Generality and Difficulty in Genetic Programming: Evolving a Sort

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Kenneth E. {Kinnear, Jr.}",
  title =        "Generality and Difficulty in Genetic Programming:
                 Evolving a Sort",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  editor =       "Stephanie Forrest",
  publisher =    "Morgan Kaufmann",
  pages =        "287--294",
  month =        "17-21 " # jul,
  address =      "University of Illinois at Urbana-Champaign",
  keywords =     "genetic algorithms, genetic programming",
  size =         "8 pages",
  URL =          "",
  abstract =     "application of GP to evolving sorting algorithms and
                 the lessons learned from this. Plus the discovery of a
                 connection between size and generality.",
  notes =        "Adding inverse prog size decreases size of progs and
                 makes them more general.

                 Ref \cite{Bickel:1989:tsrGA} Tree structured rules in
                 GAs Kinnear IEEE Press, ICNN U M O'Reilly and F.
                 Oppacher 'An experimental Perspective on Genetic
                 Programming' in 'Parallel Problem solving from nature'
                 R.Manner and B. Manderick (eds) Holland:Elsevier.

                 Loads of references on Steady State v generational
                 (also see kim's own reference)

                 Various fiddling to find balance of size parameters.
                 Various terminal/function sets tried (less powerful ->
                 more difficult) Smaller successful programs more

                 David R. White says reproduced without problem May

Genetic Programming entries for Kenneth E Kinnear Jr