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  • 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
http://linked.open...avai/riv/aktivity
  • P(GA201/08/0802), P(GEICC/08/E018), Z(AV0Z10300504)
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
  • 305415
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/09:00333959
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • evolutionary algorithms; empirical objective functions; surrogate modelling; surrogate modelling; artificial neural networks; boosting (en)
http://linked.open.../riv/klicoveSlovo
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
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Holeňa, Martin
  • Linke, D.
  • Steinfeldt, N.
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
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-642-10682-8
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