encoded into its genetic make-up. This was achieved by combining a generative rule system commonly used to model biological growth with a genetic algorithm simulating the evolutionary process of selection to evolve an adaptive rule system capable of replicating any preselected 3-D shape. The generative modelling technique used is a string rewriting Lindenmayer system the genes of the emergent structures are the production rules of the L-system, and the spatial representation of the structures uses the geometry of iso-spatial dense-packed spheres. %K genetic algorithms, genetic programming %U https://repository.uel.ac.uk/item/86q5y %P 885-917 %0 Conference Proceedings %T Genetic Programming and Spatial Morphogenesis %A Coates, Paul %A Makris, Dimitrios %Y Patrizio, Andrew %Y Wiggins, Geraint A. %Y Pain, Helen %S AISB Symposium on Creative Evolutionary Systems %D 1999 %8 June 9 apr %C Edinburgh College of Art and Division of Informatics, University of Edinburgh %@ 1-902956-03-6 %F Coates:1999:AISBces %X This paper discusses the use of genetic programming (G.P.) for applications in the field of spatial composition. The G.P. was used to generate three-dimensional spatial forms from a set of geometrical structures. The approach uses genetic programming with a Genetic Library (G.Lib). G.P. provides a way to genetically breed a computer program to solve a problem. G. Lib enables genetic programming to define potentially useful subroutines dynamically during a run. %K genetic algorithms, genetic programming %U http://www.aisb.org.uk/publications/proceedings/proc1999/aisb1999/AISB99_Evolutionary.pdf %P 105-114 %0 Book %T Programming.Architecture %A Coates, Paul %D 2010 %8 jan 29th %I Routledge %F Coates:2010:PA %X Programming.Architecture is a simple and concise introduction to the history of computing and computational design, explaining the basics of algorithmic thinking and the use of the computer as a tool for design and architecture. Introduction 1. Falling Between Two Stools 2. Rethinking Representation 3. In the Beginning was the Word 4. The Mystery of the Machine that Invents Itself 5. Evolving the Text - Being even Lazier 6. The Text of the Vernacular. Epilogue. Glossary %K genetic algorithms, genetic programming %U http://www.routledge.com/books/details/9780415451888/ %0 Conference Proceedings %T Fitness Function Obtained from a Genetic Programming Approach for Web Document Clustering Using Evolutionary Algorithms %A Cobos, Carlos %A Munoz, Leydy %A Mendoza, Martha %A Leon Guzman, Elizabeth %A Herrera-Viedma, Enrique %Y Pavon, Juan %Y Duque-Mendez, Nestor D. %Y Fuentes-Fernandez, Ruben %S Proceedings of the 13th Ibero-American Conference on AI, IBERAMIA 2012 %S Lecture Notes in Computer Science %D 2012 %8 nov 13 16 %V 7637 %I Springer %C Cartagena de Indias, Colombia %F conf/iberamia/CobosMMLH12 %X Web document clustering (WDC) is an alternative means of searching the web and has become a rewarding research area. Algorithms for WDC still present some problems, in particular: inconsistencies in the content and description of clusters. The use of evolutionary algorithms is one approach for improving results. It uses standard index to evaluate the quality (as a fitness function) of different solutions of clustering. Indexes such as Bayesian Information Criteria (BIC), Davies-Bouldin, and others show good performance, but with much room for improvement. In this paper, a modified BIC fitness function for WDC based on evolutionary algorithms is presented. This function was discovered using a genetic program (from a reverse engineering view). Experiments on datasets based on DMOZ show promising results. %K genetic algorithms, genetic programming, web document clustering, clustering of web results, Bayesian information criteria %R 10.1007/978-3-642-34654-5_19 %U http://dx.doi.org/10.1007/978-3-642-34654-5 %U http://dx.doi.org/10.1007/978-3-642-34654-5_19 %P 179-188 %0 Conference Proceedings %T Program Boosting: Program Synthesis via Crowd-Sourcing %A Cochran, Robert A. %A D’Antoni, Loris %A Livshits, Benjamin %A Molnar, David %A Veanes, Margus %Y Gill, Andy %S Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 2015 %D 2015 %8 15 17 jan %I ACM %C Mumbai, India %F Cochran:2015:POPL %X In this paper, we investigate an approach to program synthesis that is based on crowd-sourcing. With the help of crowd-sourcing, we aim to capture the wisdom of the crowds to find good if not perfect solutions to inherently tricky programming tasks, which elude even expert developers and lack an easy-to-formalize specification. We propose an approach we call program boosting, which involves crowd-sourcing imperfect solutions to a difficult programming problem from developers and then blending these programs together in a way that improves their correctness. We implement this approach in a system called CROWDBOOST and show in our experiments that interesting and highly non-trivial tasks such as writing regular expressions for URLs or email addresses can be effectively crowd-sourced. We demonstrate that carefully blending the crowd-sourced results together consistently produces a boost, yielding results that are better than any of the starting programs. Our experiments on 465 program pairs show consistent boosts in accuracy and demonstrate that program boosting can be performed at a relatively modest monetary cost. %K genetic algorithms, genetic programming, crowd-sourcing, program synthesis, regular expressions, symbolic automata SFA %R 10.1145/2676726.2676973 %U https://www.cs.unc.edu/~rac/pdf/POPL15.pdf %U http://dx.doi.org/10.1145/2676726.2676973 %P 677-688 %0 Generic %T Scaling Genetic Programming for Source Code Modification %A Cody-Kenny, Brendan %A Barrett, Stephen %D 2012 %8 21 nov %I arXiv %F DBLP:journals/corr/abs-1211-5098 %X In Search Based Software Engineering, Genetic Programming has been used for bug fixing, performance improvement and parallelisation of programs through the modification of source code. Where an evolutionary computation algorithm, such as Genetic Programming, is to be applied to similar code manipulation tasks, the complexity and size of source code for real-world software poses a scalability problem. To address this, we intend to inspect how the Software Engineering concepts of modularity, granularity and localisation of change can be reformulated as additional mechanisms within a Genetic Programming algorithm. %K genetic algorithms, genetic programming, genetic improvement, SBSE %U http://arxiv.org/abs/1211.5098 %0 Conference Proceedings %T Self-focusing genetic programming for software optimisation %A Cody-Kenny, Brendan %A Barrett, Stephen %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 Cody-Kenny:2013:GECCOcomp %X Approaches in the area of Search Based Software Engineering (SBSE) have seen Genetic Programming (GP) algorithms applied to the optimisation of software. While the potential of GP for this task has been demonstrated, the complexity of real-world software code bases poses a scalability problem for its serious application. To address this scalability problem, we inspect a form of GP which incorporates a mechanism to focus operators to relevant locations within a program code base. When creating offspring individuals, we introduce operator node selection bias by allocating values to nodes within an individual. Offspring values are inherited and updated when a difference in behaviour between offspring and parent is found. We argue that this approach may scale to find optimal solutions in more complex code bases under further development. %K genetic algorithms, genetic programming %R 10.1145/2464576.2464681 %U http://dx.doi.org/10.1145/2464576.2464681 %P 203-204 %0 Conference Proceedings %T The Emergence of Useful Bias in Self-focusing Genetic Programming for Software Optimisation %A Cody-Kenny, Brendan %A Barrett, Stephen %Y Ruhe, Guenther %Y Zhang, Yuanyuan %S Symposium on Search-Based Software Engineering %S Lecture Notes in Computer Science %D 2013 %8 aug 24 26 %V 8084 %I Springer %C Leningrad %F Cody-Kenny:2013:SSBSE %O Graduate Student Track %X The use of Genetic Programming (GP) to optimise increasingly large software code has been enabled through biasing the application of GP operators to code areas relevant to the optimisation of interest. As previous approaches have used various forms of static bias applied before the application of GP, we show the emergence of bias learnt within the GP process itself which improves solution finding probability in a similar way. As this variant technique is sensitive to the evolutionary lineage, we argue that it may more accurately provide bias in programs which have undergone heavier modification and thus find solutions addressing more complex issues. %K genetic algorithms, genetic programming, genetic improvement, SBSE %R 10.1007/978-3-642-39742-4_29 %U http://dx.doi.org/10.1007/978-3-642-39742-4_29 %P 306-311 %0 Thesis %T Genetic Programming Bias with Software Performance Analysis %A Cody-Kenny, Brendan %D 2015 %8 jan %C Ireland %C Trinity College Dublin %F BCK-thesis %X The complexities of modern software systems make their engineering costly and time consuming. This thesis explores and develops techniques to improve software by automating re-design. Source code can be randomly modified and subsequently tested for correctness to search for improvements in existing software. By iteratively selecting useful programs for modification a randomised search of program variants can be guided toward improved programs. Genetic Programming (GP) is a search algorithm which crucially relies on selection to guide the evolution of programs. Applying GP to software improvement represents a scalability challenge given the number of possible modification locations in even the smallest of programs. The problem addressed in this thesis is locating performance improvements within programs. By randomly modifying a location within a program and measuring the change in performance and functionality we determine the probability of finding a performance improvement at that location under further modification. Locating performance improvements can be performed during GP as GP relies on mutation. A probabilistic overlay of bias values for modification emerges as GP progresses and the software evolves. Measuring different aspects of program change can fine-tune the GP algorithm To focus on code which is particularly relevant to the measured aspect. Measuring execution cost reduction can indicate where an improvement is likely to exist and increase the chances of finding an improvement during GP. %K genetic algorithms, genetic programming, genetic improvement, SBSE, locoGP, Java, AST %9 Ph.D. thesis %U http://www.tara.tcd.ie/bitstream/handle/2262/76251/BCK.thesis.april.2016%5b1%5d.pdf %0 Conference Proceedings %T locoGP: Improving Performance by Genetic Programming Java Source Code %A Cody-Kenny, Brendan %A Lopez, Edgar Galvan %A Barrett, Stephen %Y Langdon, William B. %Y Petke, Justyna %Y White, David R. %S Genetic Improvement 2015 Workshop %D 2015 %8 November 15 jul %I ACM %C Madrid %F Cody-Kenny:2015:gi %X We present locoGP, a Genetic Programming (GP) system written in Java for evolving Java source code. locoGP was designed to improve the performance of programs as measured in the number of operations executed. Variable test cases are used to maintain functional correctness during evolution. The operation of locoGP is demonstrated on a number of typically constructed off-the-shelf hand-written implementations of sort and prefix-code programs. locoGP was able to find improvement opportunities in all test problems. %K genetic algorithms, genetic programming, Genetic Improvement, Metrics complexity measures, performance measures, Execution Cost, Implementation, Performance Improvement, Java %R 10.1145/2739482.2768419 %U http://gpbib.cs.ucl.ac.uk/gi2015/locoGP_improving_performance_by_genetic_programming_java_source_code.pdf %U http://dx.doi.org/10.1145/2739482.2768419 %P 811-818 %0 Conference Proceedings %T From Problem Landscapes to Language Landscapes: Questions in Genetic Improvement %A Cody-Kenny, Brendan %A Fenton, Michael %A O’Neill, Michael %Y Petke, Justyna %Y White, David R. %Y Langdon, W. B. %Y Weimer, Westley %S GI-2017 %D 2017 %8 15 19 jul %I ACM %C Berlin %F Cody-Kenny:2017:GI %X Managing and curating software is a time consuming process particularly as programming languages, libraries, and execution environments change. To support the engineering of software, we propose applying a GP-based continuous learning system to all useful software. We take the position that search-based itemization and analysis of all commonly used software is feasible, in large part, because the requirements that people place on software can be used to bound the search space to software which is of high practical use. By repeatedly reusing the information generated during the search process we hope to attain a higher-level, but also more rigorous, understanding of our engineering material: source code. %K genetic algorithms, genetic programming, genetic improvement, Software Engineering, Search, Learning %R 10.1145/3067695.3082522 %U http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/codykenny2017_landscape_questions.pdf %U http://dx.doi.org/10.1145/3067695.3082522 %P 1509-1510 %0 Conference Proceedings %T A Search for Improved Performance in Regular Expressions %A Cody-Kenny, Brendan %A Fenton, Michael %A Ronayne, Adrian %A Considine, Eoghan %A McGuire, Thomas %A O’Neill, Michael %S Proceedings of the Genetic and Evolutionary Computation Conference %S GECCO ’17 %D 2017 %8 15 19 jul %I ACM %C Berlin, Germany %F Cody-Kenny:2017:GECCOa %X The primary aim of automated performance improvement is to reduce the running time of programs while maintaining (or improving on) functionality. In this paper, Genetic Programming is used to find performance improvements in regular expressions for an array of target programs, representing the first application of automated software improvement for run-time performance in the Regular Expression language. This particular problem is interesting as there may be many possible alternative regular expressions which perform the same task while exhibiting subtle differences in performance. A benchmark suite of candidate regular expressions is proposed for improvement. We show that the application of Genetic Programming techniques can result in performance improvements in all cases. As we start evolution from a known good regular expression, diversity is critical in escaping the local optima of the seed expression. In order to understand diversity during evolution we compare an initial population consisting of only seed programs with a population initialised using a combination of a single seed individual with individuals generated using PI Grow and Ramped-half-and-half initialisation mechanisms. %K genetic algorithms, genetic programming, performance, regular expressions %R 10.1145/3071178.3071196 %U http://doi.acm.org/10.1145/3071178.3071196 %U http://dx.doi.org/10.1145/3071178.3071196 %P 1280-1287 %0 Journal Article %T Genetic Improvement Workshop at GECCO 2017 %A Cody-Kenny, Brendan %J SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation %D 2017 %8 oct %V 10 %N 3 %F Cody-Kenny:2017:sigevolution %K genetic algorithms, genetic programming, Genetic Improvement %9 journal article %R 10.1145/3231560.3231562 %U http://www.sigevolution.org/issues/SIGEVOlution1003.pdf %U http://dx.doi.org/10.1145/3231560.3231562 %P 7-8 %0 Conference Proceedings %T Investigating the Evolvability of Web Page Load Time %A Cody-Kenny, Brendan %A Manganiello, Umberto %A Farrelly, John %A Ronayne, Adrian %A Considine, Eoghan %A McGuire, Thomas %A O’Neill, Michael %Y Esparcia-Alcazar, Anna I. %Y Silva, Sara %S 21st International Conference on the Applications of Evolutionary Computation, EvoSET 2018 %S LNCS %D 2018 %8 April 6 apr %V 10784 %I Springer %C Parma, Italy %F Cody-Kenny:2018:evoApplications %X Client-side Javascript execution environments (browsers) allow anonymous functions and event-based programming concepts such as callbacks. We investigate whether a mutate-and-test approach can be used to optimise web page load time in these environments. First, we characterise a web page load issue in a benchmark web page and derive performance metrics from page load event traces.We parse Javascript source code to an AST and make changes to method calls which appear in a web page load event trace.We present an operator based solely on code deletion and evaluate an existing community-contributed performance optimising code transform. By exploring Javascript code changes and exploiting combinations of non-destructive changes, we can optimise page load time by 41percent in our benchmark web page. %K genetic algorithms, genetic programming, genetic improvement, Search-based software engineering, SBSE, Javascript, Performance, Web applications %R 10.1007/978-3-319-77538-8_51 %U https://arxiv.org/pdf/1803.01683 %U http://dx.doi.org/10.1007/978-3-319-77538-8_51 %P 769-777 %0 Conference Proceedings %T Performance Localisation %A Cody-Kenny, Brendan %A O’Neill, Michael %A Barrett, Stephen %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 Cody-Kenny:2018:GI %X Profiling techniques highlight where performance issues manifest and provide a starting point for tracing cause back through a program. While people diagnose and understand the cause of performance to guide formulation of a performance improvement, we seek automated techniques for highlighting performance improvement opportunities to guide search algorithms. We investigate mutation-based approaches for highlighting where a performance improvement is likely to exist. For all modification locations in a program, we make all possible modifications and analyse how often modifications reduce execution count. We compare the resulting code location rankings against rankings derived using a profiler and find that mutation analysis provides the higher accuracy in highlighting performance improvement locations in a set of benchmark problems, though at a much higher execution cost. We see both approaches as complimentary and consider how they may be used to further guide Genetic Programming in finding performance improvements %K genetic algorithms, genetic programming, genetic improvement %R 10.1145/3194810.3194815 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/Cody-Kenny_2018_GI.pdf %U http://dx.doi.org/10.1145/3194810.3194815 %P 27-34 %0 Journal Article %T Inducing multi-objective clustering ensembles with genetic programming %A Coelho, Andre L. V. %A Fernandes, Everlandio %A Faceli, Katti %J Neurocomputing %D 2010 %V 74 %N 1-3 %@ 0925-2312 %F Coelho2010494 %O Artificial Brains %X The recent years have witnessed a growing interest in two advanced strategies to cope with the data clustering problem, namely, clustering ensembles and multi-objective clustering. In this paper, we present a genetic programming based approach that can be considered as a hybrid of these strategies, thereby allowing that different hierarchical clustering ensembles be simultaneously evolved taking into account complementary validity indices. Results of computational experiments conducted with artificial and real datasets indicate that, in most of the cases, at least one of the Pareto optimal partitions returned by the proposed approach compares favourably or go in par with the consensual partitions yielded by two well-known clustering ensemble methods in terms of clustering quality, as gauged by the corrected Rand index. %K genetic algorithms, genetic programming, Cluster analysis, Ensembles, Multi-objective optimization %9 journal article %R 10.1016/j.neucom.2010.09.014 %U http://www.sciencedirect.com/science/article/B6V10-517YN4X-P/2/7322b78e25061d5ecbaa12f058216cd0 %U http://dx.doi.org/10.1016/j.neucom.2010.09.014 %P 494-498 %0 Journal Article %T Multi-objective design of hierarchical consensus functions for clustering ensembles via genetic programming %A Coelho, Andre L. V. %A Fernandes, Everlandio %A Faceli, Katti %J Decision Support Systems %D 2011 %V 51 %N 4 %@ 0167-9236 %F Coelho2011 %X This paper investigates a genetic programming (GP) approach aimed at the multi-objective design of hierarchical consensus functions for clustering ensembles. By this means, data partitions obtained via different clustering techniques can be continuously refined (via selection and merging) by a population of fusion hierarchies having complementary validation indices as objective functions. To assess the potential of the novel framework in terms of efficiency and effectiveness, a series of systematic experiments, involving eleven variants of the proposed GP-based algorithm and a comparison with basic as well as advanced clustering methods (of which some are clustering ensembles and/or multi-objective in nature), have been conducted on a number of artificial, benchmark and bioinformatics datasets. Overall, the results corroborate the perspective that having fusion hierarchies operating on well-chosen subsets of data partitions is a fine strategy that may yield significant gains in terms of clustering robustness. %K genetic algorithms, genetic programming, Cluster analysis, Clustering ensembles, Multi-objective clustering, Hierarchical fusion, Partition selection %9 journal article %R 10.1016/j.dss.2011.01.014 %U http://dx.doi.org/10.1016/j.dss.2011.01.014 %P 794-809 %0 Conference Proceedings %T Classifier ensemble based analysis of a genome-wide SNP dataset concerning Late-Onset Alzheimer Disease %A Coelho, Lucio %A Goertzel, Ben %A Pennachin, Cassio %A Heward, Chris %S 8th IEEE International Conference on Cognitive Informatics, ICCI ’09 %D 2009 %8 jun %F Coelho:2009:ICCI %X The OpenBiomind toolkit is used to apply GA, GP and local search methods to analyze a large SNP dataset concerning late-onset Alzheimer’s disease (LOAD). Classification models identifying LOAD with statistically significant accuracy are identified, and ensemble-based important features analysis is used to identify brain genes related to LOAD, most notably the solute carrier gene SLC6A15. Ensemble analysis is used to identify potentially significant interactions between genes in the context of LOAD. %K genetic algorithms, genetic programming, OpenBiomind toolkit, SLC6A15, brain genes, classifier ensemble based analysis, genome-wide SNP dataset, important features analysis, late-onset Alzheimer disease, local search methods, single-nucleotide polymorphisms, brain, diseases, genetic engineering, learning (artificial intelligence), medical administrative data processing, search problems %R 10.1109/COGINF.2009.5250695 %U http://dx.doi.org/10.1109/COGINF.2009.5250695 %P 469-475 %0 Journal Article %T Solving Multiobjective Optimization Problems Using an Artificial Immune System %A Coello Coello, Carlos A. %A Cruz Cortes, Nareli %J Genetic Programming and Evolvable Machines %D 2005 %8 jun %V 6 %N 2 %@ 1389-2576 %F coello:2004:GPEM %X we propose an algorithm based on the clonal selection principle to solve multiobjective optimisation problems (either constrained or unconstrained). The proposed approach uses Pareto dominance and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced and non-uniform mutation is applied to the ’not so good’ antibodies (which are represented by binary strings that encode the decision variables of the problem to be solved). We also use a secondary (or external) population that stores the nondominated solutions found along the search process. Such secondary population constitutes the elitist mechanism of our approach and it allows it to move towards the true Pareto front of a problem over time. Our approach is compared with three other algorithms that are representative of the state-of-the-art in evolutionary multiobjective optimisation. For our comparative study, three metrics are adopted and graphical comparisons with respect to the true Pareto front of each problem are also included. Results indicate that the proposed approach is a viable alternative to solve multiobjective optimisation problems. %K AIS, artificial immune system, multiobjective optimization, clonal selection %9 journal article %R 10.1007/s10710-005-6164-x %U http://dx.doi.org/10.1007/s10710-005-6164-x %P 163-190 %0 Conference Proceedings %T Tournament Selection Improves Cartesian Genetic Programming for Atari Games %A Cofala, Tim %A Elend, Lars %A Kramer, Oliver %S 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020, Bruges, Belgium, October 2-4, 2020 %D 2020 %F DBLP:conf/esann/CofalaEK20 %K genetic algorithms, genetic programming, Cartesian Genetic Programming %U https://www.esann.org/sites/default/files/proceedings/2020/ES2020-204.pdf %P 345-350 %0 Conference Proceedings %T CityBreeder: city design with evolutionary computation %A Cohen, Adam T. S. %A White, Tony %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 Cohen:2014:GECCOcomp %X The process of creating city designs is complex and time-consuming. This paper presents CityBreeder, a system which uses Evolutionary Computation to enable the rapid, user-guided development of city designs based on the blending of multiple existing designs. %K genetic algorithms, genetic programming: Poster %R 10.1145/2598394.2598495 %U http://doi.acm.org/10.1145/2598394.2598495 %U http://dx.doi.org/10.1145/2598394.2598495 %P 133-134 %0 Journal Article %T Normalized Compression Distance of Multisets with Applications %A Cohen, Andrew R. %A Vitanyi, Paul M. B. %J IEEE Transactions on Pattern Analysis and Machine Intelligence %D 2015 %8 aug %V 37 %N 8 %@ 0162-8828 %F Cohen:2015:ieeeTPAMI %X Pairwise normalized compression distance (NCD) is a parameter-free, feature-free, alignment-free, similarity metric based on compression. We propose an NCD of multisets that is also metric. Previously, attempts to obtain such an NCD failed. For classification purposes it is superior to the pairwise NCD in accuracy and implementation complexity. We cover the entire trajectory from theoretical underpinning to feasible practice. It is applied to biological (stem cell, organelle transport) and OCR classification questions that were earlier treated with the pairwise NCD. With the new method we achieved significantly better results. The theoretic foundation is Kolmogorov complexity. