Nonlinear System Modeling by Hybrid Genetic Programming

Abstract

Genetic Programming (GP) is a useful tool of nonlinear model building, however a simple use of GP often fails in numeric optimization since GP hangs on random number sampling in searching appropriate constant parameters in individual representing each model candidate. From this viewpoint a hybrid GP based nonlinear system identification method is proposed in this paper. We introduce a simple numerical optimization inspired by Particle swarm in GP operation to improve numeric optimization ability. Then, this hybridization is applied to nonlinear system identification by using GP. The applicability of the proposed method is shown by the results of some numerical experiments.