Publication Details
Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration.
Schwarz Josef, doc. Ing., CSc. (CM-SFE)
Estimation of Distribution Algorithms, Copula Theory, Parallel EDA, Island-based Model, Multivariate Copula Sampling, Migration of Probabilistic Models.
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.
@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/"
}