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Probabilistic Model Building and Competent Genetic Programming

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Genetic Programming Theory and Practice

Part of the book series: Genetic Programming Series ((GPEM,volume 6))

Abstract

This paper describes probabilistic model building genetic programming (PM-BGP) developed based on the extended compact genetic algorithm (eCGA). Unlike traditional genetic programming, which use fixed recombination operators, the proposed PMBGA adapts linkages. The proposed algorithms, called the extended compact genetic programming (eCGP) adaptively identifies and exchanges non-overlapping building blocks by constructing and sampling probabilistic models of promising solutions. The results show that eCGP scales-up polynomially with the problem size (the number of functionals and terminals) on both GP-easy problem and boundedly difficult GP-hard problem.

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Author information

Authors and Affiliations

  1. Illinois Genetic Algorithms Laboratory (IlliGAL) Department of General Engineering, University of Illinois, Urbana-Champaign, IL 104 S. Mathews Ave, Urbana, IL, 61801, USA

    Kumara Sastry & David E. Goldberg

Authors
  1. Kumara Sastry
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  2. David E. Goldberg
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Editor information

Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, USA

    Rick Riolo

  2. Genetics Squared, USA

    Bill Worzel

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Sastry, K., Goldberg, D.E. (2003). Probabilistic Model Building and Competent Genetic Programming. In: Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice. Genetic Programming Series, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8983-3_13

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  • DOI: https://doi.org/10.1007/978-1-4419-8983-3_13

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4747-7

  • Online ISBN: 978-1-4419-8983-3

  • eBook Packages: Springer Book Archive

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