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A Genetic Programming Approach to Solomonoff’s Probabilistic Induction

  • Conference paper
Genetic Programming (EuroGP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3905))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Institute of High Performance Computing and Networking, National Research Council of Italy (ICAR–CNR), Via P. Castellino 111, 80131, Naples, Italy

    Ivanoe De Falco & Ernesto Tarantino

  2. Natural Computation Lab – DIIIE, University of Salerno, Via Ponte don Melillo 1, 84084, Fisciano (SA), Italy

    Antonio Della Cioppa

  3. Department of Physical Sciences, University of Naples “Federico II”, Via Cinthia, 80126, Naples, Italy

    Domenico Maisto

Authors
  1. Ivanoe De Falco
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  2. Antonio Della Cioppa
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  3. Domenico Maisto
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  4. Ernesto Tarantino
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Editor information

Editors and Affiliations

  1. Université Louis Pasteur, LSIIT, FDBT, Pôle API,, F-67400, Illkirch, France

    Pierre Collet

  2. Institut des Systèmes d’Information (ISI), Université de Lausanne, Hautes Etudes Commerciales (HEC),, Switzerland

    Marco Tomassini

  3. Lehrstuhl für Informatik II, Universität Würzburg, Am Hubland,, 97074, Würzburg, Germany

    Marc Ebner

  4. GE Global Research, Niskayuna,, NY, USA

    Steven Gustafson

  5. Aston University, Birmingham, UK

    Anikó Ekárt

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© 2006 Springer-Verlag Berlin Heidelberg

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De Falco, I., Della Cioppa, A., Maisto, D., Tarantino, E. (2006). A Genetic Programming Approach to Solomonoff’s Probabilistic Induction. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds) Genetic Programming. EuroGP 2006. Lecture Notes in Computer Science, vol 3905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11729976_3

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  • DOI: https://doi.org/10.1007/11729976_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33143-8

  • Online ISBN: 978-3-540-33144-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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