Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy

0508171 - ÚI 2020 RIV US eng C - Conference Paper (international conference)
Pitra, Zbyněk - Repický, Jakub - Holeňa, Martin
Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy.
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference. New York: ACM, 2019 - (López-Ibáñez, M.), s. 691-699. ISBN 978-1-4503-6111-8.
[GECCO 2019: The Genetic and Evolutionary Computation Conference. Prague (CZ), 13.07.2019-17.07.2019]
R&D Projects: GA ČR GA17-01251S; GA ČR(CZ) GA18-18080S
Grant - others:GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian process * landscape analysis
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Gaussian processes modeling technique has been shown as a valuable surrogate model for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in continuous single-objective black-box optimization tasks, where the optimized function is expensive. In this paper, we investigate how different Gaussian process settings influence the error between the predicted and genuine population ordering in connection with features representing the fitness landscape. Apart from using features for landscape analysis known from the literature, we propose a new set of features based on CMA-ES state variables. We perform the landscape analysis of a large set of data generated using runs of a surrogate-assisted version of the CMA-ES on the noiseless part of the Comparing Continuous Optimisers benchmark function testbed.
Permanent Link: http://hdl.handle.net/11104/0299146