An Estimation of Distribution Algorithm Based on Clayton Copula and Empirical Margins
Estimation of Distribution Algorithms (EDAs) are new evolutionary algorithms which based on the estimation and sampling the distribution model of the selected population in each generation. The way of copula used in EDAs is introduced in this paper. The joint distribution of the selected population is separated into the univariate marginal distribution and a function called copula to represent the dependence structure. And the new individuals are obtained by sampling from copula and then calculating the inverse of the univariate marginal distribution function. The empirical distribution and Clayton copula are used to implement the proposed copula Estimation of Distribution Algorithm (copula EDA). The experimental results show that the proposed algorithm is equivalent to some conventional continuous EDAs in performance.
Estimation of distribution algorithms (EDAs) copula theory the joint distribution the marginal distribution Clayton copula
L.F.Wang Y.C.Wang J.C.Zeng Y.Hong
Colloge of Electrical and Information Engineering, Lanzhou University of Technology,Lanzhou, 730050, China Complex System and Computational Intelligence Laboratory,Taiyuan University of Science and Technology, Taiyuan, 030024, China
国际会议
无锡
英文
82-88
2010-09-17（万方平台首次上网日期，不代表论文的发表时间）