Controlling Effective Introns for Multi-Agent Learning by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Hitoshi Iba and Makoto Terao",
  title =        "Controlling Effective Introns for Multi-Agent Learning
                 by Genetic Programming",
  pages =        "419--426",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and 
                 Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  URL =          "",
  URL =          "",
  abstract =     "This paper presents the emergence of the cooperative
                 behavior for multiple agents by means of Genetic
                 Programming (GP). For the purpose of evolving the
                 e#ective cooperative behavior, we propose a controlling
                 strategy of introns, which are non-executed code
                 segments dependent upon the situation. The traditional
                 approach to removing introns was able to cope with only
                 a part of syntactically defined introns, which excluded
                 other frequent types of introns. The validness of our
                 approach is discussed with comparative experiments with
                 robot simulation tasks, i.e., a navigation problem and
                 an escape problem.",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of

Genetic Programming entries for Hitoshi Iba Makoto Terao