Počet záznamů: 1  

Neural Networks as Surrogate Models for Measurements in Optimization Algorithms

  1. 1.
    SYSNO ASEP0345993
    Druh ASEPC - Konferenční příspěvek (mezinárodní konf.)
    Zařazení RIVD - Článek ve sborníku
    NázevNeural Networks as Surrogate Models for Measurements in Optimization Algorithms
    Tvůrce(i) Holeňa, Martin (UIVT-O) SAI, RID
    Linke, D. (DE)
    Rodemerck, U. (DE)
    Bajer, Lukáš (UIVT-O) SAI, RID, ORCID
    Zdroj.dok.Analytical and Stochastic Modeling Techniques and Applications. - Berlin : Springer, 2010 / Al-Begain K. ; Fiems D ; Knottenbelt W. - ISSN 0302-9743 - ISBN 978-3-642-13567-5
    Rozsah strans. 351-366
    Poč.str.16 s.
    AkceASMTA 2010. International Conference /17./
    Datum konání14.06.2010-16.06.2010
    Místo konáníCardiff
    ZeměGB - Velká Británie
    Typ akceWRD
    Jazyk dok.eng - angličtina
    Země vyd.DE - Německo
    Klíč. slovafunctions evaluated via measurements ; evolutionary optimization ; surrogate modelling ; neural networks ; boosting
    Vědní obor RIVIN - Informatika
    CEPGA201/08/0802 GA ČR - Grantová agentura ČR
    CEZAV0Z10300504 - UIVT-O (2005-2011)
    UT WOS000279619100025
    EID SCOPUS77955454759
    DOI10.1007/978-3-642-13568-2_25
    AnotaceThe 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.
    PracovištěÚstav informatiky
    KontaktTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Rok sběru2011
Počet záznamů: 1  

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