Comparing Terminal Sets for Evolving CMA-ES Mean-Update Selection
Abstract
The CMA-ES algorithm searches a fitness landscape by sampling from a multivariate normal distribution and updating its mean by taking a weighted average of the highest fitness candidate solutions. In this work, we explore the possibility of using Genetic Programming to evolve new mean-update selection methods that take into account information other than just raw fitness values. These results show that CMA-ES can be tuned to specific problem classes to achieve better results.
Recommended Citation
S. N. Richter et al., "Comparing Terminal Sets for Evolving CMA-ES Mean-Update Selection," GECCO 2019 Companion -- Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, pp. 326 - 327, Association for Computing Machinery (ACM), Jul 2019.
The definitive version is available at https://doi.org/10.1145/3319619.3321977
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)
978-1-5386-9148-9
International Standard Serial Number (ISSN)
2474-249X
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
