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Neural Networks as Surrogate Models for Measurements in Optimization Algorithms

SYSNO ASEP	0345993
Document Type	C - Proceedings Paper (int. conf.)
R&D Document Type	Conference Paper
Title	Neural Networks as Surrogate Models for Measurements in Optimization Algorithms
Author(s)	Holeňa, Martin (UIVT-O)_{SAI, RID} Linke, D. (DE) Rodemerck, U. (DE) Bajer, Lukáš (UIVT-O)_{SAI, RID, ORCID}
Source Title	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
Pages	s. 351-366
Number of pages	16 s.
Action	ASMTA 2010. International Conference /17./
Event date	14.06.2010-16.06.2010
VEvent location	Cardiff
Country	GB - United Kingdom
Event type	WRD
Language	eng - English
Country	DE - Germany
Keywords	functions evaluated via measurements ; evolutionary optimization ; surrogate modelling ; neural networks ; boosting
Subject RIV	IN - Informatics, Computer Science
R&D Projects	GA201/08/0802 GA ČR - Czech Science Foundation (CSF)
CEZ	AV0Z10300504 - UIVT-O (2005-2011)
UT WOS	000279619100025
EID SCOPUS	77955454759
DOI	10.1007/978-3-642-13568-2_25
Annotation	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.
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