ACGP: Adaptable Constrained Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Cezary Z. Janikow",
  title =        "{ACGP}: Adaptable Constrained Genetic Programming",
  booktitle =    "Genetic Programming Theory and Practice {II}",
  year =         "2004",
  editor =       "Una-May O'Reilly and Tina Yu and Rick L. Riolo and 
                 Bill Worzel",
  chapter =      "12",
  pages =        "191--206",
  address =      "Ann Arbor",
  month =        "13-15 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming,
                 representation, learning, adaptation, heuristics",
  ISBN =         "0-387-23253-2",
  DOI =          "doi:10.1007/0-387-23254-0_12",
  abstract =     "Genetic Programming requires that all
                 functions/terminals (tree labels) be given a priori. In
                 the absence of specific information about the solution,
                 the user is often forced to provide a large set, thus
                 enlarging the search space often resulting in reducing
                 the search efficiency. Moreover, based on heuristics,
                 syntactic constraints, or data typing, a given subtree
                 may be undesired or invalid in a given context. Typed
                 Genetic Programming methods give users the power to
                 specify some rules for valid tree construction, and
                 thus to prune the otherwise unconstrained
                 representation in which Genetic Programming operates.
                 However, in general, the user may not be aware of the
                 best representation space to solve a particular
                 problem. Moreover, some information may be in the form
                 of weak heuristics. In this work, we present a
                 methodology, which automatically adapts the
                 representation for solving a particular problem, by
                 extracting and using such heuristics. Even though many
                 specific techniques can be implemented in the
                 methodology, in this paper we use information on local
                 first-order (parent-child) distributions of the
                 functions and terminals. The heuristics are extracted
                 from the population by observing their distribution in
                 better individuals. The methodology is illustrated and
                 validated using a number of experiments with the
                 11-multiplexer. Moreover, some preliminary empirical
                 results linking population size and the sampling rate
                 are also given.",
  notes =        "part of \cite{oreilly:2004:GPTP2}",

Genetic Programming entries for Cezary Z Janikow