Title
Evolutionary algorithms and elliptical copulas applied to continuous optimization problems.
Abstract
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).
Year
DOI
Venue
2016
10.1016/j.ins.2016.07.006
Inf. Sci.
Keywords
Field
DocType
Continuous numeric optimization,Copulas,Estimation of distribution algorithms,Evolutionary computation
EDAS,Mathematical optimization,Joint probability distribution,Evolutionary algorithm,Estimation of distribution algorithm,Premature convergence,Copula (linguistics),Probability distribution,CMA-ES,Mathematics
Journal
Volume
Issue
ISSN
369
C
0020-0255
Citations 
PageRank 
References 
1
0.38
0
Authors
4