Multivariate Gaussian copula in Estimation of Distribution Algorithm with model migration

Abstract

The paper presents a new concept of an island-based model of Estimation of Distribution Algorithms (EDAs) with a bidirectional topology in the field of numerical optimization in continuous domain. The traditional migration of individuals is replaced by the probability model migration. Instead of a classical joint probability distribution model, the multivariate Gaussian copula is used which must be specified by correlation coefficients and parameters of a univariate marginal distributions. The idea of the proposed Gaussian Copula EDA algorithm with model migration (GC-mEDA) is to modify the parameters of a resident model respective to each island by the immigrant model of the neighbour island. The performance of the proposed algorithm is tested over a group of five well-known benchmarks.