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SCHWARZ, J. JAROŠ, J.
Original Title
Parallel Bivariate Marginal Distribution Algorithm with Probability Model Migration
Type
book chapter
Language
English
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.
Keywords
BMDA, Model migration, parallel architectures
Authors
SCHWARZ, J.; JAROŠ, J.
RIV year
2008
Released
10. 9. 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
Pages from
3
Pages to
23
Pages count
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/" }