Detail publikace

Multivariate Gaussian Copula in Estimation of Distribution Algorithm with Model Migration

HYRŠ, M. SCHWARZ, J.

Originální název

Multivariate Gaussian Copula in Estimation of Distribution Algorithm with Model Migration

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The paper presents a new concept of an island-based model of Estimation of Distribution Algorithms (EDAs) with a bidirectional topology in the field of numerical optimization in continuous domain. The traditional migration of individuals is replaced by the probability model migration. Instead of a classical joint probability distribution model, the multivariate Gaussian copula is used which must be specified by correlation coefficients and parameters of a univariate marginal distributions. The idea of the proposed Gaussian Copula EDA algorithm with model migration (GC-mEDA) is to modify the parameters of a resident model respective to each island by the immigrant model of the neighbour island. The performance of the proposed algorithm is tested over a group of five well-known benchmarks.

Klíčová slova

Estimation of distribution algorithms, Copula Theory, Sklar's theorem, multivariate Gaussian copula, island-based model, model migration, optimization problems. 

Autoři

HYRŠ, M.; SCHWARZ, J.

Rok RIV

2014

Vydáno

11. 12. 2014

Nakladatel

Institute of Electrical and Electronics Engineers

Místo

Piscataway

ISBN

978-1-4799-4492-7

Kniha

2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Proceedings

Strany od

114

Strany do

119

Strany počet

6

BibTex

@inproceedings{BUT111681,
  author="Martin {Hyrš} and Josef {Schwarz}",
  title="Multivariate Gaussian Copula in Estimation of Distribution Algorithm with Model Migration",
  booktitle="2014 IEEE Symposium on Foundations of Computational  Intelligence (FOCI) Proceedings",
  year="2014",
  pages="114--119",
  publisher="Institute of Electrical and Electronics Engineers",
  address="Piscataway",
  doi="10.1109/FOCI.2014.7007815",
  isbn="978-1-4799-4492-7"
}