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HYRŠ, M. SCHWARZ, J.
Original Title
Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration.
Type
conference paper
Language
English
Original Abstract
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that are based on building and sampling a probability model. Copula theory provides methods that simplify the estimation of a probability model. An island-based version of copula-based EDA with probabilistic model migration (mCEDA) was tested on a set of well-known standard optimization benchmarks in the continuous domain. We investigated two families of copulas - Archimedean and elliptical. Experimental results confirm that this concept of model migration (mCEDA) yields better convergence as compared with the sequential version (sCEDA) and other recently published copula-based EDAs.
Keywords
Estimation of Distribution Algorithms, Copula Theory, Parallel EDA, Island-based Model, Multivariate Copula Sampling, Migration of Probabilistic Models.
Authors
HYRŠ, M.; SCHWARZ, J.
RIV year
2015
Released
12. 11. 2015
Publisher
SciTePress - Science and Technology Publications
Location
Lisbon
ISBN
978-989-758-157-1
Book
Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015)
Pages from
212
Pages to
219
Pages count
8
URL
https://www.fit.vut.cz/research/publication/11013/
BibTex
@inproceedings{BUT119927, author="Martin {Hyrš} and Josef {Schwarz}", title="Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration.", booktitle="Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015)", year="2015", pages="212--219", publisher="SciTePress - Science and Technology Publications", address="Lisbon", isbn="978-989-758-157-1", url="https://www.fit.vut.cz/research/publication/11013/" }
Documents
ecta15-0925-CR.pdf