Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization

Authors: Lukáš Bajer, Z. Pitra, Martin Holeňa
Publication year: 2015
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: GECCO Companion '15. Genetic and Evolutionary Computation Conference. Companion Material Proceedings
Projects: Constructing Advanced Comprehensible Classifiers
Bibliographic citation: BAJER, Lukáš, PITRA, Z., HOLEŇA, Martin. Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization. In: SILVA, S., ed. GECCO Companion '15. Genetic and Evolutionary Computation Conference. Companion Material Proceedings. New York: ACM, 2015, S. 1351-1352. ISBN 978-1-4503-3488-4
Dostupné z: doi:10.1145/2739482.2764692
DOI: 10.1145/2739482.2764692
ID: 446913

Abstract

This paper introduces two surrogate models for continous black-box optimization, Gaussian processes and random forests, as an alternative to the already used ordinal SVM regression. We employ the CMA-ES as the reference optimization method with which the surrogate models are combined and also compared on subset of the noisless BBOB testing set.