Genetic Network Programming with Reinforcement Learning and its Performance Evaluation

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Shingo Mabu and Kotaro Hirasawa and Jinglu Hu",
  title =        "Genetic Network Programming with Reinforcement
                 Learning and its Performance Evaluation",
  booktitle =    "Late Breaking Papers at the 2004 Genetic and
                 Evolutionary Computation Conference",
  year =         "2004",
  editor =       "Maarten Keijzer",
  address =      "Seattle, Washington, USA",
  month =        "26 " # jul,
  keywords =     "genetic algorithms, genetic programming, GNP",
  URL =          "",
  abstract =     "A new graph-based evolutionary algorithm named
                 'Genetic Network Programming, GNP' has been proposed.
                 GNP represents its solutions as graph structures, which
                 can improve the expression ability and performance.
                 Since GA, GP and GNP already proposed are based on
                 evolution and they cannot change their solutions until
                 one generation ends, we propose GNP with Reinforcement
                 Learning (GNP with RL) in this paper in order to search
                 solutions quickly. Evolutionary algorithm of GNP makes
                 very compact graph structure which contributes to
                 reducing the size of the Q-table and saving memory.
                 Reinforcement Learning of GNP improves search speed for
                 solutions because it can use the information obtained
                 during task execution.",
  notes =        "Part of \cite{keijzer:2004:GECCO:lbp}",

Genetic Programming entries for Shingo Mabu Kotaro Hirasawa Jinglu Hu