An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
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
Paper: The parallel genetic algorithm as function optimizer
Parallel Computing
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
Evolving crossover operators for function optimization
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
The automatic generation of mutation operators for genetic algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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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 configuration 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.