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Journal articles on the topic 'Genetic Programming'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Hirasawa, Kotaro, Masafumi Okubo, Hironobu Katagiri, Jinglu Hu, and Junichi Murata. "Comparison between Genetic Network Programing and Genetic Programming using evolution of ant's behaviors." IEEJ Transactions on Electronics, Information and Systems 121, no. 6 (2001): 1001–9. http://dx.doi.org/10.1541/ieejeiss1987.121.6_1001.

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12

Seo, Ki-Sung, and Young-Kyun Kim. "Scale and Rotation Robust Genetic Programming-Based Corner Detectors." Journal of Institute of Control, Robotics and Systems 16, no. 4 (2010): 339–45. http://dx.doi.org/10.5302/j.icros.2010.16.4.339.

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13

Palmer, John M. "Relative Referenced Genetic Programming." Complex Systems 17, no. 4 (2008): 339–56. http://dx.doi.org/10.25088/complexsystems.17.4.339.

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This paper presents a linear code referencing approach to the representation of individuals within a genetic programming scheme. This approach has been devised in order to confront various problems associated with genetic programming schemes. These are primarily the size of the available search space, the ability to pass through this search space, the construction of valid individuals after crossover and mutation, and the probability for the use of terminals and subpieces of an individual's solution. A comparison is made with existing methods and for the problems tested the presented method gi

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14

Banzhaf, Wolfgang. "Genetic Programming and Emergence." Genetic Programming and Evolvable Machines 15, no. 1 (2013): 63–73. http://dx.doi.org/10.1007/s10710-013-9196-7.

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15

Ekárt, Anikó. "Emergence in genetic programming." Genetic Programming and Evolvable Machines 15, no. 1 (2013): 83–85. http://dx.doi.org/10.1007/s10710-013-9199-4.

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16

Fogel, David B. "Advances in genetic programming." Biosystems 36, no. 1 (1995): 82–85. http://dx.doi.org/10.1016/0303-2647(95)90007-1.

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17

Ribeiro Filho, J. L., P. C. Treleaven, and C. Alippi. "Genetic-algorithm programming environments." Computer 27, no. 6 (1994): 28–43. http://dx.doi.org/10.1109/2.294850.

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18

Merta, Jan, and Tomáš Brandejský. "Two-layer genetic programming." Neural Network World 32, no. 4 (2022): 215–31. http://dx.doi.org/10.14311/nnw.2022.32.013.

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This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accur

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19

Merta, Jan, and Tomáš Brandejský. "Two-layer genetic programming." Neural Network World 32, no. 4 (2022): 215–31. http://dx.doi.org/10.14311/nnw.2022.27.013.

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This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accur

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20

Giot, Romain, and Christophe Rosenberger. "Genetic programming for multibiometrics." Expert Systems with Applications 39, no. 2 (2012): 1837–47. http://dx.doi.org/10.1016/j.eswa.2011.08.066.

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21

Toulouse. "Automatic Quantum Computer Programming: A Genetic Programming Approach." Genetic Programming and Evolvable Machines 7, no. 1 (2006): 125. http://dx.doi.org/10.1007/s10710-005-4866-8.

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22

Toulouse, Michel. "Automatic Quantum Computer Programming: A Genetic Programming Approach." Genetic Programming and Evolvable Machines 7, no. 1 (2006): 125–26. http://dx.doi.org/10.1007/s10710-006-4866-3.

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23

Lajovic Carneiro, Marcos, Leonardo Cunha Brito, Sergio Granato Araujo, Paulo Cesar Miranda Machado, and Paulo Henrique Portela Carvalho. "Genetic programming applied to programmable logic controllers programming." IEEE Latin America Transactions 9, no. 3 (2011): 266–75. http://dx.doi.org/10.1109/tla.2011.5893771.

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24

PETRY, FREDERICK E., and BERTRAND DANIEL DUNAY. "AUTOMATIC PROGRAMMING AND PROGRAM MAINTENANCE WITH GENETIC PROGRAMMING." International Journal of Software Engineering and Knowledge Engineering 05, no. 02 (1995): 165–77. http://dx.doi.org/10.1142/s0218194095000095.

