Modeling with Copulas and Vines in Estimation of Distribution Algorithms
Neural and Evolutionary Computing
2015-01-14 v1 Methodology
Abstract
The aim of this work is studying the use of copulas and vines in the optimization with Estimation of Distribution Algorithms (EDAs). Two EDAs are built around the multivariate product and normal copulas, and other two are based on pair-copula decomposition of vine models. Empirically we study the effect of both marginal distributions and dependence structure separately, and show that both aspects play a crucial role in the success of the optimization. The results show that the use of copulas and vines opens new opportunities to a more appropriate modeling of search distributions in EDAs.
Cite
@article{arxiv.1210.5500,
title = {Modeling with Copulas and Vines in Estimation of Distribution Algorithms},
author = {Marta Soto and Yasser González-Fernández and Alberto Ochoa},
journal= {arXiv preprint arXiv:1210.5500},
year = {2015}
}