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  • The paper is focused on the problem of aggregation of probability distribution applicable for parallel Bivariate Marginal Distribution Algorithm (pBMDA). A new approach based on 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.
  • The paper is focused on the problem of aggregation of probability distribution applicable for parallel Bivariate Marginal Distribution Algorithm (pBMDA). A new approach based on 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. (en)
Title
  • Parallel BMDA with an Aggregation of Probability Models
  • Parallel BMDA with an Aggregation of Probability Models (en)
skos:prefLabel
  • Parallel BMDA with an Aggregation of Probability Models
  • Parallel BMDA with an Aggregation of Probability Models (en)
skos:notation
  • RIV/00216305:26230/09:PU82612!RIV10-MSM-26230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/07/0850), Z(MSM0021630528)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 332631
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26230/09:PU82612
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • BMDA, aggregation, probability distributions, migration (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [D14312F8B4A3]
http://linked.open...v/mistoKonaniAkce
  • Trondheim
http://linked.open...i/riv/mistoVydani
  • Trondheim
http://linked.open...i/riv/nazevZdroje
  • Proceeding of 2009 IEEE Congress on Evolutionary Computation
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Jaroš, Jiří
  • Schwarz, Josef
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computational Intelligence Society
https://schema.org/isbn
  • 978-1-4244-2959-2
http://localhost/t...ganizacniJednotka
  • 26230
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