Article

Online program simplification in genetic programming

Authors:

Mengjie Zhang,

Phillip Wong,

Dongping QianAuthors Info & Claims

SEAL'06: Proceedings of the 6th international conference on Simulated Evolution And Learning

Pages 592 - 600

https://doi.org/10.1007/11903697_75

Published: 15 October 2006 Publication History

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Abstract

This paper describes an approach to online simplification of evolved programs in genetic programming (GP). Rather than manually simplifying genetic programs after evolution for interpretation purposes only, this approach automatically simplifies programs during evolution. In this approach, algebraic simplification rules, algebraic equivalence and prime techniques are used to simplify genetic programs. The simplification based GP system is examined and compared to a standard GP system on a regression problem and a classification problem. The results suggest that, at certain frequencies or proportions, this system can not only achieve superior performance to the standard system on these problems, but also significantly reduce the sizes of evolved programs.

References

[1]

Poli, R.: Genetic programming for image analysis. In Koza, J. R., Goldberg, D. E., Fogel, D. B., RioloOB, R. L., eds.: Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, MIT Press (1996) 363-368

Digital Library

Google Scholar

[2]

Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In Foo, N., ed.: 12th Australian Joint Conference on Artificial Intelligence. Volume 1747 of LNAI., Sydney, Australia, Springer-Verlag (1999) 180-192

Digital Library

Google Scholar

[3]

Koza, J. R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA (1992).

Digital Library

Google Scholar

[4]

Busch, J., Ziegler, J., Aue, C., Ross, A., Sawitzki, D., Banzhaf, W.: Automatic generation of control programs for walking robots using genetic programming. In: EuroGP '02: Proceedings of the 5th European Conference on Genetic Programming, London, UK (2002) 258-267

Digital Library

Google Scholar

[5]

Tackett, W. A.: Genetic programming for feature discovery and image discrimination. In Forrest, S., ed.: Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93, University of Illinois at Urbana-Champaign, Morgan Kaufmann (1993) 303-309

Digital Library

Google Scholar

[6]

Martin, W. A.: Determining the equivalence of algebraic expressions by hash coding. j-J-ACM 18(4) (1971) 549-558

Digital Library

Google Scholar

[7]

Gonnet, G. H.: Determining equivalence of expressions in random polynomial time. In: STOC '84: Proceedings of the sixteenth annual ACM symposium on Theory of computing, New York, NY, USA, ACM Press (1984) 334-341

Digital Library

Google Scholar

[8]

Lidl, R., Niederreiter, H.: Introduction to finite fields and their applications. Cambridge University Press, New York, NY, USA (1986).

Digital Library

Google Scholar

[9]

Trappe, W., Washington, L.C.: Introduction to Cryptograpy with Coding theory. 2ed edn. Prentice-Hall (2006).

Digital Library

Google Scholar

[10]

Cherowitzo, B.: (2006) Lecture Notes. http://www-math.cudenver.edu/~ wcherowi/courses/m5410/exeucalg.html. Visited on 7 January 2006.

Google Scholar

[11]

Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6) (2001) 643-660

Digital Library

Google Scholar

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Fong KWongso SMotani MSilva SPaquete L(2023)Evolutionary Symbolic Regression: Mechanisms from the Perspectives of Morphology and AdaptabilityProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3595830(21-22)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3595830
Vanneschi LCastelli MSilva S(2018)A survey of semantic methods in genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-013-9210-015:2(195-214)Online publication date: 24-Dec-2018
https://dl.acm.org/doi/10.1007/s10710-013-9210-0
Zhan HArnold D(2014)A quantitative analysis of the simplification genetic operatorProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2605684(1077-1080)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2605684
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Index Terms

Online program simplification in genetic programming
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

Index terms have been assigned to the content through auto-classification.

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Published In

SEAL'06: Proceedings of the 6th international conference on Simulated Evolution And Learning

October 2006

940 pages

ISBN:3540473319

Editors:
Tzai-Der Wang
Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC
,
Xiaodong Li
School of Computer Science and information Technology, RMIT University, Melbourne, Australia
,
Shu-Heng Chen
AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan
,
Xufa Wang
Department of Computer Science and Technology, University of Sci. & Tech. of China, Hefei, Anhui, P.R. China
,
Hussein Abbass
School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

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Springer-Verlag

Berlin, Heidelberg

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Published: 15 October 2006

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Fong KWongso SMotani MSilva SPaquete L(2023)Evolutionary Symbolic Regression: Mechanisms from the Perspectives of Morphology and AdaptabilityProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3595830(21-22)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3595830
Vanneschi LCastelli MSilva S(2018)A survey of semantic methods in genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-013-9210-015:2(195-214)Online publication date: 24-Dec-2018
https://dl.acm.org/doi/10.1007/s10710-013-9210-0
Zhan HArnold D(2014)A quantitative analysis of the simplification genetic operatorProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2605684(1077-1080)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2605684
Johnston MLiddle TZhang M(2010)A relaxed approach to simplification in genetic programmingProceedings of the 13th European conference on Genetic Programming10.1007/978-3-642-12148-7_10(110-121)Online publication date: 7-Apr-2010
https://dl.acm.org/doi/10.1007/978-3-642-12148-7_10

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Program simplification in genetic programming for object classification

Neural network crossover in genetic algorithms using genetic programming

How online simplification affects building blocks in genetic programming

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