Publication Details
Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration
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{FITPUB11013,
author = "Martin Hyr\v{s} and Josef Schwarz",
title = "Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration",
pages = "212--219",
booktitle = "Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015)",
year = 2015,
location = "Lisbon, PT",
publisher = "SciTePress - Science and Technology Publications",
ISBN = "978-989-758-157-1",
language = "english",
url = "https://www.fit.vut.cz/research/publication/11013"
}