Review and comparative analysis of geometric semantic crossovers

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@Article{Pawlak:2015:GPEM,
  author =       "Tomasz P. Pawlak and Bartosz Wieloch and 
                 Krzysztof Krawiec",
  title =        "Review and comparative analysis of geometric semantic
                 crossovers",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2015",
  volume =       "16",
  number =       "3",
  pages =        "351--386",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Fitness
                 landscape, Crossover, Theory, Experiment",
  ISSN =         "1389-2576",
  publisher =    "Springer US",
  language =     "English",
  DOI =          "doi:10.1007/s10710-014-9239-8",
  size =         "36 pages",
  abstract =     "This paper provides a structured, unified, formal and
                 empirical perspective on all geometric semantic
                 crossover operators proposed so far, including the
                 exact geometric crossover by Moraglio, Krawiec, and
                 Johnson, as well as the approximately geometric
                 operators. We start with presenting the theory of
                 geometric semantic genetic programming, and discuss the
                 implications of geometric operators for the structure
                 of fitness landscape. We prove that geometric semantic
                 crossover can by construction produce an offspring that
                 is not worse than the fitter parent, and that under
                 certain conditions such an offspring is guaranteed to
                 be not worse than the worse parent. We review all
                 geometric semantic crossover operators presented to
                 date in the literature, and conduct a comprehensive
                 experimental comparison using a tree-based genetic
                 programming framework and a representative suite of
                 nine symbolic regression and nine Boolean function
                 synthesis tasks. We scrutinise the performance (program
                 error and success rate), generalisation, computational
                 cost, bloat, population diversity, and the operators'
                 capability to generate geometric offspring. The
                 experiment leads to several interesting conclusions,
                 the primary one being that an operator's capability to
                 produce geometric offspring is positively correlated
                 with performance. The paper is concluded by
                 recommendations regarding the suitability of operators
                 for the particular domains of program induction
                 tasks.",
  notes =        "early access, open access",
}

Genetic Programming entries for Tomasz Pawlak Bartosz Wieloch Krzysztof Krawiec

Citations