Adaptable Representation in GP

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

  author =       "Cezary Z. Janikow",
  title =        "Adaptable Representation in {GP}",
  booktitle =    "Genetic and Evolutionary Computation Conference
                 {(GECCO2005)} workshop program",
  year =         "2005",
  month =        "25-29 " # jun,
  editor =       "Franz Rothlauf and Misty Blowers and 
                 J{\"u}rgen Branke and Stefano Cagnoni and Ivan I. Garibay and 
                 Ozlem Garibay and J{\"o}rn Grahl and Gregory Hornby and 
                 Edwin D. {de Jong} and Tim Kovacs and Sanjeev Kumar and 
                 Claudio F. Lima and Xavier Llor{\`a} and 
                 Fernando Lobo and Laurence D. Merkle and Julian Miller and 
                 Jason H. Moore and Michael O'Neill and Martin Pelikan and 
                 Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and 
                 Stephen L. Smith and Hal Stringer and 
                 Keiki Takadama and Marc Toussaint and Stephen C. Upton and 
                 Alden H. Wright",
  publisher =    "ACM Press",
  address =      "Washington, D.C., USA",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "327--331",
  URL =          "",
  abstract =     "Genetic Programming uses trees to represent
                 chromosomes. The user defines the representation space
                 by defining the set of functions and terminals to label
                 the nodes in the trees. The sufficiency principle
                 requires that the set be sufficient to label the
                 desired solution trees, often forcing the user to
                 enlarge the set, thus also enlarging the search space.
                 Structure-preserving crossover, STGP, CGP, and
                 CFG-based GP give the user the power to reduce the
                 space by specifying rules for valid tree construction,
                 based on types, syntax, and heuristics. These rules in
                 effect change the representation. However, in general
                 the user may not be aware of the best representation,
                 including heuristics, to solve a particular problem.
                 Last year, ACGP methodology was introduced for
                 extracting local problem-specific heuristics, that is
                 for learning a local model of the problem domain. ACGP
                 discovers representation, in the space of probabilistic
                 representations, one that improves the search itself
                 and that provides the user with heuristics about the
                 domain. We discuss and illustrate the probabilistic
  notes =        "Distributed on CD-ROM at GECCO-2005. ACM

Genetic Programming entries for Cezary Z Janikow