Self-tuning geometric semantic Genetic Programming

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@Article{Castelli:2016:GPEM,
  author =       "Mauro Castelli and Luca Manzoni and 
                 Leonardo Vanneschi and Sara Silva and Ales Popovic",
  title =        "Self-tuning geometric semantic Genetic Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2016",
  volume =       "17",
  number =       "1",
  pages =        "55--74",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Semantics,
                 Parameters Tuning",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-015-9251-7",
  size =         "20 pages",
  abstract =     "The process of tuning the parameters that characterize
                 evolutionary algorithms is difficult and can be time
                 consuming. This paper presents a self-tuning algorithm
                 for dynamically updating the crossover and mutation
                 probabilities during a run of genetic programming. The
                 genetic operators that are considered in this work are
                 the geometric semantic genetic operators introduced by
                 Moraglio et al. Differently from other existing
                 self-tuning algorithms, the proposed one works by
                 assigning a (different) crossover and mutation
                 probability to each individual of the population. The
                 experimental results we present show the
                 appropriateness of the proposed self-tuning algorithm:
                 on seven different test problems, the proposed
                 algorithm finds solutions of a quality that is better
                 than, or comparable to, the one achieved using the best
                 known values for the geometric semantic crossover and
                 mutation rates for the same problems. Also, we study
                 how the mutation and crossover probabilities change
                 during the execution of the proposed self-tuning
                 algorithm, pointing out an interesting insight:
                 mutation is basically the only operator used in the
                 exploration phase, while crossover is used for
                 exploitation, further improving good quality
                 solutions.",
}

Genetic Programming entries for Mauro Castelli Luca Manzoni Leonardo Vanneschi Sara Silva Ales Popovic

Citations