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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: 9 June 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 (May 2000): 74–84. http://dx.doi.org/10.1109/5254.846288.

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Gielen, C. "Genetic programming." Neurocomputing 6, no. 1 (February 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 (June 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 programming that enforces data-type constraints and whose use of generic functions and generic data types makes it more powerful than other approaches to type-constraint enforcement. After describing its operation, we illustrate its use on problems in two domains, matrix/vector manipulation and list manipulation, which require its generality. The examples are (1) the multidimensional least-squares regression problem, (2) the multidimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR.

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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 (February 4, 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 combinations are sometimes unable to improve on Value Line. Moreover, simple averaging of forecasts results in better predictive accuracy than Genetic Programming-combining of forecasts. Hence, as implemented in this study, Genetic Programming is not superior to traditional methodologies in either forecasting or forecast combining of quarterly earnings.

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Fajfar, Iztok, Žiga Rojec, Árpád Bűrmen, Matevž Kunaver, Tadej Tuma, Sašo Tomažič, and Janez Puhan. "Imperative Genetic Programming." Symmetry 16, no. 9 (September 3, 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 encoding scheme that universally supports genetic operators without constraints and consistently generates syntactically correct Python programs from scratch. By blending the symmetrical structure of tree-based representations with the inherent asymmetry of linear sequences, we created a versatile environment for program evolution. Our approach was rigorously tested on 35 problems characterized by varying Halstead complexity metrics, to delineate the approach’s boundaries. While expected brute-force program solutions were observed, our method yielded more sophisticated strategies, such as optimizing a program by restricting the division trials to the values up to the square root of the number when counting its proper divisors. Despite the recent groundbreaking advancements in large language models, we assert that the GP field warrants continued research. GP embodies a fundamentally different computational paradigm, crucial for advancing our understanding of natural evolutionary processes.

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Ciesielski, Vic. "Linear genetic programming." Genetic Programming and Evolvable Machines 9, no. 1 (August 15, 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 (April 2001): 106–13. http://dx.doi.org/10.1007/s005000000077.

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McDermott, James, Gabriel Kronberger, Patryk Orzechowski, Leonardo Vanneschi, Luca Manzoni, Roman Kalkreuth, and Mauro Castelli. "Genetic programming benchmarks." ACM SIGEVOlution 15, no. 3 (September 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 (August 25, 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 (December 1, 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 novo Sistema Fuzzy-Genético Genérico, denominado Genetic Programming Fuzzy Inference System (GPFIS). O principal aspecto do modelo GPFIS são as componentes do seu processo de Inferência Fuzzy. Esta estrutura é composta em sua base pela Programação Genética Multigênica e pretende: (i ) possibilitar o uso de operadores de agregação, negação e modificadores linguísticos de forma simplificada; (ii ) empregar heurísticas de definição do consequente mais apropriado para uma parte antecedente; e (iii ) usar um procedimento de defuzzificação, que induzido pela forma de fuzzificação e sobre determinadas condições, pode proporcionar uma estimativa mais acurada. Todas estas são contribuições que podem ser estendidas a outros Sistemas Fuzzy-Genéticos. Para demonstrar o aspecto genérico, o desempenho e a importância de cada componente para o modelo proposto, são formuladas uma série de investigações empíricas. Cada investigação compreende um tipo de problema: Classificação, Previsão, Regressão e Controle. Para cada problema, a melhor configuração obtida durante as investigações é usada no modelo GPFIS e os resultados são comparados com os de outros Sistemas Fuzzy-Genéticos e modelos presentes na literatura. Por fim, para cada problema é apresentada uma aplicação detalhada do modelo GPFIS em um caso real.<br>Genetic Fuzzy Systems constitute an area that brings together Fuzzy Inference Systems and Meta-Heuristics that are often related to natural selection and genetic recombination. This area attracts great interest from the scientific community, due to the knowledge discovery capability in situations where the comprehension of the phenomenon under analysis is lacking. It can also provides support to decision makers. This dissertation aims at developing a new Generic Genetic Fuzzy System, called Genetic Programming Fuzzy Inference System (GPFIS). The main aspects of GPFIS model are the components which are part of its Fuzzy Inference procedure. This structure is basically composed of Multi-Gene Genetic Programming and intends to: (i ) apply aggregation operators, negation and linguistic hedges in a simple manner; (ii ) make use of heuristics to define the consequent term most appropriate to the antecedent part; (iii ) employ a defuzzification procedure that, driven by the fuzzification step and under some assumptions, can provide a most accurate estimate. All these features are contributions that can be extended to other Genetic Fuzzy Systems. In order to demonstrate the general aspect of GPFIS, its performance and the relevance of each of its components, several investigations have been performed. They deal with Classification, Forecasting, Regression and Control problems. By using the best configuration obtained for each of the four problems, results are compared to other Genetic Fuzzy Systems and models in the literature. Finally, applications of GPFIS actual cases in each category is reported.

