Evolving Mean-Update Selection Methods for CMA-ES
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.
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
2019 Genetic and Evolutionary Computation Conference, GECCO 2019 (2019: Jul. 13-17, Prague, Czech Republic)
Keywords and Phrases
CMA-ES; Genetic Programming; Hyper-heuristic; Selection
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2019 Association for Computing Machinery (ACM), All rights reserved.
01 Jul 2019