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Genetic Programming Model Regularization

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Computational Intelligence (IJCCI 2013)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 613))

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Abstract

We propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials), show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalization error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria.

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Acknowledgments

This work is partially supported by spanish grant TIN2011-27479-C04-04.

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Authors and Affiliations

  1. Centro de Inteligencia Artificial, Campus de Gijón, Universidad de Oviedo, 33271, Gijón, Spain

    César L. Alonso

  2. Departamento de Matemáticas, Estad ística y Computación, Universidad de Cantabria, 39005, Santander, Spain

    José Luis Montaña

  3. DeustoTech (Energy Unit), Universidad de Deusto, 48007, Bilbao, Spain

    Cruz Enrique Borges

Authors
  1. César L. Alonso
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  2. José Luis Montaña
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  3. Cruz Enrique Borges
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Corresponding author

Correspondence to José Luis Montaña .

Editor information

Editors and Affiliations

  1. Images, Signals and Intelligence Systems Laboratory, University PARIS-EST Creteil (UPEC), Créteil, France

    Kurosh Madani

  2. Departamento de Engenharia Informatica Polo II - Pinhal de Marrocos, University of Coimbra, Coimbra, Portugal

    António Dourado

  3. aSEEB-ISR-IST Av. Rovisco Pais, 1, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  4. Rua do Vale de Chaves, Estefanilha, Polytechnic Institute of Setúbal / INSTICC, Setúbal, Portugal

    Joaquim Filipe

  5. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Alonso, C.L., Montaña, J.L., Borges, C.E. (2016). Genetic Programming Model Regularization. In: Madani, K., Dourado, A., Rosa, A., Filipe, J., Kacprzyk, J. (eds) Computational Intelligence. IJCCI 2013. Studies in Computational Intelligence, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-23392-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-23392-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23391-8

  • Online ISBN: 978-3-319-23392-5

  • eBook Packages: EngineeringEngineering (R0)

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