Detail výsledku
Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm
Schwarz Josef, doc. Ing., CSc., UPSY (FIT)
Estimation of distribution algorithm (EDA) is a variant of evolution algorithms, which is based on construction and sampling of probability model. Nowadays the copula theory is often utilized for the probability model estimation to simplify this process. We made comparison of two classes of copulas - elliptical and Archimedean ones - for the set of standard optimization benchmarks. The experimental results con firm our assumption that the elliptical copulas outperform the Archimedean ones namely in the case of the complex optimization problems.
Estimation of distribution algorithms, copula theory, multivariate copula sampling, Clayton copula, Gumbel copula, Frank copula, Gaussian copula, Student t-copula
@inproceedings{BUT119926,
author="Martin {Hyrš} and Josef {Schwarz}",
title="Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm",
booktitle="MENDEL 2015 21st International Conference on Soft Computing",
year="2015",
pages="19--26",
publisher="Faculty of Mechanical Engineering BUT",
address="Brno",
isbn="978-80-214-4984-8",
url="https://www.fit.vut.cz/research/publication/11012/"
}