Result Details
Advanced Parallel Copula Based EDA
Schwarz Josef, doc. Ing., CSc., DCSY (FIT)
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.
Estimation of distribution algorithm (EDA)
Copula theory
Parallel island-based algorithm
Migration of model
Benchmarks CEC 2013
@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/"
}