Publication detail

Parallel Bivariate Marginal Distribution Algorithm with Probability Model Migration

SCHWARZ, J. JAROŠ, J.

Original Title

Parallel Bivariate Marginal Distribution Algorithm with Probability Model Migration

English Title

Parallel Bivariate Marginal Distribution Algorithm with Probability Model Migration

Type

book chapter

Language

en

Original Abstract

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.

English abstract

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.

Keywords

BMDA, Model migration, parallel architectures

RIV year

2008

Released

10.09.2008

Publisher

Springer Verlag

Location

Berlin / Heidelberg

ISBN

978-3-540-85067-0

Book

Linkage in Evolutionary Computation

Edition

LNSC, Studies in Computational Intelligence Vol. 157

Edition number

NEUVEDEN

Pages from

3

Pages to

23

Pages count

21

URL

BibTex


@inbook{BUT55784,
  author="Josef {Schwarz} and Jiří {Jaroš}",
  title="Parallel Bivariate Marginal Distribution Algorithm with Probability Model Migration",
  annote="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.",
  address="Springer Verlag",
  booktitle="Linkage in Evolutionary Computation",
  chapter="55784",
  edition="LNSC, Studies in Computational Intelligence Vol. 157",
  howpublished="print",
  institution="Springer Verlag",
  year="2008",
  month="september",
  pages="3--23",
  publisher="Springer Verlag",
  type="book chapter"
}