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/TPAMI.2014.2375175 %U http://dx.doi.org/10.1109/TPAMI.2014.2375175 %P 1602-1614 %0 Conference Proceedings %T Dynamic System Identification from Scarce and Noisy Data Using Symbolic Regression %A Cohen, Benjamin %A Beykal, Burcu %A Bollas, George %S 2023 62nd IEEE Conference on Decision and Control (CDC) %D 2023 %8 dec %F Cohen:2023:CDC %X A framework for dynamic system model identification from scarce and noisy data is proposed. This framework uses symbolic regression via genetic programming with a gradient-based parameter estimation step to identify a differential equation model and its parameters from available system data. The effectiveness of the method is demonstrated by identifying four synthetic systems: an ideal plug flow reactor (PFR) with an irreversible chemical reaction, an ideal continuously stirred tank reactor (CSTR) with an irreversible chemical reaction, a system described by Burgers’ Equation, and an ideal PFR with a reversible chemical reaction. The results show that this framework can identify PDE models of systems from broadly spaced and noisy data. When the data was not sufficiently rich, the framework discovered a surrogate model that described the observations in equal or fewer terms than the true system model. Additionally, the method can select relevant physics terms to describe a system from a list of candidate arguments, providing valuable models for use in controls applications. %K genetic algorithms, genetic programming, Parameter estimation, Chemical reactions, Mathematical models, Data models, Noise measurement, Inductors %R 10.1109/CDC49753.2023.10383906 %U http://dx.doi.org/10.1109/CDC49753.2023.10383906 %P 3670-3675 %0 Journal Article %T Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data %A Cohen, Benjamin G. %A Beykal, Burcu %A Bollas, George M. %J Journal of Computational Physics %D 2024 %V 514 %@ 0021-9991 %F Cohen:2024:jcp %X A novel framework is proposed that uses symbolic regression via genetic programming to identify free-form partial differential equations from scarce and noisy data. The framework successfully identified ground truth models for four synthetic systems (an isothermal plug flow reactor, a continuously stirred tank reactor, a nonisothermal reactor, and viscous flow governed by Burgers’ equation) from time-variant data collected at one location. A comparative analysis against the so-called weak Sparse Identification of Nonlinear Dynamics (SINDy) demonstrated the proposed framework’s superior ability to identify meaningful partial differential equation (PDE) models when data was scarce. The framework was further tested for robustness to noise and scarcity, showing successful model recovery from as few as eight time series data points collected at a single point in space with 50percent noise. These results emphasize the potential of the proposed framework for the discovery of PDE models when data collection is expensive or otherwise difficult %K genetic algorithms, genetic programming, Model discovery, Symbolic regression, Partial differential equations %9 journal article %R 10.1016/j.jcp.2024.113261 %U https://www.sciencedirect.com/science/article/pii/S0021999124005096 %U http://dx.doi.org/10.1016/j.jcp.2024.113261 %P 113261 %0 Conference Proceedings %T It’s all in the Semantics: When are Genetically Improved Programs Still Correct? %A Cohen, Myra B. %S "12th International Workshop on Genetic Improvement %F Cohen:2023:GI %0 Journal Article %D 2023 %8 20 may %I IEEE %C Melbourne, Australia %F 2023"b %O Invited Keynote %X Genetic improvement (GI) is a powerful technique to automatically optimise programs, often for nonfunctional properties. As such, we expect to retain the original program semantics, hence GI is guided by both a functional test suite and at least one other objective such as program efficiency, memory usage, energy efficiency, etc. An assumption made is that it is possible to improve a program’s non-functional objective while retaining the program’s correctness, however, this assumption may not hold for all types of non-functional properties. In this talk I show why GI is naturally a multi-objective optimization problem and argue that it may be necessary to relax part of the program correctness to satisfy our non-functional goals. I discuss a few recent examples where we have had to balance functional correctness and non-functional objectives and demonstrate how this may lead to programs that are of higher quality in the end. This raises an important question about when it is possible to completely satisfy multiple (potentially competing) program objectives during GI, and when it is semantically impossible. This leads to the ultimate question of what it means for a program to be correct when using GI. %K genetic algorithms, genetic programming, Genetic Improvement %9 journal article %R 10.1109/GI59320.2023.00008 %U http://gpbib.cs.ucl.ac.uk/gi2023/keynote_2023_gi.pdf %U http://dx.doi.org/10.1109/GI59320.2023.00008 %P ix %0 Thesis %T Automatic Evolution of Conceptual Building Architectures %A Coia, Corrado %D 2011 %8 nov %C Brock University %F Coia:mastersthesis %X This thesis describes research in which genetic programming is used to automatically evolve shape grammars that construct three dimensional models of possible external building architectures. A completely automated fitness function is used, which evaluates the three dimensional building models according to different geometric properties such as surface normals, height, building footprint, and more. In order to evaluate the buildings on the different criteria, a multi-objective fitness function is used. The results obtained from the automated system were successful in satisfying the multiple objective criteria as well as creating interesting and unique designs that a human-aided system might not discover. In this study of evolutionary design, the architectures created are not meant to be fully functional and structurally sound blueprints for constructing a building, but are meant to be inspirational ideas for possible architectural designs. The evolved models are applicable for today’s architectural industries as well as in the video game and movie industries. Many new avenues for future work have also been discovered and highlighted. %K genetic algorithms, genetic programming %9 Masters thesis %U http://hdl.handle.net/10464/3961 %0 Conference Proceedings %T Automatic Evolution of Conceptual Building Architectures %A Coia, Corrado %A Ross, Brian %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 Coia:2011:AEoCBA %X An evolutionary approach to the automatic generation of 3D building topologies is presented. Genetic programming is used to evolve shape grammars. When interpreted, the shape grammars generate 3D models of buildings. Fitness evaluation considers user-specified criteria that evaluate different aspects of the model geometry. Such criteria might include maximising the number of unique normals, satisfying target height requirements, and conforming to supplied shape contours. Multi-objective evaluation is used to analyse and rank model fitness, based on the varied user-supplied criteria. A number of interesting models complying to given geometric specifications have been successfully evolved with the approach. A motivation for this research application is that it can be used as a generator of conceptual designs, to be used as inspirations for refinement or further exploration. %K genetic algorithms, genetic programming, 3D building model, automatic 3D building topologies generation, automatic shape grammar, conceptual building architecture, geometric specification, model geometry, target height requirement, user supplied criteria, building, shapes (structures), solid modelling, structural engineering computing %R 10.1109/CEC.2011.5949745 %U http://dx.doi.org/10.1109/CEC.2011.5949745 %P 1145-1152 %0 Conference Proceedings %T Design and Optimization of Digital Circuits by Artificial Evolution Using Hybrid Multi Chromosome Cartesian Genetic Programming %A Coimbra, Vitor %A Lamar, Marcus Vinicius %Y Bonato, Vanderlei %Y Bouganis, Christos %Y Gorgon, Marek %S Applied Reconfigurable Computing - 12th International Symposium, ARC 2016, Mangaratiba, RJ, Brazil, March 22-24, 2016, Proceedings %S Lecture Notes in Computer Science %D 2016 %V 9625 %I Springer %F conf/arc/CoimbraL16 %K genetic algorithms, genetic programming %U http://dx.doi.org/10.1007/978-3-319-30481-6 %P 195-206 %0 Journal Article %T Surface roughness prediction by extreme learning machine constructed with abrasive water jet %A Cojbasic, Zarko %A Petkovic, Dalibor %A Shamshirband, Shahaboddin %A Tong, Chong Wen %A Ch, Sudheer %A Jankovic, Predrag %A Ducic, Nedeljko %A Baralic, Jelena %J Precision Engineering %D 2016 %V 43 %@ 0141-6359 %F Cojbasic:2016:PE %X In this study, the novel method based on extreme learning machine (ELM) is adapted to estimate roughness of surface machined with abrasive water jet. Roughness of surface is one of the main attributes of quality of products derived from water jet processing, and directly depends on the cutting parameters, such as thickness of the workpiece, abrasive flow rate, cutting speed and others. In this study, in order to provide data on influence of parameters on surface roughness, extensive experiments were carried out for different cutting regimes. Measured data were used to model the process by using ELM model. Estimation and prediction results of ELM model were compared with genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ELM approach in comparison with GP and ANN. Moreover, achieved results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy for roughness of the surface machined with abrasive water jet. In conclusion, it is conclusively found that application of ELM is particularly promising as an alternative method to estimate the roughness of the surface machined with abrasive water jet. %K genetic algorithms, genetic programming, Abrasive water jet, Cutting, Surface roughness, Estimation, Extreme learning machine (ELM) %9 journal article %R 10.1016/j.precisioneng.2015.06.013 %U http://www.sciencedirect.com/science/article/pii/S0141635915001154 %U http://dx.doi.org/10.1016/j.precisioneng.2015.06.013 %P 86-92 %0 Conference Proceedings %T SASS: Self-adaptation using stochastic search %A Coker, Zack %A Garlan, David %A Le Goues, Claire %Y Canfora, Gerardo %Y Elbaum, Sebastian %Y Bertolino, Antonia %S 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems %D 2015 %8 may 18 19 %I IEEE %C Florence Italy %F Coker:SEAMS:2015 %X Future-generation self-adaptive systems will need to be able to optimise for multiple interrelated, difficult to measure, and evolving quality properties. To navigate this complex search space, current self-adaptive planning techniques need to be improved. In this position paper, we argue that the research community should more directly pursue the application of stochastic search techniques, such as hill climbing or genetic algorithms, that incorporate an element of randomness to self-adaptive systems research. These techniques are well-suited to handling multi-dimensional search spaces and complex problems, situations which arise often for self-adaptive systems. We believe that recent advances in both fields make this a particularly promising research trajectory. We demonstrate one way to apply some of these advances in a search-based planning prototype technique to illustrate both the feasibility and the potential of the proposed research. This strategy informs a number of potentially interesting research directions and problems. In the long term, this general technique could enable sophisticated plan generation techniques that improve domain specific knowledge, decrease human effort, and increase the application of self-adaptive systems. %K genetic algorithms, genetic programming, genetic improvement, SBSE %R 10.1109/SEAMS.2015.16 %U https://www.cs.cmu.edu/~clegoues/docs/seams15-position.pdf %U http://dx.doi.org/10.1109/SEAMS.2015.16 %P 168-174 %0 Journal Article %T Milling surface roughness prediction using evolutionary programming methods %A Colak, Oguz %A Kurbanoglu, Cahit %A Kayacan, M. Cengiz %J Materials & Design %D 2007 %V 28 %N 2 %@ 0261-3069 %F Colak2007657 %X CNC milling has become one of the most competent, productive and flexible manufacturing methods, for complicated or sculptured surfaces. In order to design, optimize, built up to sophisticated, multi-axis milling centers, their expected manufacturing output is at least beneficial. Therefore data, such as the surface roughness, cutting parameters and dynamic cutting behavior are very helpful, especially when they are computationally produced, by artificial intelligent techniques. Predicting of surface roughness is very difficult using mathematical equations. In this study gene expression programming method is used for predicting surface roughness of milling surface with related to cutting parameters. Cutting speed, feed and depth of cut of end milling operations are collected for predicting surface roughness. End of the study a linear equation is predicted for surface roughness related to experimental study. %K genetic algorithms, genetic programming, gene expression programming, Surface roughness, CNC end milling, Genetic expression programming %9 journal article %R 10.1016/j.matdes.2005.07.004 %U http://www.sciencedirect.com/science/article/B6TX5-4GYNXVH-3/2/9f33fbb56f37b01600d2773bc207696f %U http://dx.doi.org/10.1016/j.matdes.2005.07.004 %P 657-666 %0 Journal Article %T Modelling formulations using gene expression programming - A comparative analysis with artificial neural networks %A Colbourn, E. A. %A Roskilly, S. J. %A Rowe, R. C. %A York, P. %J European Journal of Pharmaceutical Sciences %D 2011 %V 44 %N 3 %@ 0928-0987 %F Colbourn2011366 %X This study has investigated the utility and potential advantages of gene expression programming (GEP) - a new development in evolutionary computing for modelling data and automatically generating equations that describe the cause-and-effect relationships in a system- to four types of pharmaceutical formulation and compared the models with those generated by neural networks, a technique now widely used in the formulation development. Both methods were capable of discovering subtle and non-linear relationships within the data, with no requirement from the user to specify the functional forms that should be used. Although the neural networks rapidly developed models with higher values for the ANOVA R2 these were black box and provided little insight into the key relationships. However, GEP, although significantly slower at developing models, generated relatively simple equations describing the relationships that could be interpreted directly. The results indicate that GEP can be considered an effective and efficient modelling technique for formulation data. %K genetic algorithms, genetic programming, gene expression programming, Neural networks, Modelling, Formulation %9 journal article %R 10.1016/j.ejps.2011.08.021 %U http://www.sciencedirect.com/science/article/pii/S0928098711002958 %U http://dx.doi.org/10.1016/j.ejps.2011.08.021 %P 366-374 %0 Conference Proceedings %T Boosting Blackjack Returns with Machine Learned Betting Criteria %A Coleman, Ron %S Third International Conference on Information Technology: New Generations (ITNG 2006) %D 2006 %8 October 12 apr %I IEEE Computer Society %C Las Vegas, Nevada, USA %@ 0-7695-2497-4 %F DBLP:conf/itng/Coleman06 %K genetic algorithms, genetic programming %R 10.1109/ITNG.2006.40 %U http://dx.doi.org/10.1109/ITNG.2006.40 %P 669-673 %0 Conference Proceedings %T Comparing Performance of the Learnable Evolution Model and Genetic Algorithms %A Coletti, Mark %A Lash, Thomas D. %A Michalski, Ryszard %A Mandsager, Craig %A Moustafa, Rida %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 coletti:1999:CPLEMGA %K genetic algorithms and classifier systems, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-386.pdf %P 779 %0 Conference Proceedings %T Library for Evolutionary Algorithms in Python (LEAP) %A Coletti, Mark A. %A Scott, Eric O. %A Bassett, Jeffrey K. %Y Allmendinger, Richard %Y Marin, Hugo Terashima %Y Montes, Efren Mezura %Y Bartz-Beielstein, Thomas %Y Filipic, Bogdan %Y Tang, Ke %Y Howard, David %Y Hart, Emma %Y Eiben, Gusz %Y Eftimov, Tome %Y La Cava, William %Y Naujoks, Boris %Y Oliveto, Pietro %Y Volz, Vanessa %Y Weise, Thomas %Y Derbel, Bilel %Y Li, Ke %Y Li, Xiaodong %Y Zapotecas, Saul %Y Zhang, Qingfu %Y Wang, Rui %Y Cheng, Ran %Y Wu, Guohua %Y Li, Miqing %Y Ishibuchi, Hisao %Y Fieldsend, Jonathan %Y Akman, Ozgur %Y Alyahya, Khulood %Y Branke, Juergen %Y Woodward, John R. %Y Tauritz, Daniel R. %Y Baioletti, Marco %Y Uribe, Josu Ceberio %Y McCall, John %Y Milani, Alfredo %Y Wagner, Stefan %Y Affenzeller, Michael %Y Alexander, Bradley %Y Brownlee, Alexander (Sandy) %Y Haraldsson, Saemundur O. %Y Wagner, Markus %Y Sanchez-Pi, Nayat %Y Marti, Luis %Y Fernandez Alzueta, Silvino %Y Valledor Pellicer, Pablo %Y Stuetzle, Thomas %Y Johns, Matthew %Y Ross, Nick %Y Keedwell, Ed %Y Mahmoud, Herman %Y Walker, David %Y Stein, Anthony %Y Nakata, Masaya %Y Paetzel, David %Y Vaughan, Neil %Y Smith, Stephen %Y Cagnoni, Stefano %Y Patton, Robert M. %Y De Falco, Ivanoe %Y Della Cioppa, Antonio %Y Scafuri, Umberto %Y Tarantino, Ernesto %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 Hemberg, Erik %Y Alshammari, Riyad %Y Makanju, Tokunbo %Y Zelinka, Ivan %Y Das, Swagatam %Y Nagaratnam, Ponnuthurai %Y Senkerik, Roman %E Fuijimino-shi %S Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion %S GECCO ’20 %D 2020 %8 jul 8 12 %I Association for Computing Machinery %C internet %F Coletti:2020:GECCOcomp %X There are generally three types of scientific software users: users that solve problems using existing science software tools, researchers that explore new approaches by extending existing code, and educators that teach students scientific concepts. Python is a general-purpose programming language that is accessible to beginners, such as students, but also as a language that has a rich scientific programming ecosystem that facilitates writing research software. Additionally, as high-performance computing (HPC) resources become more readily available, software support for parallel processing becomes more relevant to scientific software. There currently are no Python-based evolutionary computation frameworks that adequately support all three types of scientific software users. Moreover, some support synchronous concurrent fitness evaluation that do not efficiently use HPC resources. We pose here a new Python-based EC framework that uses an established generalized unified approach to EA concepts to provide an easy to use toolkit for users wishing to use an EA to solve a problem, for researchers to implement novel approaches, and for providing a low-bar to entry to EA concepts for students. Additionally, this toolkit provides a scalable asynchronous fitness evaluation implementation friendly to HPC that has been vetted on hardware ranging from laptops to the worlds fastest supercomputer, Summit. %K genetic algorithms, genetic programming %R 10.1145/3377929.3398147 %U https://www.osti.gov/biblio/1649229 %U http://dx.doi.org/10.1145/3377929.3398147 %P 1571-1579 %0 Journal Article %T Data-Mining and Genetic Programming %A Colin, Andre %J PC AI %D 1997 %8 sep / oct %V 11 %N 5 %I Knowledge Technology, Inc. %C Phoenix, AZ, USA %@ 0894-0711 %F colin:1997:DMGP %X To make intelligent real-world decisions, a data-mining package must often align with other technologies. One such technology is genetic programming, which derives rules by looking through a ’space’ of possibilities. Andrew Colin shows how data-mining can use genetic programming in important applications. %K genetic algorithms, genetic programming, data mining %9 journal article %U http://www.pcai.com/web/issues/pcai_11_5_toc.html %P 23 %0 Journal Article %T Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming %A Colins, Andrea %A Gerdtzen, Ziomara P. %A Nunez, Marco T. %A Salgado, J. Cristian %J PLOS one %D 2017 %8 jan 10 %V 12 %N 1 %F Colins:2017:pone %X Iron is a trace metal, key for the development of living organisms. Its absorption process is complex and highly regulated at the transcriptional, translational and systemic levels. Recently, the internalization of the DMT1 transporter has been proposed as an additional regulatory mechanism at the intestinal level, associated to the mucosal block phenomenon. The short-term effect of iron exposure in apical uptake and initial absorption rates was studied in Caco-2 cells at different apical iron concentrations, using both an experimental approach and a mathematical modelling framework. This is the first report of short-term studies for this system. A non-linear behaviour in the apical uptake dynamics was observed, which does not follow the classic saturation dynamics of traditional biochemical models. We propose a method for developing mathematical models for complex systems, based on a genetic programming algorithm. The algorithm is aimed at obtaining models with a high predictive capacity, and considers an additional parameter fitting stage and an additional Jack-knife stage for estimating the generalization error. We developed a model for the iron uptake system with a higher predictive capacity than classic biochemical models. This was observed both with the apical uptake dataset used for generating the model and with an independent initial rates dataset used to test the predictive capacity of the model. The model obtained is a function of time and the initial apical iron concentration, with a linear component that captures the global tendency of the system, and a non-linear component that can be associated to the movement of DMT1 transporters. The model presented in this paper allows the detailed analysis, interpretation of experimental data, and identification of key relevant components for this complex biological process. This general method holds great potential for application to the elucidation of biological mechanisms and their key components in other complex systems. %K genetic algorithms, genetic programming %9 journal article %R 10.1371/journal.pone.0169601 %U http://dx.doi.org/10.1371/journal.pone.0169601 %P e0169601 %0 Generic %T Learning of Behavior Trees for Autonomous Agents %A Colledanchise, Michele %A Parasuraman, Ramviyas %A Oegren, Petter %D 2015 %8 apr 22 %F oai:arXiv.org:1504.05811 %X Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behaviour Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, reactiveness, scalability and domain-independence. In this paper, we propose a model-free AP framework using Genetic Programming (GP) to derive an optimal BT for an autonomous agent to achieve a given goal in unknown (but fully observable) environments. We illustrate the proposed framework using experiments conducted with an open source benchmark Mario AI for automated generation of BTs that can play the game character Mario to complete a certain level at various levels of difficulty to include enemies and obstacles. %K genetic algorithms, genetic programming, computer science - robotics, computer science - artificial intelligence, computer science - learning %U http://arxiv.org/abs/1504.05811 %0 Journal Article %T Learning of Behavior Trees for Autonomous Agents %A Colledanchise, Michele %A Parasuraman, Ramviyas Nattanmai %A Ogren, Petter %J IEEE Transactions on Games %D 2018 %@ 2475-1502 %F Colledanchise:2018:ieeeTOG %X we study the problem of automatically synthesizing a successful Behaviour Tree (BT) in an a-priori unknown dynamic environment. Starting with a given set of behaviours, a reward function, and sensing in terms of a set of binary conditions, the proposed algorithm incrementally learns a switching structure in terms of a BT, that is able to handle the situations encountered. Exploiting the fact that BTs generalise And-Or-Trees and also provide very natural chromosome mappings for genetic programming, we combine the long term performance of Genetic Programming with a greedy element and use the And-Or analogy to limit the size of the resulting structure. Finally, earlier results on BTs enable us to provide certain safety guarantees for the resulting system. Using the testing environment Mario AI we compare our approach to alternative methods for learning BTs and Finite State Machines. The evaluation shows that the proposed approach generated solutions with better performance, and often fewer nodes than the other two methods. %K genetic algorithms, genetic programming, Artificial intelligence, Games, Genetics, Heuristic algorithms, Planning, Safety, Stochastic processes %9 journal article %R 10.1109/TG.2018.2816806 %U http://dx.doi.org/10.1109/TG.2018.2816806 %0 Book %T Behavior Trees in Robotics and AI: An Introduction %A Colledanchise, Michele %A Ogren, Petter %S AI and Robotics %D 2019 %7 v6 %I Chapman & Hall %F Colledanchise_2018 %X Behaviour Trees (BTs) provide a way to structure the behavior of an artificial agent such as a robot or a non-player character in a computer game. Traditional design methods, such as finite state machines, are known to produce brittle behaviors when complexity increases, making it very hard to add features without breaking existing functionality. BTs were created to address this very problem, and enables the creation of systems that are both modular and reactive. Behavior Trees in Robotics and AI: An Introduction provides a broad introduction as well as an in-depth exploration of the topic, and is the first comprehensive book on the use of BTs. %K genetic algorithms, genetic programming %R 10.1201/9780429489105 %U https://www.routledge.com/Behavior-Trees-in-Robotics-and-Al-An-Introduction/Colledanchise-Ogren/p/book/9781138593732 %U http://dx.doi.org/10.1201/9780429489105 %0 Conference Proceedings %T Individual GP: an Alternative Viewpoint for the Resolution of Complex Problems %A Collet, Pierre %A Lutton, Evelyne %A Raynal, Frederic %A Schoenauer, Marc %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 collet:1999:IGAVRCP %X An unususal GP implementation is proposed, based on a more ’economic’ exploitation of the GP algorithm: the ’individual’ approach, where each individual of the population embodies a single function rather than a set of functions. The final solution is then a set of individuals. Examples are presented where results are obtained more rapidly than with the conventional approach, where all individuals of the final generation but one are discarded. %K genetic algorithms, genetic programming, IFS, fractals %U http://minimum.inria.fr/evo-lab/Publications/GP-467.ps.gz %P 974-981 %0 Report %T Polar IFS + Individual Genetic Programming = Efficient IFS Inverse Problem Solving %A Collet, Pierre %A Lutton, Evelyne %A Raynal, Frederic %A Schoenauer, Marc %D 1999 %8 dec %N RR-3849 %I INRIA %C Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le Chesnay Cedex France %F collet:1999:RR-3849 %X The inverse problem for Iterated Functions Systems (finding an IFS whose attractor is a target 2D shape) with non-affine IFS is a very complex task. Successful approaches have been made using Genetic Programming, but there is still room for improvement in both the IFS and the GP parts. The main difficulty with non-linear IFS is the efficient handling of contractance constraints. This paper introduces Polar IFS, a specific representation of IFS functions that shrinks the search space to mostly contractive functions. Moreover, the Polar representation gives direct access to the fixed points of the functions, whereas the fixed point of general non-linear