Supportive Coevolution

Abstract

Automatically configuring and dynamically controlling an Evolutionary Algorithm's (EA's) parameters is a complex task, yet doing so allows EAs to become more powerful and require less problem specific tuning to become effective. Supportive Coevolution is a new form of Evolutionary Algorithm (EA) that uses multiple populations to overcome the limitations of other automatic configuration techniques like self-adaptation, giving it the potential to concurrently evolve all of the parameters and operators in an EA. As a proof of concept experimentation comparing selfadaptation of n uncorrelated mutation step sizes with Supportive Coevolution for mutation step sizes was performed on the Rastrigin and Shifted Rastrigin benchmark functions. Statistical analysis showed Supportive Coevolution outperforming self-adaptation on all but one of the problem instances tested. Furthermore, analysis of instantaneous mutation success rate showed that this new technique is better able to adapt to the changes in the population fitness. Further study using multiple evolving parameters is needed to fully test Supportive Coevolution, but the results presented here show a promising outlook.

Meeting Name

14th International Conference on Genetic and Evolutionary Computation, GECCO'12 (2012: Jul. 7-11, Philadelphia, PA)

Department(s)

Computer Science

Sponsor(s)

Missouri University of Science and Technology. Natural Computation Laboratory

Keywords and Phrases

Coevolution; Parameter Control; Self-Adaptation; Supportive Coevolution

International Standard Book Number (ISBN)

978-1450311786

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2012 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Jan 2012

Link to Full Text

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