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Statements

Subject Item
n2:RIV%2F67985807%3A_____%2F09%3A00333959%21RIV10-AV0-67985807
rdf:type
n13:Vysledek skos:Concept
dcterms:description
The paper deals with a neural-network-based version of surrogate modelling, a modern approach to the optimization of empirical objective functions. The approach leads to a substantial decrease of time and costs of evaluation of the objective function, a property that is particularly attractive in evolutionary optimization. In the paper, an extension of surrogate modelling with regression boosting is proposed, which increases the accuracy of surrogate models, thus also the agreement between results obtained with the model and those obtained with the original objective function. The extension is illustrated on a case study in materials science. Presented case study results clearly confirm the usefulness of boosting for neural-network-based surrogate models. The paper deals with a neural-network-based version of surrogate modelling, a modern approach to the optimization of empirical objective functions. The approach leads to a substantial decrease of time and costs of evaluation of the objective function, a property that is particularly attractive in evolutionary optimization. In the paper, an extension of surrogate modelling with regression boosting is proposed, which increases the accuracy of surrogate models, thus also the agreement between results obtained with the model and those obtained with the original objective function. The extension is illustrated on a case study in materials science. Presented case study results clearly confirm the usefulness of boosting for neural-network-based surrogate models.
dcterms:title
Boosted Neural Networks in Evolutionary Computation Boosted Neural Networks in Evolutionary Computation
skos:prefLabel
Boosted Neural Networks in Evolutionary Computation Boosted Neural Networks in Evolutionary Computation
skos:notation
RIV/67985807:_____/09:00333959!RIV10-AV0-67985807
n3:aktivita
n7:P n7:Z
n3:aktivity
P(GA201/08/0802), P(GEICC/08/E018), Z(AV0Z10300504)
n3:dodaniDat
n10:2010
n3:domaciTvurceVysledku
n11:6036627
n3:druhVysledku
n12:D
n3:duvernostUdaju
n21:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
305415
n3:idVysledku
RIV/67985807:_____/09:00333959
n3:jazykVysledku
n15:eng
n3:klicovaSlova
evolutionary algorithms; empirical objective functions; surrogate modelling; surrogate modelling; artificial neural networks; boosting
n3:klicoveSlovo
n6:artificial%20neural%20networks n6:surrogate%20modelling n6:boosting n6:evolutionary%20algorithms n6:empirical%20objective%20functions
n3:kontrolniKodProRIV
[9F9C746A493B]
n3:mistoKonaniAkce
Bangkok
n3:mistoVydani
Berlin
n3:nazevZdroje
Neural Information Processing
n3:obor
n8:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:projekt
n9:GEICC%2F08%2FE018 n9:GA201%2F08%2F0802
n3:rokUplatneniVysledku
n10:2009
n3:tvurceVysledku
Linke, D. Steinfeldt, N. Holeňa, Martin
n3:typAkce
n17:WRD
n3:zahajeniAkce
2009-12-01+01:00
n3:zamer
n14:AV0Z10300504
s:numberOfPages
10
n20:hasPublisher
Springer-Verlag
n5:isbn
978-3-642-10682-8