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Wise Breeding GA via Machine Learning Techniques for Function Optimization

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Book cover Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

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Abstract

This paper explores how inductive machine learning can guide the breeding process of evolutionary algorithms for black-box function optimization. In particular, decision trees are used to identify the underlying characteristics of good and bad individuals, using the mined knowledge for wise breeding purposes. Inductive learning is complemented with statistical learning in order to define the breeding process. The proposed evolutionary process optimizes the fitness function in a dual manner, both maximizing and minimizing it. The paper also summarize some tuning and population sizing issues, as well as some preliminary results obtained using the proposed algorithm.

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Author information

Authors and Affiliations

  1. Illinois Genetic Algorithms Laboratory (IlliGAL), National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 104 S. Mathews Avenue, Urbana, IL, 61801

    Xavier Llorà & David E. Goldberg

Authors
  1. Xavier Llorà
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  2. David E. Goldberg
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Llorà, X., Goldberg, D.E. (2003). Wise Breeding GA via Machine Learning Techniques for Function Optimization. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_125

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  • DOI: https://doi.org/10.1007/3-540-45105-6_125

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

  • eBook Packages: Springer Book Archive

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