Rule Accumulation Method Based on Credit Genetic Network Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  title =        "Rule Accumulation Method Based on Credit Genetic
                 Network Programming",
  author =       "Lutao Wang and Wei Xu and Shingo Mabu and 
                 Kotaro Hirasawa",
  pages =        "3651--3658",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6253004",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 games and multi-agent systems, Evolutionary
                 simulation-based optimization, Intelligent systems
                 applications, Genetic Network Programming",
  abstract =     "As a new promising evolutionary computation method,
                 Genetic Network Programming (GNP) is good at generating
                 action rules for multi-agent control in dynamic
                 environments. However, some unimportant nodes exist in
                 the program of GNP. These nodes serve as some redundant
                 information which decreases the performance of GNP and
                 the quality of the generated rules. In order to prune
                 these nodes, this paper proposes a novel method named
                 Credit GNP, where a credit branch is added to each
                 node. When the credit branch is visited, the node is
                 neglected and its function is not executed, so that the
                 unimportant nodes could be jumped. The probability of
                 visiting this credit branch and to which node it is
                 jumped is determined by both evolution and
                 Sarsa-learning, therefore, the unimportant nodes could
                 be pruned automatically. Simulation results on the
                 Tile-world problem show that the proposed method could
                 get better programs and generate better and more
                 general rules.",
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",

Genetic Programming entries for Lutao Wang Wei Xu Shingo Mabu Kotaro Hirasawa