Detail publikace

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

HYRŠ, M. SCHWARZ, J.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Estimation of Distribution Algorithms, Copula Theory, Parallel EDA, Island-based Model, Multivariate Copula Sampling, Migration of Probabilistic Models.

Autoři

HYRŠ, M.; SCHWARZ, J.

Rok RIV

2015

Vydáno

12. 11. 2015

Nakladatel

SciTePress - Science and Technology Publications

Místo

Lisbon

ISBN

978-989-758-157-1

Kniha

Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015)

Strany od

212

Strany do

219

Strany počet

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/"
}