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.
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References
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, pp. 363–368. MIT Press, Cambridge (1996)
Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In: Foo, N.Y. (ed.) Canadian AI 1999. LNCS, vol. 1747, pp. 180–192. Springer, Heidelberg (1999)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Busch, J., Ziegler, J., Aue, C., Ross, A., Sawitzki, D., Banzhaf, W.: Automatic generation of control programs for walking robots using genetic programming. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 258–267. Springer, Heidelberg (2002)
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 193, University of Illinois at Urbana-Champaign, pp. 303–309. Morgan Kaufmann, San Francisco (1993)
Martin, W.A.: Determining the equivalence of algebraic expressions by hash coding. j-J-ACM 18(4), 549–558 (1971)
Gonnet, G.H.: Determining equivalence of expressions in random polynomial time. In: STOC 1984: Proceedings of the sixteenth annual ACM symposium on Theory of computing, pp. 334–341. ACM Press, New York (1984)
Lidl, R., Niederreiter, H.: Introduction to finite fields and their applications. Cambridge University Press, New York (1986)
Trappe, W., Washington, L.C.: Introduction to Cryptograpy with Coding theory, 2nd edn. Prentice-Hall, Englewood Cliffs (2006)
Cherowitzo, B.: Lecture Notes (2006), Visited on January 7, 2006, http://www-math.cudenver.edu/~wcherowi/courses/m5410/exeucalg.html
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), 643–660 (2001)
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Zhang, M., Wong, P., Qian, D. (2006). Online Program Simplification in Genetic Programming. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_75
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DOI: https://doi.org/10.1007/11903697_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
eBook Packages: Computer ScienceComputer Science (R0)