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Program Evolution by Integrating EDP and GP
Kohsuke Yanai and Hitoshi Iba
Dept. of Frontier Informatics, Graduate School of Frontier Science, The University of Tokyo., 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japanyanai@iba.k.u-tokyo.ac.jp
iba@iba.k.u-tokyo.ac.jp
Abstract. This paper discusses the performance of a hybrid system which consists of EDP and GP. EDP, Estimation of Distribution Programming, is the program evolution method based on the probabilistic model, where the probability distribution of a program is estimated by using a Bayesian network, and a population evolves repeating estimation of distribution and program generation without crossover and mutation. Applying the hybrid system of EDP and GP to various problems, we discovered some important tendencies in the behavior of this hybrid system. The hybrid system was not only superior to pure GP in a search performance but also had interesting features in program evolution. More tests revealed how and when EDP and GP compensate for each other. We show some experimental results of program evolution by the hybrid system and discuss the characteristics of both EDP and GP.
LNCS 3102, p. 774 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004