Using Gumbel copula and empirical marginal distribution in Estimation of Distribution Algorithm

Estimation of Distribution Algorithms (EDAs) is a novel evolutionary algorithm originated from Genetic Algorithms. The probability distribution model of promising population is estimated iteratively in EDAs, and the new generation is sampled from the estimated model. An EDA with Gumbel copula is proposed in this paper. In order to estimating the joint, the empirical margins of each variable are estimated separately, and the relationship of variables is presented by Gumbel copula. On the ground of copula theory, the joint is the composite function of the copula and the margins. This algorithm simplifies the operator to estimating the multivariate distribution. The experimental results show that the proposed algorithm is equivalent to some conventional continuous EDAs in performance.