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Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks
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SYSNO ASEP 0389195 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks Author(s) Bajer, Lukáš (UIVT-O) SAI, RID, ORCID
Holeňa, Martin (UIVT-O) SAI, RIDSource Title SOFSEM 2013. Theory and Practice of Computer Science. - Berlin : Springer, 2013 / van Emde Boas P. ; Groen F.C.A. ; Italiano G.F. ; Nawrocki J. ; Sack H. - ISSN 0302-9743 - ISBN 978-3-642-35842-5 Pages s. 481-490 Number of pages 10 s. Publication form Print - P Action SOFSEM 2013. Conference on Current Trends in Theory and Practice of Computer Science /39./ Event date 26.01.2013-31.01.2013 VEvent location Špindlerův Mlýn Country CZ - Czech Republic Event type WRD Language eng - English Country DE - Germany Keywords surrogate modelling ; RBF networks ; genetic algorithms ; mixed-variables optimization ; continuous and discrete variables Subject RIV IN - Informatics, Computer Science R&D Projects GAP202/11/1368 GA ČR - Czech Science Foundation (CSF) GA201/08/0802 GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000389226900041 EID SCOPUS 84872558861 DOI 10.1007/978-3-642-35843-2_41 Annotation Approximation of costly objective functions by surrogate models is an increasingly popular method in many engineering optimization tasks. Surrogate models can substantially decrease the number of expensive experiments or simulations needed to achieve an optimal or near-optimal solution. In this paper, a novel surrogate model is presented. Compared to the most of the surrogate models reported in the literature, it has an advantage of explicitly dealing with mixed continuous and discrete variables. The model use radial basis function networks for continuous and clustering and a generalized linear model for the discrete covariates. The applicability of the model is shown on a benchmark problem, and the model’s regression performance is further measured on a dataset from a real-world application. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2013
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