Polymorphism and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Tina Yu",
  title =        "Polymorphism and Genetic Programming",
  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 =        "218--233",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Polymorphism,
                 Strongly Typed GP, STGP, Multi-objective optimisation,
                 Typed GP, Constraint handling, PolyGP",
  ISBN =         "3-540-41899-7",
  URL =          "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/poly.pdf",
  URL =          "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/poly.pdf",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=218",
  size =         "16 pages",
  abstract =     "Types have been introduced to Genetic Programming (GP)
                 by researchers with different motivation. We present
                 the concept of types in GP and introduce a typed GP
                 system, PolyGP, that supports polymorphism through the
                 use of three different kinds of type variable. We
                 demonstrate the usefulness of this kind of polymorphism
                 in GP by evolving two polymorphic programs (nth and
                 map) using the system. Based on the analysis of a
                 series of experimental results, we conclude that this
                 implementation of polymorphism is effective in
                 assisting GP evolutionary search to generate these two
                 programs. PolyGP may enhance the applicability of GP to
                 a new class of problems that are difficult for other
                 polymorphic GP systems to solve.",
  notes =        "EuroGP'2001, part of miller:2001:gp. Best

Genetic Programming entries for Tina Yu