A multi-population parallel estimation of distribution algorithms based on Clayton and Gumbel copulas

Authors:
Chunyan Chang;Lifang Wang
Affiliations:
College of Textile Engineering and Art, Taiyuan University of Technology, Yuci, Jingzhong, P.R. China;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, P.R. China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Year:
2011

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Abstract

The idea of multi-population parallel strategy and the copula theory are introduced into the Estimation of Distribution Algorithm (EDA), and a new parallel EDA is proposed in this paper. In this algorithm, the population is divided into some subpopulations. Different copula is used to estimate the distribution model in each subpopulation. Two copulas, Clayton and Gumbel, are used in this paper. To estimate the distribution function is to estimate the copula and the margins. New individuals are generated according to the copula and the margins. In order to increase the diversity of the subpopulation, the elites of one subpopulation are learned by the other subpopulation. The experiments show the proposed algorithm performs better than the basic copula EDA and some classical EDAs in speed and in precision.