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HYRŠ, M. SCHWARZ, J.
Original Title
Multivariate Gaussian Copula in Estimation of Distribution Algorithm with Model Migration
Type
conference paper
Language
English
Original Abstract
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.
Keywords
Estimation of distribution algorithms, Copula Theory, Sklar's theorem, multivariate Gaussian copula, island-based model, model migration, optimization problems.
Authors
HYRŠ, M.; SCHWARZ, J.
RIV year
2014
Released
11. 12. 2014
Publisher
Institute of Electrical and Electronics Engineers
Location
Piscataway
ISBN
978-1-4799-4492-7
Book
2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Proceedings
Pages from
114
Pages to
119
Pages count
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" }