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Journal articles on the topic 'Genetic Programming'

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Published: 4 June 2021
Last updated: 25 May 2024

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

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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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 (April 1, 2010): 339–45. http://dx.doi.org/10.5302/j.icros.2010.16.4.339.

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12

Banzhaf, Wolfgang. "Genetic Programming and Emergence." Genetic Programming and Evolvable Machines 15, no. 1 (August 21, 2013): 63–73. http://dx.doi.org/10.1007/s10710-013-9196-7.

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13

Ekárt, Anikó. "Emergence in genetic programming." Genetic Programming and Evolvable Machines 15, no. 1 (October 10, 2013): 83–85. http://dx.doi.org/10.1007/s10710-013-9199-4.

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14

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

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15

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

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16

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 accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.
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17

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 accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.
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18

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

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19

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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20

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

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21

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 (June 2011): 266–75. http://dx.doi.org/10.1109/tla.2011.5893771.

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22

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 (June 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 problems by combining library components with program primitives to synthesize new programs. When a new problem is solved or a known problem is solved more efficiently, the genetic program maintains the library so as to keep it valid and efficient.
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23

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

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24

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

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25

Seo, Kisung, and Chulhyuk Pang. "Tree-Structure-Aware Genetic Operators in Genetic Programming." Journal of Electrical Engineering and Technology 9, no. 2 (March 1, 2014): 749–54. http://dx.doi.org/10.5370/jeet.2014.9.2.749.

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26

Zhang, Du, and Michael D. Kramer. "GAPS: A Genetic Programming System." International Journal on Artificial Intelligence Tools 12, no. 02 (June 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 process and improved termination criteria. (2) It includes an extensible language tailored to the needs of genetic programming. And (3) It is a complete, standalone system that allows for genetic programming tasks to be carried out without requiring other tools such as compilers. Results with GAPS have been satisfactory.
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27

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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28

M.Ibrahem, Hani, Mohammed M. Nasef, and Mahmoud Emam. "Genetic Programming based Face Recognition." International Journal of Computer Applications 69, no. 27 (May 31, 2013): 1–6. http://dx.doi.org/10.5120/12140-8187.

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29

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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30

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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31

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

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32

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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33

O'Neill, Michael, and Anthony Brabazon. "Recent Patents on Genetic Programming." Recent Patents on Computer Science 2, no. 1 (January 16, 2009): 43–49. http://dx.doi.org/10.2174/1874479600902010043.

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34

O'Neill, Michael, and Anthony Brabazon. "Recent Patents on Genetic Programming." Recent Patents on Computer Science 2, no. 1 (January 9, 2010): 43–49. http://dx.doi.org/10.2174/1874479610902010043.

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35

., Anjaneya, and Bimlesh Kumar. "Genetic Programming for Vegetated Channel." International Journal Of Recent Advances in Engineering & Technology 08, no. 01 (January 30, 2020): 27–32. http://dx.doi.org/10.46564/ijraet.2020.v08i01.005.

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36

Mweshi, George. "Feature Selection using Genetic Programming." Zambia ICT Journal 3, no. 2 (November 30, 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 can be used to remove these unnecessary features. Feature selection consequently reduces the dimension of the data as well as the search space which in turn increases the efficiency and the accuracy of the mining algorithms. In this paper, we investigate the ability of Genetic Programming (GP), an evolutionary algorithm searching strategy capable of automatically finding solutions in complex and large search spaces, to perform feature selection. We implement a basic GP algorithm and perform feature selection on 5 benchmark classification datasets from UCI repository. To test the competitiveness and feasibility of the GP approach, we examine the classification performance of four classifiers namely J48, Naives Bayes, PART, and Random Forests using the GP selected features, all the original features and the features selected by the other commonly used feature selection techniques i.e. principal component analysis, information gain, relief-f and cfs. The experimental results show that not only does GP select a smaller set of features from the original features, classifiers using GP selected features achieve a better classification performance than using all the original features. Furthermore, compared to the other well-known feature selection techniques, GP achieves very competitive results.
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37

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 (September 30, 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 (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and $$(1+\lambda )$$ ( 1 + λ ) . In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the ($$1 + \lambda$$ 1 + λ ) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP’s number of registers and CGP’s levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse.
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38

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

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39

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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40

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

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41

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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42

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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43

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

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44

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

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45

Song, Andy, and Vic Ciesielski. "Texture Segmentation by Genetic Programming." Evolutionary Computation 16, no. 4 (December 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 construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.
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46

Preen, Richard J., and Larry Bull. "Dynamical Genetic Programming in XCSF." Evolutionary Computation 21, no. 3 (September 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 a number of composite polynomial tasks. In addition, the network outputs are later repeatedly sampled at varying temporal intervals to perform multistep-ahead predictions of a financial time series.
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47

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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48

Forsyth, Richard S. "The genesis of genetic programming." ACM SIGEVOlution 9, no. 3 (March 17, 2017): 3–11. http://dx.doi.org/10.1145/3066862.3066863.

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49

Gang Li, Jin Feng Wang, Kin Hong Lee, and Kwong-Sak Leung. "Instruction-Matrix-Based Genetic Programming." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 4 (August 2008): 1036–49. http://dx.doi.org/10.1109/tsmcb.2008.922054.

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50

Willis, M. "Systems Modelling using Genetic Programming." Computers & Chemical Engineering 21, no. 1-2 (1997): S1161—S1166. http://dx.doi.org/10.1016/s0098-1354(97)00206-8.

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