Detail publikace

Parallel Bivariate Marginal Distribution Algorithm with Probability Model Migration

SCHWARZ, J. JAROŠ, J.

Originální název

Parallel Bivariate Marginal Distribution Algorithm with Probability Model Migration

Typ

kapitola v knize

Jazyk

angličtina

Originální abstrakt

This chapter presents a new concept of parallel Bivariate Marginal Distribution Algorithm (BMDA) using the stepping stone communication model with the unidirectional ring topology. The traditional migration of individuals is compared with a newly proposed technique of probability model migration. The idea of the new adaptive BMDA (aBMDA) algorithms is to modify the classic learning of the probability model (applied in the sequential BMDA). In the proposed strategy, the adap-tive learning of the resident probability model is used. The evaluation of pair dependency, using Pearson's chi-square statistics is influenced by the relevant immigrant pair dependency according to the quality of resident and immigrant subpopulation. Experimental results show that the proposed aBMDA significantly outperforms the traditional concept of migration of individuals.

Klíčová slova

BMDA, Model migration, parallel architectures

Autoři

SCHWARZ, J.; JAROŠ, J.

Rok RIV

2008

Vydáno

10. 9. 2008

Nakladatel

Springer Verlag

Místo

Berlin / Heidelberg

ISBN

978-3-540-85067-0

Kniha

Linkage in Evolutionary Computation

Edice

LNSC, Studies in Computational Intelligence Vol. 157

Strany od

3

Strany do

23

Strany počet

21

URL

https://www.fit.vut.cz/research/publication/8773/

BibTex

@inbook{BUT55784,
  author="Josef {Schwarz} and Jiří {Jaroš}",
  title="Parallel Bivariate Marginal Distribution Algorithm with Probability Model Migration",
  booktitle="Linkage in Evolutionary Computation",
  year="2008",
  publisher="Springer Verlag",
  address="Berlin / Heidelberg",
  series="LNSC, Studies in Computational Intelligence Vol. 157",
  pages="3--23",
  isbn="978-3-540-85067-0",
  url="https://www.fit.vut.cz/research/publication/8773/"
}