Genetic programming for dynamic chaotic systems modelling

This work presents an investigation into the use of genetic programming (GP) applied to chaotic systems modelling. A difference equation model representation was proposed for being the basis of the hierarchical tree encoding in GP. Based upon the NARMA difference equation model and formulating the identification as a multiobjective optimisation problem, Chua´s circuit was studied. The formulation of the GP fitness function, defined as a multiobjective function, generated a set of nondominated chaotic models. This approach considered criteria related to the complexity, performance and also statistical validation of the models in the fitness evaluation. The final set of non-dominated model solutions were able to capture the dynamic characteristics of the system and reproduce the chaotic motion of the double scroll attractor