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Relevant bibliographies by topics / Genetic Programming

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Academic literature on the topic 'Genetic Programming'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Genetic Programming"

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Banzhaf, W., J. R. Koza, C. Ryan, L. Spector, and C. Jacob. "Genetic programming." IEEE Intelligent Systems 15, no. 3 (2000): 74–84. http://dx.doi.org/10.1109/5254.846288.

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Gielen, C. "Genetic programming." Neurocomputing 6, no. 1 (1994): 120–22. http://dx.doi.org/10.1016/0925-2312(94)90038-8.

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Montana, David J. "Strongly Typed Genetic Programming." Evolutionary Computation 3, no. 2 (1995): 199–230. http://dx.doi.org/10.1162/evco.1995.3.2.199.

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Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection (Koza, 1992). However, in its standard form, there is no way to restrict the programs it generates to those where the functions operate on appropriate data types. In the case when the programs manipulate multiple data types and contain functions designed to operate on particular data types, this can lead to unnecessarily large search times and/or unnecessarily poor generalization performance. Strongly typed genetic programming (STGP) is an enhanced version of genetic pro

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Rodríguez, Arturo, and Joaquín Trigueros. "Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming." Estudios de Administración 15, no. 2 (2020): 47. http://dx.doi.org/10.5354/0719-0816.2008.56413.

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In this study we examine different methodologies to estimate earnings. More specifically, we evaluate the viability of Genetic Programming as both a forecasting model estimator and a forecast-combining methodology. When we compare the performance of traditional mechanical forecasting (ARIMA) models and models developed using Genetic Programming we observe that Genetic Programming can be used to create time-series models for quarterly earnings as accurate as the traditional linear models. Genetic Programming can also effectively combine forecasts. However, Genetic Programming's forecast combina

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Fajfar, Iztok, Žiga Rojec, Árpád Bűrmen, et al. "Imperative Genetic Programming." Symmetry 16, no. 9 (2024): 1146. http://dx.doi.org/10.3390/sym16091146.

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Genetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly utilizing tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve ’real’ Turing-like imperative programs (as contrasted with functional programming) from the ground up. Existing research focuses mainly on specific special cases where the structure of the solution is partly known. This paper explores the potential of integrating tree and linear GP paradigms to develop an e

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Ciesielski, Vic. "Linear genetic programming." Genetic Programming and Evolvable Machines 9, no. 1 (2007): 105–6. http://dx.doi.org/10.1007/s10710-007-9036-8.

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Wa¸siewicz, P., and J. J. Mulawka. "Molecular genetic programming." Soft Computing 5, no. 2 (2001): 106–13. http://dx.doi.org/10.1007/s005000000077.

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McDermott, James, Gabriel Kronberger, Patryk Orzechowski, et al. "Genetic programming benchmarks." ACM SIGEVOlution 15, no. 3 (2022): 1–19. http://dx.doi.org/10.1145/3578482.3578483.

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The top image shows a set of scales, which are intended to bring to mind the ideas of balance and fair experimentation which are the focus of our article on genetic programming benchmarks in this issue. Image by Elena Mozhvilo and made available under the Unsplash license on https://unsplash.com/photos/j06gLuKK0GM.

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Kim, Young-Kyun, and Ki-Sung Seo. "Automated Generation of Corner Detectors Using Genetic Programming." Journal of Korean Institute of Intelligent Systems 19, no. 4 (2009): 580–85. http://dx.doi.org/10.5391/jkiis.2009.19.4.580.

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Seo, Kisung. "Genetic Programming Based Plant/Controller Simultaneous Optimization Methodology." Transactions of The Korean Institute of Electrical Engineers 65, no. 12 (2016): 2069–74. http://dx.doi.org/10.5370/kiee.2016.65.12.2069.

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Dissertations / Theses on the topic "Genetic Programming"

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Lones, Michael Adam. "Enzyme genetic programming : modelling biological evolvability in genetic programming." Thesis, University of York, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399653.

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KOSHIYAMA, ADRIANO SOARES. "GPFIS: A GENERIC GENETIC-FUZZY SYSTEM BASED ON GENETIC PROGRAMMING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26560@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Sistemas Fuzzy-Genéticos compreendem uma área que une Sistemas de Inferência Fuzzy e Meta-Heurísticas prevalentes nos conceitos de seleção natural e recombinação genética. Esta é de grande interesse para a comunidade científica, pois propicia a descoberta de conhecimento em áreas onde a compreensão do fenômeno em estudo é exíguo, além de servir de apoio à decisão para gestores público-privados. O objetivo desta dissertação é desenvolver um no

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Martin, Peter N. "Genetic programming in hardware." Thesis, University of Essex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272585.

