Parsimony doesn't mean simplicity: genetic programming for inductive inference on noisy data

Authors:
Ivanoe De Falco;Antonio Della Cioppa;Domenico Maisto;Umberto Scafuri;Ernesto Tarantino
Affiliations:
Institute of High Performance Computing and Networking, National Research Council of Italy, Naples, Italy;Natural Computation Lab, DIIIE, University of Salerno, Fisciano, SA, Italy;University of Modena and Reggio Emilia, Modena, Italy;Institute of High Performance Computing and Networking, National Research Council of Italy, Naples, Italy;Institute of High Performance Computing and Networking, National Research Council of Italy, Naples, Italy
Venue:
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Year:
2007

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Abstract

A Genetic Programming algorithm based on Solomonoff's probabilistic induction is designed and used to face an Inductive Inference task, i.e., symbolic regression. To this aim, some test functions are dressed with increasing levels of noise and the algorithm is employed to denoise the resulting function and recover the starting functions. Then, the algorithm is compared against a classical parsimony-based GP. The results shows the superiority of the Solomonoff-based approach.