Publication Details
Multivariate Gaussian Copula in Estimation of Distribution Algorithm with Model Migration
Estimation of distribution algorithms, Copula Theory, Sklar's theorem, multivariate Gaussian copula, island-based model, model migration, optimization problems.
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.
@INPROCEEDINGS{FITPUB10786,
author = "Martin Hyr\v{s} and Josef Schwarz",
title = "Multivariate Gaussian Copula in Estimation of Distribution Algorithm with Model Migration",
pages = "114--119",
booktitle = "2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Proceedings",
year = 2014,
location = "Piscataway, US",
publisher = "Institute of Electrical and Electronics Engineers",
ISBN = "978-1-4799-4492-7",
doi = "10.1109/FOCI.2014.7007815",
language = "english",
url = "https://www.fit.vut.cz/research/publication/10786"
}