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International Conference on Artificial Evolution (Evolution Artificielle)

EA 2001: Artificial Evolution pp 255–266Cite as

Avoiding the Bloat with Stochastic Grammar-Based Genetic Programming

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

Abstract

The application of Genetic Programming to the discovery of empirical laws is often impaired by the huge size of the search space, and consequently by the computer resources needed. In many cases, the extreme demand for memory and CPU is due to the massive growth of non-coding segments, the introns. The paper presents a new program evolution framework which combines distribution-based evolution in the PBIL spirit, with grammar-based genetic programming; the information is stored as a probability distribution on the grammar rules, rather than in a population. Experiments on a real-world like problem show that this approach gives a practical solution to the problem of introns growth.

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Author information

Authors and Affiliations

  1. LRMA, Institut Supérieur de l’Automobile et des Transports, 58027, Nevers, France

    Alain Ratle

  2. LMS CNRS UMR 76-49, Ecole Polytechnique, 91128, Palaiseau, France

    Alain Ratle & Michèle Sebag

Authors
  1. Alain Ratle
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  2. Michèle Sebag
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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-VerlagBerlin Heidelberg

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Ratle, A., Sebag, M. (2002). Avoiding the Bloat with Stochastic Grammar-Based 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_21

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  • DOI: https://doi.org/10.1007/3-540-46033-0_21

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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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