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Automatic programming is discussed in the context of software engineering. An approach to automatic programming is presented, which utilizes software engineering principles in the synthesis and maintenance of programs. As a simple demonstration, program-equivalent Turing machines are synthesized, encapsulated, reused, and maintained by genetic programming. Turing machines are synthesized from input-output pairs for a variety of simple problems. When a problem is solved, the solution is encapsulated and becomes part of a software library. The genetic program uses the library to solve new proble

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25

Eriksson, R., and B. Olsson. "Adapting genetic regulatory models by genetic programming." Biosystems 76, no. 1-3 (2004): 217–27. http://dx.doi.org/10.1016/j.biosystems.2004.05.014.

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26

Wong, Man Leung. "An adaptive knowledge-acquisition system using generic genetic programming." Expert Systems with Applications 15, no. 1 (1998): 47–58. http://dx.doi.org/10.1016/s0957-4174(98)00010-4.

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27

Zhang, Du, and Michael D. Kramer. "GAPS: A Genetic Programming System." International Journal on Artificial Intelligence Tools 12, no. 02 (2003): 187–206. http://dx.doi.org/10.1142/s0218213003001198.

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One of the major approaches in the field of evolutionary computation is genetic programming. Genetic programming tackles the issue of how to automatically create a computer program for a given problem from some initial problem statement. The goal is accomplished by genetically breeding a population of computer programs in terms of genetic operations. In this paper, we describe a genetic programming system called GAPS. GAPS has the following features: (1) It implements the standard generational algorithm for genetic programming with some refinement on controlling introns growth during evolution

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28

Evangelista Gorres, Teotima. "Symbolic Regression via Genetic Programming; Philippines Population Prediction: 2010-2020." International Journal of Science and Research (IJSR) 10, no. 5 (2021): 772–74. https://doi.org/10.21275/art2019289.

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29

Seo, Kisung, and Chulhyuk Pang. "Tree-Structure-Aware Genetic Operators in Genetic Programming." Journal of Electrical Engineering and Technology 9, no. 2 (2014): 749–54. http://dx.doi.org/10.5370/jeet.2014.9.2.749.

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30

Bakurov, Illya, Mauro Castelli, Olivier Gau, Francesco Fontanella, and Leonardo Vanneschi. "Genetic programming for stacked generalization." Swarm and Evolutionary Computation 65 (August 2021): 100913. http://dx.doi.org/10.1016/j.swevo.2021.100913.

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31

M.Ibrahem, Hani, Mohammed M. Nasef, and Mahmoud Emam. "Genetic Programming based Face Recognition." International Journal of Computer Applications 69, no. 27 (2013): 1–6. http://dx.doi.org/10.5120/12140-8187.

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32

Junior, Airton Bordin, Nádia Félix F. da Silva, Thierson Couto Rosa, and Celso G. C. Junior. "Sentiment analysis with genetic programming." Information Sciences 562 (July 2021): 116–35. http://dx.doi.org/10.1016/j.ins.2021.01.025.

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33

Katagiri, Hironobu, Kotaro Hirasawa, Jinglu Hu, and Junichi Murata. "Variable Size Genetic Network Programming." IEEJ Transactions on Electronics, Information and Systems 123, no. 1 (2003): 57–66. http://dx.doi.org/10.1541/ieejeiss.123.57.

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34

O'Neill, Michael, and Anthony Brabazon. "Recent Patents on Genetic Programming." Recent Patents on Computer Sciencee 2, no. 1 (2009): 43–49. http://dx.doi.org/10.2174/2213275910902010043.

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35

Sastry, Kumara, D. D. Johnson, David E. Goldberg, and Pascal Bellon. "Genetic Programming for Multiscale Modeling." International Journal for Multiscale Computational Engineering 2, no. 2 (2004): 239–56. http://dx.doi.org/10.1615/intjmultcompeng.v2.i2.50.

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36

O'Neill, Michael, and Anthony Brabazon. "Recent Patents on Genetic Programming." Recent Patents on Computer Science 2, no. 1 (2009): 43–49. http://dx.doi.org/10.2174/1874479600902010043.

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37

O'Neill, Michael, and Anthony Brabazon. "Recent Patents on Genetic Programming." Recent Patents on Computer Science 2, no. 1 (2010): 43–49. http://dx.doi.org/10.2174/1874479610902010043.

