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.
B. W. Goldman and D. R. Tauritz, "Supportive Coevolution," Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion (GECCO 2012), pp. 59-66, Association for Computing Machinery (ACM), Jan 2012.
The definitive version is available at http://dx.doi.org/10.1145/2330784.2330795
14th International Conference on Genetic and Evolutionary Computation, GECCO'12 (2012: Jul. 7-11, Philadelphia, PA)
Missouri University of Science and Technology. Natural Computation Laboratory
Keywords and Phrases
Coevolution; Parameter Control; Self-Adaptation; Supportive Coevolution
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2012 Association for Computing Machinery (ACM), All rights reserved.