Towards the maintenance of population diversity: A hybrid genetic network programming

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@Article{Li:2010:TJSEC,
  author =       "Xianneng Li and Shingo Mabu and Kotaro Hirasawa",
  title =        "Towards the maintenance of population diversity: A
                 hybrid genetic network programming",
  journal =      "Transaction of the Japanese Society for Evolutionary
                 Computation",
  year =         "2010",
  volume =       "1",
  number =       "1",
  pages =        "89--101",
  month =        "12",
  email =        "sennou@asagi.waseda.jp",
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming, probabilistic model building
                 evolutionary algorithm, PMBEA, estimation of
                 distribution algorithm, EDA, GNP, probabilistic model
                 building genetic network programming, PMBGNP, diversity
                 maintenance",
  ISSN =         "2185-7385",
  URL =          "http://www.jpnsec.org/online_journal/1_1/1_89.pdf",
  size =         "13 pages",
  abstract =     "Some researchers have investigated that the diversity
                 loss will significantly decrease the performance of
                 Probabilistic Model Building Genetic Algorithm (PMBGA),
                 especially under large search space, leading to the
                 premature convergence and local optimum. However, few
                 work has been done on the diversity maintenance in the
                 Probabilistic Model Building Evolutionary Algorithms
                 (PMBEAs) with more complex chromosome structures, such
                 as tree structure based Probabilistic Model Building
                 Genetic Programming (PMBGP) and graph structure based
                 Probabilistic Model Building Genetic Network
                 Programming (PMBGNP). For the PMBEAs with more complex
                 chromosome structures, the required sample size is
                 usually much larger than that of binary structure based
                 PMBGA. Therefore, these algorithms usually become much
                 more sensitive to the population diversity. In order to
                 obtain enough population diversity, the large
                 population size is needed, which is not the best way.
                 the maintenance of the population diversity is studied
                 in PMBGNP, which is a kind of PMBEA, but has its unique
                 characteristics because of its directed graph
                 structure. This paper proposed a hybrid PMBGNP
                 algorithm to maintain the population diversity to avoid
                 the premature convergence and local optimum, and
                 presented a theoretical analysis of the diversity loss
                 in PMBGA, PMBGP and PMBGNP. Two techniques have been
                 proposed for the diversity maintenance when the
                 population size is set at not large values, which are
                 multiple probability vectors and genetic operators. The
                 proposed algorithm is applied and evaluated in a kind
                 of autonomous robot, Khepera robot. The simulation
                 study demonstrates that the proposed hybrid PMBGNP is
                 often able to achieve a better performance than the
                 conventional algorithms.",
}

Genetic Programming entries for Xianneng Li Shingo Mabu Kotaro Hirasawa

Citations