Number of the records: 1
Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks
- 1.
SYSNO ASEP 0347773 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks Author(s) Bajer, L. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDSource Title 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 Pages s. 251-258 Number of pages 8 s. Action IDEAL 2010. International Conference on Intelligent Data Engineering and Automated Learning /11./ Event date 01.09.2010-03.09.2010 VEvent location Paisley Country GB - United Kingdom Event type WRD Language eng - English Country DE - Germany Keywords surrogate modelling ; RBF networks ; genetic algorithms ; continuous and discrete variables Subject RIV IN - Informatics, Computer Science R&D Projects GD201/09/H057 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000284820400031 EID SCOPUS 78049364129 DOI 10.1007/978-3-642-15381-5_31 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2011
Number of the records: 1