Identifying Nonlinear Model Structures Using Genetic Programming Techniques

This paper points out how combined Genetic Programming techniques can be applied to the identification of nonlinear structures in experimental data. In many cases it is essential to generate an algebraic expression as a part of an equation that describes the physical representation of a dynamic system. Genetic Programming is an optimization procedure that can be used to identify such nonlinear structures, and evolution strategies as a second step can be applied to the tuning of constant parameters and delays. In this paper we present a new approach for identifying nonlinear models of mechatronic systems in which the Evolutionary Computation concepts of both Genetic Programming and Evolution Strategies are used.