Committee Learning of Partial Functions in Fitness-Shared Genetic Programming

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

  publisher_address = "Piscataway, NJ, USA",
  author =       "R. I. (Bob) McKay",
  booktitle =    "Industrial Electronics Society, 2000. IECON 2000. 26th
                 Annual Confjerence of the IEEE Third Asia-Pacific
                 Conference on Simulated Evolution and Learning 2000",
  DOI =          "doi:10.1109/IECON.2000.972452",
  ISBN =         "0-7803-6456-2",
  address =      "Nagoya, Japan",
  month =        oct # " 22-28",
  notes =        "Refereed International Conference Papers",
  pages =        "2861--2866",
  publisher =    "IEEE Press",
  title =        "Committee Learning of Partial Functions in
                 Fitness-Shared Genetic Programming",
  URL =          "",
  url1 =         "",
  volume =       "4",
  year =         "2000",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper investigates the application of committee
                 learning to fitness-shared genetic programming.
                 Committee learning is applied to populations of either
                 partial and total functions, and using either fitness
                 sharing or raw fitness, giving four treatments in all.
                 The approaches are compared on three problems, the 6-
                 and 11-multiplexer problems, and learning recursive
                 list membership functions. As expected, fitness sharing
                 gave better performance on all problems than raw
                 fitness. The comparison between populations of partial
                 and total functions with fitness sharing is more
                 equivocal. The results are very similar, though
                 slightly in favour of total functions. However there
                 are strong indications that the average size of
                 individuals in the partial function populations are
                 smaller, and hence might be expected to generalise
                 better, though this was not investigated in this

Genetic Programming entries for R I (Bob) McKay