Issues in nonlinear model structure identification using genetic programming

Genetic programming (GP) is a powerful nonlinear optimisation tool which can be applied to the identification of the nonlinear structure of dynamic systems. Several issues must be considered. The model format must be defined and a simulation routine integrated with the GP optimisation code to evaluate each candidate model. Numerical parameters of the model must be identified and the model's "goodness-of-fit" must be quantified. The GP algorithm must be configured for model identification and optimised for computation time. Finally, general nonlinear modelling issues such as experimental design and model validation must be considered. All these issues are addressed in this paper.