Evolving problem heuristics with on-line ACGP

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Cezary Z. Janikow",
  title =        "Evolving problem heuristics with on-line ACGP",
  booktitle =    "Late breaking paper at Genetic and Evolutionary
                 Computation Conference {(GECCO'2007)}",
  year =         "2007",
  month =        "7-11 " # jul,
  editor =       "Peter A. N. Bosman",
  isbn13 =       "978-1-59593-698-1",
  pages =        "2503--2508",
  address =      "London, United Kingdom",
  keywords =     "genetic algorithms, genetic programming, heuristics,
                 machine learning, STGP, artificial ant",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2503.pdf",
  DOI =          "doi:10.1145/1274000.1274017",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  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:
                 types, syntax, and heuristics. However, in general the
                 user may not be aware of the best representation space,
                 including heuristics, to solve a particular problem.
                 Recently, the ACGP methodology for extracting
                 problem-specific heuristics, and thus for learning
                 model of the problem domain, was introduced with
                 preliminary off-line results. This paper overviews
                 ACGP, pointing out its strength and limitations in the
                 off-line mode. It then introduces a new on-line model,
                 for learning while solving a problem, illustrated with
                 experiments involving the multiplexer and the Santa Fe
  notes =        "Distributed on CD-ROM at GECCO-2007 ACM Order No.

Genetic Programming entries for Cezary Z Janikow