IFS can only be numerically estimated. On the evolutionary side, the ’individual’ approach is similar to the Michigan approach of Classifier Systems: each individual of the population embodies a single function rather than the whole IFS. A solution to the inverse problem is then built from a set of individuals. Both improvements show a drastic cut-down on CPU-time: good results are obtained with small populations in few generations. %K genetic algorithms, genetic programming %U http://minimum.inria.fr/evo-lab/Publications/RR-PolarIFS.ps.gz %0 Conference Proceedings %T Take it EASEA %A Collet, Pierre %A Lutton, Evelyne %A Schoenauer, Marc %A Louchet, Jean %Y Schoenauer, Marc %Y Deb, Kalyanmoy %Y Rudolph, Günter %Y Yao, Evelyne Lutton Xin %Y Merelo, Juan Julian %Y Schwefel, Hans-Paul %S Parallel Problem Solving from Nature - PPSN VI 6th International Conference %S LNCS %D 2000 %8 sep 16 20 %V 1917 %I Springer-Verlag %C Paris, France %F ColletPPSN2000 %X Evolutionary algorithms are not straightforward to implement and the lack of any specialised language forces users to reinvent the wheel every time they want to write a new program. Over the last years, evolutionary libraries have appeared, trying to reduce the amount of work involved in writing such algorithms from scratch, by offering standard engines, strategies and tools. Unfortunately, most of these libraries are quite complex to use, and imply a deep knowledge of object programming and C++. To further reduce the amount of work needed to implement a new algorithm, without however throwing down the drain all the man-years already spent in the development of such libraries, we have designed EASEA (acronym for EAsy Specification of Evolutionary Algorithms): a new high-level language dedicated to the specification of evolutionary algorithms. EASEA compiles .ez files into C++ object files, containing function calls to a chosen existing library. The resulting C++ file is in turn compiled and linked with the library to produce an executable file implementing the evolutionary algorithm specified in the original .ez file. %K genetic algorithms, genetic programming %U http://minimum.inria.fr/evo-lab/Publications/PPSNVI.ps.gz %P 891-901 %0 Journal Article %T Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving %A Collet, Pierre %A Lutton, Evelyne %A Raynal, Frederic %A Schoenauer, Marc %J Genetic Programming and Evolvable Machines %D 2000 %8 oct %V 1 %N 4 %@ 1389-2576 %F collet:2000:IFSpGP %X This paper proposes a new method for treating the inverse problem for Iterated Functions Systems (IFS) using Genetic Programming. This method is based on two original aspects. On the fractal side, a new representation of the IFS functions, termed Polar Iterated Functions Systems, is designed, shrinking the search space to mostly contractive functions. Moreover, the Polar representation gives direct access to the fixed points of the functions. On the evolutionary side, a new variant of GP, the Parisian approach is presented. The paper explains its similarity to the Michigan approach of Classifier Systems: each individual of the population only represents a part of the global solution. The solution to the inverse problem for IFS is then built from a set of individuals. A local contribution to the global fitness of an IFS is carefully defined for each one of its member functions and plays a major role in the fitness of each individual. It is argued here that both proposals result in a large improvement in the algorithms. We observe a drastic cut-down on CPU-time, obtaining good results with small populations in few generations. %K genetic algorithms, genetic programming, fractals, Iterated Functions System, inverse problem for IFS, polar IFS %9 journal article %R 10.1023/A:1010065123132 %U http://minimum.inria.fr/evo-lab/Publications/PolarIFS-GPEM-New.ps.gz %U http://dx.doi.org/10.1023/A:1010065123132 %P 339-361 %0 Report %T EASEA : un langage de specification pour les algorithmes evolutionnaires %A Collet, Pierre %A Schoenauer, Marc %A Lutton, Evelyne %A Louchet, Jean %D 2001 %8 jun %N RR-4218 %I INRIA %C Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le Chesnay Cedex France %F collet:2001:RR4421 %X Contrairement aux apparences, il n’est pas simple d’ecrire un programme informatique realisant un algorithme evolutionnaire, d’autant que le manque de langage specialise oblige l’utilisateur a utiliser C, C++ ou JAVA. La plupart des algorithmes evolutionnaires, cependant, possedent une structure commune, et la part reellement specifique est constituee par une faible portion du code. Ainsi, il semble que rien ne s’oppose en theorie a ce qu’un utilisateur puisse construire, puis faire tourner son algorithme evolutionnaire a partir d’une interface graphique, afin de limiter son effort de programmation a la fonction a optimiser. L’ecriture d’une telle interface graphique pose tout d’abord le probleme de sauver et de recharger l’algorithme evolutionnaire sur lequel l’utilisateur travaille, puis celui de transformer ces informations en code compilable. Cela ressemble fort a un language de specification et son compilateur. Le logiciel EASEA a ete cree dans ce but, et a notre connaissance, il est actuellement le seul et unique compilateur de langage specifique aux algorithmes evolutionnaires. Ce rapport decrit comment EASEA a ete construit et quels sont les problemes qui restent a resoudre pour achever son implantation informatique. %K genetic algorithms, genetic programming, EASEA, Java %U ftp://ftp.inria.fr/INRIA/publication/publi-pdf/RR/RR-4218.pdf %0 Conference Proceedings %T Issues on the Optimisation of Evolutionary Algorithms Code %A Collet, Pierre %A Louchet, Jean %A Lutton, Evelyne %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 Collet:2002:IotOoEAC %X The aim of this paper is to show that the common belief, in the evolutionary community, that evaluation time usually takes over 90percent of the total time, is far from being always true. In fact, many real-world applications showed a much lower percentage. This raises several questions, one of them being the balance between fitness and operators computational complexity: what is the use of elaborating smart evolutionary operators to reduce the number of evaluations if as a result, the total computation time is increased? %K genetic algorithms, genetic programming, code optimisation, computation time, computational complexity, evolutionary algorithms, fitness, testbenches, computational complexity, evolutionary computation, %R 10.1109/CEC.2002.1004397 %U http://dx.doi.org/10.1109/CEC.2002.1004397 %P 1103-1108 %0 Conference Proceedings %T GUIDE: Unifying Evolutionary Engines through a Graphical User Interface %A Collet, Pierre %A Schoenauer, Marc %Y Liardet, Pierre %Y Collet, Pierre %Y Fonlupt, Cyril %Y Lutton, Evelyne %Y Schoenauer, Marc %S Evolution Artificielle, 6th International Conference %S Lecture Notes in Computer Science %D 2003 %8 27 30 oct %V 2936 %I Springer %C Marseilles, France %@ 3-540-21523-9 %F collet:2003:EA %O Revised Selected Papers %X Many kinds of Evolutionary Algorithms (EAs) have been described in the literature since the last 30 years. However, though most of them share a common structure, no existing software package allows the user to actually shift from one model to another by simply changing a few parameters, e.g. in a single window of a Graphical User Interface. GUIDE, a graphical user interface for DREAM experiments that, among other user-friendly features, unifies all kinds of EAs into a single panel, as far as evolution parameters are concerned. Such a window can be used either to ask for one of the well known ready-to-use algorithms, or to very easily explore new combinations that have not yet been studied. Another advantage of grouping all necessary elements to describe virtually all kinds of EAs is that it creates a fantastic pedagogic tool to teach EAs to students and newcomers to the field. %K genetic algorithms, genetic programming, Artificial Evolution %R 10.1007/b96080 %U http://dx.doi.org/10.1007/b96080 %P 203-215 %0 Conference Proceedings %T Proceedings of the 9th European Conference on Genetic Programming %E Collet, Pierre %E Tomassini, Marco %E Ebner, Marc %E Gustafson, Steven %E Ekárt, Anikó %S Lecture Notes in Computer Science %D 2006 %8 October 12 apr %V 3905 %I Springer %C Budapest, Hungary %@ 3-540-33143-3 %F collet:2006:GP %K genetic algorithms, genetic programming %R 10.1007/11729976 %U http://dx.doi.org/10.1007/11729976 %0 Book Section %T Genetic Programming %A Collet, Pierre %E Rennard, Jean-Philippe %B Handbook of Research on Nature-Inspired Computing for Economics and Management %D 2007 %V I %I Idea Group Inc. %C 1200 E. Colton Ave %@ 1-59140-984-5 %F collet:2007:nicem %X The aim of genetic programming is to evolve programs or functions (symbolic regression) thanks to artificial evolution. This technique is now mature and can routinely yield results on par with (or even better than) human intelligence. This chapter sums up the basics of genetic programming and outlines the main subtleties one should be aware of in order to obtain good results. %K genetic algorithms, genetic programming, GP-std/same, homologous crossover, interval arithmetic, problem dependence, over fitting and bloat %R 10.4018/978-1-59140-984-7.ch005 %U http://dx.doi.org/10.4018/978-1-59140-984-7.ch005 %P 59-73 %0 Journal Article %T Husbands, Holland, and Wheeler (eds): Review of the book ’The Mechanical Mind in History’ MIT Press, 2008, ISBN 978-0-262-08377-5 %A Collet, Pierre %J Genetic Programming and Evolvable Machines %D 2009 %8 mar %V 10 %N 1 %@ 1389-2576 %F Collet:2009:GPEM %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-008-9070-1 %U https://rdcu.be/dR8iK %U http://dx.doi.org/10.1007/s10710-008-9070-1 %P 91-93 %0 Journal Article %T Evolutionary algorithms for data mining %A Collet, Pierre %A Wong, Man Leung %J Genetic Programming and Evolvable Machines %D 2012 %8 mar %V 13 %N 1 %@ 1389-2576 %F Collet:2012:GPEM %O Editorial Introduction to Special Section on Evolutionary Algorithms for Data Mining %K genetic algorithms, genetic programming %9 journal article %R 10.1007/s10710-011-9156-z %U http://dx.doi.org/10.1007/s10710-011-9156-z %P 69-70 %0 Conference Proceedings %T Evolutionary algorithms and genetic programming on graphic processing units (GPU) %A Collet, Pierre %A Harding, Simon %Y Ochoa, Gabriela %S GECCO 2012 Specialized techniques and applications tutorials %D 2012 %8 July 11 jul %I ACM %C Philadelphia, Pennsylvania, USA %F Collet:2012:GECCOcomp %K genetic algorithms, genetic programming, GPU %R 10.1145/2330784.2330933 %U http://dx.doi.org/10.1145/2330784.2330933 %P 1117-1138 %0 Conference Proceedings %T Modeling the Behaviour of Interacting Autonomous Intelligent Agents %A Collins, J. J. %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 collins:1998:mbiaia %K genetic algorithms, genetic programming %U http://www.csis.ul.ie/staff/jjcollins/gp98.html %P 29and253 %0 Conference Proceedings %T Genetic Planner for a Mobile Robot Navigation System %A Collins, J. J. %A Sheehan, Lucia %A Casey, Conor %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 collins:1999:GPMRNS %K genetic algorithms and classifier systems, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-399.pdf %P 782 %0 Conference Proceedings %T Non-stationary Function Optimization using Polygenic Inheritance %A Collins, J. J. %A Ryan, Conor %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 collins:1999:NFOPI %K genetic algorithms and classifier systems, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-398.pdf %P 781 %0 Conference Proceedings %T Counting Solutions in Reduced Boolean Parity %A Collins, M. %Y Poli, R. %Y Cagnoni, S. %Y Keijzer, M. %Y Costa, E. %Y Pereira, F. %Y Raidl, G. %Y Upton, S. C. %Y Goldberg, D. %Y Lipson, H. %Y de Jong, E. %Y Koza, J. %Y Suzuki, H. %Y Sawai, H. %Y Parmee, I. %Y Pelikan, M. %Y Sastry, K. %Y Thierens, D. %Y Stolzmann, W. %Y Lanzi, P. L. %Y Wilson, S. W. %Y O’Neill, M. %Y Ryan, C. %Y Yu, T. %Y Miller, J. F. %Y Garibay, I. %Y Holifield, G. %Y Wu, A. S. %Y Riopka, T. %Y Meysenburg, M. M. %Y Wright, A. W. %Y Richter, N. %Y Moore, J. H. %Y Ritchie, M. D. %Y Davis, L. %Y Roy, R. %Y Jakiela, M. %S GECCO 2004 Workshop Proceedings %D 2004 %8 26 30 jun %C Seattle, Washington, USA %F collins:2004:nue:mcol %K genetic algorithms, genetic programming %U http://gpbib.cs.ucl.ac.uk/gecco2004/WNUE001.pdf %0 Conference Proceedings %T Finding needles in haystacks is harder with neutrality %A Collins, M. %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 1068282 %K genetic algorithms, genetic programming, Cartesian genetic programming, reduced Boolean parity, search space %R 10.1145/1068009.1068282 %U http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1613.pdf %U http://dx.doi.org/10.1145/1068009.1068282 %P 1613-1618 %0 Journal Article %T Finding needles in haystacks is harder with neutrality %A Collins, Mark %J Genetic Programming and Evolvable Machines %D 2006 %8 aug %V 7 %N 2 %@ 1389-2576 %F Collins:2006:GPEM %O Special Issue: Best of GECCO 2005 %X an extended analysis of the reported successes of the Cartesian Genetic Programming method on a simplified form of the Boolean parity problem. We show the method of sampling used by the CGP is significantly less effective at locating solutions than the solution density of the corresponding formula space would warrant. We present results indicating that the loss of performance is caused by the sampling bias of the CGP, due to the neutrality friendly representation. We implement a simple intron free random sampling algorithm which performs considerably better on the same problem and then explain how such performance is possible. %K genetic algorithms, genetic programming, Cartesian genetic programming, random sampling, solution density %9 journal article %R 10.1007/s10710-006-9001-y %U http://dx.doi.org/10.1007/s10710-006-9001-y %P 131-144 %0 Thesis %T An Algorithm for Evolving Protocol Constraints %A Collins, Mark %D 2006 %C Artificial Intelligence Applications Institute, School of Informatics, University of Edinburgh %F Collins:thesis %X We present an investigation into the design of an evolutionary mechanism for multiagent protocol constraint optimisation. Starting with a review of common population based mechanisms we discuss the properties of the mechanisms used by these search methods. We derive a novel algorithm for optimisation of vectors of real numbers and empirically validate the efficacy of the design by comparing against well known results from the literature. We discuss the application of an optimiser to a novel problem and remark upon the relevance of the no free lunch theorem. We show the relative performance of the optimiser is strong and publish details of a new best result for the Keane optimisation problem. We apply the final algorithm to the multi-agent protocol optimisation problem and show the design process was successful. %K genetic algorithms %9 Ph.D. thesis %U http://www.cisa.informatics.ed.ac.uk/ssp/pubs/collins_phd.pdf %0 Conference Proceedings %T A Comparison of Search Space Visualization Techniques %A Collins, Trevor D. %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 collins:1999:ACSSVT %K genetic algorithms and classifier systems, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-395.ps %P 780 %0 Conference Proceedings %T Evolvable Hardware for Security through Diverse Variants %A Collins, Zachary %A King, Bayley %A Jha, Rashmi %A Kapp, David %A Ralescu, Anca %S 2019 IEEE National Aerospace and Electronics Conference (NAECON) %D 2019 %8 jul %F Collins:2019:NAECON %X Evolvable hardware is attractive as a design strategy to hardware engineers, but suffers due to its lack of scalability to larger hardware systems. This work examines how hardware designers can make use of evolvable hardware to improve the security of their systems, and to create hardware systems that are better resistant to reverse engineering. %K genetic algorithms, genetic programming %R 10.1109/NAECON46414.2019.9058062 %U http://dx.doi.org/10.1109/NAECON46414.2019.9058062 %P 257-261 %0 Thesis %T Multi-Dimensional Analysis of Software Power Consumptions in Multi-Core Architectures %A Colmant, Maxime %D 2016 %8 30 nov %C France %C University de Lille %F Colmant:thesis %X Energy-efficient computing is becoming increasingly important. Among the reasons, one can mention the massive consumption of large data centres that consume as much as 180,000 homes. This trend, combined with environmental concerns, makes energy efficiency a prime technological and societal challenge. Currently, widely used power distribution units (PDUs) are often shared amongst nodes to deliver aggregated power consumption reports, in the range of hours and minutes. However, in order to improve the energy efficiency of software systems, we need to support process-level power estimation in real-time, which goes beyond the capacity of a PDUs. In particular, the CPU is considered by the research community as the major power consumer within a node and draws attention while trying to model the system power consumption. Over the last few years, a dozen of ad hoc power models have been proposed to cope with the wide diversity and the growing complexity of modern CPU architectures. In this thesis, we rather propose PowerAPI for learning power models and building software-defined power meters that provide accurate power estimation on modern architectures. With the emergence of cloud computing, we propose BitWatts and WattsKit for leveraging software power estimation in virtual machines VMs and clusters. A finer level of estimation may be required to further evaluate the effectiveness of the software optimizations and we therefore propose codEnergy for helping developers to understand how the energy is really consumed by a software. We deeply assessed all above approaches, thus demonstrating the usefulness of PowerAPI to better understand the software power consumption on modern architectures. %K genetic improvement, SBSE, software power consumption, software-defined power meter, machine learning %9 Ph.D. thesis %U https://tel.archives-ouvertes.fr/tel-01403559/file/colmant-thesis.pdf %0 Conference Proceedings %T Improving reliability of embedded systems through dynamic memory manager optimization using grammatical evolution %A Colmenar, J. Manuel %A Risco-Martin, Jose L. %A Atienza, David %A Garnica, Oscar %A Hidalgo, J. Ignacio %A Lanchares, Juan %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 Colmenar:2010:gecco %X Technology scaling has offered advantages to embedded systems, such as increased performance, more available memory and reduced energy consumption. However, scaling also brings a number of problems like reliability degradation mechanisms. The intensive activity of devices and high operating temperatures are key factors for reliability degradation in latest technology nodes. Focusing on embedded systems, the memory is prone to suffer reliability problems due to the intensive use of dynamic memory on wireless and multimedia applications. In this work we present a new approach to automatically design dynamic memory managers considering reliability, and improving performance, memory footprint and energy consumption. Our approach, based on Grammatical Evolution, obtains a maximum improvement of 39percent in execution time, 38percent in memory usage and 50percent in energy consumption over state-of-the-art dynamic memory managers for several real-life applications. In addition, the resulting distributions of memory accesses improve reliability. To the best of our knowledge, this is the first proposal for automatic dynamic memory manager design that considers reliability. Categories and Subject %K genetic algorithms, genetic programming, grammatical evolution, genetic improvement, SBSE %R 10.1145/1830483.1830705 %U http://dx.doi.org/10.1145/1830483.1830705 %P 1227-1234 %0 Conference Proceedings %T Multi-objective optimization of dynamic memory managers using grammatical evolution %A Colmenar, J. Manuel %A Risco-Martin, Jose L. %A Atienza, David %A Hidalgo, J. Ignacio %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 Colmenar:2011:GECCO %X The dynamic memory manager (DMM) is a key element whose customization for a target application reports great benefits in terms of execution time, memory usage and energy consumption. Previous works presented algorithms to automatically obtain custom DMMs for a given application. Nevertheless, those approaches are based on grammatical evolution where the fitness is built as an aggregate objective function, which does not completely exploit the search space, returning the designer the DMM solution with best fitness. However, this approach may not find solutions that could fit in a concrete hardware platform due to a very low value of one of the objectives while the others remain high, which may represent a high fitness. In this work we present the first multi-objective optimisation methodology applied to DMM optimisation where the Pareto dominance is considered, thus providing the designer with a set of non-dominated DMM implementations on each optimisation run. Our results show that the multi-objective optimisation provides Pareto-optimal alternatives due to a better exploitation of the search space obtaining better hypervolume values than the aggregate objective function approach. %K genetic algorithms, genetic programming, grammatical evolution, genetic improvement, SBSE, Real world applications %R 10.1145/2001576.2001820 %U http://dx.doi.org/10.1145/2001576.2001820 %P 1819-1826 %0 Conference Proceedings %T An evolutionary methodology for automatic design of finite state machines %A Colmenar, J. Manuel %A Cuesta-Infante, Alfredo %A Risco-Martin, Jose L. %A Hidalgo, J. Ignacio %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 Colmenar:2013:GECCOcomp %X We propose an evolutionary flow for finite state machine inference through the cooperation of grammatical evolution and a genetic algorithm. This coevolution has two main advantages. First, a high-level description of the target problem is accepted by the flow, being easier and affordable for system designers. Second, the designer does not need to define a training set of input values because it is automatically generated by the genetic algorithm at run time. Our experiments on the sequence recogniser and the vending machine problems obtained the FSM solution in 99.96percent and 100percent of the optimisation runs, respectively. %K genetic algorithms, genetic programming %R 10.1145/2464576.2464645 %U http://dx.doi.org/10.1145/2464576.2464645 %P 139-140 %0 Conference Proceedings %T Compilable Phenotypes: Speeding-Up the Evaluation of Glucose Models in Grammatical Evolution %A Colmenar, J. Manuel %A Hidalgo, Jose Ignacio %A Lanchares, Juan %A Garnica, Oscar %A Risco-Martin, Jose L. %A Contreras, Ivan %A Sanchez, Almudena %A Velasco, J. Manuel %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 9598 %I Springer %C Porto, Portugal %F conf/evoW/ColmenarHLGRCSV16 %X This paper presents a method for accelerating the evaluation of individuals in Grammatical Evolution. The method is applied for identification and modelling problems, where, in order to obtain the fitness value of one individual, we need to compute a mathematical expression for different time events. We propose to evaluate all necessary values of each individual using only one mathematical Java code. For this purpose we take profit of the flexibility of grammars, which allows us to generate Java compilable expressions. We test the methodology with a real problem: modeling glucose level on diabetic patients. Experiments confirms that our approach (compilable phenotypes) can get up to 300x reductions in execution time. %K genetic algorithms, genetic programming, Grammatical evolution, Model identification, Diabetes mellitus, EvoPAR %R 10.1007/978-3-319-31153-1_9 %U http://dx.doi.org/10.1007/978-3-319-31153-1_9 %P 118-133 %0 Conference Proceedings %T Predicting Glycemia in Diabetic Patients By Evolutionary Computation and Continuous Glucose Monitoring %A Colmenar, J. Manuel %A Winkler, Stephan M. %A Kronberger, Gabriel %A Maqueda, Esther %A Botella, Marta %A Hidalgo, J. Ignacio %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 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation %D 2016 %8 20 24 jul %I ACM %C Denver, Colorado, USA %F Colmenar:2016:GECCOcomp %X Diabetes mellitus is a disease that affects more than three hundreds million people worldwide. Maintaining a good control of the disease is critical to avoid not only severe long-term complications but also dangerous short-term situations. Diabetics need to decide the appropriate insulin injection, thus they need to be able to estimate the level of glucose they are going to have after a meal. In this paper we use machine learning techniques for predicting glycemia in diabetic patients. The algorithms use data collected from real patients by a continuous glucose monitoring system, the estimated number of carbohydrates, and insulin administration for each meal. We compare (1) non-linear regression with fixed model structure, (2) identification of prognosis models by symbolic regression using genetic programming, (3) prognosis by k-nearest-neighbour time series search, and (4) identification of prediction models by grammatical evolution. We consider predictions horizons of 30, 60, 90 and 120 minutes. %K genetic algorithms, genetic programming, grammatical evolution %R 10.1145/2908961.2931734 %U http://dx.doi.org/10.1145/2908961.2931734 %P 1393-1400 %0 Journal Article %T Automatic generation of models for energy demand estimation using Grammatical Evolution %A Colmenar, J. M. %A Hidalgo, J. I. %A Salcedo-Sanz, S. %J Energy %D 2018 %V 164 %@ 0360-5442 %F COLMENAR:2018:Energy %X The estimation of total energy demand in a country from macro-economic variables is an important problem useful to evaluate the robustness of the country’s economy. Since the first years of this century, meta-heuristics approaches have been successfully applied to this problem, for different countries and problem’s parameterizations. Many of these works optimize prediction models which are based on classical polynomial or simple exponential relationships, which may not be the best option for an accurate energy demand estimation prediction. In this paper the use of Grammatical Evolution is proposed to generate new models for total energy demand estimation at country level. Grammatical Evolution is a class of Genetic Programming algorithm, which is able to automatically generate new models from input variables. In this case, Grammatical Evolution considers macro-economic variables from which it is able to generate new models for total energy demand estimation of a country, with a temporal prediction horizon of one year. The models generated by the Grammatical Evolution are further optimized in order to adjust their parameters to the energy demand estimation. This