Inductive Inference of Chaotic Series by Genetic Programming: a Solomonoff-based Approach

A Genetic Programming approach to inductive inference of chaotic series, with reference to Solomonoff complexity, is presented. It consists in evolving a population of mathematical expressions looking for the 'optimal' one that generates a given chaotic data series. Validation is performed on the Logistic, the Henon and the Mackey-Glass series. The method is shown effective in obtaining the analytical expression of the first two series, and in achieving very good results on the third one.