Modelling Evolvability in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Fowler:2016:EuroGP,
  author =       "Benjamin Fowler and Wolfgang Banzhaf",
  title =        "Modelling Evolvability in Genetic Programming",
  booktitle =    "EuroGP 2016: Proceedings of the 19th European
                 Conference on Genetic Programming",
  year =         "2016",
  month =        "30 " # mar # "--1 " # apr,
  editor =       "Malcolm I. Heywood and James McDermott and 
                 Mauro Castelli and Ernesto Costa and Kevin Sim",
  series =       "LNCS",
  volume =       "9594",
  publisher =    "Springer Verlag",
  address =      "Porto, Portugal",
  pages =        "215--229",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, evolvability,
                 meta-learning, artificial neural networks",
  isbn13 =       "978-3-319-30668-1",
  DOI =          "doi:10.1007/978-3-319-30668-1_14",
  abstract =     "We develop a tree-based genetic programming system
                 capable of modelling evolvability during evolution
                 through machine learning algorithms, and exploiting
                 those models to increase the efficiency and final
                 fitness. Existing methods of determining evolvability
                 require too much computational time to be effective in
                 any practical sense. By being able to model
                 evolvability instead, computational time may be
                 reduced. This will be done first by demonstrating the
                 effectiveness of modelling these properties \emph{a
                 priori}, before expanding the system to show its
                 effectiveness as evolution occurs.",
  notes =        "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in
                 conjunction with EvoCOP2016, EvoMusArt2016 and
                 EvoApplications2016",
}

Genetic Programming entries for Benjamin Fowler Wolfgang Banzhaf

Citations