Počet záznamů: 1
Combining Gaussian Processes and Neural Networks in Surrogate Modeling for Covariance Matrix Adaptation Evolution Strategy
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SYSNO ASEP 0546157 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Combining Gaussian Processes and Neural Networks in Surrogate Modeling for Covariance Matrix Adaptation Evolution Strategy Tvůrce(i) Koza, J. (CZ)
Tumpach, J. (CZ)
Pitra, Z. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDCelkový počet autorů 4 Zdroj.dok. Proceedings of the 21st Conference Information Technologies – Applications and Theory (ITAT 2021). - Aachen : Technical University & CreateSpace Independent Publishing, 2021 / Brejová B. ; Ciencialová L. ; Holeňa M. ; Mráz F. ; Pardubská D. ; Plátek M. ; Vinař T. - ISSN 1613-0073 Rozsah stran s. 29-38 Poč.str. 10 s. Forma vydání Online - E Akce ITAT 2021: Information Technologies - Applications and Theory /21./ Datum konání 24.09.2021 - 28.09.2021 Místo konání Heľpa Země SK - Slovensko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova black-box optimization ; surrogate modeling ; artificial neural networks ; Gaussian processes ; covariance functions Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA18-18080S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85116711806 Anotace This paper focuses on surrogate models for Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in continuous black-box optimization. Surrogate modeling has proven to be able to decrease the number of evaluations of the objective function, which is an important requirement in some real-world applications where the evaluation can be costly or time-demanding. Surrogate models achieve this by providing an approximation instead of the evaluation of the true objective function. One of the stateof-the-art models for this task is the Gaussian process. We present an approach to combining Gaussian processes with artificial neural networks, which was previously successfully applied to other machine learning domains. The experimental part employs data recorded from previous CMA-ES runs, allowing us to assess different settings of surrogate models without running the whole CMA-ES algorithm. The data were collected using 24 noiseless benchmark functions of the platform for comparing continuous optimizers COCO in 5 different dimensions. Overall, we used data samples from over 2.8 million generations of CMA-ES runs. The results examine and statistically compare six covariance functions of Gaussian processes with the neural network extension. So far, the combined model did not show up to outperform the Gaussian process alone. Therefore, in conclusion, we discuss possible reasons for this and ideas for future research. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2022 Elektronická adresa http://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper27.pdf
Počet záznamů: 1