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Evolutionary algorithms and elliptical copulas applied to continuous optimization problems

INFORMATION SCIENCES 2016年第0期369卷 419-440页

作者： de Mello Junior, Harold Dias Marti, Luis Abs da Cruz, Andre V. Rebuzzi Vellasco, Marley M. B. Univ Estado Rio De Janeiro Dept Elect Engn Rio De Janeiro Brazil Univ Fed Fluminense Inst Comp Rio De Janeiro Brazil State Univ West Side Comp Univ Unit Rio De Janeiro Brazil Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn Rio De Janeiro Brazil

Estimation of Distribution Algorithms (EDAs) constitutes a class of evolutionary algorithms that can extract and exploit knowledge acquired throughout the optimization process. The most critical step in the EDAs is the estimation of the joint probability distribution associated to the variables from the most promising solutions determined by the evaluation function. Recently, a new approach to EDAs has been developed, based on copula theory, to improve the estimation of the joint probability distribution function. However, most copula-based EDAs still present two major drawbacks: focus on copulas with constant parameters, and premature convergence. This paper presents a new copula-based estimation of distribution algorithm for numerical optimization problems, named EDA based on Multivariate Elliptical Copulas (EDA-MEC). This model,uses multivariate copulas to estimate the probability distribution for generating a population of individuals. The EDA-MEC differs from Other copula-based EDAs in several aspects: the copula parameter is dynamically estimated, using dependence measures;it uses a variation of the learned probability distribution to generate individuals that help to avoid premature convergence;and uses a heuristic to reinitialize the population as an additional technique to preserve the diversity of solutions. The paper shows, by means of a set of parametric tests, that this approach improves the overall performance of the optimization process when compared with other copula-based EDAs and with other efficient heuristics such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). (C) 2016 Elsevier Inc. All rights reserved.

关键词： continuous numeric optimization Copulas Estimation of distribution algorithms Evolutionary computation

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Estimation of Distribution Algorithm based on a Multivariate Extension of the Archimedean Copula

Estimation of Distribution Algorithm based on a Multivariate...

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1st BRICS Countries Congress on Computational Intelligence / 11th Brazilian Congress on Computational Intelligence (BRICS-CCI and CBIC)

作者： de Mello, Harold D., Jr. Abs da Cruz, Andre V. Vellasco, Marley M. B. R. Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn Rio De Janeiro RJ Brazil

ISBN: (纸本)9781479931941

This paper presents a Copula-based Estimation of Distribution Algorithm with Parameter Updating for numeric optimization problems. This model implements an estimation of distribution algorithm using a multivariate extension of the Archimedean copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional crossover and elitism operators during the optimization. We show that this approach improves the overall performance of the optimization when compared to other copula-based EDAs.

关键词： continuous numeric optimization evolutionary computation estimation of distribution algorithms copulas

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