Počet záznamů: 1
Combining Gaussian Processes with Neural Networks for Active Learning in Optimization
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SYSNO ASEP 0555604 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Combining Gaussian Processes with Neural Networks for Active Learning in Optimization Tvůrce(i) Růžička, J. (CZ)
Koza, J. (CZ)
Tumpach, J. (CZ)
Pitra, Z. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDCelkový počet autorů 5 Zdroj.dok. IAL@ECML PKDD 2021: Workshop on Interactive Adaptive Learning. Proceedings. - Aachen : Technical University & CreateSpace Independent Publishing, 2021 / Krempl G. ; Lemaire V. ; Kottke D. ; Holzinger A. ; Hammer B. - ISSN 1613-0073 Rozsah stran s. 105-120 Poč.str. 16 s. Forma vydání Tištěná - P Akce IAL 2021: Workshop on Interactive Adaptive Learning /5./ Datum konání 13.09.2021 - 13.09.2021 Místo konání Bilbao / virtual Země ES - Španělsko Typ akce WRD Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova active learning ; black-box optimization ; artificial neural networks ; Gaussian processes ; covariance functions 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 GA18-18080S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85124088084 Anotace One area where active learning plays an important role is black-box optimization of objective functions with expensive evaluations. To deal with such evaluations, continuous black-box optimization has adopted an approach called surrogate modelling or metamodelling, which consists in replacing the true black-box objective in some of its evaluations with a suitable regression model, the selection of evaluations for replacement being an active learning task. This paper concerns surrogate modelling in the context of a surrogate-assisted variant of the continuous black-box optimizer Covariance Matrix Adaptation Evolution Strategy. It reports the experimental investigation of surrogate models combining artificial neural networks with Gaussian processes, for which it considers six different covariance functions. The experiments were performed on the set of 24 noiseless benchmark functions of the platform Comparing Continuous Optimizers COCO with 5 different dimensionalities. Their results revealed that the most suitable covariance function for this combined kind of surrogate models is the rational quadratic followed by the Matérn 25 and squared exponential. Moreover, the rational quadratic and squared exponential covariances were found interchangeable in the sense that for no function, no group of functions, no dimension and combination of them, the performance of the respective surrogate models was significantly different. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2022 Elektronická adresa http://ceur-ws.org/Vol-3079/ial2021_paper9.pdf
Počet záznamů: 1