Evolving modules in Genetic Programming by subtree encapsulation

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

  author =       "Simon C. Roberts and Daniel Howard and John R. Koza",
  title =        "Evolving modules in Genetic Programming by subtree
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and 
                 Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and 
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "160--175",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming,
                 Modularisation, Code Reuse, Subtree Encapsulation,
                 Image Processing",
  ISBN =         "3-540-41899-7",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=160",
  DOI =          "doi:10.1007/3-540-45355-5_13",
  size =         "16 pages",
  abstract =     "In tree-based genetic programming (GP), the most
                 frequent subtrees on later generations are likely to
                 constitute useful partial solutions. This paper
                 investigates the effect of encapsulating such subtrees
                 by representing them as atoms in the terminal set, so
                 that the subtree evaluations can be exploited as
                 terminal data. The encapsulation scheme is compared
                 against a second scheme which depends on random subtree
                 selection. Empirical results show that both schemes
                 improve upon standard GP.",
  notes =        "EuroGP'2001, part of \cite{miller:2001:gp}",

Genetic Programming entries for Simon C Roberts Daniel Howard John Koza