Detail publikace

Parallel BMDA with an Aggregation of Probability Models

Originální název

Parallel BMDA with an Aggregation of Probability Models

Anglický název

Parallel BMDA with an Aggregation of Probability Models

Jazyk

en

Originální abstrakt

The paper is focused on the problem of aggregation of probability distribution applicable for parallel Bivariate Marginal Distribution Algorithm (pBMDA). A new approach based 000274803100222on quantitative combination of probabilistic models is presented. Using this concept, the traditional migration of individuals is replaced with a newly proposed technique of probability parameter migration. In the proposed strategy, the adaptive learning of the resident probability model is used. The short theoretical study is completed by an experimental works for the implemented parallel BMDA algorithm (pBMDA). The performance of pBMDA algorithm is evaluated for various problem size (scalability) and interconnection topology. In addition, the comparison with the previously published aBMDA  is presented.

Anglický abstrakt

The paper is focused on the problem of aggregation of probability distribution applicable for parallel Bivariate Marginal Distribution Algorithm (pBMDA). A new approach based 000274803100222on quantitative combination of probabilistic models is presented. Using this concept, the traditional migration of individuals is replaced with a newly proposed technique of probability parameter migration. In the proposed strategy, the adaptive learning of the resident probability model is used. The short theoretical study is completed by an experimental works for the implemented parallel BMDA algorithm (pBMDA). The performance of pBMDA algorithm is evaluated for various problem size (scalability) and interconnection topology. In addition, the comparison with the previously published aBMDA  is presented.

BibTex


@inproceedings{BUT33724,
  author="Jiří {Jaroš} and Josef {Schwarz}",
  title="Parallel BMDA with an Aggregation of Probability Models",
  annote="The paper is focused on the problem of aggregation of probability distribution
applicable for parallel Bivariate Marginal Distribution Algorithm (pBMDA). A new
approach based 000274803100222on quantitative combination of probabilistic models
is presented. Using this concept, the traditional migration of individuals is
replaced with a newly proposed technique of probability parameter migration. In
the proposed strategy, the adaptive learning of the resident probability model is
used. The short theoretical study is completed by an experimental works for the
implemented parallel BMDA algorithm (pBMDA). The performance of pBMDA algorithm
is evaluated for various problem size (scalability) and interconnection topology.
In addition, the comparison with the previously published aBMDA  is presented.",
  address="IEEE Computational Intelligence Society",
  booktitle="Proceeding of 2009 IEEE Congress on Evolutionary Computation",
  chapter="33724",
  edition="NEUVEDEN",
  howpublished="online",
  institution="IEEE Computational Intelligence Society",
  year="2009",
  month="may",
  pages="1683--1690",
  publisher="IEEE Computational Intelligence Society",
  type="conference paper"
}