process is carried out by means of a Differential Evolution approach, which is run for every model generated by the Grammatical Evolution. Thus, the algorithmic proposal consists of a hybrid method, involving Grammatical Evolution for model generation and a Differential Evolution meta-heuristic for the models’ parameter tuning. The performance of the proposed approach has been evaluated in two different problems of total energy demand estimation in Spain and France, with excellent results in terms of prediction accuracy %K genetic algorithms, genetic programming, Grammatical evolution, Energy demand estimation, Macro-economic variables, Meta-heuristics %9 journal article %R 10.1016/j.energy.2018.08.199 %U http://www.sciencedirect.com/science/article/pii/S0360544218317353 %U http://dx.doi.org/10.1016/j.energy.2018.08.199 %P 183-193 %0 Conference Proceedings %T WebGE: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution %A Manuel Colmenar, J. %A Martin-Santamaria, Raul %A Hidalgo, J. Ignacio %Y Laredo, Juan Luis Jimenez %Y Hidalgo, J. Ignacio %Y Babaagba, Kehinde Oluwatoyin %S 25th International Conference, EvoApplications 2022 %S LNCS %D 2022 %8 20 22 apr %V 13224 %I Springer %C Madrid %F Colmenar:2022:evoapplications %X Many frameworks and libraries are available for researchers working on optimization. However, the majority of them require programming knowledge, lack of a friendly user interface and cannot be run on different operating systems. WebGE is a new optimization tool which provides a web-based graphical user interface allowing any researcher to use Grammatical Evolution and Differential Evolution on symbolic regression problems. In addition, the fact that it can be deployed on any server as a web service also incorporating user authentication, makes it a versatile and portable tool that can be shared by multiple researchers. Finally, the modular software architecture allows to easily extend WebGE to other algorithms and types of problems. %K genetic algorithms, genetic programming, Grammatical Evolution, Differential Evolution Symbolic regression, Open-source software %R 10.1007/978-3-031-02462-7_18 %U http://dx.doi.org/10.1007/978-3-031-02462-7_18 %P 269-282 %0 Conference Proceedings %T Mapping the Field of Metaheuristic and Bioinspired Portfolio Optimization %A Colomine Duran, Feijoo %A Cotta, Carlos %A Fernandez-Leiva, Antonio J. %Y Mora, Antonio M. %Y Esparcia-Alcazar, Anna I. %S Evostar 2022 Late breaking abstracts %D 2022 %8 20 22 apr %C Madrid %F Colomine-Duran:2022:evostarLBA %X We analyze the bibliography related to portfolio optimization using metaheuristics and bioinspired algorithms. To this end, we perform data clustering based on lexical similarity between bibliographical descriptors and propose an internal arrangement of each cluster using evolutionary algorithms. We also conduct a network analysis in order to determine relevant keywords and their associations %K genetic algorithms, genetic programming, Metaheuristics, Bioinspired Algorithms, Evolutionary Algorithms, Multiobjective, Portfolio %U https://arxiv.org/abs/arXiv:2208.00555 %P 1-4 %0 Conference Proceedings %T Evolving Simple Art-based Games %A Colton, Simon %A Browne, Cameron %Y Giacobini, Mario %Y De Falco, Ivanoe %Y Ebner, Marc %S Applications of Evolutionary Computing, EvoWorkshops2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoPhD, EvoSTOC, EvoTRANSLOG %S LNCS %D 2009 %8 15 17 apr %V 5484 %I Springer Verlag %C Tubingen, Germany %F Colton:evows09 %X Evolutionary art has a long and distinguished history, and genetic programming is one of only a handful of AI techniques which is used in graphic design and the visual arts. A recent trend in so-called ’new media’ art is to design online pieces which are dynamic and have an element of interaction and sometimes simple game-playing aspects. This defines the challenge addressed here: to automatically evolve dynamic, interactive art pieces with game elements. We do this by extending the Avera user-driven evolutionary art system to produce programs which generate spirograph-style images by repeatedly placing, scaling, rotating and colouring geometric objects such as squares and circles. Such images are produced in an inherently causal way which provides the dynamic element to the pieces.We further extend the system to produce programs which react to mouse clicks, and to evolve sequential patterns of clicks for the user to uncover. We wrap the programs in a simple front end which provides the user with feedback on how close they are to uncovering the pattern, adding a lightweight gameplaying element to the pieces. The evolved interactive artworks are a preliminary step in the creation of more sophisticated multimedia pieces. %K genetic algorithms, genetic programming %R 10.1007/978-3-642-01129-0_32 %U http://dx.doi.org/10.1007/978-3-642-01129-0_32 %P 283-292 %0 Journal Article %T Genetic Programming to Design Communication Algorithms for Parallel Architectures %A Comellas, F. %A Giménez, G. %J Parallel Processing Letters %D 1998 %V 8 %N 4 %@ 0129-6264 %F Comellas:1998:GPD %X Broadcasting is an information dissemination problem in which a message originating at one node of a communication network (modelled as a graph) is to be sent to all other nodes as quickly as possible. This paper describes a new way of producing broadcasting schemes using genetic programming. This technique has proven successful by easily finding optimal algorithms for several well-known families of networks (grids, hypercubes and cycle connected cubes) and has indeed generated a new scheme for butterflies that improves the known upper bound for the broadcasting time of these networks. %K genetic algorithms, genetic programming, broadcasting, networks, butterfly graph %9 journal article %R 10.1142/S0129626498000547 %U http://www-mat.upc.es/~comellas/genprog/genprog_f.pdf %U http://dx.doi.org/10.1142/S0129626498000547 %P 549-560 %0 Conference Proceedings %T Using Genetic Programming to Design Broadcasting Algorithms for Manhattan Street Networks %A Comellas, Francesc %A Dalfo, Cristina %Y Raidl, Guenther R. %Y Cagnoni, Stefano %Y Branke, Jurgen %Y Corne, David W. %Y Drechsler, Rolf %Y Jin, Yaochu %Y Johnson, Colin R. %Y Machado, Penousal %Y Marchiori, Elena %Y Rothlauf, Franz %Y Smith, George D. %Y Squillero, Giovanni %S Applications of Evolutionary Computing, EvoWorkshops2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC %S LNCS %D 2004 %8 May 7 apr %V 3005 %I Springer Verlag %C Coimbra, Portugal %@ 3-540-21378-3 %F comellas:evows04 %X Broadcasting is the process of disseminating a message from a node of a communication network to all other nodes as quickly as possible. We consider Manhattan Street Networks (MSNs) which are mesh-structured, toroidal, directed, regular networks such that locally they resemble the geographical topology of the avenues and streets of Manhattan. With the use of genetic programming we have generated broadcasting algorithms for 2-dimensional and 3-dimensional MSNs. %K genetic algorithms, genetic programming, evolutionary computation %R 10.1007/978-3-540-24653-4_18 %U http://dx.doi.org/10.1007/978-3-540-24653-4_18 %P 170-177 %0 Conference Proceedings %T Optimal Broadcasting in 2-Dimensional Manhattan Street Networks %A Comellas, Francesc %A Dalfo, Cristina %Y Fahringer, T. %Y Hamza, M. H. %S Parallel and Distributed Computing and Networks - 2005 %D 2005 %8 feb 15 17 %V 246 %I Acta Press %C Innsbruck, Austria %@ 0-88986-468-3 %F Comellas:2005:PDCN %X Broadcasting is the process of disseminating a message from a node of a communication network to all other nodes as quickly as possible. In this paper we consider Manhattan Street Networks (MSNs) which are mesh-structured, toroidal, directed, regular networks such that locally they resemble the geographical topology of the avenues and streets of Manhattan. Previous work on these networks has been mainly devoted to the study of the average distance and point-to-point routing schemes. Here we provide an algorithm which broadcasts optimally in a 2-dimensional M N Manhattan Street Network (M and N even). %K genetic algorithms, genetic programming, Manhattan Street Networks, broadcasting, communication networks %U http://www.actapress.com/Abstract.aspx?paperId=19188 %P 135-140 %0 Conference Proceedings %T Design & Implementation of Parallel Linear GP for the IBM Cell Processor %A Comte, Pascal %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 Comte:2009:CIGPU %X We present two different single-core parallel SIMD linear genetic programming (LGP) systems for the IBM Cell Processor on the Playstation3. Our algorithms harness their computational power from the parallel capabilities of the Cell Processor. We implement two evolutionary algorithms and look at the classical problem of symbolic regression of functions. The first LGP generates a single offspring and selection from the population occurs randomly. The second algorithm generates two offspring and selection from the population is performed using k-tournament with k = 2. Mutation occurs at macro and micro levels. Both SIMD instructions and register operands are subject to mutation. We use a static population of 648 individuals due to memory and data transfer restrictions and, experiments are constrained to 300 seconds of computational time. Our results indicate that both EAs perform equally well though the first algorithm is faster and outperforms the 2nd algorithm in some cases. We speculate that the speed at which generations are iterated through is significantly greater than that of a typical tree-based GP and sequential linear GP. %K genetic algorithms, genetic programming %R 10.1145/1569901.1596274 %U http://dx.doi.org/10.1145/1569901.1596274 %0 Conference Proceedings %T Design & Implementation of Real-time Parallel GA Operators on the IBM Cell Processor %A Comte, Pascal %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 Compte:2009:CIGPU2 %X We present a set of single-core designed parallel SIMD Genetic Algorithm (GA) operators aimed at increasing computational speed of genetic algorithms. We use a discrete-valued chromosome representation. The explored operators include: single gene mutation, uniform crossover and a fitness evaluation function. We discuss their low-level hardware implementations on the Cell Processor. We use the Knapsack problem as a proof of concept, demonstrating performances of our operators. We measure the scalability in terms of generations per second. Using the architecture of the Cell Processor and a static population size of 648 individuals, we achieved 11.6 million generations per second on one Synergetic Processing Element (SPE) core for a problem size n = 8 and 9.5 million generations per second for a problem size n = 16. Generality for a problem size n multiple of 8 is also shown. Executing six independent concurrent GA runs, one per SPE core, allows for a rough overall estimate of 70 million generations per second and 57 million generations per second for problem sizes of n = 8 and n = 16 respectively. %K genetic algorithms %R 10.1145/1569901.1596275 %U http://dx.doi.org/10.1145/1569901.1596275 %0 Journal Article %T Developing a Genetic Programming System %A Cona, John %J AI Expert %D 1995 %8 feb %@ 0888-3785 %F ga95aCona:1995:dGPs %X We can use an object-oriented C++ approach to develop gentic base classes. Discusses practical speed/memory tradeoffs for an (IBM) PC environment. %K genetic algorithms, genetic programming, C++, Object Orientated %9 journal article %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ga95aCona_1995_dGPs.pdf %P 20-29 %0 Conference Proceedings %T Nominal-Yield-Area Tradeoff in Automatic Synthesis of Analog Circuits: A Genetic Programming Approach using Immune-Inspired Operators %A Conca, Piero %A Nicosia, Giuseppe %A Stracquadanio, Giovanni %A Timmis, Jon %Y Arslan, Tughrul %Y Keymeulen, Didier %S NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2009) %D 2009 %8 jul 29 aug 1 %C San Francisco, California, USA %F Conca:2009:AHS %X The synthesis of analog circuits is a complex and expensive task; whilst there are various approaches for the synthesis of digital circuits, analog design is intrinsically more difficult since analog circuits process voltages in a continuous range. In the field of analog circuit design, the genetic programming approach has received great attention, affording the possibility to design and optimize a circuit at the same time. However, these algorithms have limited industrial relevance, since they work with ideal components. Starting from the well known results of Koza and co-authors, we introduce a new evolutionary algorithm, called elitist Immune Programming (EIP), that is able to synthesize an analog circuit using industrial components series in order to produce reliable and low cost circuits. The algorithm has been used for the synthesis of low-pass filters; the results were compared with the genetic programming, and the analysis shows that EIP is able to design better circuits in terms of frequency response and number of components. In addition we conduct a complete yield analysis of the discovered circuits, and discover that EIP circuits attain a higher yield than the circuits generated via a genetic programming approach, and, in particular, the algorithm discovers a Pareto Front which respects nominal performance (sizing), number of components (area) and yield (robustness). %K genetic algorithms, genetic programming, AIS, ElP, Pareto Front, analog circuit automatic synthesis, analog circuit design, circuit reliability, elitist immune programming, evolutionary algorithm, frequency response, genetic programming approach, immune-inspired operators, industrial components series, low-pass filter synthesis, nominal-yield-area tradeoff, Pareto optimisation, analogue circuits, circuit CAD, circuit reliability, frequency response, low-pass filters %R 10.1109/AHS.2009.32 %U http://dx.doi.org/10.1109/AHS.2009.32 %P 399-406 %0 Conference Proceedings %T Phytopigments Profiling of Lactuca Sativa Leaf Chloroplast Photosystems via Vision-based Planar Chromatography %A Concepcion, Ronnie S. %A Dadios, Elmer P. %A Carpio, Joy N. %A Bandala, Argel A. %A Sybingco, Edwin %S 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) %D 2020 %8 dec %F Concepcion:2020:HNICEM %X Phytopigments are essential indicators of plant growth. However, current methodologies use expensive laboratory devices. In this study, a low-cost approach of lettuce leaf phytopigments profiling is employed using a consumer-grade camera and integrated computational intelligence via paper chromatography. Hybrid neighborhood component analysis and ReliefF selected the blue reflectance extracted from chromatography to have the most significant impact with other leaf biophysical signatures. Chl b exhibits more complex reflectance spectrum than other pigments and considered as strong indicator of energy absorbance variations. Xanthophyll and carotenoid have the strongest and weakest retardation factor and retention time, respectively. Chl a-b has weak affinity to acetone and their average blue reflectance is confirmed to absorb the highest number of photons in white light cultivation. Leaf absorbance varies by plus-minus1307.04 μmol m^-2 s^-1 PPFD per plus-minus0.1 of blue reflectance. Among other machine learning models, Gaussian processing regression bested out multigene symbolic genetic programming and recurrent neural network in predicting the average chloroplast photosystems I and II blue reflectance with R^2 of 0.9974. This developed approach extends the application of paper chromatography from segmenting to phytopigment profiling. %K genetic algorithms, genetic programming %R 10.1109/HNICEM51456.2020.9400156 %U http://dx.doi.org/10.1109/HNICEM51456.2020.9400156 %0 Conference Proceedings %T Aquaphotomics Determination of Total Organic Carbon and Hydrogen Biomarkers on Aquaponic Pond Water and Concentration Prediction Using Genetic Programming %A Concepcion II, Ronnie %A Lauguico, Sandy %A Alejandrino, Jonnel %A De Guia, Justin %A Dadios, Elmer %A Bandala, Argel %S 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC) %D 2020 %8 dec %F Concepcion:2020:HTC %X Crops that are cultivated in aquaponics setup highly relies on the nutrients supplied by the aqueous system through fish effluents. Continuous monitoring of essential elemental nutrients requires expensive sensors and arrays of it for full scale deployment. However, sustainable agriculture demands energy consumption reduction and cost-effectiveness. This study employed device minimization by using a combination of physical water sensors, namely temperature and electrical conductivity sensors, to predict total organic carbon (TOC) and hydrogen ion (H) concentrations in pond water. Aquaphotomics through ultraviolet (UV) and visible light (Vis) wavelength sweeping from 250 to 500 nm was explored to determine the nutrient biomarkers of pond water samples that undergoes temperature perturbation from 16 to 36 degreeC with 2 degreeC increment per testing. Principal component analysis (PCA) selected the most relevant activated water bands which are 275 nm for TOC and 415 nm for H. Direct spectrophotometric TOC concentration data was passed through a Savitzky-Golay filter to smoothen the nutrient signal. Recurrent neural network (RNN) exhibited the fastest inference time of 3.5 seconds on the average with R2 of 0.8583 and 0.9686 for predicting TOC and H concentrations. Multigene symbolic regression genetic programming (MSRGP) exhibited the best R2 performances of 0.9280 and 0.9693 in predicting TOC and H concentrations by using only the temperature and electrical conductivity sensoracquired data. This developed model is an innovative approach on measuring chemical concentrations of water using physical limnological sensors which resulted to energy consumption reduction of 50percent for complete 42-day crop life cycle of lettuce. %K genetic algorithms, genetic programming %R 10.1109/R10-HTC49770.2020.9357030 %U http://dx.doi.org/10.1109/R10-HTC49770.2020.9357030 %0 Journal Article %T Aquaphotomics determination of nutrient biomarker for spectrophotometric parameterization of crop growth primary macronutrients using genetic programming %A Concepcion, Ronnie %A Lauguico, Sandy %A Alejandrino, Jonnel %A Dadios, Elmer %A Sybingco, Edwin %A Bandala, Argel %J Information Processing in Agriculture %D 2021 %@ 2214-3173 %F CONCEPCION:2021:IPA %X Water quality assessment is currently based on time-consuming and costly laboratory procedures and numerous expensive physicochemical sensors deployment. In response to the trend of device minimization and reduced outlays in sustainable aquaponic water monitoring, the integration of aquaphotomics and computational intelligence is presented in this paper. This study used the combination of temperature, pH, and electrical conductivity sensors in predicting crop growth primary macronutrient concentration (nitrate, phosphate, and potassium (NPK)), thus, limiting the number of deployed sensors. A total of 220 water samples collected from an outdoor artificial aquaponic pond were temperature perturbed from 16 to 36 degreeC with 2 degreeC increments to mimic ambient range, which varies water compositional structure. Aquaphotomics was applied on ultraviolet, visible light, and near-infrared spectral regions, 100 to 1 000 nm, to determine NPK compounds. Principal component analysis emphasized nutrient dynamics through selecting the highly correlated water absorption bands resulting in 250 nm, 840 nm, and 765 nm for nitrate, phosphate, and potassium respectively. These activated water bands were used as wavelength protocols to spectrophotometrically measure macronutrient concentrations. Experiments have shown that multigene symbolic regression genetic programming (MSRGP) obtained the optimal performance in parameterizing and predicting nitrate, phosphate, and potassium concentrations based on water physical properties with an accuracy of 87.63percent, 88.73percent, and 99.91percent, respectively. The results have shown the established 4-dimensional nutrient dynamics map reveals that temperature significantly strengthens nitrate and potassium above 30 degreeC and phosphate below 25 degreeC with pH and electrical conductivity ranging between 7 and 8 and 0.1 to 0.2 mS cm-1 respectively. This novel approach of developing a physicochemical estimation model predicted macronutrient concentrations in real-time using physical limnological sensors with a 50percent reduction of energy consumption. This same approach can be extended to measure secondary macronutrients and micronutrients %K genetic algorithms, genetic programming, Aquaphotomics, Plant nutrients, Physicochemical composition, Spectrophotometry, Water quality monitoring %9 journal article %R 10.1016/j.inpa.2021.12.007 %U https://www.sciencedirect.com/science/article/pii/S2214317321000998 %U http://dx.doi.org/10.1016/j.inpa.2021.12.007 %0 Conference Proceedings %T Optimizing Low Power Near L-band Capacitive Resistive Antenna Design for in Silico Plant Root Tomography Based on Genetic Big Bang-Big Crunch %A Concepcion, Ronnie %A Relano, R-Jay %A Francisco, Kate %A Baun, Jonah Jahara %A Janairo, Adrian Genevie %A De Leon, Joseph Aristotle %A Espiritu, Llewelyn %A Mayol, Andres Philip %A Enriquez, Mike Louie %A Vicerra, Ryan Rhay %A Bandala, Argel %S 2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM) %D 2023 %8 jan %F Concepcion:2023:IMCOM %X Root system architecture (RSA) phenotyping is essential in formulating suitable organic fertilizers, irrigation, and protective regiments concerning its functional role in resource acquisition for plant growth. However, Ground Penetrating Radar, and Magnetic Resonance, Positron Emission, and X-Ray Micro Computed Tomography Scanning have high power requirements, and RGB imaging demands an intrusive scheme. Existing antenna-based imaging systems are not intelligently optimized yet. To address these challenges, this study developed a low power (10 W) near L-band capacitive resistive antenna system for in silico maize root tomography optimized using three novel advanced evolutionary computing, namely, Genetic Particle Collision Algorithm (gPCA), Genetic Integrated Radiation Algorithm (gIRA), and Genetic Big Bang-Big Crunch Algorithm (gBB-BC). Two capacitive resistive antenna designs were developed using CADFEKO: single parallel plate and 90-electrode dipole-dipole, where root information acquisition and processing from healthy maize seedling inside a PVC pipe intact with soil were done. Maize root permittivity and soil quality were set to resemble actual biological experiments. Transmitter frequency was determined using multigene (10 genes) genetic programming (MGGP) integrated with PCA, IRA, and BB-BC to determine the global maximum voltage at the receiver dipole. Based on in silico experiments, gBB-BC resulted in 0.984463 GHz operating frequency that lies within the global solutions of gPCA (> 1 GHz) and gIRA (< gBB-BC). The root tomography generated from electric field mapping using the gBB-BC-based antenna exhibited more pronounced RSA, while gIRA-based antenna is sensitive only to root tips. Hence, the established root imaging protocol here supports faster, low-power, and non-destructive approaches. %K genetic algorithms, genetic programming %R 10.1109/IMCOM56909.2023.10035574 %U http://dx.doi.org/10.1109/IMCOM56909.2023.10035574 %0 Conference Proceedings %T Characterization of Potassium Chloride Stress on Philippine Vigna radiata Varieties in Temperature-stabilized Hydroponics Using Genetic Programming %A Concepcion, Ronnie %A Duarte, Bernardo %A Bandala, Argel %A Cuello, Joel %A Vicerra, Ryan Rhay %A Dadios, Elmer %S 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) %D 2021 %8 28 30 nov %C Manila, Philippines %F Concepcion:2021:HNICEM %X Chloride is an important micronutrient for crop plant life. Excess chloride dehydrates the plant system and accumulates salt-like residue in leaves causing them to undergo chlorosis and necrosis. Micronutrient stress through potassium chloride that is used as fertilizers to common and industrial farms was not yet comprehensively explored concerning mung beans. This study aims to characterize the effects of potassium chloride (KCl) fertilization on stems and roots of two Philippine mung bean (Vigna radiata L.) varieties which are the yellow and green mongo. A temperature-stabilized hydroponics setup was developed based on Peltier technology. Three KCl treatments were employed: 50 μM (control), 10 μM (deficient), and 100 μM (toxic or excess). Morphological assay confirmed that KCl deficient mung beans have longer root and shoot systems and higher number of spanning leaves. Lowering KCl concentration to 10 10 μM also increases the germination rate by 111.53percent than the control. Light microscopy was performed and confirmed that there is thicker cortex, denser vascular cambium, broader xylem and phloem vessels, and larger parenchyma cells in KCl deficient seedlings. Only the green mung bean seedling variety exposed in excess KCl have formed trichomes within 14 days. Multigene genetic programming was applied to generate mathematical models of seedling architectural traits as functions of KCl concentration and cultivation period. It was found out that less than 0.05 mM, 0.9 mM 0.7 mM, 4 mM of KCl promotes root growth, shoot length, leaf expansion, and the number of spanning leaves, respectively. Overall, chloride deficiency improves mung bean growth. %K genetic algorithms, genetic programming %R 10.1109/HNICEM54116.2021.9731922 %U http://dx.doi.org/10.1109/HNICEM54116.2021.9731922 %0 Journal Article %T Screen-printed graphite electrode on polyvinyl chloride and parchment strips integrated with genetic programming for in situ nitrate sensing of aquaponic pond water %A Concepcion, Ronnie %A Duarte, Bernardo %A Gemel Palconit, Maria %A Baun, Jonah Jahara %A Bandala, Argel %A Rhay Vicerra, Ryan %A Dadios, Elmer %J Information Processing in Agriculture %D 2023 %@ 2214-3173 %F CONCEPCION:2023:inpa %X Nitrate is the primary water-soluble macronutrient essential for plant growth that is converted from excess fish feeds, fish effluents, and degrading biomaterials on the aquaponic pond floor, and when aquacultural malpractices occur, large amounts of it retain in the water system causing increase rate in eutrophication and toxifies fish and aquaculture plants. Recent nitrate sensor prototypes still require performing the additional steps of water sample deionization and dilution and were constructed with expensive materials. In response to the challenge of sensor enhancement and aquaponic water quality monitoring, this study developed sensitive, repeatable, and reproducible screen-printed graphite electrodes on polyvinyl chloride and parchment paper substrates with silver as electrode material and 60:40 graphite powder:nail polish formulated conductive ink for electrical traces, integrated with 9-gene genetic expression