Genetic Network Programming with Automatically Generated Macro Nodes

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@Article{Nakagoe:2004:ieejTEIS,
  author =       "Hiroshi Nakagoe and Shingo Mabu and 
                 Kotaro Hirasawa and Takayuki Hurutsuki",
  title =        "Genetic Network Programming with Automatically
                 Generated Macro Nodes",
  journal =      "IEEJ Transactions on Electronics, Information and
                 Systems",
  year =         "2004",
  volume =       "124",
  number =       "8",
  pages =        "1619--1625",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Network Programming, Automatically Defined Functions
                 (ADFs), Artificial Intelligence",
  ISSN =         "0385-4221",
  URL =          "https://www.jstage.jst.go.jp/article/ieejeiss/124/8/124_8_1619/_article",
  DOI =          "doi:10.1541/ieejeiss.124.1619",
  abstract =     "Genetic Network Programming (GNP) extended from other
                 evolutionary computations such as Genetic Algorithm
                 (GA) and Genetic Programming (GP) has network
                 structures as gene. Previously, the program size of
                 conventional GNP was fixed and GNP programs have not
                 introduced the concept of sub-routines, although GA and
                 GP paid attention to sub-routines. In this paper, a new
                 method where GNP with Automatically Generated Macro
                 Nodes (GNP with AGMs) composed of a number of nodes is
                 proposed for improving the performance of GNP. These
                 AGMs also have network structures and are evolved like
                 main GNP. In addition to that, AGMs have multiple
                 inputs and outputs that have not been introduced in the
                 past. In the simulations, comparisons between GNP
                 program only and GNP with AGMs are carried out using
                 the tile world. Simulation results shows that the
                 proposed method brings better results compared with
                 traditional GNP. And it is clarified from simulations
                 that the node transition rules obtained by AGMs show
                 the generalised rules able to deal with unknown
                 environments.",
  notes =        "Waseda University, Graduate School of Information,
                 Production, and Systems, Waseda University

                 Also known as \cite{Hiroshi Nakagoe20041619}",
}

Genetic Programming entries for Hiroshi Nakagoe Shingo Mabu Kotaro Hirasawa Takayuki Hurutsuki

Citations