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Cavill, Rachel. "Multi-chromosomal genetic programming." Thesis, University of York, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437617.

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Suleman, Hussein. "Genetic Programming in Mathematica." Thesis, University of Durban-Westville, 1997. http://hdl.handle.net/10919/71533.

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GP has traditionally been implemented in LISP but there is a slow migration towards faster languages like C++. Any implementation language is dictated not only by the speed of the platform but also by the desirability of such an implementation. With a large number of scientists migrating to scientifically-biased programming languages like Mathematica, such provides an ideal testbed for GP.In this study it was attempted to implement GP on a Mathematica platform, exploiting the advantages of Mathematica's unique capabilities. Wherever possible, optimizations have been applied to drive the GP alg

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Steinhoff, Robert J. "A Performance Comparison of Tree-Based Genetic Programming versus Stack-Based Genetic Programming versus Stack-Based Genetic Programming Using the Java Virtual Machine." NSUWorks, 2000. http://nsuworks.nova.edu/gscis_etd/859.

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Stack-based genetic programming uses variable length, linear programs executing on a virtual stack machine. This concept was proposed and evaluated by Timothy Perkis. The Java programming language uses a stack-based virtual machine to perform operations. This paper examined the possibility of performing stack-based genetic programming directly using the stack on the Java virtual machine. The problems of combining stack based genetic programming with the Java virtual machine stack were explored. Excessive runtime delay on bytecode verification of the chromosome carrying classes undergoing fitne

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Smith, Steven Lee Al-Mahmood Mohammed Abdul latif. "Knowledge discovery using genetic programming /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA276224.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, December 1993.<br>Thesis advisor(s): B. Ramesh ; William R. Gates. "December 1993." Includes bibliographical references. Also available online.

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Johansson, Magnus. "Financial application of genetic programming." Thesis, Linköping University, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17091.

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Norin, Martin. "Automatic Modularization in Genetic Programming." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-150822.

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This master’s thesis is an investigation into automatically created functions when applying Genetic Programming to a problem. The thesis begins with a description of Genetic Programming and the different parts that is required to apply Genetic Programming to a problem. This is followed by descriptions of different methods for automatically creating functions when using Genetic Programming as a problem solving method. A new method for automatically creating functions is proposed with the name Structural Module Creation. Structural Module Creation creates new functions based on the most frequent

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Smith, Steven Lee, and Mohammed Abdul latif Al-Mahmood. "Knowledge discovery using genetic programming." Thesis, Monterey, California. Naval Postgraduate School, 1993. http://hdl.handle.net/10945/26608.

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Dramatic growth in database technology has outpaced the ability to analyze the information stored in databases for new knowledge and has created an increasing potential for the loss of undiscovered knowledge. This potential gains for such knowledge discovery are particularly large in the Department of Defense where millions of transactions, from maintenance to medical information, are recorded yearly. Due to the limitations of traditional knowledge discovery methods in analyzing this data, there is a growing need to utilize new knowledge discovery methods to glean knowledge from vast databases

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Books on the topic "Genetic Programming"

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Hu, Ting, Nuno Lourenço, and Eric Medvet, eds. Genetic Programming. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72812-0.

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Medvet, Eric, Gisele Pappa, and Bing Xue, eds. Genetic Programming. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02056-8.

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Castelli, Mauro, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, and Pablo García-Sánchez, eds. Genetic Programming. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77553-1.

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Ebner, Marc, Michael O’Neill, Anikó Ekárt, Leonardo Vanneschi, and Anna Isabel Esparcia-Alcázar, eds. Genetic Programming. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71605-1.

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Esparcia-Alcázar, Anna Isabel, Anikó Ekárt, Sara Silva, Stephen Dignum, and A. Şima Uyar, eds. Genetic Programming. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12148-7.

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Foster, James A., Evelyne Lutton, Julian Miller, Conor Ryan, and Andrea Tettamanzi, eds. Genetic Programming. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45984-7.

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Machado, Penousal, Malcolm I. Heywood, James McDermott, et al., eds. Genetic Programming. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16501-1.

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McDermott, James, Mauro Castelli, Lukas Sekanina, Evert Haasdijk, and Pablo García-Sánchez, eds. Genetic Programming. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55696-3.

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Hu, Ting, Nuno Lourenço, Eric Medvet, and Federico Divina, eds. Genetic Programming. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44094-7.

