Assessment of Surrogate Model Settings Using Landscape Analysis

0533901 - ÚI 2021 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Dvořák, M. - Pitra, Zbyněk - Holeňa, Martin
Assessment of Surrogate Model Settings Using Landscape Analysis.
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.), s. 81-89. CEUR Workshop Proceedings, 2718. ISSN 1613-0073.
[ITAT 2020: Information Technologies - Applications and Theory /20./. Oravská Lesná (SK), 18.09.2020-22.09.2020]
Grant CEP: GA ČR(CZ) GA18-18080S
Institucionální podpora: RVO:67985807
Klíčová slova: Black-box optimization * CMA-ES * Surrogate modelling * Gaussian process * Landscape analysis
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2718/paper20.pdf

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.

Trvalý link: http://hdl.handle.net/11104/0312131