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Combining Gaussian Processes with Neural Networks for Active Learning in Optimization
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SYSNO ASEP 0555604 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Combining Gaussian Processes with Neural Networks for Active Learning in Optimization Author(s) Růžička, J. (CZ)
Koza, J. (CZ)
Tumpach, J. (CZ)
Pitra, Z. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDNumber of authors 5 Source Title 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 Pages s. 105-120 Number of pages 16 s. Publication form Print - P Action IAL 2021: Workshop on Interactive Adaptive Learning /5./ Event date 13.09.2021 - 13.09.2021 VEvent location Bilbao / virtual Country ES - Spain Event type WRD Language eng - English Country DE - Germany Keywords active learning ; black-box optimization ; artificial neural networks ; Gaussian processes ; covariance functions Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA18-18080S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 EID SCOPUS 85124088084 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2022 Electronic address http://ceur-ws.org/Vol-3079/ial2021_paper9.pdf
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