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Traditional Gaussian Process Surrogates in the BBOB Framework
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SYSNO ASEP 0462909 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Traditional Gaussian Process Surrogates in the BBOB Framework Author(s) Repický, J. (CZ)
Bajer, L. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDSource Title Proceedings ITAT 2016: Information Technologies - Applications and Theory. - Aachen & Charleston : Technical University & CreateSpace Independent Publishing Platform, 2016 / Brejová B. - ISSN 1613-0073 - ISBN 978-1-5370-1674-0 Pages s. 163-171 Number of pages 9 s. Publication form Online - E Action ITAT 2016. Conference on Theory and Practice of Information Technologies /16./ Event date 15.09.2016-19.09.2016 VEvent location Tatranské Matliare Country SK - Slovakia Event type EUR Language eng - English Country DE - Germany Keywords continuous optimization ; objective function evaluation ; black-box optimization ; Gaussian process ; surrogate modelling Subject RIV IN - Informatics, Computer Science R&D Projects NV15-33250A GA MZd - Ministry of Health (MZ) Institutional support UIVT-O - RVO:67985807 EID SCOPUS 85046289742 Annotation Objective function evaluation in continuous optimization tasks is often the operation that dominates the algorithm’s cost. In particular in the case of black-box functions, i.e. when no analytical description is available, and the function is evaluated empirically. In such a situation, utilizing information from a surrogate model of the objective function is a well known technique to accelerate the search. In this paper, we review two traditional approaches to surrogate modelling based on Gaussian processes that we have newly reimplemented in MATLAB: Metamodel Assisted Evolution Strategy using probability of improvement and Gaussian Process Optimization Procedure. In the research reported in this paper, both approaches have been for the first time evaluated on Black-Box Optimization Benchmarking framework (BBOB), a comprehensive benchmark for continuous optimizers. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2017 Electronic address http://ceur-ws.org/Vol-1649/163.pdf
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