Title

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.

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.

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