A Genetic Programming Approach to Solomonoff’s Probabilistic Induction

  • Ivanoe De Falco
  • Antonio Della Cioppa
  • Domenico Maisto
  • Ernesto Tarantino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3905)


In the context of Solomonoff’s Inductive Inference theory, Induction operator plays a key role in modeling and correctly predicting the behavior of a given phenomenon. Unfortunately, this operator is not algorithmically computable. The present paper deals with a Genetic Programming approach to Inductive Inference, with reference to Solomonoff’s algorithmic probability theory, that consists in evolving a population of mathematical expressions looking for the ‘optimal’ one that generates a collection of data and has a maximal a priori probability. Validation is performed on Coulomb’s Law, on the Henon series and on the Arosa Ozone time series. The results show that the method is effective in obtaining the analytical expression of the first two problems, and in achieving a very good approximation and forecasting of the third.


Production Rule Inductive Inference Kolmogorov Complexity Derivation Tree Probabilistic Induction 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ivanoe De Falco
    • 1
  • Antonio Della Cioppa
    • 2
  • Domenico Maisto
    • 3
  • Ernesto Tarantino
    • 1
  1. 1.Institute of High Performance Computing and Networking, National Research Council of Italy (ICAR–CNR)NaplesItaly
  2. 2.Natural Computation Lab – DIIIEUniversity of SalernoFisciano (SA)Italy
  3. 3.Department of Physical SciencesUniversity of Naples “Federico II”NaplesItaly

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