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Evaluating the Effects of Local Search in Genetic Programming

  • Emigdio Z-Flores
  • Leonardo Trujillo
  • Oliver Schütze
  • Pierrick Legrand
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Genetic Programming Local Search Memetic Algorithms 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Emigdio Z-Flores
    • 1
  • Leonardo Trujillo
    • 1
  • Oliver Schütze
    • 2
  • Pierrick Legrand
    • 3
    • 4
  1. 1.TREE-LAB, Doctorado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y ElectrónicaInstituto Tecnológico de TijuanaTijuanaMéxico
  2. 2.Computer Science DepartmentCINVESTAV-IPNMexico CityMéxico
  3. 3.UMR CNRS 5251Université Victor Segalen Bordeaux 2 and The Institut de Mathématiques de BordeauxBordeauxFrance
  4. 4.ALEA TeamINRIA Bordeaux Sud-OuestTalenceFrance

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