Estimation of Distribution Algorithm Based on a Multivariate Extension of the Archimedean Copula
Harold Dias de Mello, Andre V. Abs da Cruz, Marley M. B. R. Vellasco · 2013
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.