Improving Geometric Semantic Genetic Programming with Safe Tree Initialisation

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Grant Dick",
  title =        "Improving Geometric Semantic Genetic Programming with
                 Safe Tree Initialisation",
  booktitle =    "18th European Conference on Genetic Programming",
  year =         "2015",
  editor =       "Penousal Machado and Malcolm I. Heywood and 
                 James McDermott and Mauro Castelli and 
                 Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
  series =       "LNCS",
  volume =       "9025",
  publisher =    "Springer",
  pages =        "28--40",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Semantic
                 methods, Interval arithmetic, Safe initialisation,
                 Symbolic regression",
  isbn13 =       "978-3-319-16500-4",
  DOI =          "doi:10.1007/978-3-319-16501-1_3",
  abstract =     "Researchers in genetic programming (GP) are
                 increasingly looking to semantic methods to increase
                 the efficacy of search. Semantic methods aim to
                 increase the likelihood that a structural change made
                 in an individual will be correlated with a change in
                 behaviour. Recent work has promoted the use of
                 geometric semantic methods, where offspring are
                 generated within a bounded interval of the parents
                 behavioural space. Extensions of this approach use
                 random trees wrapped in logistic functions to
                 parametrise the blending of parents. This paper
                 identifies limitations in the logistic wrapper
                 approach, and suggests an alternative approach based on
                 safe initialisation using interval arithmetic to
                 produce offspring. The proposed method demonstrates
                 greater search performance than using a logistic
                 wrapper approach, while maintaining an ability to
                 produce offspring that exhibit good generalisation
  notes =        "Part of \cite{Machado:2015:GP} EuroGP'2015 held in
                 conjunction with EvoCOP2015, EvoMusArt2015 and

Genetic Programming entries for Grant Dick