self-configuring crossover
Crossover is a core genetic operator in many evolutionary algorithms (EAs). The performance of such EAs on a given problem is dependent on properly configuring crossover. A small set of common crossover operators is used in the vast majority of EAs, typically fixed for the entire evolutionary run. Selecting which crossover operator to use and tuning its associated parameters to obtain acceptable performance on a specific problem often is a time consuming manual process. Even then a custom crossover operator may be required to achieve optimal performance. Finally, the best crossover configuration may be dependent on the state of the evolutionary run. This paper introduces the Self-Configuring Crossover operator encoded with linear genetic programming which addresses these shortcomings while relieving the user from the burden of crossover configuration. To demonstrate its general applicability, the novel crossover operator was applied without any problem specific tuning. Results are presented showing it to outperform the traditional crossover operators arithmetic crossover , uniform crossover , and n-point crossover on the Rosenbrock, Rastrigin, Offset Rastrigin, DTrap, and NK Landscapes benchmark problems.
self-configuration linear genetic programming dynamic crossover parameter control