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Applying Boosting Techniques to Genetic Programming

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Artificial Evolution (EA 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2310))

Abstract

This article deals with an improvement for genetic programming based on a technique originating from the machine learning field: boosting. In a first part of this paper, we test the improvements offered by boosting on binary problems. Then we propose to deal with regression problems, and propose an algorithm, called GPboost, that keeps closer to the original idea of distribution in Adaboost than what has been done in previous implementation of boosting for genetic programming.

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References

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Author information

Authors and Affiliations

  1. LIL, Université du Littoral-Côte d’Opale, BP 719, 62228, Calais Cedex, France

    Gregory Paris, Denis Robilliard & Cyril Fonlupt

Authors
  1. Gregory Paris
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  2. Denis Robilliard
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  3. Cyril Fonlupt
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Editor information

Editors and Affiliations

  1. Centre de Mathématiques Appliquées, Ecole Polytechnique, 91128, Palaiseau Cedex, France

    Pierre Collet

  2. LIL, Université du Littoral — Côte d’Opale, BP 719, 62228, Calais Cedex, France

    Cyril Fonlupt

  3. LERIA, Université d’Angers, 2 Boulevard Lavoisier, 49045, Angers Cedex 01, France

    Jin-Kao Hao

  4. Projet FRACTALES, INRIA Rocquencourt, Domaine de Voluceau, BP 105, 78153, Le Chesnay Cedex, France

    Evelyne Lutton  & Marc Schoenauer  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Paris, G., Robilliard, D., Fonlupt, C. (2002). Applying Boosting Techniques to Genetic Programming. In: Collet, P., Fonlupt, C., Hao, JK., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2001. Lecture Notes in Computer Science, vol 2310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46033-0_22

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  • DOI: https://doi.org/10.1007/3-540-46033-0_22

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43544-0

  • Online ISBN: 978-3-540-46033-6

  • eBook Packages: Springer Book Archive

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