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Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization
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SYSNO ASEP 0447919 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization Tvůrce(i) Kudinov, A. (CZ)
Bajer, L. (CZ)
Pitra, Z. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. Proceedings ITAT 2015: Information Technologies - Applications and Theory. - Aachen & Charleston : Technical University & CreateSpace Independent Publishing Platform, 2015 / Yaghob J. - ISSN 1613-0073 - ISBN 978-1-5151-2065-0 Rozsah stran s. 159-166 Poč.str. 8 s. Forma vydání Online - E Akce ITAT 2015. Conference on Theory and Practice of Information Technologies /15./ Datum konání 17.09.2015-21.09.2015 Místo konání Slovenský Raj Země SK - Slovensko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova black-box optimization ; evolutionary optimization ; multimodal optimization ; surrogate modelling ; Gaussian process Vědní obor RIV IN - Informatika CEP GA13-17187S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 84944342806 Anotace Minimizing the number of function evaluations became a very challenging problem in the field of blackbox optimization, when one evaluation of the objective function may be very expensive or time-consuming. Gaussian processes (GPs) are one of the approaches suggested to this end, already nearly 20 years ago, in the area of general global optimization. So far, however, they received only little attention in the area of evolutionary black-box optimization. This work investigates the performance of GPs in the context of black-box continuous optimization, using multimodal functions from the CEC 2013 competition. It shows the performance of two methods based on GPs, Model Guided Sampling Optimization (MGSO) and GPs as a surrogate model for CMA-ES. The paper compares the speed-up of both methods with respect to the number of function evaluations using different settings to CMAES with no surrogate model. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2016
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