Copula Estimation of Distribution Algorithm with PMLE

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Xiaodong Guo, Lifang Wang, Jianchao Zeng, Xueliang Zhang

Published: 2011, Last Modified: 28 Feb 2026ICNC 2011EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Estimation of Distribution Algorithms (EDAs) have the task to estimate the distribution model of samples. Copula Estimation of Distribution Algorithms (cEDAs) introduce the copula theory into EDAs which divide the multivariate distribution estimation into two parts: the marginal distribution estimation and the estimation of the dependant structure of variables. The parameter of copula influences the shape of dependant structure. PMLE is used in cEDA to estimate the parameter of copula. The experimental results show that the proposed algorithms are feasible and effective.