model as a function of peak anodic current and electrochemical test time for nitrate concentration prediction that is embedded into low-power Arduino ESP32 for in situ nitrate sensing in aquaponic pond water. Five SPE electrical traces were designed on the two types of substrates. Scanning electron microscopy with energy dispersive X-ray confirmed the electrode surface morphology. Electrochemical cyclic voltammetry using 10 to 100 mg/L KNO3 and water from three-depth regions of the actual pond established the electrochemical test time (10.5 s) and electrode potential (0.135 V) protocol necessary to produce peak current that corresponds to the strength of nitrate ions during redox. The findings from in situ testing revealed that the proposed sensors have strong linear predictions (R2=0.968 MSE=1.659 for nSPEv and R2=0.966 MSE=4.697 for nSPEp) in the range of 10 to 100 mg/L and best detection limit of 3.15 ?g/L, which are comparable to other sensors of more complex construction. The developed three-electrode electrochemical nitrate sensor confirms that it is reliable for both biosensing in controlled solutions and in situ aquaponic pond water systems %K genetic algorithms, genetic programming, aquaponic water quality, electrochemical technology, graphite electrode, nitrate sensor, precision agriculture, printed electronics, scanning electron microscopy, screen-printed electrode, voltammetry %9 journal article %R 10.1016/j.inpa.2023.02.002 %U https://www.sciencedirect.com/science/article/pii/S2214317323000124 %U http://dx.doi.org/10.1016/j.inpa.2023.02.002 %0 Conference Proceedings %T Genetic Termite Colony-Optimized Arbuscular Mycorrhizal Fungi Concentration for Glycophyte Plant Resilience to Saline Environment %A Concepcion II, Ronnie %A Janairo, Adrian Genevie %A Martinez, Raneiel Angelo %A Relano, R-Jay %A Guillermo, Marielet %A Bandala, Argel %A Vicerra, Ryan Rhay %S 2023 8th International Conference on Business and Industrial Research (ICBIR) %D 2023 %8 may %F Concepcion:2023:ICBIR %X Saline environments, such as coastal and agricultural areas with excessive fertigation that remained uncultivated, impede glycophyte growth. However, arbuscular mycorrhizal fungi (AMF) can help regulate plant water balance, but its overpopulation exhibits competition with roots. To address this challenge, this study developed three hybrid evolutionary and bio-inspired optimisation models namely, genetic termite colony (GTC), genetic bacterial foraging, and genetic sperm swarm in determining the optimum concentration of Glomus spp. AMF inoculant to induce papaya var. Sinta F1 plant growth in terms of root and stem lengths, stem thickness, leaf count, and total leaf chlorophyll when exposed to a saline environment after 15 and 30 days of sowing. Four treatments were performed: control, and mycorrhizal with 5, 10, and 15 mg/L concentrations. Salinity was maintained at 6 dS/m using NaCl solution. Variance-based Factor Analysis confirmed stem length $(L_s)$ as the most significant phenotype with highest communality. A 4-gene Genetic Programming model was formulated for $L_s$ fitness function. With most acceptable results, GTC recommended 11.149 mg/L which resulted in 1937.5percent, 388.43percent, 650percent, 480percent, and 238.889percent improvement in root and stem lengths, stem thickness, leaf count, and total leaf chlorophyll respectively, than the non-mycorrhizal plant. This established protocol increased glycophyte resistance to high salinity. %K genetic algorithms, genetic programming, Fungi, Reactive power, Protocols, Salinity (geophysical), Plants (biology), Sea measurements, abiotic stress, bio-inspired optimisation, digital agriculture, evolutionary computing, mycorrhizal fungi %R 10.1109/ICBIR57571.2023.10147624 %U http://dx.doi.org/10.1109/ICBIR57571.2023.10147624 %P 812-817 %0 Journal Article %T Genetic atom search-optimized in vivo bioelectricity harnessing from live dragon fruit plant based on intercellular two-electrode placement %A Concepcion II, Ronnie %A Francisco, Kate %A Janairo, Adrian Genevie %A Baun, Jonah Jahara %A Izzo, Luigi Gennaro %J Renewable Energy %D 2023 %V 219 %@ 0960-1481 %F CONCEPCIONII:2023:renene %X Bioelectricity is a promising alternative renewable energy source that can be produced from live plants and trees. However, previous experimental studies mostly applied non-sustainable bioelectricity extraction techniques from cut-off stem or leaves and neglected the optimum placement of electrodes for maximizing energy extraction without impeding plant growth. Electrode placement and penetration are crucial in energy extraction since they greatly influence electrical generated output enhancement. Relatively, along with the common plants used for bioelectricity extraction, the dragon fruit tree has the potential to be explored as an alternative bioelectricity source since it is widely abundant in many regions. With that, this work introduced a novel integrated genetic-population metaheuristic-based optimization model that was developed centered on in vivo stem bioelectricity extraction from dragon fruit tree to determine the exact optimum distance of silver-coated copper pin-type anodes and cathodes for maximum bioelectricity extraction through intercellular across vascular bundle (icVB) and inter-parenchymal cells (iPC) electrode penetration techniques, and incorporated the cradle-to-gate Life Cycle Assessment methodology to properly account the environmental impacts of the two intercellular penetration approaches. Multigene genetic programming was performed to formulate the fitness function followed by a comparative atom search (ASO), shuffle frog-leaping, and elephant herding-based bioelectricity harnessing optimization. Thus, ASO demonstrated the highest attainable fitness value and conformed well with both electrode placement treatments. This subsequently verified that ASO-based iPC penetration, yielding 58.923 J, surpasses icVB, which only yielded 13.909 J in terms of the total harnessed energy stored throughout the 30-day experiment. Overall, the genetic ASO-iPC with an electrode distance of 4.488 inches produced a higher yield of harnessed bioelectricity while incurring no significant damage and causing fewer environmental impacts compared to the ASO-icVB treatment. This developed technique can minimize greenhouse gas emissions while also expanding the application of evolutionary computing in agriculture and alternative energy domains %K genetic algorithms, genetic programming, Affordable and clean energy, Alternative energy source, Bioenergy, Low-carbon power, Renewable energy, Sustainable agriculture %9 journal article %R 10.1016/j.renene.2023.119528 %U https://www.sciencedirect.com/science/article/pii/S096014812301443X %U http://dx.doi.org/10.1016/j.renene.2023.119528 %P 119528 %0 Journal Article %T Effects of optimized interaction of short-term hypergravity stimulation and nitrate-deficient cultivation in maize root using genetic-immunological algorithms %A Concepcion II, Ronnie %A Relano, R-Jay %A Janairo, Adrian Genevie %A Francisco, Kate %A Garcia, Lance %A Montanvert, Hugo %J Plant Stress %D 2024 %V 14 %@ 2667-064X %F Concepcion:2024:stress %X Nitrate is a macronutrient substantial for plant root and shoot growth, however, the availability of nitrate within soil-based and soilless cultivation environments is not consistently optimal, presenting a significant challenge for plant growth and development. Traditional seed stimulation includes scarification, soaking, hormone application and microbial application but they are all invasive. This study pioneered an experimental approach to address the challenges posed by nutrient deficiency in hydroponic environment by integrating Multigene Genetic Programming (MGGP) with immunological computation algorithms, namely Clonal Selection Algorithm (CSA), Ant Colony Optimisation Algorithm (ACOA), and COVID Optimisation Algorithm (COVIDOA) in determining the exact optimal time exposure to 2 g hypergravity that can induced the growth of three maize genotypes (PSB 92-97, NSIC CN 302, and NSIC CN 282). Through varying dry seed exposure times to hypergravity (6, 12, and 24 h), labeled models gCSA, gACOA, and gCOVIDOA converged to 20.120 h, 22.466, and 19.700 h, respectively, based on the formulated 2-gene model of root-to-shoot ratio as a function of exposure time. Exposure time between 20 and 24 h increased the root-to-shoot ratio (R/S) by at least a factor of 2.631 and the seedling’s dry weight by 13.430 g while between 10 and 15 h of exposure reduced the overall biomass. gACOA-treated seedings exhibited an R/S of 3.732 plus-minus 0.067 having the highest uniformity among the control, gCSA, and gCOVIDOA treatments. gACOA-treated seedlings have healthier root hair compared to unexposed seeds after 14 days and revealed the highest rate of increase in metaxylem, xylem, phloem, and radicle diameters with a factor of 3.651 mum/hr, 1.440 mum/hr, 0.872 mum/hr, and 71.602 mum/hr of exposure in 2 g hypergravity. This study implies that stimulating corn seeds using hypergravity can help lessen the introduction of nutrient fertilizers in the long run which could help in reducing the farm expenses %K genetic algorithms, genetic programming, Abiotic stress, Biostimulant, Complex environment, Digital agriculture, Evolutionary computing, Optimization algorithm, Seed germination, Simulated gravity, Space agriculture, Sustainable agriculture %9 journal article %R 10.1016/j.stress.2024.100702 %U https://www.sciencedirect.com/science/article/pii/S2667064X24003555 %U http://dx.doi.org/10.1016/j.stress.2024.100702 %P 100702 %0 Conference Proceedings %T Gaphyl: A genetic algorithm approach to cladistics %A Congdon, Clare Bates %A Greenfest, Emily F. %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 congdon:2000:GA %X his research investigates the use of genetic algorithms (GA’s) to solve problems from cladistics — a technique used by biologists to hypothesise the evolutionary relationships between organisms. Since exhaustive search is not practical in this domain, typical cladistics software packages use heuristic search methods to navigate through the space of possible trees in an attempt to find one or more ’best’ solutions. We have developed a system called GAphyl, which uses the GA... %K genetic algorithms, genetic programming %U http://www.cs.colby.edu/~congdon/Publications/gaphyl-gecco00.ps %P 85-88 %0 Conference Proceedings %T Phylogenetic trees using evolutionary search: Initial progress in extending gaphyl to work with genetic data %A Congdon, Clare Bates %A Septor, Kevin J. %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 congdon:2003:ptuesipiegtwwgd %X Gaphyl is an application of evolutionary algorithms to phylogenetics, an approach used by biologists to investigate evolutionary relationships among organisms. For datasets larger than 20-30 species, exhaustive search is not practical in this domain. Gaphyl uses an evolutionary search mechanism to search the space of possible phylogenetic trees, in an attempt to find the most plausible evolutionary hypotheses, while typical phylogenetic software packages use heuristic search methods. In previous work, Gaphyl has been shown to be a promising approach for searching for phylogentic trees using data with binary attributes and Wagner parsimony to evaluate the trees. In the work reported here, Gaphyl is extended to work with genetic data. Initial results with this extension further suggest that evolutionary search is a promising approach for phylogenetic work. %K genetic algorithms, genetic programming, Application software, Computer science, DNA, Drives, Educational institutions, Evolutionary computation, Genetics, Organisms, Phylogeny, Sequences, biology computing, evolutionary computation, genetics, tree searching, trees (mathematics), Gaphyl, Wagner parsimony, binary attributes, datasets, evolutionary algorithm application, evolutionary relationships, evolutionary search, exhaustive search, genetic data, heuristic search method, phylogenetic software package, phylogenetic trees, phylogenetic work, tree evaluation %R 10.1109/CEC.2003.1299592 %U http://dx.doi.org/10.1109/CEC.2003.1299592 %P 320-326 %0 Journal Article %T SQL, Data Mining, & Genetic Programming %A Conolly, Brian %J Dr. Dobb’s %D 2004 %8 apr 01 %F Connolly:2004:drdobbs %X Evolutionary algorithms solve problems by mimicking the process of natural evolution. The practical side of evolutionary algorithms %K genetic algorithms, genetic programming, Database %9 journal article %U https://www.drdobbs.com/database/sql-data-mining-genetic-programming/184405616 %0 Journal Article %T Genetic Algorithms Survival of the Fittest: Natural Selection with Windows Forms %A Connolly, Brian %J MSDN Magazine %D 2004 %8 aug %V 19 %N 8 %I Microsoft %F Connolly:2004:MSDN %X Genetic Programming is an evolutionary algorithm that employs reproduction and natural selection to breed better and better executable computer programs. It can create programs that implement subtle, non-intuitive solutions to complex problems. By taking a well-known example from the Genetic Programming community and implementing it with the .NET Framework, this article demonstrates that CodeDOM and Reflection provide all the facilities that are needed to do Genetic Programming effectively This article discusses: * Genetic programming definition * Breeding new algorithm generations * Cross breeding * Mutations * Increasing fitness %K genetic algorithms, genetic programming %9 journal article %U http://msdn.microsoft.com/en-gb/magazine/cc163934.aspx %0 Conference Proceedings %T Optimising Team Sport Training Plans With Grammatical Evolution %A Connor, Mark %A Fagan, David %A O’Neill, Michael %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 Connor:2019:CEC %X We present a novel approach to generating seasonal training plans for elite athletes using the grammatical evolution approach to genetic programming. A grammatical encoding of a team sport training plan dictates the plan structure. The quality of the training plan is calculated using the widely adopted fitness-fatigue model, which in this study incorporates four performance metrics, namely distance covered at low to medium speed, distance covered at high speed, distance covered accelerating, and distance covered decelerating. We compare performance of the evolved training plans to a control set up which generates plans using a pseudo-random search process, and baseline against the training plan adopted by an elite team of Gaelic Football Players. Significant potential performance gains are achieved over the control setup and baseline elite team plan. %K genetic algorithms, genetic programming, grammatical evolution, sports analytics %R 10.1109/CEC.2019.8790369 %U http://dx.doi.org/10.1109/CEC.2019.8790369 %P 2474-2481 %0 Book Section %T The Price of Programmability %A Conrad, Michael %E Herken, Rolf %B The Universal Turing Machine A Half-Century Survey %D 1988 %I Oxford University Press %@ 0-19-853741-7 %F Con88 %X Programmability and computational efficiency are fundamental attributes of computing systems. A third attribute is evolutionary adaptability, the ability of a system to self-organise through a variation and selection process. The author has previously proposed that these three attributes of computing are linked by a trade-off principle, which may be roughly stated thus: a computing system cannot at the same time have high programmability, high computational efficiency, and high evolutionary adaptability (e.g., Conrad 1972, 1974, 1985). The purpose of the present paper is to outline the reasons for the trade-off principle in a manner which, though not entirely formal, is sufficiently detailed to allow for a well-defined formulation. We also consider the implications of the principle, first for alternative computer architectures. suited to solving problems by methods of evolutionary search and second, for limits on the capacity of programmable machines to simulate nature and duplicate intelligence. %K genetic algorithms, genetic programming, cellular automata, evolvable hardware, quantum computing, DNA and molecular computing %R 10.1093/oso/9780198537748.003.0011 %U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Con88.pdf %U http://dx.doi.org/10.1093/oso/9780198537748.003.0011 %P 285-307 %0 Conference Proceedings %T Speech Sound Discrimination With Genetic Programming %A Conrads, Markus %A Nordin, Peter %A Banzhaf, Wolfgang %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 conrads:1998:ssdGP %X The question that we investigate in this paper is, whether it is possible for Genetic Programming to extract certain regularities from raw time series data of human speech. We examine whether a genetic programming algorithm can find programs that are able to discriminate certain spoken vowels and consonan ts. We present evidence that this can indeed be achieved with a surprisingly simple approach that does not need preprocessing. The data we have collec ted on the system’s behavior show that even speaker-independent discriminatio n is possible with GP. %K genetic algorithms, genetic programming %R 10.1007/BFb0055932 %U http://dx.doi.org/10.1007/BFb0055932 %P 113-129 %0 Conference Proceedings %T Can clustering improve glucose forecasting with genetic programming models? %A Contador, Sergio %A Hidalgo, J. Ignacio %A Garnica, Oscar %A Velasco, J. Manuel %A Lanchares, Juan %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 Contador:2019:GECCOcomp %K genetic algorithms, genetic programming %R 10.1145/3319619.3326809 %U http://dx.doi.org/10.1145/3319619.3326809 %P 1829-1836 %0 Conference Proceedings %T Profiled Glucose Forecasting using Genetic Programming and Clustering %A Contador, Sergio %A Velasco, J. Manuel %A Garnica, Oscar %A Hidalgo, J. Ignacio %Y Divina, Federico %Y Torres, Miguel Garcia %S The 35th ACM/SIGAPP Symposium On Applied Computing %D 2020 %8 mar 30 – apr 3 %I ACM %C Brno, Czech Republic %F Contador:2020:SAC %X This paper proposes a method to obtain accurate forecastings of the subcutaneous glucose values from diabetic patients. Statistical techniques are applied to identify everyday situations of glucose behaviors and discover glucose profiles. This knowledge is used to create predictive models with genetic programming. The time series of glucose values, measured using continuous glucose monitoring systems, are divided into 4-hour, non-overlapping slots and clustered using a technique based on decision trees called chi-square automatic interaction detection. The glucose profiles are classified using the decision variables in order to customize the models for different profiles. Genetic programming models created with glucose values from the original dataset are compared to those of models created with classified glucose values. Significant differences and associations are observed between the glucose profiles. In general, using profiled glucose models improves the accuracy of the predictions with respect to those of models created with the original dataset. %K genetic algorithms, genetic programming %R 10.1145/3341105.3374003 %U http://dx.doi.org/10.1145/3341105.3374003 %P 529-536 %0 Conference Proceedings %T Short and Medium Term Blood Glucose Prediction Using Multi-objective Grammatical Evolution %A Contador, Sergio %A Colmenar, J. Manuel %A Garnica, Oscar %A Hidalgo, J. Ignacio %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 Contador:2020:evoapplications %X we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of grammatical evolution. In particular, we continue with previous research about finding expressions to model the glucose levels in blood of diabetic patients. We use here a multi-objective Grammatical Evolution approach based on NSGA-II algorithm, considering the root mean squared error and an ad-hoc fitness function as objectives. This ad-hoc function is based on the Clarke Error Grid analysis, which is useful for showing the potential danger of mispredictions. Experimental results show that the multi-objective approach improves previous results in terms of Clarke Error Grid analysis reducing the number of dangerous mispredictions. %K genetic algorithms, genetic programming, Grammatical Evolution, Multi-objective optimization, medicine, Human Glucose blood concentration prediction, Diabetes %R 10.1007/978-3-030-43722-0_32 %U http://dx.doi.org/10.1007/978-3-030-43722-0_32 %P 494-509 %0 Journal Article %T Glucose forecasting using genetic programming and latent glucose variability features %A Contador, Sergio %A Velasco, J. Manuel %A Garnica, Oscar %A Hidalgo, J. Ignacio %J Applied Soft Computing %D 2021 %V 110 %@ 1568-4946 %F CONTADOR:2021:ASC %X This paper investigates a set of genetic programming methods to obtain accurate predictions of subcutaneous glucose values from diabetic patients. We explore the usefulness of different features that identify the latent glucose variability. New features, including average glucose, glucose variability and glycemic risk, are generated as input variables of the genetic programming algorithm in order to improve the accuracy of the models in the prediction phase. The performance of traditional genetic programming, and models created with classified glucose values, are compared to those using latent glucose variability features. We experimented with a set of 18 different features and we also performed a study of the importance of the variables in the models. The Bayesian statistical analysis shows that the use of glucose variability as latent variables improved the predictions of the models, not only in terms of RMSE, but also in the reduction of dangerous predictions, i.e., those predictions that could lead to wrong decisions in the clinical practice %K genetic algorithms, genetic programming, Diabetes, Continuous glucose monitoring, Glucose variability %9 journal article %R 10.1016/j.asoc.2021.107609 %U https://www.sciencedirect.com/science/article/pii/S1568494621005305 %U http://dx.doi.org/10.1016/j.asoc.2021.107609 %P 107609 %0 Journal Article %T Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios %A Contador, Sergio %A Colmenar, J. Manuel %A Garnica, Oscar %A Velasco, J. Manuel %A Hidalgo, J. Ignacio %J Genetic Programming and Evolvable Machines %D 2022 %8 jun %V 23 %N 2 %@ 1389-2576 %F Contador:2022:GPEM %X we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of Grammatical Evolution. In particular, we extend previous work, obtaining mathematical expressions to model glucose levels in the blood of diabetic patients. Here we use a multi-objective Grammatical Evolution approach based on the NSGA-II algorithm, considering the root-mean-square error and an ad-hoc fitness function as objectives. This ad-hoc function is based on the Clarke Error Grid analysis, which is useful for showing the potential danger of mispredictions in diabetic patients. we use two datasets to analyse two different scenarios: What-if and Agnostic, the most common in daily clinical practice. In the What-if scenario, where future events are evaluated, results show that the multi-objective approach improves previous results in terms of Clarke Error Grid analysis by reducing the number of dangerous mispredictions. In the Agnostic situation, with no available information about future events, results suggest that we can obtain good predictions with only information from the previous hour for both Grammatical Evolution and Multi-Objective Grammatical Evolution. %K genetic algorithms, genetic programming, Grammatical evolution, Multi-objective optimization, MOGA, Glucose prediction, Diabetes %9 journal article %R 10.1007/s10710-021-09424-6 %U https://rdcu.be/cBKAs %U http://dx.doi.org/10.1007/s10710-021-09424-6 %P 161-192 %0 Conference Proceedings %T Combining Technical Analysis and Grammatical Evolution in a Trading System %A Contreras, Ivan %A Hidalgo, J. Ignacio %A Nunez-Letamendia, Laura %Y Esparcia-Alcazar, Anna I. %Y Cioppa, Antonio Della %Y De Falco, Ivanoe %Y Tarantino, Ernesto %Y Cotta, Carlos %Y Schaefer, Robert %Y Diwold, Konrad %Y Glette, Kyrre %Y Tettamanzi, Andrea %Y Agapitos, Alexandros %Y Burrelli, Paolo %Y Merelo, J. J. %Y Cagnoni, Stefano %Y Zhang, Mengjie %Y Urquhart, Neil %Y Sim, Kevin %Y Ekart, Aniko %Y Fernandez de Vega, Francisco %Y Silva, Sara %Y Haasdijk, Evert %Y Eiben, Gusz %Y Simoes, Anabela %Y Rohlfshagen, Philipp %S Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC %S LNCS %D 2013 %8 March 5 apr %V 7835 %I Springer Verlag %C Vienna %F Contreras:evoapps13 %X Trading Systems are beneficial for financial investments due to the complexity of nowadays markets. On one hand, finance markets are influenced by a great amount of factors of different sources such as government policies, natural disasters, international trade, political factors etc. On the other hand, traders, brokers or practitioners in general could be affected by human emotions, so their behaviour in the stock market becomes nonobjective. The high pressure induced by handling a large volume of money is the main reason of the so-called market psychology. Trading systems are able to avoid a great amount of these factors, allowing investors to abstract the complex flow of information and the emotions related to the investments. In this paper we compare two trading systems based on Evolutionary Computation. The first is a GA-based one and was already proposed and tested with data from 2006. The second one is a grammatical evolution approach which uses a new evaluation method. Experimental results show that the later outperforms the GA approach with a set of selected companies of the Spanish market with 2012 data. %K genetic algorithms, genetic programming, Grammatical Evolution %R 10.1007/978-3-642-37192-9_25 %U http://dx.doi.org/10.1007/978-3-642-37192-9_25 %P 244-253 %0 Journal Article %T A meta-grammatical evolutionary process for portfolio selection and trading %A Contreras, Ivan %A Hidalgo, J. Ignacio %A Nunez-Letamendia, Laura %A Velasco, J. Manuel %J Genetic Programming and Evolvable Machines %D 2017 %8 dec %V 18 %N 4 %@ 1389-2576 %F Contreras:2017:GPEM %X This study presents the implementation of an automated trading system that uses three critical analyses to determine time-decisions and portfolios for investment. The approach is based on a meta-grammatical evolution methodology that combines technical, fundamental and macroeconomic analysis on a hybrid top-down paradigm. First, the method provides a low-risk portfolio by analysing countries and industries. Next, aiming to focus on the most robust companies, the system filters the portfolio by analyzing their economic variables. Finally, the system analyses prices and volumes to optimize investment decisions during a given period. System validation involves a series of experiments in the European financial markets, which are reflected with a data set of over nine hundred companies. The final solutions have been compared with static strategies and other evolutionary implementations and the results show the effectiveness of