Genetic network programming with learning and evolution for adapting to dynamical environments

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

  author =       "Shingo Mabu and Kotaro Hirasawa and Jinglu Hu",
  title =        "Genetic network programming with learning and
                 evolution for adapting to dynamical environments",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "69--76",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  ISBN =         "0-7803-7804-0",
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming, Decision making, Dynamic
                 programming, Economic indicators, Evolutionary
                 computation, Learning systems, Optimisation methods,
                 Tree data structures, Tree graphs, learning (artificial
                 intelligence), search problems, dynamical environments,
                 evolutionary algorithm, learning algorithm, network
                 structures, search ability, wide solution space,",
  DOI =          "doi:10.1109/CEC.2003.1299558",
  abstract =     "A new evolutionary algorithm named genetic network
                 programming, GNP has been proposed. GNP represents its
                 solutions as network structures, which can improve the
                 expression and search ability. 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 learning and evolution in order to
                 adapt to a dynamical environment quickly. Learning
                 algorithm improves search speed for solutions and
                 evolutionary algorithm enables GNP to search wide
                 solution space efficiently.",
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",

Genetic Programming entries for Shingo Mabu Kotaro Hirasawa Jinglu Hu