Inductive Inference on Noisy Data by Genetic Programming

In this paper a Genetic Programming algorithm based on Solomonoff probabilistic induction concepts is designed and used to face an Inductive Inference task, i.e. symbolic regression. To this aim, Schwefel function is dressed with increasing levels of additive noise and the algorithm is employed to denoise the resulting function and recover the starting one. The proposed algorithm is compared against a classical parsimony-based GP. The earliest results seem to show a superiority of the Solomonoff-based approach.