Estimation of distribution algorithm based on archimedean copulas

Authors:
Li-Fang Wang;Jian-Chao Zeng;Yi Hong
Affiliations:
Colloge of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China;Colloge of Electrical and Information Engineering, Lanzhou University of Technology, LANzhou, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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Abstract

Both Estimation of Distribution Algorithms (EDAs) and Copula Theory are hot topics in different research domains. The key of EDAs is modeling and sampling the probability distribution function which need much time in the available algorithms. Moreover, the modeled probability distribution function can not reflect the correct relationship between variables of the optimization target. Copula Theory provides a correlation between univariable marginal distribution functions and the joint probability distribution function. Therefore, Copula Theory could be used in EDAs. Because Archimedean copulas possess many nice properties, an EDA based on Archimedean copulas is presented in this paper. The experimental results show the effectiveness of the proposed algorithm.