Abstract
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.
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