Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP)

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{hirasawa:2001:cgnpgp,
  author =       "Kotaro Hirasawa and M. Okubo and J. Hu and J. Murata",
  title =        "Comparison between Genetic Network Programming (GNP)
                 and Genetic Programming (GP)",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "1276--1282",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, genetic
                 programming Network, Evolution, Ant behaviors, ant
                 behaviour simulation, bloat, complicated programs,
                 evolutionary computation, evolutionary method, genetic
                 algorithm, genome, real world problems, searching
                 efficiency, string structure, tree structure,
                 behavioural sciences computing, biology computing,
                 genetic algorithms, tree data structures, trees
                 (mathematics), zoology,",
  ISBN =         "0-7803-6658-1",
  DOI =          "doi:10.1109/CEC.2001.934337",
  abstract =     "Recently, many methods of evolutionary computation
                 such as genetic algorithm (GA) and genetic programming
                 (GP) have been developed as a basic tool for modelling
                 and optimising of complex systems. Generally speaking,
                 GA has the genome of a string structure, while the
                 genome in GP is the tree structure. Therefore, GP is
                 suitable for constructing complicated programs, which
                 can be applied to many real world problems. However, GP
                 might sometimes be difficult to search for a solution
                 because of its bloat. A novel evolutionary method named
                 Genetic Network Programming (GNP), whose genome is a
                 network structure is proposed to overcome the low
                 searching efficiency of GP and is applied to the
                 problem of the evolution of ant behaviour in order to
                 study the effectiveness of GNP. In addition, the
                 comparison of the performances between GNP and GP is
                 carried out in simulations on ant behaviors",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 GNP directed graph: judgement, time delay, processing
                 nodes. Network genome. subnet swapping crossover. Ant
                 pheremone square 32 by 32 grid world, food gathering.",
}

Genetic Programming entries for Kotaro Hirasawa Masafumi Okubo Jinglu Hu Junichi Murata

Citations