Automatic Generation of Boolean Functions Using Genetic Network Programming

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

  author =       "Yuko Matsuya and Kotaro Hirasawa and Jinglu Hu and 
                 Junichi Murata",
  title =        "Automatic Generation of {Boolean} Functions Using
                 Genetic Network Programming",
  booktitle =    "Proceedings of the 4th Asia-Pacific Conference on
                 Simulated Evolution And Learning (SEAL'02)",
  year =         "2002",
  editor =       "Lipo Wang and Kay Chen Tan and Takeshi Furuhashi and 
                 Jong-Hwan Kim and Xin Yao",
  address =      "Orchid Country Club, Singapore",
  month =        "18-22 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "981-04-7522-5",
  URL =          "",
  abstract =     "In this paper, a recently proposed Evolutionary
                 Computation method called Genetic Network Programming
                 (GNP) is applied to generate Boolean functions. GNP is
                 based on Genetic Algorithm (GA) and Genetic Programming
                 (GP). It has a network structure and can search for
                 solutions effectively. GNP has been mainly applied to
                 dynamic problems and has shown better performance
                 compared to GP. However, its application to static
                 problems has not yet been studied well. Thus in this
                 paper, GNP is applied to generate Boolean functions as
                 its extension to solving static problems. In the
                 simulations, GNP succeeded in solving Even-n-Parity
                 problem and Mirror Symmetry problem.",
  notes =        "SEAL 2002 see

Genetic Programming entries for Yuko Matsuya Kotaro Hirasawa Jinglu Hu Junichi Murata