the proposal. %K genetic algorithms, Grammatical evolution, Automated trading systems, Meta-GE, Technical analysis, Fundamental analysis, Macroeconomic analysis %9 journal article %R 10.1007/s10710-017-9304-1 %U http://dx.doi.org/10.1007/s10710-017-9304-1 %P 411-431 %0 Journal Article %T A hybrid automated trading system based on multi-objective grammatical evolution %A Contreras, Ivan %A Hidalgo, Jose Ignacio %A Nunez-Letamendia, Laura %J Journal of Intelligent and Fuzzy Systems %D 2017 %V 32 %N 3 %@ 1064-1246 %F journals/jifs/ContrerasHN17 %X This paper describes a hybrid automated trading system (ATS) based on grammatical evolution and microeconomic analysis. The proposed system takes advantage from the flexibility of grammars for introducing and testing novel characteristics. The ATS introduces the self-generation of new technical indicators and multi-strategies for stopping unforeseen losses. Additionally, this work copes with a novel optimization method combining multi-objective optimization with a grammatical evolution methodology. We implemented the ATS testing three different fitness functions under three mono-objective approaches and also two multi-objective ATSs. Experimental results test and compare them to the Buy and Hold strategy and a previous approach, beating both in returns and in number of positive operations. In particular, the multi-objective approach demonstrated returns up to 20percent in very volatile periods, proving that the combination of fitness functions is beneficial for the ATS. %K genetic algorithms, genetic programming, grammatical evolution %9 journal article %R 10.3233/JIFS-16435 %U http://dx.doi.org/10.3233/JIFS-16435 %P 2461-2475 %0 Conference Proceedings %T Using Grammatical Evolution to Generate Short-term Blood Glucose Prediction Models %A Contreras, Ivan %A Bertachi, Arthur %A Biagi, Lyvia %A Vehi, Josep %A Oviedo, Silvia %Y Bach, Kerstin %Y Bunescu, Razvan C. %Y Farri, Oladimeji %Y Guo, Aili %Y Hasan, Sadid A. %Y Ibrahim, Zina M. %Y Marling, Cindy %Y Raffa, Jesse %Y Rubin, Jonathan %Y Wu, Honghan %S KDH@IJCAI-ECAI 2018 The 3rd International Workshop on Knowledge Discovery in Healthcare Data %S CEUR Workshop Proceedings %D 2018 %8 jul 13 %V Vol-2148 %I CEUR-WS.org %C Stockholm %F Contreras:2018:KDH %X Blood glucose levels prediction provides the possibility to issue early warnings related to ineffective or poor treatments. Advance notifications of adverse glycemic events can provide sufficient time windows to issue appropriate responses and adjust the therapy. Consequently, patients could avoid hyperglycemia and hypoglycemia conditions which would improve overall health, safety, and the quality of life of insulin dependent patients. This report concerns to the application of a search-based algorithm to generate models able to capture the dynamics of the blood glucose at a personalized patient level. The grammar-based feature generation allows to build complex empirical models using the data gathered by a sensor augmented therapy, a fitness band and a basic knowledge of T1D dynamics. Final model solutions provide blood glucose levels estimations using prediction horizons of 30, 60 and 90 minutes. %K genetic algorithms, genetic programming, grammatical evolution %U http://ceur-ws.org/Vol-2148/paper15.pdf %P 91-96 %0 Conference Proceedings %T Automatic design of algorithms for optimization problems %A Contreras-Bolton, Carlos %A Parada, Victor %S 2015 Latin America Congress on Computational Intelligence (LA-CCI) %D 2015 %8 oct %F Contreras-Bolton:2015:LA-CCI %X The design of efficient algorithms for difficult combinatorial optimisation problems remains a challenging field. Many heuristic, meta-heuristic and hyper-heuristic methods exist. In the specialized literature, it is observed that for some problems, the combined algorithms have better computational performance than individual performance. However, the automatic combination of the existing methods or the automatic design of new algorithms has received less attention in the literature. In this study, a method to automatically design algorithms is put into practice for two optimisation problems of recognised computational difficulty: the travelling salesman problem and the automatic clustering problem. The new algorithms are generated by means of genetic programming and are numerically evaluated with sets of typical instances for each problem. From an initial population of randomly generated algorithms, a systematic convergence towards the better algorithms is observed after a few hundred generations. Numerical results obtained from the evaluation of each of the designed algorithms suggest that for each set of instances with similar characteristics, specialized algorithms are required. %K genetic algorithms, genetic programming %R 10.1109/LA-CCI.2015.7435977 %U http://dx.doi.org/10.1109/LA-CCI.2015.7435977 %0 Journal Article %T A genetic programming framework in the automatic design of combination models for salient object detection %A Contreras-Cruz, Marco A. %A Martinez-Rodriguez, Diana E. %A Hernandez-Belmonte, Uriel H. %A Ayala-Ramirez, Victor %J Genetic Programming and Evolvable Machines %D 2019 %8 sep %V 20 %N 3 %@ 1389-2576 %F Contreras-Cruz:GPEM %X In computer vision, the salient object detection problem consists of finding the most attention-grabbing objects in images. In the last years, many researchers have proposed salient object detection algorithms to address this problem. However, most of the algorithms perform well only on images with specific conditions and they do not solve the general problem. To cope with a more significant number of image types than those where each standalone saliency detection method performs well, novel methods search to generate a combination model that improves the overall performance of detecting salient objects in images. The contribution of this work is oriented towards the automatic design of combination models by using genetic programming. The proposed approach automatically selects the algorithms to be combined and the combination operators that result in an improvement in the overall performance. The evolutionary approach uses as input a set of candidate saliency detection methods ... %K genetic algorithms, genetic programming, Visual attention, Contrast based methods, Evolutionary computation, Fusion strategies, Saliency enhancement, Saliency aggregation %9 journal article %R 10.1007/s10710-019-09345-5 %U http://dx.doi.org/10.1007/s10710-019-09345-5 %P 285-325 %0 Generic %T GPRS-kit 0.2 User Guide %A Cook, Henry %D 2007 %F GPRS-kit %K genetic algorithms, genetic programming, C++, Matlab, gprscreate, aedoecreate, Linux, OSX %U https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=53464b2d3973d4ae2afbd40d10d5ed8e841a3b64 %0 Report %T Genetically Programmed Response Surfaces for Efficient Design Space Exploration %A Cook, Henry %A Skadron, Kevin %D 2007 %8 aug %N CS-2007-12 %I Department of Computer Science, University of Virginia %C USA %F Cook:CS-2007-12 %X In spite of many efforts to speed up cycle-accurate architecture simulation, exponential increases in architectural design complexity threaten to make traditional design optimization techniques completely intractable. Response surface methodologies address this challenge by transforming the optimization process from a lengthy series of detailed simulations into the tractable formulation and rapid evaluation of a marginally less accurate but easy to evaluate analytical expression—a predictive model. We propose genetic programming as a powerful method for creating these predictive response surface models out of sampled architectural performance data. Genetically programmed response surfaces (GPRSs) allow the architect to make rapid design optimizations (because only a small number of detailed simulations are needed) while simultaneously obtaining insight into the problem domain (because the resulting response surface, a non-linear polynomial in our case, exposes relationships and relative weights among the design variables). We validate our methodology on realistic datasets and compare it to recently proposed techniques for predictive design space exploration. GPRSs are highly accurate when making global predictions about architectural performance behavior based on only small samples of performance data: global predictions of IPC incur less than 3 percent mean percentage error based on sample sizes of less than 1 percent of one target processor design space, and no worse than than mean 6 percent error at sample sizes as small as 0.0000002 percent out of over one billion possible design points from a second target space. GPRSs can therefore reduce required simulation costs by up to six orders of magnitude. %K genetic algorithms, genetic programming %U https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=74e47f95ea3a0f963dc30ed86835927f8ac8cf4d %0 Conference Proceedings %T Predictive design space exploration using genetically programmed response surfaces %A Cook, Henry %A Skadron, Kevin %S 45th ACM/IEEE Design Automation Conference, DAC 2008 %D 2008 %8 jun %F Cook:2008:DAC %X Exponential increases in architectural design complexity threaten to make traditional processor design optimization techniques intractable. Genetically programmed response surfaces (GPRS) address this challenge by transforming the optimization process from a lengthy series of detailed simulations into the tractable formulation and rapid evaluation of a predictive model. We validate GPRS methodology on realistic processor design spaces and compare it to recently proposed techniques for predictive microarchitectural design space exploration. %K genetic algorithms, genetic programming, genetically programmed response surfaces, GPRS, microarchitectural design space exploration, optimization process, predictive design space exploration, aircraft computers, computer architecture %R 10.1145/1391469.1391711 %U http://www.cs.virginia.edu/~skadron/Papers/gprs_dac08.pdf %U http://dx.doi.org/10.1145/1391469.1391711 %P 960-965 %0 Conference Proceedings %T Multi-Faceted Evolution Of Simple Arcade Games %A Cook, Michael %A Colton, Simon %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 Cook:2011:CIG %X We present a system for generating complete game designs by evolving rulesets, character layouts and terrain maps in an orchestrated way. In contrast to existing approaches to generate such game components in isolation, our ANGELINA system develops game components in unison with an appreciation for their interrelatedness. We describe this multi-faceted evolutionary approach, and give some results from a first round of experimentation.Y %K genetic algorithms %U http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper64.pdf %P 289-296 %0 Journal Article %T Behold the Ch!Ld %A Cook, Perry R. %J Communications of the ACM %D 2021 %8 may %V 64 %N 5 %I Association for Computing Machinery %@ 0001-0782 %F Cook:2021:CACM %X From the intersection of computational science and technological speculation, with boundaries limited only by our ability to imagine what could be. Opportunity can come calling when you least expect it. %K genetic algorithms, genetic programming, genetic improvement, ficton %9 journal article %R 10.1145/3453712 %U https://doi.org/10.1145/3453712 %U http://dx.doi.org/10.1145/3453712 %P 188-120 %0 Book Section %T Circuit Synthesis through Genetic Programming %A Coon, Brett W. %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 coon:1994:csgp %K genetic algorithms, genetic programming %P 11-20 %0 Conference Proceedings %T Comparison Of Evolving Against Peers And Fixed Opponents Using Corewars %A Cooper, Jason %A Hinde, Chris %Y Langdon, W. B. %Y Cantú-Paz, E. %Y Mathias, K. %Y Roy, R. %Y Davis, D. %Y Poli, R. %Y Balakrishnan, K. %Y Honavar, V. %Y Rudolph, G. %Y Wegener, J. %Y Bull, L. %Y Potter, M. A. %Y Schultz, A. C. %Y Miller, J. F. %Y Burke, E. %Y Jonoska, N. %S GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %D 2002 %8 September 13 jul %I Morgan Kaufmann Publishers %C New York %@ 1-55860-878-8 %F cooper:2002:gecco %K genetic algorithms, genetic programming, poster paper, Corewars %U http://gpbib.cs.ucl.ac.uk/gecco2002/GP082.ps %P 887 %0 Conference Proceedings %T EvoGeneS, a New Evolutionary Approach to Graph Generation %A Cordella, Luigi Pietro %A De Stefano, Claudio %A Fontanella, Francesco %A Marcelli, Angelo %Y Raidl, Günther R. %Y Gottlieb, Jens %S Evolutionary Computation in Combinatorial Optimization – EvoCOP 2005 %S LNCS %D 2005 %8 30 mar 1 apr %V 3448 %I Springer Verlag %C Lausanne, Switzerland %F cordella:evocop05 %X Graphs are powerful and versatile data structures, useful to represent complex and structured information of interest in various fields of science and engineering. We present a system, called EvoGeneS, based on an evolutionary approach, for generating undirected graphs whose number of nodes is not a priori known. The method is based on a special data structure, called multilist, which encodes undirected attributed relational graphs. Two novel crossover and mutation operators are defined in order to evolve such structure. The developed system has been tested on a wireless network configuration and the results compared with those obtained by a genetic programming based approach recently proposed in the literature. %K evolutionary computation %R 10.1007/978-3-540-31996-2_5 %U http://dx.doi.org/10.1007/978-3-540-31996-2_5 %P 46-57 %0 Conference Proceedings %T Genetic Programming for Generating Prototypes in Classification Problems %A Cordella, L. P. %A De Stefano, C. %A Fontanella, F. %A Marcelli, A. %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 cordella:2005:CEC %X We propose a genetic programming based approach for generating prototypes in a classification problem. In this context, the set of prototypes to which the samples of a data set can be traced back is coded by a multitree, i.e. a set of trees, which represents the chromosome. Differently from other approaches, our chromosomes are of variable length. This allows coping with those classification problems in which one or more classes consist of subclasses. The devised approach has been tested on several problems and the results compared with those obtained by a different genetic programming based approach recently proposed in the literature. %K genetic algorithms, genetic programming %R 10.1109/CEC.2005.1554820 %U http://dx.doi.org/10.1109/CEC.2005.1554820 %P 1149-1155 %0 Conference Proceedings %T A Novel Genetic Programming Based Approach for Classification Problems %A Cordella, Luigi P. %A De Stefano, Claudio %A Fontanella, Francesco %A Marcelli, Angelo %Y Roli, Fabio %Y Vitulano, Sergio %S Proceedings 13th International Conference Image Analysis and Processing - ICIAP 2005 %S Lecture Notes in Computer Science %D 2005 %8 sep 6 8 %V 3617 %I Springer %C Cagliari, Italy %@ 3-540-28869-4 %F conf/iciap/CordellaSFM05 %X A new genetic programming based approach to classification problems is proposed. Differently from other approaches, the number of prototypes in the classifier is not a priori fixed, but automatically found by the system. In fact, in many problems a single class may contain a variable number of subclasses. Hence, a single prototype, may be inadequate to represent all the members of the class. The devised approach has been tested on several problems and the results compared with those obtained by a different genetic programming based approach recently proposed in the literature. %K genetic algorithms, genetic programming %R 10.1007/11553595_89 %U http://dx.doi.org/10.1007/11553595_89 %P 727-734 %0 Conference Proceedings %T Looking for Prototypes by Genetic Programming %A Cordella, Luigi P. %A De Stefano, Claudio %A Fontanella, Francesco %A Marcelli, Angelo %Y Zheng, Nanning %Y Jiang, Xiaoyi %Y Lan, Xuguang %S Advances in Machine Vision, Image Processing, and Pattern Analysis, International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, IWICPAS 2006, Proceedings %S Lecture Notes in Computer Science %D 2006 %8 aug 26 27 %V 4153 %I Springer %C Xi’an, China %@ 3-540-37597-X %F DBLP:conf/iwicpas/CordellaSFM06 %X In this paper we propose a new genetic programming based approach for prototype generation in Pattern Recognition problems. Prototypes consist of mathematical expressions and are encoded as derivation trees. The devised system is able to cope with classification problems in which the number of prototypes is not a priori known. The approach has been tested on several problems and the results compared with those obtained by other genetic programming based approaches previously proposed. %K genetic algorithms, genetic programming %R 10.1007/11821045_16 %U http://dx.doi.org/10.1007/11821045_16 %P 152-159 %0 Journal Article %T Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques %A Cordon, Oscar %A Herrera, Francisco %A Sanchez, Luciano %J Applied Intelligence %D 1999 %8 jan %V 10 %N 1 %@ 0924-669X %F cordon:1999:sedpuhedat %K genetic algorithms, genetic programming, electrical engineering, data analysis, evolutionary algorithms, genetic algorithm program, genetic fuzzy rule-based systems %9 journal article %U ftp://decsai.ugr.es/pub/arai/tech_rep/ga-fl/tr-98106.ps.Z %P 5-24 %0 Conference Proceedings %T Learning Queries for a Fuzzy Information Retrieval System by means of GA-P Techniques %A Cordon, Oscar %A de Moya Anegon, Felix %A Zarco, Carmen %Y Mayor, Gaspar %Y Suñer, Jaume %S Proceedings of the EUSFLAT-ESTYLF Joint Conference %D 1999 %8 sep 22 25 %I Universitat de les Illes Balears %C Palma de Mallorca, Spain %F DBLP:conf/eusflat/CordonAZ99 %K genetic algorithms, genetic programming %U http://www.eusflat.org/publications/proceedings/EUSFLAT-ESTYLF_1999/papers/335-cordon.pdf %P 335-338 %0 Journal Article %T A GA-P Algorithm to Automatically Formulate Extended Boolean Queries for a Fuzzy Information Retrieval System %A Cordon, O. %A de Moya, F. %A Zarco, C. %J Mathware & Soft Computing %D 2000 %V 7 %N 2-3 %@ 1134-5632 %F Cordon:2000:MSC %X Although the fuzzy retrieval model constitutes a powerful extension of the boolean one, being able to deal with the imprecision and subjectivity existing in the Information Retrieval process, users are not usually able to express their query requirements in the form of an extended boolean query including weights. To solve this problem, different tools to assist the user in the query formulation have been proposed. In this paper, the genetic algorithm-programming technique is considered to build an algorithm of this kind that will be able to automatically learn weighted queries -modeling the user’s needs- for a fuzzy information retrieval system by applying an off-line adaptive process starting from a set of relevant documents. %K genetic algorithms, genetic programming %9 journal article %U http://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/0450_MATHWARE_2000_07_02-03_18.pdf %P 309-322 %0 Conference Proceedings %T An Inductive Query by Example Technique for Extended Boolean Queries Based on Simulated-Annealing Programming %A Cordon, O. %A Herrera-Viedma, E. %A Luque, Maria %A Moya, Felix %A Zarco, Carmen %Y Lopez-Huertas, M. J. %S Challenges in Knowledge Representation and Organization for the 21st Century. Integration of Knowledge across Boundaries. Proceedings of the 7th International ISKO Conference (ISKO’2002) %S Advances in knowledge organization %D 2002 %8 jul 10 13 %V 8 %I Ergon %C Granada, Spain %F Cordon:2002:ISKO %K genetic algorithms, genetic programming %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.163.4687 %P 429-436 %0 Journal Article %T A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems %A Cordon, O. %A Moya, F. %A Zarco, C. %J Soft Computing - A Fusion of Foundations, Methodologies and Applications %D 2002 %8 aug %V 6 %N 5 %@ 1432-7643 %F cordon:2002:SC %X Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.’s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased. %K genetic algorithms, genetic programming, Fuzzy information retrieval, Relevance feedback, Evolutionary algorithms, Simulated annealing %9 journal article %R 10.1007/s00500-002-0184-8 %U http://dx.doi.org/10.1007/s00500-002-0184-8 %P 308-319 %0 Conference Proceedings %T Evolutionary Learning of Boolean Queries by Multiobjective Genetic Programming %A Cordon, Oscar %A Herrera-Viedma, Enrique %A Luque, Maria %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 cordon:ppsn2002:pp710 %X The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimisation of any of its components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of these applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme. In this paper, we will tackle with a usual information retrieval problem, the automatic derivation of Boolean queries, by incorporating a well known Pareto-based multiobjective evolutionary approach, MOGA, into a previous proposal of a genetic programming technique for this task. %K genetic algorithms, genetic programming, MOGA, Pattern recognition and classification/datamining,Web services, Multi-objective %R 10.1007/3-540-45712-7_68 %U http://dx.doi.org/10.1007/3-540-45712-7_68 %P 710-719 %0 Conference Proceedings %T Analyzing the Performance of a Multiobjective GA-P Algorithm for Learning Fuzzy Queries in a Machine Learning Environment %A Cordon, Oscar %A Herrera-Viedma, Enrique %A Luque, Maria %A de Moya Anegon, Felix %A Zarco, Carmen %Y Bilgiç, Taner %Y Baets, Bernard De %Y Kaynak, Okyay %S Proceedings of the 10th International Fuzzy Systems Association World Congress, Fuzzy Sets and Systems - IFSA 2003 %S Lecture Notes in Computer Science %D 2003 %8 jun 30 jul 2 %V 2715 %I Springer %C Istanbul, Turkey %@ 3-540-40383-3 %F DBLP:conf/ifsa/CordonHLMZ03 %X The fuzzy information retrieval model was proposed some years ago to solve several limitations of the Boolean model without a need of a complete redesign of the information retrieval system. However, the complexity of the fuzzy query language makes it difficult to formulate user queries. Among other proposed approaches to solve this problem, we find the Inductive Query by Example (IQBE) framework, where queries are automatically derived from sets of documents provided by the user. In this work we test the applicability of a multiobjective evolutionary IQBE technique for fuzzy queries in a machine learning environment. To do so, the Cranfield documentary collection is divided into two different document sets, labeled training and test, and the algorithm is run on the former to obtain several queries that are then validated on the latter. %K genetic algorithms, genetic programming %R 10.1007/3-540-44967-1_73 %U http://www.scimago.es/publications/ifsa03-cordon.pdf %U http://dx.doi.org/10.1007/3-540-44967-1_73 %P 611-619 %0 Conference Proceedings %T A Realistic Information Retrieval Environment to Validate a Multiobjective GA-P Algorithm for Learning Fuzzy Queries %A Cordon, Oscar %A Herrera-Viedma, Enrique %A Luque, Maria %A Moya, Felix %A Zarco, Carmen %S Proceedings if the 8th Online World Conference on Soft Computing in Industrial Applications (WSC8) %S Advances in Soft Computing %D 2003 %V 32 %I Springer %F cordon:2003:WSC %O published by Springer 2005 as Soft Computing: Methodologies and Applications %X IQBE has been shown as a promising technique to assist the users in the query formulation process. In this framework, queries are automatically derived from sets of documents provided by them. However, the different proposals found in the specialized literature are usually validated in non realistic information retrieval environments. In this work, we design several experimental setups to create real-like retrieval environments and validate the applicability of a previously proposed multiobjective evolutionary IQBE technique for fuzzy queries on them. %K genetic algorithms, genetic programming %R 10.1007/3-540-32400-3_23 %U http://dx.doi.org/10.1007/3-540-32400-3_23 %P 299-309 %0 Conference Proceedings %T Fuzzy logic and multiobjective evolutionary algorithms as soft computing tools for persistent query learning in text retrieval environments %A Cordon, Oscar %A de Moya, Felix %A Zarco, Carmen %S Proceedings of the 2004 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2004) %D 2004 %8 25 29 jul %V 1 %I IEEE Press %C Budapest, Hungary %F Cordon:2004:FUZZ-IEEE %X Persistent queries are a specific kind of queries used in information retrieval systems to represent a user’s long-term standing information need. These queries can present many different structures, being the ’bag of words’ that most commonly used. They can be sometimes formulated by the user, although this task is usually difficult for him and the persistent query is then automatically derived from a set of sample documents he provides. In this work we aim at getting persistent queries with a more representative structure for text retrieval issues. To do so, we make use of soft computing tools: fuzzy logic is considered for representation and inference purposes by dealing with the extended Boolean query structure, and multiobjective evolutionary algorithms are applied to build the persistent fuzzy query. Experimental results show how both an expressive fuzzy logic-based query structure and a proper learning process to derive it are needed in order to get a good retrieval efficacy, when comparing our process to single-objective evolutionary methods to derive both classic Boolean and extended Boolean queries. %K genetic algorithms, genetic programming, Boolean functions, evolutionary computation, fuzzy logic, knowledge engineering, query processing extended Boolean query structure, fuzzy logic, information retrieval systems, multiobjective evolutionary algorithms, persistent query learning, soft computing tools, text retrieval environment %R 10.1109/FUZZY.2004.1375799 %U http://dx.doi.org/10.1109/FUZZY.2004.1375799 %P 571-576 %0 Book Section %T Automatic Learning of Multiple Extended Boolean Queries by Multiobjective GA-P Algorithms %A Cordon, O. %A Moya, F. %A Zarco, C. %E Loia, V. %E Nikravesh, M. %E Zadeh, L. A. %B Fuzzy Logic and the Internet %S STUDIES IN FUZZINESS AND SOFT COMPUTING %D 2004 %V 137 %I PHYSICA-VERLAG %C Germany %F Cordon:2004:fzi %X In this contribution, a new Inductive Query by Example process is proposed to automatically derive extended Boolean queries for fuzzy information retrieval systems from a set of relevant documents provided by a user. The novelty of our approach is that it is able to simultaneously generate several queries with a different precision-recall tradeoff in a single run. To do so, it is based on an advanced. evolutionary algorithm, GA-P, specially designed to tackle with multiobjective problems by means of a Pareto-based multi-objective technique. The performance of the new proposal will be tested on the usual Cranfield collection and compared to the well-known Kraft et al.’s process. %K genetic algorithms, genetic programming %R 10.1007/978-3-540-39988-9_3 %U http://direct.bl.uk/research/18/0E/RN143659018.html %U