Estimation of distribution algorithm based on multivariate Gaussian copulas

Abstract

Copula is a powerful tool for multivariate probability analysis. Estimation of distribution algorithms are a class of optimization algorithms based on probability distribution model. This paper introduces a new estimation of distribution algorithm with multivariate Gaussian copulas. In the algorithm, Gaussian copula parameters are firstly estimated by estimating Kendall's tau and using the relationship of Kendall's tau and correlation matrix, thus, joint distribution is estimated. Then, the Monte Carte simulation is used to generate new individuals. The relative experimental results show that the new algorithm is effective.