Pair-copula estimation of distribution algorithms
Huimin Gao, Xiaoping Wang · International Journal of Computing Science and Mathematics · 2013
Copula theory provides a promising solution for the estimation of population probability in estimation distribution algorithms (EDAs), and more and more researchers pay attention to copula-EDAs. Most of the copula-EDAs researches are related to two variables case, in this paper, by taking advantage of the ability of pair-copula in high-dimensional correlation construction, a new algorithm is proposed, called pair-copula estimation distribution algorithms (pcEDAs). The architecture of pcEDAs is provided, and sampling method of the probability model is discussed, the simulation results based on two different vines, namely C-vine and D-vine, show that the proposed algorithm is not only feasible, but also perform very well.