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

Author

De Mello, Harold D. ; Abs da Cruz, Andre V. ; Vellasco, Marley M. B. R.

Author_Institution

Dept. of Electr. Eng., Pontifical Catholic Univ. of Rio de Janeiro, Rio de Janeiro, Brazil

fYear

2013

fDate

8-11 Sept. 2013

Firstpage

75

Lastpage

80

Abstract

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.

Keywords

estimation theory; optimisation; statistical distributions; Archimedean copula; MEC-EDA; conditional probability; copula-based estimation; distribution algorithm; multivariate extension; numeric optimization problems; Distribution functions; Estimation; Evolutionary computation; Joints; Optimization; Sociology; Statistics; continuous numeric optimization; copulas; estimation of distribution algorithms; evolutionary computation;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013 BRICS Congress on

Conference_Location

Ipojuca

Type

conf

DOI

10.1109/BRICS-CCI-CBIC.2013.23

Filename

6855832