Polynomial Genetic Programming for Response Surface Modeling

Abstract

This paper deals with generating optimal polynomials using genetic programming (GP). Low order Taylor series are used to make the polynomial easily approximate nonlinear response surfaces or function. The overfitting problem is unavoidable because in real applications, the size of learning samples is minimal. This problem can be handled with the extended data set and function node stabilization method. Two examples are presented to demonstrate our method.