About: Boosted Neural Networks in Evolutionary Computation

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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. (en)
Title	Boosted Neural Networks in Evolutionary Computation Boosted Neural Networks in Evolutionary Computation (en)
skos:prefLabel	Boosted Neural Networks in Evolutionary Computation Boosted Neural Networks in Evolutionary Computation (en)
skos:notation	RIV/67985807:_____/09:00333959!RIV10-AV0-67985807
http://linked.open...avai/riv/aktivita	P Z
http://linked.open...avai/riv/aktivity	P(GA201/08/0802), P(GEICC/08/E018), Z(AV0Z10300504)
http://linked.open...vai/riv/dodaniDat	2010
http://linked.open...aciTvurceVysledku	Holeňa, Martin
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Ústav informatiky AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	305415
http://linked.open...ai/riv/idVysledku	RIV/67985807:_____/09:00333959
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	evolutionary algorithms; empirical objective functions; surrogate modelling; surrogate modelling; artificial neural networks; boosting (en)
http://linked.open.../riv/klicoveSlovo	evolutionary algorithms surrogate modelling boosting artificial neural networks empirical objective functions
http://linked.open...ontrolniKodProRIV	[9F9C746A493B]
http://linked.open...v/mistoKonaniAkce	Bangkok
http://linked.open...i/riv/mistoVydani	Berlin
http://linked.open...i/riv/nazevZdroje	Neural Information Processing
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Applications of Methods of Knowledge Engineering in Data Mining Logical Models of Reasoning with Vague Information
http://linked.open...UplatneniVysledku	2009
http://linked.open...iv/tvurceVysledku	Holeňa, Martin Linke, D. Steinfeldt, N.
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2009-12-01 (xsd:date)
http://linked.open...n/vavai/riv/zamer	Informatika pro informační společnost: modely, algoritmy, aplikace
number of pages	10 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-642-10682-8

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