Modeling with Copulas and Vines in Estimation of Distribution Algorithms

The aim of this work is studying the use of copulas and vines in numerical optimization with Estimation of Distribution Algorithms (EDAs). Two EDAs built around the multivariate product and normal copulas, and other two based on pair-copula decomposition of vine models are studied. We analyze empirically the effect of both marginal distributions and dependence structure in order to show that both aspects play a crucial role in the success of the optimization process. The results show that the use of copulas and vines opens new opportunities to a more appropriate modeling of search distributions in EDAs.