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

Martin Hyrš, Josef Schwarz

2015

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.

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Paper Citation


in Harvard Style

Hyrš M. and Schwarz J. (2015). Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 212-219. DOI: 10.5220/0005594602120219


in Bibtex Style

@conference{ecta15,
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 - Volume 1: ECTA,},
year={2015},
pages={212-219},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005594602120219},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration
SN - 978-989-758-157-1
AU - Hyrš M.
AU - Schwarz J.
PY - 2015
SP - 212
EP - 219
DO - 10.5220/0005594602120219