Evolving Crossover Operators for Function Optimization

Abstract. A new model for evolving crossover operators for evolutionary function optimization is proposed in this paper. The model is a hybrid technique that combines a Genetic Programming (GP) algorithm and a Genetic Algorithm (GA). Each GP chromosome is a tree encoding a crossover operator used for function optimization. The evolved crossover is embedded into a standard Genetic Algorithm which is used for solving a particular problem. Several crossover operators for function optimization are evolved using the considered model. The evolved crossover operators are compared to the human-designed convex crossover. Numerical experiments show that the evolved crossover operators perform similarly or sometimes even better than standard approaches for several well-known benchmarking problems.

• Type: Conference
• Year: 2006
• Where: EUROGP
• Authors: Laura Diosan, Mihai Oltean
• Keywords: Crossover Operators, EUROGP 2006, Evolved Crossover, Evolved Crossover Operators, Optimization