Počet záznamů: 1
Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy
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SYSNO ASEP 0478629 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy Tvůrce(i) Pitra, Zbyněk (UIVT-O) RID, ORCID, SAI
Bajer, Lukáš (UIVT-O) SAI, RID, ORCID
Repický, Jakub (UIVT-O) ORCID, SAI
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. Proceedings ITAT 2017: Information Technologies - Applications and Theory. - Aachen & Charleston : Technical University & CreateSpace Independent Publishing Platform, 2017 / Hlaváčová J. - ISSN 1613-0073 - ISBN 978-1974274741 Rozsah stran s. 120-128 Poč.str. 9 s. Forma vydání Online - E Akce ITAT 2017. Conference on Theory and Practice of Information Technologies - Applications and Theory /17./ Datum konání 22.09.2017 - 26.09.2017 Místo konání Martinské hole Země SK - Slovensko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova black-box optimization ; evolutionary optimization ; surrogate modelling ; Gaussian process ; CMA-ES Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA17-01251S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85045738878 Anotace An area of increasingly frequent applications of evolutionary optimization to real-world problems is continuous black-box optimization. However, evaluating realworld black-box fitness functions is sometimes very timeconsuming or expensive, which interferes with the need of evolutionary algorithms for many fitness evaluations. Therefore, surrogate regression models replacing the original expensive fitness in some of the evaluated points have been in use since the early 2000s. The Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy (DTS-CMA-ES) represents a surrogate-assisted version of the state-of-the-art algorithm for continuous blackbox optimization CMA-ES. The DTS-CMA-ES saves expensive function evaluations through using a surrogate model. However, the model inaccuracy on some functions can slow-down the algorithm convergence. This paper investigates an extension of DTS-CMA-ES which controls the usage of the model according to the model’s error. Results of testing an adaptive and the original version of DTS-CMA-ES on the set of noiseless benchmarks are reported. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2018 Elektronická adresa http://ceur-ws.org/Vol-1885/120.pdf
Počet záznamů: 1