Using Supportive Coevolution to Evolve Self-configuring Crossover


Creating an Evolutionary Algorithm (EA) which is capable of automatically configuring itself and dynamically controlling its parameters is a challenging problem. However, solving this problem can reduce the amount of manual con- figuration required to implement an EA, allow the EA to be more adaptable, and produce better results on a range of problems without requiring problem specific tuning. Using Supportive Coevolution (SuCo) to evolve Self-Configuring Crossover (SCX) combines the automatic configuration technique of multiple populations from SuCo with the dynamic crossover operator creation and evolution of SCX. This paper reports an empirical comparison and analysis of several different combinations of mutation and crossover techniques including SuCo and SCX. The Rosenbrock, Rastrigin, and Offset Rastrigin benchmark problems were selected for testing purposes. The benefits and drawbacks of self-adaptation and evolution of SCX are also discussed. SuCo of mutation step sizes and SCX operators produced results that were at least as good as previous work, and some experiments produced results that were significantly better.

Meeting Name

15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013 (2013: Jul. 6-10, Amsterdam, The Netherlands)


Computer Science


Missouri University of Science and Technology. Natural Computation Laboratory

Keywords and Phrases

Coevolution; Dynamic Crossover; Linear Genetic Programming; Parameter Control; Self-Adaptation; Self-Configuration; Supportive Coevolution

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2013 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Jan 2013