Publication detail

Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration.

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/"
}