Detail publikace

Parallel BMDA with Probability Model Migration

Originální název

Parallel BMDA with Probability Model Migration

Anglický název

Parallel BMDA with Probability Model Migration

Jazyk

en

Originální abstrakt

The paper presents a new concept of parallel bivariate marginal distribution algorithm using the stepping stone based model of communication 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 xBMDA algorithms is to modify the learning of classic probability model (applied in the sequential BMDA). In the first strategy, the adaptive 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. In the second proposed strategy, the evaluation metric is applied for the diploid mode of the aggregated resident and immigrant subpopulation. Experimental results show that the proposed adaptive BMDA outperforms the traditional concept of individual migration.

Anglický abstrakt

The paper presents a new concept of parallel bivariate marginal distribution algorithm using the stepping stone based model of communication 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 xBMDA algorithms is to modify the learning of classic probability model (applied in the sequential BMDA). In the first strategy, the adaptive 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. In the second proposed strategy, the evaluation metric is applied for the diploid mode of the aggregated resident and immigrant subpopulation. Experimental results show that the proposed adaptive BMDA outperforms the traditional concept of individual migration.

BibTex


@inproceedings{BUT28814,
  author="Jiří {Jaroš} and Josef {Schwarz}",
  title="Parallel BMDA with Probability Model Migration",
  annote="The paper presents a new concept of parallel bivariate marginal distribution
algorithm using the stepping stone based model of communication 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 xBMDA algorithms is to modify the learning of classic probability
model (applied in the sequential BMDA). In the first strategy, the adaptive
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. In the second proposed strategy, the evaluation metric is applied
for the diploid mode of the aggregated resident and immigrant subpopulation.
Experimental results show that the proposed adaptive BMDA outperforms the
traditional concept of individual migration.",
  address="IEEE Computer Society",
  booktitle="Proceeding of 2007 IEEE Congress on Evolutionary Computation",
  chapter="28814",
  howpublished="print",
  institution="IEEE Computer Society",
  year="2007",
  month="august",
  pages="1059--1066",
  publisher="IEEE Computer Society",
  type="conference paper"
}