Using supportive coevolution to evolve self-configuring crossover
Nathaniel R. Kamrath, Brian W. Goldman, Daniel R. Tauritz · 2013
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.