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Evaluating the Effects of Local Search in Genetic Programming

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EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 288))

Abstract

Genetic programming (GP) is an evolutionary computation paradigm for the automatic induction of syntactic expressions. In general, GP performs an evolutionary search within the space of possible program syntaxes, for the expression that best solves a given problem. The most common application domain for GP is symbolic regression, where the goal is to find the syntactic expression that best fits a given set of training data. However, canonical GP only employs a syntactic search, thus it is intrinsically unable to efficiently adjust the (implicit) parameters of a particular expression. This work studies a Lamarckian memetic GP, that incorporates a local search (LS) strategy to refine GP individuals expressed as syntax trees. In particular, a simple parametrization for GP trees is proposed, and different heuristic methods are tested to determine which individuals should be subject to a LS, tested over several benchmark and real-world problems. The experimental results provide necessary insights in this insufficiently studied aspect of GP, suggesting promising directions for future work aimed at developing new memetic GP systems.

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Author information

Authors and Affiliations

  1. TREE-LAB, Doctorado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Tijuana, Blvd. Industrial y Av. ITR Tijuana S/N, Mesa Otay, C.P. 22500, Tijuana, B.C., México

    Emigdio Z-Flores & Leonardo Trujillo

  2. Computer Science Department, CINVESTAV-IPN, Av. IPN 2508, Col. San Pedro Zacatenco, 07360, Mexico City, México

    Oliver Schütze

  3. UMR CNRS 5251, Université Victor Segalen Bordeaux 2 and The Institut de Mathématiques de Bordeaux, Bordeaux, France

    Pierrick Legrand

  4. ALEA Team, INRIA Bordeaux Sud-Ouest, Talence, France

    Pierrick Legrand

Authors
  1. Emigdio Z-Flores
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  2. Leonardo Trujillo
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  3. Oliver Schütze
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  4. Pierrick Legrand
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Corresponding author

Correspondence to Emigdio Z-Flores .

Editor information

Editors and Affiliations

  1. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg

    Alexandru-Adrian Tantar

  2. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg

    Emilia Tantar

  3. School of Engineering, University of California, Merced, California, USA

    Jian-Qiao Sun

  4. College of Mechanical Engineering, Beijing University of Technology, Beijing, China

    Wei Zhang

  5. Department of Mechanics, Tianjin University, Tianjin, China

    Qian Ding

  6. Depto. Computación, CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  7. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Michael Emmerich

  8. Université de Bordeaux, Bordeaux, France

    Pierrick Legrand

  9. School of Mathematics and Statistics, University of New South Wales, Sydney, New South Wales, Australia

    Pierre Del Moral

  10. Computer Science Department, CINVESTAV-IPN, Mexico City, Mexico

    Carlos A. Coello Coello

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Z-Flores, E., Trujillo, L., Schütze, O., Legrand, P. (2014). Evaluating the Effects of Local Search in Genetic Programming. In: Tantar, AA., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. Advances in Intelligent Systems and Computing, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-319-07494-8_15

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  • DOI: https://doi.org/10.1007/978-3-319-07494-8_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07493-1

  • Online ISBN: 978-3-319-07494-8

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