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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 algorithm towards realistic goals. At an early stage it was noted that the standard GP algorithm could be significantly speeded up by parallelisation and the distribution of processing. This was incorporated into the algorithm, using known techniques and Mathematica-specific knowledge.

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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 fitness evaluation was identified. Another problem is that the Java virtual machine stack must be tightly controlled and cannot have illegal operands. Direct comparison of stack-based genetic programming on the Java virtual machine to common tree-based genetic programming was not performed due to discovered flaws in the architecture. A practical model to implement stack-based genetic programming on the Java virtual machine using a class bytecode assembler was proposed. This model combines the GPsys genetic programming system with the JAS bytecode assembler resulting in an architecture called GPsys-JAS. A further recommendation to compare stack-based genetic programming on the Java virtual machine against stack-based genetic programming using the Java Stack class was suggested.

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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 subtrees in the population of individuals. Experiments are conducted on the even-k-parity problem to compare Structural Module Creation to Module Acquisition (another modularization method) and Genetic Programming without a modularization method. The result of the different experiments showed no improvement when using Structural Module Creation compared to Module Acquisition and Genetic Programming without a modularization method. The conclusion that can be drawn is that Structural Module Creation is not applicable to the even-k-parity problem. Appendix A contains graphs depicting the different experiments. Appendix B contains a description of the implementation of the system.

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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. This research compares a new knowledge discovery approach using a genetic program (GP) developed at the Naval Postgraduate School that produces data associations expressed as IF X THEN Y rules. In determining validity of this GP approach, the program is compared to traditional statistical and inductive methods of knowledge discovery. Results of this comparison indicate the viability of using a GP approach in knowledge discovery by three findings. First, the GP discovered interesting patterns from the data set. Second, the GP discovered new relationships not uncovered by the traditional methods. Third, the GP demonstrated a greater ability to focus the knowledge discovery search towards particular relationships, such as producing exact or general rules

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Books on the topic "Genetic Programming"

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Hu, Ting, Nuno Lourenço, and Eric Medvet, eds. Genetic Programming. Cham: 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. Cham: 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. Cham: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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, Mauro Castelli, Pablo García-Sánchez, Paolo Burelli, Sebastian Risi, and Kevin Sim, eds. Genetic Programming. Cham: 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. Cham: 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. Cham: 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. Berlin, Heidelberg: 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, 71–82. Cham: 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, 143–85. Boston, MA: 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, 845–69. Berlin, Heidelberg: 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, 95–114. Berlin, Heidelberg: 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, 185–225. Boston, MA: 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, 5–15. Boston, MA: 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, Zhao Xu, Yaakov Engel, Claude Sammut, Mark Reid, et al. "Genetic Programming." In Encyclopedia of Machine Learning, 457. Boston, MA: 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, 205–57. Cham: 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, 43–50. Wiesbaden: 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, 101–14. Berlin, Heidelberg: 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), 1–8. 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. New York, New York, USA: 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. New York, New York, USA: 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. New York, New York, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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), October 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. Fort Belvoir, VA: Defense Technical Information Center, September 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. Fort Belvoir, VA: Defense Technical Information Center, January 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), January 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), March 2015. http://dx.doi.org/10.2172/1177599.

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

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Rauss, Patrick. Hyperspectral and Spectro-Polarimetric Pixel-Level Classification Using Genetic Programming. Fort Belvoir, VA: Defense Technical Information Center, November 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), October 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, September 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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