Modeling with Copulas and Vines in Estimation of Distribution Algorithms
This work provides incremental improvements to optimization algorithms for researchers in evolutionary computation.
The authors studied how copulas and vines can improve optimization in Estimation of Distribution Algorithms by building four EDAs based on different copula models and showing that both marginal distributions and dependence structures are crucial for success.
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.