Towards Low-Dimensional Gaussian Process Metamodels for CMA-ES

0432404 - ÚI 2015 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Bajer, Lukáš - Holeňa, Martin
Towards Low-Dimensional Gaussian Process Metamodels for CMA-ES.
ITAT 2014. Information Technologies - Applications and Theory. Part II. Prague: Institute of Computer Science AS CR, 2014 - (Kůrková, V.; Bajer, L.; Peška, L.; Vojtáš, R.; Holeňa, M.; Nehéz, M.), s. 33-37. ISBN 978-80-87136-19-5.
[ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./. Demänovská dolina (SK), 25.09.2014-29.09.2014]
Grant CEP: GA ČR GA13-17187S
Institucionální podpora: RVO:67985807
Klíčová slova: CMA-ES * Gaussian processes * global optimization * surrogate model * metamodel
Kód oboru RIV: IN - Informatika

Gaussian processes and kriging models has attracted attention of researchers from different areas of black-box optimization, especially since Jones’ introduction of the Efficient Global Optimization (EGO) algorithm. However, current implementations of the EGO or real-world applications are rather few. We conjecture that the EGO is not suitable for higher-dimensional optimization and try to investigate whether hybridization of a low-dimensional local optimization with the current state-of-the-art continuous black-box optimizer CMA-ES (Covariance Matrix Adaptation Evolution Strategy) could help. In this paper, only a first proposal of such a GP/CMA-ES connection is described and some preliminary tests are presented.
Trvalý link: http://hdl.handle.net/11104/0236768