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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F07%3APU70837%21RIV08-MSM-26230___
rdf:type
n18:Vysledek skos:Concept
dcterms:description
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. 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. 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.
dcterms:title
Parallel BMDA with Probability Model Migration Parallel BMDA with Probability Model Migration Parallel BMDA with Probability Model Migration
skos:prefLabel
Parallel BMDA with Probability Model Migration Parallel BMDA with Probability Model Migration Parallel BMDA with Probability Model Migration
skos:notation
RIV/00216305:26230/07:PU70837!RIV08-MSM-26230___
n4:strany
1059-1066
n4:aktivita
n15:Z n15:P
n4:aktivity
P(GA102/07/0850), Z(MSM0021630528)
n4:dodaniDat
n16:2008
n4:domaciTvurceVysledku
n5:2457652 n5:5444284
n4:druhVysledku
n20:D
n4:duvernostUdaju
n10:S
n4:entitaPredkladatele
n11:predkladatel
n4:idSjednocenehoVysledku
440577
n4:idVysledku
RIV/00216305:26230/07:PU70837
n4:jazykVysledku
n17:eng
n4:klicovaSlova
Evolutionary algorithms, EDA algorithms, island-based models, migration, learning of probability models
n4:klicoveSlovo
n9:learning%20of%20probability%20models n9:island-based%20models n9:Evolutionary%20algorithms n9:migration n9:EDA%20algorithms
n4:kontrolniKodProRIV
[BB8B47761971]
n4:mistoKonaniAkce
Singapore
n4:mistoVydani
Singapore
n4:nazevZdroje
Proceeding of 2007 IEEE Congress on Evolutionary Computation
n4:obor
n14:JC
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:projekt
n6:GA102%2F07%2F0850
n4:rokUplatneniVysledku
n16:2007
n4:tvurceVysledku
Jaroš, Jiří Schwarz, Josef
n4:typAkce
n19:WRD
n4:zahajeniAkce
2007-09-25+02:00
n4:zamer
n21:MSM0021630528
s:numberOfPages
8
n7:hasPublisher
IEEE Computer Society
n22:isbn
1-4244-1340-0
n13:organizacniJednotka
26230