Genetic Network Programming with Estimation of Distribution Algorithms for class association rule mining in traffic prediction

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@InProceedings{Li:2010:cec,
  author =       "Xianneng Li and Shingo Mabu and Huiyu Zhou and 
                 Kaoru Shimada and Kotaro Hirasawa",
  title =        "Genetic Network Programming with Estimation of
                 Distribution Algorithms for class association rule
                 mining in traffic prediction",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Network Programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "As an extension of Genetic Algorithm (GA) and Genetic
                 Programming (GP), a new approach named Genetic Network
                 Programming (GNP) has been proposed in the evolutionary
                 computation field. GNP uses multiple reusable nodes to
                 construct directed-graph structures to represent its
                 solutions. Recently, many research has clarified that
                 GNP can work well in data mining area. In this paper, a
                 novel evolutionary paradigm named GNP with Estimation
                 of Distribution Algorithms (GNP-EDAs) is proposed and
                 used to solve traffic prediction problems using class
                 association rule mining. In GNP-EDAs, a probabilistic
                 model is constructed by estimating the probability
                 distribution from the selected elite individuals of the
                 previous generation to replace the conventional genetic
                 operators, such as crossover and mutation. The
                 probabilistic model is capable of enhancing the
                 evolution to achieve the ultimate objective. In this
                 paper, two methods are proposed based on extracting the
                 probabilistic information on the node connections and
                 node transitions of GNP-EDAs to construct the
                 probabilistic model. A comparative study of the
                 proposed paradigm and the conventional GNP is made to
                 solve the traffic prediction problems using class
                 association rule mining. The simulation results showed
                 that GNP-EDAs can extract the class association rules
                 more effectively, when the number of the candidate
                 class association rules increases. And the
                 classification accuracy of the proposed method shows
                 good results in traffic prediction systems.",
  DOI =          "doi:10.1109/CEC.2010.5586456",
  notes =        "WCCI 2010. Also known as \cite{5586456}",
}

Genetic Programming entries for Xianneng Li Shingo Mabu Huiyu Zhou Kaoru Shimada Kotaro Hirasawa

Citations