Genetic Programming for Inductive Inference of Chaotic Series
In the context of inductive inference Solomonoff complexity plays a key role in correctly predicting the behavior of a given phenomenon. Unfortunately, Solomonoff complexity is not algorithmically computable. This paper deals with a Genetic Programming approach to inductive inference of chaotic series, with reference to Solomonoff complexity, that consists in evolving a population of mathematical expressions looking for the ‘optimal’ one that generates a given series of chaotic data. Validation is performed on the Logistic, the Henon and the Mackey–Glass series. The results show that the method is effective in obtaining the analytical expression of the first two series, and in achieving a very good approximation and forecasting of the Mackey–Glass series.
KeywordsGenetic programming Solomonoff complexity chaotic series
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