This HTML5 document contains 47 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n12http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n21http://localhost/temp/predkladatel/
n16http://purl.org/net/nknouf/ns/bibtex#
n20http://linked.opendata.cz/resource/domain/vavai/projekt/
n18http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n10http://linked.opendata.cz/ontology/domain/vavai/
n11https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n4http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F00216305%3A26230%2F02%3APU36222%21RIV%2F2005%2FGA0%2F262305%2FN/
n7http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n17http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n6http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n19http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n15http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n14http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F02%3APU36222%21RIV%2F2005%2FGA0%2F262305%2FN
rdf:type
skos:Concept n10:Vysledek
dcterms:description
Cílem článku je ověřt použitelnost Bayesovských optimalizačních algoritmů pro multikriteriální optimalizační úlohy s aproximací Paretovské hranice. Navržený optimalizační algoritmus na bázi binárních rozhodovacích stromů byl testován na bikriteriální úloze o batohu In recent years, several researchers have concentrated on using probabilistic models in evolutionary algorithms. These Estimation Distribution Algorithms (EDA) incorporate methods for automated learning of correlations between variables of the encoded solutions. The process of sampling new individuals from a probabilistic model respects these mutual dependencies among genes such that disruption of important building blocks is avoided, in comparison with classical recombination operators. The goal of thiis paper is to investigate the usefulness of this concept in multi-objective evolutionary optimization, where the aim is to approximate the set of Pareto-optimal solutions. We integrate the model building and sampling techniques of a special EDA called Bayesian Optimization Algorithm based on binary decision trees into a general evolutionary multi-objective optimizer. A potential performance gain is empirically tested in comparison with other state-of-the-art multi-objective EA on the bi-objective 0/1 k In recent years, several researchers have concentrated on using probabilistic models in evolutionary algorithms. These Estimation Distribution Algorithms (EDA) incorporate methods for automated learning of correlations between variables of the encoded solutions. The process of sampling new individuals from a probabilistic model respects these mutual dependencies among genes such that disruption of important building blocks is avoided, in comparison with classical recombination operators. The goal of thiis paper is to investigate the usefulness of this concept in multi-objective evolutionary optimization, where the aim is to approximate the set of Pareto-optimal solutions. We integrate the model building and sampling techniques of a special EDA called Bayesian Optimization Algorithm based on binary decision trees into a general evolutionary multi-objective optimizer. A potential performance gain is empirically tested in comparison with other state-of-the-art multi-objective EA on the bi-objective 0/1 k
dcterms:title
Bayesovský optimalizační algoritmus pro multikriteriální optimalizaci Bayesian Optimization Algorithms for Multi-Objective Optimization Bayesian Optimization Algorithms for Multi-Objective Optimization
skos:prefLabel
Bayesian Optimization Algorithms for Multi-Objective Optimization Bayesian Optimization Algorithms for Multi-Objective Optimization Bayesovský optimalizační algoritmus pro multikriteriální optimalizaci
skos:notation
RIV/00216305:26230/02:PU36222!RIV/2005/GA0/262305/N
n3:strany
298-307
n3:aktivita
n17:P
n3:aktivity
P(GA102/02/0503)
n3:dodaniDat
n14:2005
n3:domaciTvurceVysledku
n18:8428190
n3:druhVysledku
n19:D
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
639404
n3:idVysledku
RIV/00216305:26230/02:PU36222
n3:jazykVysledku
n6:eng
n3:klicovaSlova
probabilistic models,Estimation Distribution Algorithms, multi-objective evolutionary optimization, Pareto-optimal solutions, Bayesian Optimization Algorithm, binary decision trees, knapsack problem.
n3:klicoveSlovo
n7:Bayesian%20Optimization%20Algorithm n7:knapsack%20problem. n7:probabilistic%20models n7:multi-objective%20evolutionary%20optimization n7:binary%20decision%20trees n7:Estimation%20Distribution%20Algorithms n7:Pareto-optimal%20solutions
n3:kontrolniKodProRIV
[8BB8315C1053]
n3:mistoKonaniAkce
Granada
n3:mistoVydani
Granada
n3:nazevZdroje
Parallel Problem Solving from Nature - PPSN VII
n3:obor
n15:JC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:projekt
n20:GA102%2F02%2F0503
n3:rokUplatneniVysledku
n14:2002
n3:tvurceVysledku
Očenášek, Jiří
n3:typAkce
n12:WRD
n3:zahajeniAkce
2002-09-07+02:00
s:numberOfPages
10
n16:hasPublisher
Springer-Verlag
n11:isbn
3-540-444139-5
n21:organizacniJednotka
26230