Publication Details
Advanced Parallel Copula Based EDA
Schwarz Josef, doc. Ing., CSc. (CM-SFE)
Estimation of distribution algorithm (EDA) Copula theory Parallel island-based algorithm Migration of model Benchmarks CEC 2013
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 the probability model. To improve the efficiency of current copula based EDAs (CEDAs) new modifications of parallel CEDA were proposed. We investigated eight variants of island-based algorithms utilizing the capability of promising copula families, inter-island migration and additional adaptation of marginal parameters using CT-AVS technique. The proposed algorithms were tested on two sets of well-known standard optimization benchmarks in the continuous domain. The results of the experiments validate the efficiency of our algorithms.
@inproceedings{BUT133499,
author="Martin {Hyrš} and Josef {Schwarz}",
title="Advanced Parallel Copula Based EDA",
booktitle="2016 IEEE Symposium Series on Computational Intelligence",
year="2016",
pages="1--8",
publisher="Institute of Electrical and Electronics Engineers",
address="Athens",
doi="10.1109/SSCI.2016.7850202",
isbn="978-1-5090-4239-5",
url="https://www.fit.vut.cz/research/publication/11225/"
}