Evolution Strategies for Constants Optimization in Genetic Programming

Evolutionary computation methods have been used to solve several optimization and learning problems. This paper describes an application of evolutionary computation methods to constants optimization in genetic programming. A general evolution strategy technique is proposed for approximating the optimal constants in a computer program representing the solution of a symbolic regression problem. The new algorithm has been compared with a recent linear genetic programming approach based on straight-line programs. The experimental results show that the proposed algorithm improves such technique.