Title

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.

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.

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