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Assessment of Surrogate Model Settings Using Landscape Analysis
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SYSNO ASEP 0533901 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Assessment of Surrogate Model Settings Using Landscape Analysis Author(s) Dvořák, M. (CZ)
Pitra, Zbyněk (UIVT-O) RID, ORCID, SAI
Holeňa, Martin (UIVT-O) SAI, RIDNumber of authors 3 Source Title Proceedings of the 20th Conference Information Technologies - Applications and Theory. - Aachen : Technical University & CreateSpace Independent Publishing, 2020 / Holeňa M. ; Horváth T. ; Kelemenová A. ; Mráz F. ; Pardubská D. ; Plátek M. ; Sosík P. - ISSN 1613-0073 Pages s. 81-89 Number of pages 9 s. Publication form Online - E Action ITAT 2020: Information Technologies - Applications and Theory /20./ Event date 18.09.2020 - 22.09.2020 VEvent location Oravská Lesná Country SK - Slovakia Event type EUR Language eng - English Country DE - Germany Keywords Black-box optimization ; CMA-ES ; Surrogate modelling ; Gaussian process ; Landscape analysis 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 85095964843 Annotation This work in progress concerns assessment of surrogate model settings for expensive black-box optimization. The assessment is performed in the context of Gaussian process models used in the Doubly Trained Surrogate (DTS) variant of the state-of-the-art black-box optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). This work focuses on the connection between Gaussian process surrogate model predictive accuracy and an essential model hyper-parameter – the covariance function. The performance of DTS-CMA-ES is related to the results of landscape analysis of the objective function. To this end various classification and regression methods are used, proposed in the traditional framework for algorithm selection by Rice. Several single-label classification, multi-label classification, and regression methods are experimentally evaluated on data from DTS-CMAES runs on the noiseless benchmark functions from the COCO platform for comparing continuous optimizers in black-box settings.
Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2021 Electronic address http://ceur-ws.org/Vol-2718/paper20.pdf
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