An Endosymbiotic Evolutionary Algorithm for Optimization
Applied Intelligence
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Parameter-less evolutionary search
Proceedings of the 10th annual conference on Genetic and evolutionary computation
General Layout of City Pedestrian Bridge
ICIII '08 Proceedings of the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 03
Learning individual mating preferences
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Meta-evolved empirical evidence of the effectiveness of dynamic parameters
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Using supportive coevolution to evolve self-configuring crossover
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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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 self-adaptation 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.