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Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES
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SYSNO ASEP 0447920 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES Tvůrce(i) Pitra, Z. (CZ)
Bajer, Lukáš (UIVT-O) SAI, RID, ORCID
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. 186-193 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 ; surrogate modelling ; CMA-ES ; Gaussian process ; random forest Vědní obor RIV IN - Informatika CEP GA13-17187S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 84944323389 Anotace In practical optimization tasks, it is more and more frequent that the objective function is black-box which means that it cannot be described mathematically. Such functions can be evaluated only empirically, usually through some costly or time-consuming measurement, numerical simulation or experimental testing. Therefore, an important direction of research is the approximation of these objective functions with a suitable regression model, also called surrogate model of the objective functions. This paper evaluates two different approaches to the continuous black-box optimization which both integrates surrogate models with the state-of-the-art optimizer CMAES. The first Ranking SVM surrogate model estimates the ordering of the sampled points as the CMA-ES utilizes only the ranking of the fitness values. However, we show that continuous Gaussian processes model provides in the early states of the optimization comparable results. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2016
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