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Moraglio, Alberto, Sara Silva, Krzysztof Krawiec, Penousal Machado, and Carlos Cotta, eds. Genetic Programming. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29139-5.

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Book chapters on the topic "Genetic Programming"

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Du, Ke-Lin, and M. N. S. Swamy. "Genetic Programming." In Search and Optimization by Metaheuristics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_4.

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Poli, Riccardo, and John Koza. "Genetic Programming." In Search Methodologies. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6940-7_6.

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McDermott, James, and Una-May O’Reilly. "Genetic Programming." In Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43505-2_43.

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Brabazon, Anthony, Michael O’Neill, and Seán McGarraghy. "Genetic Programming." In Natural Computing Algorithms. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_7.

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Langdon, William B., Robert I. McKay, and Lee Spector. "Genetic Programming." In Handbook of Metaheuristics. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1665-5_7.

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Ryan, Conor. "Genetic Programming." In Automatic Re-engineering of Software Using Genetic Programming. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4631-3_2.

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Zhang, Xinhua, Novi Quadrianto, Kristian Kersting, et al. "Genetic Programming." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_340.

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Vanneschi, Leonardo, and Sara Silva. "Genetic Programming." In Natural Computing Series. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17922-8_8.

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Labaca Castro, Raphael. "Genetic Programming." In Machine Learning under Malware Attack. Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40442-0_5.

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Eiben, A. E., and J. E. Smith. "Genetic Programming." In Natural Computing Series. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05094-1_6.

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Conference papers on the topic "Genetic Programming"

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Marchetti, Francesco, Mauro Castelli, Illya Bakurov, and Leonardo Vanneschi. "Full Inclusive Genetic Programming." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10611808.

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Piszcz, Alan, and Terence Soule. "Genetic programming." In the 2005 workshops. ACM Press, 2005. http://dx.doi.org/10.1145/1102256.1102309.

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O'Reilly, Una-May. "Genetic programming." In Proceeding of the fifteenth annual conference companion. ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2480797.

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Piszcz, Alan, and Terence Soule. "Genetic programming." In the 8th annual conference. ACM Press, 2006. http://dx.doi.org/10.1145/1143997.1144166.

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O'Reilly, Una-May. "Genetic Programming." In GECCO '15: Genetic and Evolutionary Computation Conference. ACM, 2015. http://dx.doi.org/10.1145/2739482.2756592.

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O'Reilly, Una-May. "Genetic programming." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2605336.

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O'Reilly, Una-May, and Erik Hemberg. "Genetic programming." In GECCO '20: Genetic and Evolutionary Computation Conference. ACM, 2020. http://dx.doi.org/10.1145/3377929.3389882.

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OReilly, Una-May. "Genetic programming." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3067695.3067709.

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OReilly, Una-May. "Genetic programming." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3207870.

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O'Reilly, Una-May. "Genetic Programming." In GECCO '16: Genetic and Evolutionary Computation Conference. ACM, 2016. http://dx.doi.org/10.1145/2908961.2926984.

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Reports on the topic "Genetic Programming"

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Othling, Andrew S., John A. Kelly, Richard J. Pryor, and Grant V. Farnsworth. Successful technical trading agents using genetic programming. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/919656.

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Wildman, Raymond A., and George A. Gazonas. Genetic Programming-based Phononic Bandgap Structure Design. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada553044.

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Spears, William M., and Vic Anand. A Study of Crossover Operators in Genetic Programming. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada294071.

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Pryor, R. J. Developing robotic behavior using a genetic programming model. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/569136.

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Harris, Sean. A Comparison of Genetic Programming Variants for Hyper-Heuristics. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1177599.

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Spector, Lee. Multi-Type Self Adaptive Genetic Programming for Complex Applications. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada432974.

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Rauss, Patrick. Hyperspectral and Spectro-Polarimetric Pixel-Level Classification Using Genetic Programming. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada397832.

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Pryor, Richard J., and Mark J. Schaller. Developing close combat behaviors for simulated soldiers using genetic programming techniques. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/918361.

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Esfahani, Naser Madani, Behzad Heidari, Hamed Boustanzar, and Ghasem Sattari. Modeling of Uniaxial Compressive Strength via Genetic Programming and Neuro-Fuzzy. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0304.

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Neely, Christopher J., and Paul A. Weller. Predicting Exchange Rate Volatility: Genetic Programming vs. GARCH and Risk Metrics™. Federal Reserve Bank of St. Louis, 2001. http://dx.doi.org/10.20955/wp.2001.009.

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