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  • The paper deals with surrogate modelling, a modern approach to the optimization of objective functions evaluated via measurements. 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. The paper recalls common strategies for using surrogate models in evolutionary optimization, and proposes two extensions to those strategies - extension to boosted surrogate models and extension to using a set of models. These are currently being implemented, in connection with surrogate modelling based on feed-forward neural networks, in a software tool for problem-tailored evolutionary optimization of catalytic materials. The paper presents results of experimentally testing already implemented parts and comparing boosted surrogate models with models without boosting, which clearly confirms the usefulness of both proposed extensions.
  • The paper deals with surrogate modelling, a modern approach to the optimization of objective functions evaluated via measurements. 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. The paper recalls common strategies for using surrogate models in evolutionary optimization, and proposes two extensions to those strategies - extension to boosted surrogate models and extension to using a set of models. These are currently being implemented, in connection with surrogate modelling based on feed-forward neural networks, in a software tool for problem-tailored evolutionary optimization of catalytic materials. The paper presents results of experimentally testing already implemented parts and comparing boosted surrogate models with models without boosting, which clearly confirms the usefulness of both proposed extensions. (en)
Title
  • Neural Networks as Surrogate Models for Measurements in Optimization Algorithms
  • Neural Networks as Surrogate Models for Measurements in Optimization Algorithms (en)
skos:prefLabel
  • Neural Networks as Surrogate Models for Measurements in Optimization Algorithms
  • Neural Networks as Surrogate Models for Measurements in Optimization Algorithms (en)
skos:notation
  • RIV/67985807:_____/10:00345993!RIV11-GA0-67985807
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA201/08/0802), 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
  • 274507
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/10:00345993
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • functions evaluated via measurements; evolutionary optimization; surrogate modelling; neural networks; boosting (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [710205420F0F]
http://linked.open...v/mistoKonaniAkce
  • Cardiff
http://linked.open...i/riv/mistoVydani
  • Berlin
http://linked.open...i/riv/nazevZdroje
  • Analytical and Stochastic Modeling Techniques and Applications
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
  • Bajer, Lukáš
  • Holeňa, Martin
  • Linke, D.
  • Rodemerck, U.
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000279619100025
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
  • 978-3-642-13567-5
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