Rule Accumulation Method with Modified Fitness Function based on Genetic Network Programming

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@InProceedings{Wang:2009:ICCAS-SICE,
  author =       "Lutao Wang and Shingo Mabu and Fengming Ye and 
                 Kotaro Hirasawa",
  title =        "Rule Accumulation Method with Modified Fitness
                 Function based on Genetic Network Programming",
  booktitle =    "ICCAS-SICE, 2009",
  year =         "2009",
  month =        "18-21 " # aug,
  address =      "Fukuoka",
  pages =        "1000--1005",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming, GNP-RA, Agent, directed graph
                 structure, fitness function, implicit memory function,
                 node reusability, rule accumulation method, tile-world
                 simulation environment, directed graphs, logic
                 programming",
  isbn13 =       "978-4-9077-6433-3",
  URL =          "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5334897",
  size =         "6 pages",
  abstract =     "Genetic Network Programming (GNP) extended from GA and
                 GP is competent for the complex problems in dynamic
                 environments because of its directed graph structure,
                 reusability of nodes and implicit memory function. In
                 this paper, a new method to extract and accumulate
                 rules from GNP is proposed. The general idea is to
                 update the fitness values of the rules accumulatively,
                 rather than just replacing them in the former research.
                 That is, the rules which appear frequently in different
                 generations are given higher fitness values because
                 they represent good universal experiences from the past
                 behaviors. By extracting the rules during the
                 evolutionary period and then matching them with agents'
                 environments, we could guide the agents properly and
                 get better rewards. In order to test the efficiency and
                 effectiveness of the proposed method, we applied the
                 proposed method to the problem of Tile-world as the
                 simulation environment. Simulation results demonstrate
                 the effectiveness of the proposed method.",
  notes =        "Also known as \cite{5334897}",
}

Genetic Programming entries for Lutao Wang Shingo Mabu Fengming Ye Kotaro Hirasawa

Citations