Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization

Authors: A. Kudinov, L. Bajer, Z. Pitra, Martin Holeňa
Publication year: 2015
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: Proceedings ITAT 2015: Information Technologies - Applications and Theory
Projects: Constructing Advanced Comprehensible Classifiers
Bibliographic citation: KUDINOV, A., BAJER, L., PITRA, Z., HOLEŇA, Martin. Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization. In: YAGHOB, J., ed. Proceedings ITAT 2015: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2015, S. 159-166. CEUR Workshop Proceedings, V-1422. ISBN 978-1-5151-2065-0. ISSN 1613-0073.
ID: 447919

Abstract

Minimizing the number of function evaluations became a very challenging problem in the field of blackbox optimization, when one evaluation of the objective function may be very expensive or time-consuming. Gaussian processes (GPs) are one of the approaches suggested to this end, already nearly 20 years ago, in the area of general global optimization. So far, however, they received only little attention in the area of evolutionary black-box optimization. This work investigates the performance of GPs in the context of black-box continuous optimization, using multimodal functions from the CEC 2013 competition. It shows the performance of two methods based on GPs, Model Guided Sampling Optimization (MGSO) and GPs as a surrogate model for CMA-ES. The paper compares the speed-up of both methods with respect to the number of function evaluations using different settings to CMAES with no surrogate model.