Evaluating the Effects of Local Search in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Emigdio Z-Flores and Leonardo Trujillo and 
                 Oliver Schuetze and Pierrick Legrand",
  title =        "Evaluating the Effects of Local Search in Genetic
  booktitle =    "EVOLVE - A Bridge between Probability, Set Oriented
                 Numerics, and Evolutionary Computation V",
  year =         "2014",
  editor =       "Alexandru-Adrian Tantar and Emilia Tantar and 
                 Jian-Qiao Sun and Wei Zhang and Qian Ding and 
                 Oliver Schuetze and Michael Emmerich and Pierrick Legrand and 
                 Pierre {Del Moral} and Carlos A. {Coello Coello}",
  volume =       "288",
  series =       "Advances in Intelligent Systems and Computing",
  pages =        "213--228",
  address =      "Peking",
  month =        "1-4 " # jul,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Local Search,
                 Memetic Algorithms",
  isbn13 =       "978-3-319-07493-1",
  oai =          "oai:HAL:hal-01060315v1",
  URL =          "https://hal.inria.fr/hal-01060315",
  DOI =          "doi:10.1007/978-3-319-07494-8_15",
  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 parametrisation 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.",

Genetic Programming entries for Emigdio Z-Flores Leonardo Trujillo Oliver Schuetze Pierrick Legrand