Comparing Terminal Sets for Evolving CMA-ES Mean-Update Selection
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.
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
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)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2019 Association for Computing Machinery (ACM), All rights reserved.