http://dx.doi.org/10.1007/978-3-540-39988-9_3 %P 47-70 %0 Book Section %T A Realistic Information Retrieval Environment to Validate a Multiobjective GA-P Algorithm for Learning Fuzzy Queries %A Cordon, Oscar %A Herrera-Viedma, Enrique %A Luque, Maria %A Moya, Felix %A Zarco, Carmen %E Hoffmann, F. %E Koppen, M. %E Klawonn, F. %E Roy, R. %B Soft Computing: Methodologies and Applications %S Advances in Soft Computing %D 2005 %V 32 %I Springer-Verlag %F cordon:2005:SCMA %X IQBE has been shown as a promising technique to assist the users in the query formulation process. In this framework, queries are automatically derived from sets of documents provided by them. However, the different proposals found in the specialized literature are usually validated in non realistic information retrieval environments. In this work, we design several experimental setups to create real-like retrieval environments and validate the applicability of a previously proposed multiobjective evolutionary IQBE technique for fuzzy queries on them. %K genetic algorithms, genetic programming %R 10.1007/3-540-32400-3_23 %U http://dx.doi.org/10.1007/3-540-32400-3_23 %P 299-309 %0 Journal Article %T Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm %A Cordon, O. %A Herrera-Viedma, E. %A Luque, M. %J Information Processing and Management %D 2006 %8 may %V 42 %N 3 %F CHL:IPM:06 %X The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431 \citeMartinPSmith:1997:JIS] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a weighted combination of the basic performance criteria, precision and recall. A desirable aspect of IRSs, especially of those based on the IQBE paradigm, is to be able to get more than one query for the same information needs, with high precision arid recall values or with different trade-offs between both. IQBE process is proposed combining a previous basic algorithm to automatically derive Boolean queries for Boolean IRSs [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431] and an advanced evolutionary multiobjective approach [Coello, C. A., Van Veldhuizen, D. A., & Lamant, G. B. (2002). Evolutionary algorithms for solving multiobjective problems. Kluwer Academic Publishers], which obtains several queries with a different precision recall trade-off in a single run. The performance of the new proposal will be tested on the Cranfield and CACM collections and compared to the well-known Smith and Smith’s algorithm, showing how it improves the learning of queries and thus it could better assist the user in the query formulation process. %K genetic algorithms, genetic programming, Boolean information retrieval systems, Inductive query by example, Multiobjective evolutionary algorithms, Query learning %9 journal article %R 10.1016/j.ipm.2005.02.006 %U http://dx.doi.org/10.1016/j.ipm.2005.02.006 %P 615-632 %0 Journal Article %T Data-Fusion Techniques for Open-set Recognition Problems %A Cordova Neira, Manuel A. %A Mendes Junior, Pedro R. %A Rocha, Anderson %A da S. Torres, Ricardo %J IEEE Access %D 2018 %8 June %V 6 %@ 2169-3536 %F Cordova-Neira:2018:ieeeAccess %X Most pattern classification techniques are focused on solving closed-set problems - in which a classifier is trained with samples of all classes that may appear during the testing phase. In many situations, however, samples of unknown classes, i.e., whose classes did not have any example during the training stage, need to be properly handled during testing. This specific set up is referred to in the literature as open-set recognition. Open-set problems are harder as they might be ill-sampled, not sampled at all, or even undefined. Differently from existing literature, here, we aim at solving open-set recognition problems combining different classifiers and features while, at the same time, taking care of unknown classes. Researchers have greatly benefited from combining different methods in order to achieve more robust and reliable classifiers in daring recognition conditions, but those solutions have often focused on closed-set set ups. In this work, we propose the integration of a newly designed open set graph-based Optimum-Path Forest (OSOPF) classifier with Genetic Programming (GP) and Majority Voting fusion techniques. While OSOPF takes care of learning decision boundaries more resilient to unknown classes and outliers, the GP, combines different problem features to discover appropriate similarity functions and allow a more robust classification through early fusion. Finally, the Majority-Voting approach combines different classification evidence from different classifier outcomes and features through late-fusion techniques. Performed experiments show the proposed data-fusion approaches yield effective results for open-set recognition problems, significantly outperforming existing counterparts in the literature and paving the way for investigations in this field. %K genetic algorithms, genetic programming, Pattern recognition, Open-set Recognition, Data Fusion, Optimum-Path Forest, Majority Voting, OSOPF %9 journal article %R 10.1109/ACCESS.2018.2824240 %U http://dx.doi.org/10.1109/ACCESS.2018.2824240 %P 21242-21265 %0 Generic %T Evolving Reinforcement Learning Algorithms %A Co-Reyes, John D. %A Miao, Yingjie %A Peng, Daiyi %A Real, Esteban %A Levine, Sergey %A Le, Quoc V. %A Lee, Honglak %A Faust, Aleksandra %D 2021 %8 August %I ArXiv %F coreyes2021evolving %X We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, we highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behaviour shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods. %K genetic algorithms, genetic programming, genetic improvement, computer video games %U https://arxiv.org/abs/2101.03958 %0 Conference Proceedings %T Automated Gelatinous Zooplankton Acquisition and Recognition %A Corgnati, Lorenzo %A Mazzei, Luca %A Marini, Simone %A Aliani, Stefano %A Conversi, Alessandra %A Griffa, Annalisa %A Isoppo, Bruno %A Ottaviani, Ennio %S ICPR Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI 2014) %D 2014 %8 24 24 aug %C Stockholm %F Corgnati:2014:CVAUI %X Much is still unknown about marine plankton abundance and dynamics in the open and interior ocean. Especially challenging is the knowledge of gelatinous zooplankton distribution, since it has a very fragile structure and cannot be directly sampled using traditional net based techniques. In the last decades there has been an increasing interest in the oceanographic community toward imaging systems. In this paper the performance of three different methodologies, Tikhonov regularisation, Support Vector Machines, and Genetic Programming, are analysed for the recognition of gelatinous zooplankton. The three methods have been tested on images acquired in the Ligurian Sea by a low cost under-water standalone system (GUARD1). The results indicate that the three methods provide gelatinous zooplankton identification with high accuracy showing a good capability in robustly selecting relevant features, thus avoiding computational-consuming preprocessing stages. These aspects fit the requirements for running on an autonomous imaging system designed for long lasting deployments. %K genetic algorithms, genetic programming, SVM, pattern recognition, gelatinous zooplankton, underwater imaging, feature selection, underwater camera, GUARD1, autonomous vehicle %R 10.1109/CVAUI.2014.12 %U http://dx.doi.org/10.1109/CVAUI.2014.12 %0 Journal Article %T Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton %A Corgnati, Lorenzo %A Marini, Simone %A Mazzei, Luca %A Ottaviani, Ennio %A Aliani, Stefano %A Conversi, Alessandra %A Griffa, Annalisa %J Sensors %D 2016 %8 14 dec %V 16 %N 12 %@ 1424-8220 %F s16122124 %O Special Issue Sensing Technologies for Autonomy and Cooperation in Underwater Networked Robot Systems %X Marine plankton abundance and dynamics in the open and interior ocean is still an unknown field. The knowledge of gelatinous zooplankton distribution is especially challenging, because this type of plankton has a very fragile structure and cannot be directly sampled using traditional net based techniques. To overcome this shortcoming, Computer Vision techniques can be successfully used for the automatic monitoring of this group.This paper presents the GUARD1 imaging system, a low-cost stand-alone instrument for underwater image acquisition and recognition of gelatinous zooplankton, and discusses the performance of three different methodologies, Tikhonov Regularization, Support Vector Machines and Genetic Programming, that have been compared in order to select the one to be run onboard the system for the automatic recognition of gelatinous zooplankton. The performance comparison results highlight the high accuracy of the three methods in gelatinous zooplankton identification, showing their good capability in robustly selecting relevant features. In particular, Genetic Programming technique achieves the same performances of the other two methods by using a smaller set of features, thus being the most efficient in avoiding computationally consuming preprocessing stages, that is a crucial requirement for running on an autonomous imaging system designed for long lasting deployments, like the GUARD1. The Genetic Programming algorithm has been installed onboard the system, that has been operationally tested in a two-months survey in the Ligurian Sea, providing satisfactory results in terms of monitoring and recognition performances. %K genetic algorithms, genetic programming, content-based image recognition, feature selection, gelatinous zooplankton, autonomous underwater imaging, GUARD1 %9 journal article %R 10.3390/s16122124 %U http://www.mdpi.com/1424-8220/16/12/2124 %U http://dx.doi.org/10.3390/s16122124 %0 Journal Article %T Genetic programming hyperheuristic parameter configuration using fitness landscape analysis %A Coric, Rebeka %A DHumic, Mateja %A Jakobovic, Domagoj %J Applied Intelligence %D 2021 %8 oct %V 51 %N 10 %@ 0924-669X %F DBLP:journals/apin/CoricEJ21 %X Fitness landscape analysis is a tool that can help us gain insight into a problem, determine how hard it is to solve a problem using a given algorithm, choose an algorithm for solving a given problem, or choose good algorithm parameters for solving the problem. fitness landscape analysis of hyperheuristics is used for clustering instances of three scheduling problems. After that, good parameters for tree-based genetic programming that can solve a given scheduling problem are calculated automatically for every cluster. Additionally, we introduce tree editing operators which help in the calculation of fitness landscape features in tree based genetic programming. A heuristic is proposed based on introduced operators, and it calculates the distance between any two trees. The results show that the proposed approach can obtain parameters that offer better performance compared to manual parameter selection. %K genetic algorithms, genetic programming, Fitness landscape analysis, Scheduling, Tree operators, Clustering, Parameter configuration %9 journal article %R 10.1007/s10489-021-02227-3 %U https://www.bib.irb.hr:8443/1123444 %U http://dx.doi.org/10.1007/s10489-021-02227-3 %P 7402-7426 %0 Journal Article %T Designing model and control system using evolutionary algorithms %A Corn, Marko %A Atanasijevic-Kunc, Maja %J IFAC-PapersOnLine %D 2015 %V 48 %N 1 %@ 2405-8963 %F Corn:2015:IFAC-PapersOnLine %O 8th Vienna International Conference on Mathematical Modelling, MATHMOD 2015 %X In the paper several types of evolutionary algorithms have been tested regarding the dynamic nonlinear multivariable system model. We have defined three problems regarding the observed system: the first is the so-called grey box identification where we search for the characteristic of the system’s valve, the second problem is black box identification where we search the model of the system with the usage of system’s measurements and the third one is a system’s controller design. We solved these problems with the usage of genetic algorithms, differential evolution, evolutionary strategies, genetic programming and a developed approach called AMEBA algorithm. All methods have been proven to be very useful for solving problems of the grey box identification and design of the controller for the mentioned system but AMEBA algorithm have also been successfully used in black box identification problem where it generated a suitable model. %K genetic algorithms, genetic programming, evolutionary algorithms, ameba, dynamic systems %9 journal article %R 10.1016/j.ifacol.2015.05.106 %U http://www.sciencedirect.com/science/article/pii/S240589631500107X %U http://dx.doi.org/10.1016/j.ifacol.2015.05.106 %P 526-531 %0 Conference Proceedings %T Optimizing MIMO control for fluidic pinball using machine learning %A Cornejo Maceda, Guy Yoslan %A Lusseyran, Francois %A Noack, Bernd R. %A Morzynski, Marek %S 89th GAMM Annual Meeting %D 2018 %8 19 23 mar %C Munich, Germany %G en %F Cornejo:2018:GAMM %X We are looking for wake stabilization in a multi input multi output (MIMO) configuration.The wake results from an obstacle made by three cylinders in an incoming flow. The meansof action are the cylinders rotation and the output is the velocity taken downstream. Previousstudies have shown that high and low frequency forcing stabilize the wake, revealing the nonlinearinteractions. Linear control being not applicable in our case we are looking for an optimal controllaw regarding drag reduction using genetic programming, a model free MLC approach. Geneticprogramming can explore a broad spectrum of laws, exploiting the nonlinearities, ranging fromopen loop control to closed loop control. %K genetic algorithms, genetic programming %U https://hal.archives-ouvertes.fr/hal-01856252 %0 Thesis %T Gradient-enriched machine learning control exemplified for shear flows in simulations and experiments %A Cornejo Maceda, Guy Y. %D 2021 %8 17 mar %C France %C Universite Paris-Saclay %G en %F Cornejo-Maceda:thesis %X As main contribution we propose a fast and automated gradient-enriched machine learning control (gMLC) algorithm to learn feedback control laws. The framework alternates between explorative and exploitive gradient-based iterations, generalizing genetic programming control (GPC) and the Explorative Gradient Method (EGM). The gMLC algorithm has been demonstrated both numerically, with the stabilization of a MIMO system, the fluidic pinball and experimentally, with the control of the open cavity. In both cases, gMLC successfully built closed-loop control laws allowing the best performances so far. We prove, in particular, that the mechanisms behind the control of the cavity rely effectively on feedback. The benchmark of gMLC with GPC on both problems, shows that gMLC outperforms GPC both in terms of convergence speed and final solution efficiency. An acceleration of at least a factor 10 between the GPC and gMLC has been achieved, allowing the control of many experiments, e.g., with a large number of inputs and outputs or multiple parameters testing for robustness. The two developed codes are both freely available online: xMLC, based on GPC and gMLC, based on our new algorithm. %K genetic algorithms, genetic programming, linear genetic programming, flow control, fluidic pinball, open cavity, machine learning control (mlc), genetic programming control (gpc), gradient-enriched machine learning control (gmlc), controle d’ecoulement, pinball fluidique, cavite ouverte, controle par apprentissage automatique (mlc), controle par programmation genetique (gpc), [PHYS, MECA, mefl]physics [physics]/mechanics [physics]/fluid mechanics [physics, class-ph], [INFO, info-ai]computer science [cs]/artificial intelligence [cs, AI], [STAT, ml]statistics [stat]/machine learning [stat, ML], [MATH, math-oc]mathematics [math]/optimisation and control [math, OC], [NLIN, nlin-cd]nonlinear sciences [physics]/chaotic dynamics [nlin, CD] %9 Ph.D. thesis %U https://tel.archives-ouvertes.fr/tel-03217787 %0 Journal Article %T Stabilization of the fluidic pinball with gradient-enriched machine learning control %A Cornejo Maceda, Guy Y. %A Li, Yiqing %A Lusseyran, Francois %A Morzynski, Marek %A Noack, Bernd R. %J Journal of Fluid Mechanics %D 2021 %8 25 jun %V 917 %I Cambridge University Press %@ 0022-1120 %F cornejo_maceda_li_lusseyran_morzynski_noack_2021 %X We stabilise the flow past a cluster of three rotating cylinders, the fluidic pinball, with automated gradient-enriched machine learning algorithms. The control laws command the rotation speed of each cylinder in an open- and closed-loop manner. These laws are optimized with respect to the average distance from the target steady solution in three successively richer search spaces. First, stabilization is pursued with steady symmetric forcing. Second, we allow for asymmetric steady forcing. And third, we determine an optimal feedback controller employing nine velocity probes downstream. As expected, the control performance increases with every generalization of the search space. Surprisingly, both open- and closed-loop optimal controllers include an asymmetric forcing, which surpasses symmetric forcing. Intriguingly, the best performance is achieved by a combination of phasor control and asymmetric steady forcing. We hypothesize that asymmetric forcing is typical for pitchfork bifurcated dynamics of nominally symmetric configurations. Key enablers are automated machine learning algorithms augmented with gradient search: explorative gradient method for the open-loop parameter optimization and a gradient-enriched machine learning control (gMLC) for the feedback optimization. Gradient-enriched machine learning control learns the control law significantly faster than previously employed genetic programming control. The gMLC source code is freely available online. %K genetic algorithms, genetic programming, flow control, machine learning, wakes %9 journal article %R 10.1017/jfm.2021.301 %U http://dx.doi.org/10.1017/jfm.2021.301 %P A42 %0 Book %T xMLC - A Toolkit for Machine Learning Control %A Cornejo Maceda, Guy Y. %A Lusseyran, Francois %A Noack, Bernd R. %S Machine Learning Tools in Fluid Mechanics %D 2022 %V 2 %7 First edition %I Technische Universitaet Braunschweig %C Braunschweig, Germany %G en %F dbbs_mods_00071130 %X xMLC is the second book of this Machine Learning Tools in Fluid Mechanics Series and focuses on Machine Learning Control (MLC). The objectives of this book are two-fold: First, provide an introduction to MLC for students, researchers, and newcomers on the field; and second, share an open-source code, xMLC, to automatically learn open and closed-loop control laws directly in the plant with only a few executable commands %K genetic algorithms, genetic programming, MATLAB, Octave %R 10.24355/dbbs.084-202208220937-0 %U https://leopard.tu-braunschweig.de/receive/dbbs_mods_00071130 %U https://leopard.tu-braunschweig.de/servlets/MCRZipServlet/dbbs_derivate_00049782 %U http://dx.doi.org/10.24355/dbbs.084-202208220937-0 %0 Conference Proceedings %T Fast Self-Learning of Turbulence Feedback Laws Using Gradient-Enriched Machine Learning Control %A Cornejo Maceda, Guy Y. %A Jiang, Zhutao %A Lusseyran, Francois %A Noack, Bernd R. %Y Hu, Ting %Y Ekart, Aniko %S Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion %S GECCO ’24 %D 2024 %8 14 18 jul %I Association for Computing Machinery %C Melbourne, Australia %F cornejo-maceda:2024:GECCOcomp %X We apply genetic programming (GP) to solve the most complex turbulence control problems. We simultaneously optimize up to dozens of parameters and dozens of control laws, listening to dozens of sensor signals with 100 to 1000 short test runs. Unlike reinforcement learning, our implementation of GP does not require any meta parameter tuning and is many orders of magnitudes faster than vanilla versions thanks to numerous enablers: 1. Parameter optimization over many dozens of experiments and simulations. 2. Smart formulation of control laws by preprocessing inputs and outputs. 3. Gradient-enriched simplex optimization of promising subspace. This work focuses on the resulting algorithm, referred to as gradient-enriched Machine Learning Control (gMLC). The applications comprise learning a multiple-input multiple-output control law to stabilize the flow past a cluster of three rotating cylinders in numerical simulations and the stabilization of the shear layer in an open cavity flow experiment. The learning acceleration achieved by gMLC opens the path to complex and time-limited experiments, including evaluation in varying operating conditions and optimization of distributed-input distributed-output control laws, i.e., functions with O(100) inputs and outputs. %K genetic algorithms, genetic programming, hybrid method, feedback control, downhill simplex: Poster %R 10.1145/3638530.3654395 %U http://dx.doi.org/10.1145/3638530.3654395 %P 495-498 %0 Conference Proceedings %T N-Dimensional Surface Mapping Using Genetic Programming %A Corney, David %A Parmee, Ian %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 corney:1999:NSMUGP %X This work introduces an extension to Genetic Programming (GP), known as GP-UDF which uses multiple User-Defined Functions (UDFs) to solve surface mapping problems. UDFs are high level primitives, such as polynomials and Gaussian hills, which simplify mapping and aid human interpretation of GP results. Preliminary results show that although UDFs do not improve GP accuracy, they may aid in landscape classification. %K genetic algorithms, genetic programming, poster papers %U http://gpbib.cs.ucl.ac.uk/gecco1999/GP-424.pdf %P 1230 %0 Conference Proceedings %T Symbolic regression of multiple-time-scale dynamical systems %A Cornforth, Theodore W. %A Lipson, Hod %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 Cornforth:2012:GECCO %X Genetic programming has been successfully used for symbolic regression of time series data in a wide variety of applications. However, previous approaches have not taken into account the presence of multiple-time-scale dynamics despite their prevalence in both natural and artificial dynamical systems. Here, we propose an algorithm that first decomposes data from such systems into components with dynamics at different time scales and then performs symbolic regression separately for each scale. Results show that this divide-and-conquer approach improves the accuracy and efficiency with which genetic programming can be used to reverse-engineer multiple-time-scale dynamical systems. %K genetic algorithms, genetic programming, Symbolic Regression, Dynamical Systems, Evolutionary Algorithms, Time Series, Multiple Time Scales, Algorithms, Design, Experimentation, Performance, Reliability, Artificial Intelligence, AI, Problem Solving, Control Methods, Search %R 10.1145/2330163.2330266 %U http://dx.doi.org/10.1145/2330163.2330266 %P 735-742 %0 Journal Article %T Inference of hidden variables in systems of differential equations with genetic programming %A Cornforth, Theodore W. %A Lipson, Hod %J Genetic Programming and Evolvable Machines %D 2013 %8 jun %V 14 %N 2 %@ 1389-2576 %F Cornforth:2013:GPEM %X The data-driven modelling of dynamical systems is an important scientific activity, and many studies have applied genetic programming (GP) to the task of automatically constructing such models in the form of systems of ordinary differential equations (ODEs). These previous studies assumed that data measurements were available for all variables in the system, whereas in real-world settings, it is typically the case that one or more variables are unmeasured or hidden. Here, we investigate the prospect of automatically constructing ODE models of dynamical systems from time series data with GP in the presence of hidden variables. Several examples with both synthetic and physical systems demonstrate the unique challenges of this problem and the circumstances under which it is possible to reverse-engineer both the form and parameters of ODE models with hidden variables. %K genetic algorithms, genetic programming, Ordinary differential equations, Hidden variables, Modelling, Symbolic identification %9 journal article %R 10.1007/s10710-012-9175-4 %U http://dx.doi.org/10.1007/s10710-012-9175-4 %P 155-190 %0 Thesis %T Data-Driven, Free-Form Modeling Of Biological Systems %A Cornforth III, Theodore William %D 2014 %8 27 jan %C USA %C Cornell University %F Cornforth:thesis %X The quantity of data available to scientists in all disciplines is increasing at an exponential rate, yet the insight necessary to distil data into scientific knowledge must still be supplied by human experts. This widening gap between data and insight can be bridged with data-driven modelling, in which computational methods shift much of the work in creating models from humans to computers. Traditional approaches to data-driven modeling require that the form of the model be fixed in advance, which requires substantial human effort and limits the complexity of problems that can be addressed. In contrast, a newer approach to automated modelling based on evolutionary computation (EC) removes such restrictions on the form of models. This free-form modelling has the potential both to reduce human effort for routine modelling and to make complex problems more tractable. Although major advances in EC-based modelling have been made in recent years, many challenges remain. These challenges include three features often seen in biological systems: complex nonlinear behaviour, multiple time scales, and hidden variables. This work addresses these challenges by developing new approaches to EC based modelling, with applications to neuroscience, systems biology, ecology, and other fields. The contributions of this work consist of three primary lines of research. In the first line of research, EC-based methods for the automated design of analogue electrical circuits are adapted for the modelling of electrical systems studied in neurophysiology that display complex, nonlinear behavior, such as ion channels. In the second line of research, EC-based methods for symbolic modelling are extended to facilitate the modelling of dynamical systems with multiple time scales, such as those found throughout ecology and other fields. Finally, in the third line of research, established EC-based algorithms are extended with the capability to model dynamical systems as systems of differential equations with hidden variables, which can contribute in an essential way to the observed dynamics of a physical system yet historically have presented a particularly difficult challenge to automated modelling. %K genetic algorithms, genetic programming, Computational Biology, Symbolic regression %9 Ph.D. thesis %U http://hdl.handle.net/1813/36187 %0 Conference Proceedings %T The Selfish Gene Algorithm: a New Evolutionary Optimization Strategy %A Corno, F. %A Sonza Reorda, M. %A Squillero, G. %S SAC: ACM Symposium on Applied Computing %D 1998 %F cad_sac98 %X This paper proposes a new general approach for optimization algorithms in