Publication detail

Advanced Parallel Copula Based EDA

HYRŠ, M. SCHWARZ, J.

Original Title

Advanced Parallel Copula Based EDA

Type

conference paper

Language

English

Original 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 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.

Keywords

Estimation of distribution algorithm (EDA) Copula theory Parallel island-based algorithm Migration of model Benchmarks CEC 2013

Authors

HYRŠ, M.; SCHWARZ, J.

Released

15. 8. 2016

Publisher

Institute of Electrical and Electronics Engineers

Location

Athens

ISBN

978-1-5090-4239-5

Book

2016 IEEE Symposium Series on Computational Intelligence

Pages from

1

Pages to

8

Pages count

8

URL

https://www.fit.vut.cz/research/publication/11225/

BibTex

@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/"
}

Documents

SSCI16_paper_197.pdf