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Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks
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SYSNO ASEP 0347773 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks Tvůrce(i) Bajer, L. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. Intelligent Data Engineering and Automated Learning - IDEAL 2010. - Berlin : Springer-Verlag, 2010 / Fyfe C. ; Tino P. ; Garcia-Osorio C. ; Yin H. - ISSN 0302-9743 - ISBN 978-3-642-15380-8 Rozsah stran s. 251-258 Poč.str. 8 s. Akce IDEAL 2010. International Conference on Intelligent Data Engineering and Automated Learning /11./ Datum konání 01.09.2010-03.09.2010 Místo konání Paisley Země GB - Velká Británie Typ akce WRD Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova surrogate modelling ; RBF networks ; genetic algorithms ; continuous and discrete variables Vědní obor RIV IN - Informatika CEP GD201/09/H057 GA ČR - Grantová agentura ČR CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000284820400031 EID SCOPUS 78049364129 DOI 10.1007/978-3-642-15381-5_31 Anotace Surrogate modelling has become a successful method improving the optimization of costly objective functions. It brings less accurate, but much faster means of evaluating candidate solutions. This paper describes a model based on radial basis function networks which takes into account both continuous and discrete variables. It shows the applicability of our surrogate model to the optimization of empirical objective functions for which mixing of discrete and continuous dimensions is typical. Results of testing with a genetic algorithm confirm considerably faster convergence in terms of the number of the original empirical fitness evaluations. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2011
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