Evolving Mean-Update Selection Methods for CMA-ES
Abstract
This paper details an investigation of the extent to which performance can be improved for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) by tuning the selection of individuals used for the mean-update algorithm. A hyper-heuristic is employed to explore the space of algorithms which select individuals from the population. We show the increase in performance obtained with a tuned selection algorithm, versus the unmodified CMA-ES mean-update algorithm. Specifically, we measure performance on instances from several real-valued benchmark function classes to demonstrate generalization of the improved performance.
Recommended Citation
S. N. Richter et al., "Evolving Mean-Update Selection Methods for CMA-ES," GECCO 2019 Companion -- Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, pp. 1513 - 1517, Association for Computing Machinery (ACM), Jul 2019.
The definitive version is available at https://doi.org/10.1145/3319619.3326827
Meeting Name
2019 Genetic and Evolutionary Computation Conference, GECCO 2019 (2019: Jul. 13-17, Prague, Czech Republic)
Department(s)
Computer Science
Keywords and Phrases
CMA-ES; Genetic Programming; Hyper-heuristic; Selection
International Standard Book Number (ISBN)
978-145036748-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
01 Jul 2019
