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

0447919 - ÚI 2016 RIV DE eng C - Conference Paper (international conference)
Kudinov, A. - Bajer, L. - Pitra, Z. - Holeňa, Martin
Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization.
Proceedings ITAT 2015: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2015 - (Yaghob, J.), s. 159-166. CEUR Workshop Proceedings, V-1422. ISBN 978-1-5151-2065-0. ISSN 1613-0073.
[ITAT 2015. Conference on Theory and Practice of Information Technologies /15./. Slovenský Raj (SK), 17.09.2015-21.09.2015]
R&D Projects: GA ČR GA13-17187S
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * multimodal optimization * surrogate modelling * Gaussian process
Subject RIV: IN - Informatics, Computer Science

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.
Permanent Link: http://hdl.handle.net/11104/0249673