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38

., Anjaneya, and Bimlesh Kumar. "Genetic Programming for Vegetated Channel." International Journal Of Recent Advances in Engineering & Technology 08, no. 01 (2020): 27–32. http://dx.doi.org/10.46564/ijraet.2020.v08i01.005.

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39

Mweshi, George. "Feature Selection using Genetic Programming." Zambia ICT Journal 3, no. 2 (2019): 11–18. http://dx.doi.org/10.33260/zictjournal.v3i2.62.

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Extracting useful and novel information from the large amount of collected data has become a necessity for corporations wishing to maintain a competitive advantage. One of the biggest issues in handling these significantly large datasets is the curse of dimensionality. As the dimension of the data increases, the performance of the data mining algorithms employed to mine the data deteriorates. This deterioration is mainly caused by the large search space created as a result of having irrelevant, noisy and redundant features in the data. Feature selection is one of the various techniques that ca

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40

Françoso Dal Piccol Sotto, Léo, Paul Kaufmann, Timothy Atkinson, Roman Kalkreuth, and Márcio Porto Basgalupp. "Graph representations in genetic programming." Genetic Programming and Evolvable Machines 22, no. 4 (2021): 607–36. http://dx.doi.org/10.1007/s10710-021-09413-9.

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AbstractGraph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming

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41

PAN, Xiao-hai, Wei-hong XU, and Kai-qing ZHOU. "New linear genetic programming approach." Journal of Computer Applications 30, no. 7 (2010): 1896–98. http://dx.doi.org/10.3724/sp.j.1087.2010.01896.

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42

Baykasoğlu, Adil, and Sultan Maral. "Fuzzy functions via genetic programming." Journal of Intelligent & Fuzzy Systems 27, no. 5 (2014): 2355–64. http://dx.doi.org/10.3233/ifs-141205.

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43

Duggan, P. M. "Book Review: Genetic Programming II." International Journal of Electrical Engineering & Education 33, no. 2 (1996): 182–83. http://dx.doi.org/10.1177/002072099603300210.

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44

Tamura, Kenji, Atsuko Mutoh, Tsuyoshi Nakamura, and Hidenori Itoh. "Virus-Evolutionary Liner Genetic Programming." IEEJ Transactions on Electronics, Information and Systems 126, no. 7 (2006): 913–18. http://dx.doi.org/10.1541/ieejeiss.126.913.

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45

MOTOKI, Tatsuya, and Yasushi NUMAGUCHI. "Diversity Maintenance in Genetic Programming." Transactions of the Japanese Society for Artificial Intelligence 21 (2006): 219–30. http://dx.doi.org/10.1527/tjsai.21.219.

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46

Fallah-Mehdipour, Elahe, Omid Bozorg Haddad, and Miguel A. Mariño. "Genetic Programming in Groundwater Modeling." Journal of Hydrologic Engineering 19, no. 12 (2014): 04014031. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000987.

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47

Kushchu, I. "Genetic programming and evolutionary generalization." IEEE Transactions on Evolutionary Computation 6, no. 5 (2002): 431–42. http://dx.doi.org/10.1109/tevc.2002.805038.

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48

Song, Andy, and Vic Ciesielski. "Texture Segmentation by Genetic Programming." Evolutionary Computation 16, no. 4 (2008): 461–81. http://dx.doi.org/10.1162/evco.2008.16.4.461.

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This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually constr

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49

Preen, Richard J., and Larry Bull. "Dynamical Genetic Programming in XCSF." Evolutionary Computation 21, no. 3 (2013): 361–87. http://dx.doi.org/10.1162/evco_a_00080.

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A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifier system. In particular, dynamical arithmetic networks are used to represent the traditional condition-action production system rules to solve continuous-valued reinforcement learning problems and to perform symbolic regression, finding competitive performance with traditional genetic programming on

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50

Hinchliffe, Mark, and Mark Willis. "DYNAMIC MODELLING USING GENETIC PROGRAMMING." IFAC Proceedings Volumes 35, no. 1 (2002): 193–98. http://dx.doi.org/10.3182/20020721-6-es-1901.00443.

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