the Evolutionary Computation field. The approach is inspired by the Selfish Gene theory, an interpretation of the Darwinian theory given by the biologist Richard Dawkins, in which the basic element of evolution is the gene, rather than the individual. The paper defines the Selfish Gene Algorithm, that implements such a view of the evolution mechanism. We tested the approach by implementing a Selfish Gene Algorithm on a case study, and we found better results than those provided by a Genetic Algorithm on the same problem and with the same fitness function. %K Genetic Algorithms, Approximate Methods, Equivalence Checking, Evolutionary Algorithms, Selfish Gene, Gate-Level, Simulation-Based Approaches %U http://www.cad.polito.it/FullDB/exact/sac98.html %P 349-355 %0 Conference Proceedings %T VEGA: A Verification Tool Based on Genetic Algorithms %A Corno, F. %A Sonza Reorda, M. %A Squillero, G. %S ICCD: International Conference on Circuit Design %D 1998 %8 May 07 oct %C Austin, TX, USA %F cad_iccd98a %X While modern state-of-the-art optimization techniques can handle designs with up to hundreds of flip-flops, equivalence verification is still a challenging task in many industrial design flows. This paper presents a new verification methodology that, while sacrificing exactness, is able to handle larger circuits and give designers the opportunity to trade off CPU time with confidence on the result. The proposed methodology is able to fruitfully support an exact verification tool, dramatically increasing the confidence on the validity of an optimization process. A prototypical tool has been developed and preliminary experimental results that support this claim are shown in the paper. %K genetic algorithms, genetic programming, EHW %R 10.1109/ICCD.1998.727069 %U http://dx.doi.org/10.1109/ICCD.1998.727069 %P 321-326 %0 Conference Proceedings %T Automatic Validation of Protocol Interfaces Described in VHDL %A Corno, Fulvio %A Sonza Reorda, Matteo %A Squillero, Giovanni %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 corno:2000:avpi %X In present days, most of the design activity is performed at a high level of abstraction, thus designers need to be sure that their designs are syntactically and semantically correct before starting the automatic synthesis process. The goal of this paper is to propose an automatic input pattern generation tool able to assist designers in the generation of a test bench for difficult parts of small or medium-sized digital protocol interfaces. The proposed approach exploit a Genetic Algorithm connected to a commercial simulator for cultivating a set of input sequence able to execute given statements in the interface description. The proposed approach has been evaluated on the new ITC-99 benchmark set, a collection of circuits offering a wide spectrum of complexity. Experimental results show that some portions of the circuits remained uncovered, and the subsequent manual analysis allowed identifying design redundancies. %K genetic algorithms, ASIC, Approximate Methods, Evolutionary Algorithms, Gate-Level, Low Power, Selfish Gene, Simulation-Based Approaches %R 10.1007/3-540-45561-2_20 %U http://www.cad.polito.it/FullDB/exact/evotel2000a.html %U http://dx.doi.org/10.1007/3-540-45561-2_20 %P 205-214 %0 Conference Proceedings %T On the test of microprocessor IP cores %A Corno, F. %A Sonza Reorda, M. %A Squillero, G. %A Violante, M. %S Proceedings of Design, Automation and Test in Europe Conference and Exhibition 2001 %D 2001 %8 13 16 mar %I IEEE Press %C Munich, Germany %F Corno:2001:DATE %X Testing is a crucial issue in SOC development and production process. A popular solution for SOCs that include microprocessor cores is based on making them execute a test program. Thus, implementing a very attractive BIST solution. This paper describes a method for the generation of effective programs for the self-test of a processor. The method can be partially automated and combines ideas from traditional functional approaches and from the ATPG field. We assess the feasibility and effectiveness of the method by applying it to a 8051 core %K genetic algorithms, genetic programming %R 10.1109/DATE.2001.915026 %U http://www.date-conference.com/conference/instructions/gl_paper04c_2.pdf %U http://dx.doi.org/10.1109/DATE.2001.915026 %P 209-213 %0 Conference Proceedings %T Efficient Machine-Code Test-Program Induction %A Corno, F. %A Cumani, G. %A Sonza Reorda, M. %A Squillero, G. %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 %C Honolulu, Hawaii, USA %@ 0-7803-7278-6 %F corno:2002:emctpi %X Technology advances allow integrating on a single chip entire system, including memories and peripherals. The test of these devices is becoming a major issue for manufacturing industries. This paper presents a methodology for inducing test-programs similar to genetic programming. However, it includes the ability to explicitly specify registers and resorts to directed acyclic graphs instead of trees. Moreover, it exploits a database containing the assembly-level semantic associated to each graph node. This approach is extremely efficient and versatile: candidate solutions are translated into source-code programs allowing millions of evaluations per second. The proposed approach is extremely versatile: the macro library allows easily changing target processor and environment. The approach was verified on three processors with different instruction sets, different formalisms and different conventions. A complete set of experiments on a test function are also reported for the SPARC processor. %K genetic algorithms, genetic programming, DAG, ATPG, Approximate Methods, Evolutionary Algorithms, Micro-Processors, Simulation-Based Approaches, SPARC processor, assembly-level semantics, conventions, database, device testing, directed acyclic graphs, evaluation speed, formalisms, genetic programming, graph nodes, instruction sets, integrated circuit manufacturing industries, machine-code test-program induction, macro library, microprocessors, registers, source-code programs, system on chip, target processor, test function, automatic test software, computer testing, directed graphs, instruction sets, integrated circuit manufacture, integrated circuit testing, macros, microprocessor chips, microprogramming, software libraries %R 10.1109/CEC.2002.1004462 %U http://www.cad.polito.it/pap/db/cec2002.pdf %U http://dx.doi.org/10.1109/CEC.2002.1004462 %P 1486-1491 %0 Conference Proceedings %T Evolutionary test program induction for microprocessor design verification %A Corno, Fulvio %A Cumani, Gianluca %A Sonza Reorda, Matteo %A Squillero, Giovanni %S Proceedings of the 11th Asian Test Symposium (ATS ’02) %D 2002 %8 18 20 nov %I IEEE Press %F corno:2002:ATS %X Design verification is a crucial step in the design of any electronic device. Particularly when microprocessor cores are considered, devising appropriate test cases may be a difficult task. This paper presents a methodology able to automatically induce a test program for maximising a given verification metric. The methodology is based on an evolutionary paradigm and exploits a syntactical description of microprocessor assembly language and an RT-level functional model. Experimental results show the effectiveness of the approach. %K genetic algorithms, genetic programming %R 10.1109/ATS.2002.1181739 %U http://www.cad.polito.it/pap/db/ats02.pdf %U http://dx.doi.org/10.1109/ATS.2002.1181739 %P 368-373 %0 Conference Proceedings %T Automatic Test Program Generation for Pipelined Processors %A Corno, F. %A Cumani, G. %A Sonza Reorda, M. %A Squillero, G. %S Proceedings of the 2003 ACM Symposium on Applied Computing (SAC) %D 2003 %8 September 12 mar %I ACM %C Melbourne, FL, USA %G en %F oai:CiteSeerPSU:573140 %X The continuous advances in micro-electronics design are creating a significant challenge to design validation in general, but tackling pipelined microprocessors is remarkably more demanding. This paper presents a methodology to automatically induce a test program for a microprocessor maximising a given verification metric. The approach exploits a new evolutionary algorithm, close to Genetic Programming, able to cultivate effective assembly language programs. The proposed methodology was used to verify the DLX/pII, an open-source processor with a 5-stage pipeline. Code-coverage was adopted in the paper, since it can be considered the required starting point for any simulation-based functional verification processes. Experimental results clearly show the effectiveness of the approach. %K genetic algorithms, genetic programming %U http://www.cad.polito.it/pap/db/sac03.pdf %0 Conference Proceedings %T Exploiting Auto-adaptive μ-GP for Highly Effective Test Programs Generation %A Corno, F. %A Cumani, F. %A Squillero, G. %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 corno:2003:ICES %X Integrated-circuit producers are shoved by competitive pressure; new devices require increasingly complex verifications to be performed at increasing pace. This paper presents a methodology to automatically induce a test program for a microprocessor that maximizes a given verification metric. The methodology is based on an auto-adaptive evolutionary algorithm and exploits a syntactical description of microprocessor assembly language and an RT-level functional model. Experimental results clearly show the effectiveness of the approach. Comparisons reveal how auto-adaptive mechanisms dramatically enhance both performances and quality of the results. %K genetic algorithms, genetic programming %R 10.1007/3-540-36553-2_24 %U http://dx.doi.org/10.1007/3-540-36553-2_24 %P 262-273 %0 Conference Proceedings %T An Enhanced Framework for Microprocessor Test-Program Generation %A Corno, F. %A Squillero, G. %Y Ryan, Conor %Y Soule, Terence %Y Keijzer, Maarten %Y Tsang, Edward %Y Poli, Riccardo %Y Costa, Ernesto %S Genetic Programming, Proceedings of EuroGP’2003 %S LNCS %D 2003 %8 14 16 apr %V 2610 %I Springer-Verlag %C Essex %@ 3-540-00971-X %F corno03 %X Test programs are fragment of code, but, unlike ordinary application programs, they are not intended to solve a problem, nor to calculate a function. Instead, they are supposed to give information about the machine that actually executes them. Today, the need for effective test programs is increasing, and, due to the inexorable increase in the number of transistor that can be integrated onto a single silicon die, devising effective test programs is getting more problematical. This paper presents GP, an efficient and versatile approach to test-program generation based on an evolutionary algorithm. The proposed methodology is highly versatile and improves previous approaches, allowing the test-program generator generating complex assembly programs that include subroutines calls. %K genetic algorithms, genetic programming: Poster %R 10.1007/3-540-36599-0_28 %U http://www.cad.polito.it/pap/db/eurogp03.pdf %U http://dx.doi.org/10.1007/3-540-36599-0_28 %P 307-316 %0 Conference Proceedings %T On The Evolution of Corewar Warriors %A Corno, Fulvio %A Sanchez, Ernesto %A Squillero, Giovanni %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 corno:2004:oteocw %X This paper analyzes corewar, a peculiar computer game where different programs fight in the memory of a virtual computer. An evolutionary assembly-program generator, is used to evolve efficient programs, and the game is exploited to evaluate new evolutionary techniques. The paper introduces a new migration model that exploits the polarization effect and a new hierarchical coarse-grained approach applicable whenever the final goal can be seen as a combination of semi-independent sub goals. Additionally, two general enhancements are proposed. Analyzed techniques are orthogonal and broadly applicable to different real-life contexts. Experimental results show that all these techniques are able to outperform a previous approach. %K genetic algorithms, genetic programming, Evolutionary Computation and Games %R 10.1109/CEC.2004.1330848 %U http://www.cad.polito.it/pap/db/cec2004b.pdf %U http://dx.doi.org/10.1109/CEC.2004.1330848 %P 133-138 %0 Journal Article %T Automatic test generation for verifying microprocessors %A Corno, F. %A Sanchez, E. %A Reorda, M. S. %A Squillero, G. %J IEEE Potentials %D 2005 %8 feb mar %V 24 %N 1 %@ 0278-6648 %F Corno:2005:ieeeP %X A pipelined processor with a high-level behavioural HDL description is presented in this paper. It generates a set of effective test programs by using a simulator, which is able to evaluate with respect to an RTL coverage metric. The proposed optimiser is based on a technique called microGP, an evolutionary system able to automatically device and optimizes the program written in an assembly language. Quantitative coverage measurement presented will guide the test-program generation. The approach is fully automatic and broadly applicable. The minimal test set with the programmable coverage is attained. %K genetic algorithms, genetic programming %9 journal article %R 10.1109/MP.2005.1405800 %U http://dx.doi.org/10.1109/MP.2005.1405800 %P 34-37 %0 Conference Proceedings %T Improving Design Diversity Using Graph Based Evolutionary Algorithms %A Corns, Steven M. %A Ashlock, Daniel A. %A McCorkle, Douglas S. %A Bryden, Kenneth Mark %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 16 21 jul %I IEEE Press %C Vancouver %@ 0-7803-9487-9 %F Corns:2006:CEC %X Graph based evolutionary algorithms (GBEAs) have been shown to have superior performance to evolutionary algorithms on a variety of evolutionary computation test problems as well as on some engineering applications. One of the motivations for creating GBEAs was to produce a diversity of solutions with little additional computational cost. This paper tests that feature of GBEAs on three problems: a real-valued multi-modal function of varying dimension, the plus-one-recall-store (PORS) problem, and an applied engineering design problem. For all of the graphs studied the number of different solutions increased as the connectivity of the graph underlying the algorithm decreased. This indicates that the choice of graph can be used to control the diversity of solutions produced. The availability of multiple solutions is an asset in a product realization system, making it possible for an engineer to explore design alternatives. %K genetic algorithms, genetic programming %R 10.1109/CEC.2006.1688327 %U http://dx.doi.org/10.1109/CEC.2006.1688327 %P 1037-1043 %0 Journal Article %T James Keller, Derong Liu, and David Fogel: Fundamentals of computational intelligence: neural networks, fuzzy systems, and evolutionary computation %A Corns, Steven Michael %J Genetic Programming and Evolvable Machines %D 2017 %8 mar %V 18 %N 1 %@ 1389-2576 %F Corns:2017:GPEM %O Book review %K genetic algorithms, EC, EHW, fuzzy, ANN %9 journal article %R 10.1007/s10710-017-9285-0 %U https://rdcu.be/dR8i0 %U http://dx.doi.org/10.1007/s10710-017-9285-0 %P 119-120 %0 Conference Proceedings %T A Grammar-based Genetic Programming Hyper-Heuristic for Corridor Allocation Problem %A Correa, Rafael F. R. %A Bernardino, Heder S. %A de Freitas, Joao M. %A Soares, Stenio S. R. F. %A Goncalves, Luciana B. %A Moreno, Lorenza L. O. %Y Xavier-Junior, Joao Carlos %Y Rios, Ricardo Araujo %S Brazilian Conference on Intelligent Systems, BRACIS 2022, part 1 %S Lecture Notes in Computer Science %D 2022 %8 nov 28 dec 1 %V 13653 %I Springer %C Campinas, Brazil %F correa:2022:IS %X Layout problems are the physical arrangement of facilities along a given area commonly used in practice. The Corridor Allocation Problem (CAP) is a class of layout problems in which no overlapping of rooms is allowed, no empty spaces are allowed between the rooms, and the two first facilities (one on each side) are placed on zero abscissa. This combinatorial problem is usually solved using heuristics, but designing and selecting the appropriate parameters is a complex task. Hyper-Heuristic can be used to alleviate this task by generating heuristics automatically. Thus, we propose a Grammar-based Genetic Programming Hyper-Heuristic (GGPHH) to generate heuristics for CAP. We investigate (i) the generation of heuristics using a subset of the instances of the problem and (ii) using a single instance. The results show that the proposed approach generates competitive heuristics, mainly when a subset of instances are used. Also, we found a single instance that can be used to generate heuristics that generalize to other cases. %K genetic algorithms, genetic programming %R 10.1007/978-3-031-21686-2_35 %U http://link.springer.com/chapter/10.1007/978-3-031-21686-2_35 %U http://dx.doi.org/10.1007/978-3-031-21686-2_35 %P 504-519 %0 Journal Article %T Combining model finder and genetic programming into a general purpose automatic program synthesizer %A Correia, Alexandre %A Iyoda, Juliano %A Mota, Alexandre %J Information Processing Letters %D 2020 %8 feb %V 154 %@ 0020-0190 %F Correia:2020:IPL %X Program synthesis aims to mechanize the task of programming from the user intent (expressed in various forms like pre/post conditions, examples, sketches, etc). There are many approaches to program synthesis that are usually implemented in isolation: deductive, syntax-based, inductive, etc. In this paper, we describe PSMF2, a program synthesizer that combines model finder and genetic programming. PSMF2 takes as user intent examples and a soft sketch: a new kind of user intent defined as a set of commands that must appear in the synthesized program (and that are in no particular order of execution). The output of PSMF2 is a general purpose imperative program. The combination of inductive synthesis and genetic programming has allowed PSMF2 to synthesize 7 programs (IntSQRT, Majority of 5, Majority of 8, Max of 4, Modulo, Factorial, and Fibonacci) found in the SyGuS competition, the iJava and IntroClass, and the Genetic programming communities. We carried out an empirical evaluation on the synthesis time of these 7 programs and the mean time varied from 56.4 seconds (Majority of 5) to 15.9 minutes (Fibonacci). %K genetic algorithms, genetic programming, Program synthesis, Alloy*, Programming languages, PSMF2, Fibonacci %9 journal article %R 10.1016/j.ipl.2019.105866 %U http://www.sciencedirect.com/science/article/pii/S0020019019301498 %U http://dx.doi.org/10.1016/j.ipl.2019.105866 %P 105866 %0 Conference Proceedings %T Improving haar cascade classifiers through the synthesis of new training examples %A Correia, Joao %A Machado, Penousal %A Romero, Juan %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 Companion ’12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion %D 2012 %8 July 11 jul %I ACM %C Philadelphia, Pennsylvania, USA %F Correia:2012:GECCOcomp %X A Genetic Programming approach for the improvement of the performance of classifier systems through the synthesis of new training instances is explored. Genetic Programming is used to exploit shortcomings of classifiers systems and generate misclassified instances. The proposed approach performs multiple parallel evolutionary runs to generate a large number of potentially misclassified samples. A supervisor module determines which of the generated images have been misclassified and which should be added to the training set. New classifiers are trained based on the original training set augmented by the selected evolved instances. The results attained while using face detection classifiers are presented and discussed. Overall they indicate that significant improvements are attained when using multiple evolutionary runs. %K genetic algorithms, Genetic programming: Poster %R 10.1145/2330784.2331001 %U http://dx.doi.org/10.1145/2330784.2331001 %P 1479-1480 %0 Conference Proceedings %T Semantic Operators for Evolutionary Art %A Correia, Joao %A Machado, Penousal %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 Correia: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/Correia.pdf %0 Conference Proceedings %T Evolving Image Enhancement Pipelines %A Correia, Joao %A Vieira, Leonardo %A Rodriguez-Fernandez, Nereida %A Romero, Juan %A Machado, Penousal %Y Romero, Juan %Y Martins, Tiago %Y Rodriguez-Fernandez, Nereida %S 10th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMusArt 2021 %S LNCS %D 2021 %8 July 9 apr %I Springer Verlag %C virtual event %F Correia:2021:evomusart %X Image enhancement is an image processing procedure in which the original information of the image is improved. It can be used to alter an image in several different ways, for instance, by highlighting a specific feature in order to ease post-processing analyses by a human or machine. In this work, we show our approach to image enhancement for digital real-estate-marketing. The aesthetic quality of the images for real-estate marketing is critical since it is the only input that clients have once browsing for options. Thus, improving and ensuring the aesthetic quality of the images is crucial for marketing success. The problem is that each set of images, even for the same real-estate item, is often taken under diverse conditions making it hard to find one solution that fits all. State of the art image enhancement pipelines applies a set of filters that tend to solve specific issues, so it is still hard to generalise that solves all type of issues encountered. With this in mind, we propose a Genetic Programming approach for the evolution of image enhancement pipelines, based on image filters from the literature. We report a set of experiments in image enhancement of real state images and analysed the results. The overall results suggest that it is possible to attain suitable pipelines that enhance the image visually and according to a set of image quality assessment metrics. The evolved pipelines show improvements across the validation metrics showing that it is possible to create image enhancement pipelines automatically. Moreover, during the experiments, some of the created pipelines end up creating non-photorealistic rendering effects in a moment of computational serendipity. Thus, we further analysed the different evolved non-photorealistic solutions, showing the potential of applying the evolved pipelines in other types of images. %K genetic algorithms, genetic programming, Image enhancement, Image processing, Computer vision, Evolutionary computation %R 10.1007/978-3-030-72914-1_6 %U http://dx.doi.org/10.1007/978-3-030-72914-1_6 %P 82-97 %0 Journal Article %T Towards Automatic Image Enhancement with Genetic Programming and Machine Learning %A Correia, Joao %A Rodriguez-Fernandez, Nereida %A Vieira, Leonardo %A Romero, Juan %A Machado, Penousal %J Applied Sciences %D 2022 %V 12 %N 4 %@ 2076-3417 %F correia:2022:AS %X Image Enhancement (IE) is an image processing procedure in which the images original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimise 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the frameworks isolated parts. %K genetic algorithms, genetic programming %9 journal article %R 10.3390/app12042212 %U https://www.mdpi.com/2076-3417/12/4/2212 %U http://dx.doi.org/10.3390/app12042212 %0 Journal Article %T Experiments in evolutionary image enhancement with ELAINE %A Correia, Joao %A Lopes, Daniel %A Vieira, Leonardo %A Rodriguez-Fernandez, Nereida %A Carballal, Adrian %A Romero, Juan %A Machado, Penousal %J Genetic Programming and Evolvable Machines %D 2022 %8 dec %V 23 %N 4 %@ 1389-2576 %F Correia:GPEM %O Special Issue: Evolutionary Computation in Art, Music and Design %X Image enhancement is an image processing procedure in which the image original information is refined, for example by highlighting specific features to ease post-processing analyses by a human or machine. This procedure remains challenging since each set of images is often taken under diverse conditions which makes it hard to find an image enhancement solution that fits all conditions. State-of-the-art image enhancement pipelines apply filters that solve specific issues; therefore, it is still hard to generalise these pipelines to all types of problems encountered. We have recently introduced a Genetic Programming approach named ELAINE (EvoLutionAry Image eNhancEment) for evolving image enhancement pipelines based on pre-defined image filters. In this paper, we showcase its potential to create solutions under a real-estate marketing scenario by comparing it with a manual approach and an existing tool for automatic image enhancement. The ELAINE obtained results far exceed those obtained by manual combinations of filters and by the one-click method, in all the metrics explored. We further explore the potential of creating non-photorealistic effects by applying the evolved pipelines to different types of images. The results highlight ELAINE potential to transform input images into either suitable real-estate images or non-photorealistic renderings, thus transforming contents and possibly enhancing its aesthetic appeal. %K genetic algorithms, genetic programming, Image enhancement, Image processing, Computer vision, Evolutionary computation, %9 journal article %R 10.1007/s10710-022-09445-9 %U https://rdcu.be/cYSxw %U http://dx.doi.org/10.1007/s10710-022-09445-9 %P 557-579 %0 Conference Proceedings %T Interpretable Safety Validation for Autonomous Vehicles %A Corso, Anthony %A Kochenderfer, Mykel J. %S 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) %D 2020 %8 sep %F Corso:2020:ITSC %X An open problem for autonomous driving is how to validate the safety of an autonomous vehicle in simulation. Automated testing procedures can find failures of an autonomous system but these failures may be difficult to interpret due to their high dimensionality and may be so unlikely as to not be important. This work describes an approach for finding interpretable failures of an autonomous system. The failures are described by signal temporal logic expressions that can be understood by a human, and are optimized to produce failures that have high likelihood. Our methodology is demonstrated for the safety validation of an autonomous vehicle in the context of an unprotected left turn and a crosswalk with a pedestrian. Compared to a baseline importance sampling approach, our methodology finds more failures with higher likelihood while retaining interpretability. %K genetic algorithms, genetic programming, Trajectory, Grammar, Autonomous vehicles, Safety, Optimization, Time series analysis %R 10.1109/ITSC45102.2020.9294490 %U http://dx.doi.org/10.1109/ITSC45102.2020.9294490 %0 Journal Article %T Evolutionary design of explainable algorithms for biomedical image segmentation %A Cortacero, Kevin %A McKenzie, Brienne %A Mueller, Sabina %A Khazen, Roxana %A Lafouresse, Fanny %A Corsaut, Gaelle %A Van Acker, Nathalie %A Frenois, Francois-Xavier %A Lamant, Laurence %A Meyer, Nicolas %A Vergier, Beatrice %A Wilson, Dennis G. %A Luga, Herve %A Staufer, Oskar %A Dustin, Michael L. %A Valitutti, Salvatore %A Cussat-Blanc, Sylvain %J Nature Communications %D 2023 %8 June %V 14 %@ 2041-1723 %F Cortacero:2