A preliminary study on mutation operators in cooperative competitive algorithms for RBFN design

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@InProceedings{Perez-Godoy:2010:ijcnn,
  author =       "Maria Dolores Perez-Godoy and Antonio J. Rivera and 
                 Cristobal J. Carmona and Maria Jose {del Jesus}",
  title =        "A preliminary study on mutation operators in
                 cooperative competitive algorithms for RBFN design",
  booktitle =    "International Joint Conference on Neural Networks
                 (IJCNN 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6917-8",
  abstract =     "Evolutionary Computation is a typical paradigm for the
                 Radial Basis Function Network design. In this
                 environment an individual represents a whole network.
                 An alternative is to use cooperative-competitive
                 methods where an individual is a part of the solution.
                 CO2RBFN is an evolutionary cooperative-competitive
                 hybrid methodology for the design of Radial Basis
                 Function Networks. In the proposed
                 cooperative-competitive environment, each individual
                 represents a Radial Basis Function, and the entire
                 population is responsible for the final solution. In
                 order to calculate the application probability of the
                 evolutive operators over a certain Radial Basis
                 Function, a Fuzzy Rule Based System has been used. In
                 this paper, CO2RBFN is adapted to the regression
                 problem and an analysis of mutation operator is
                 performed. To do so, two implementation of the mutation
                 operator, based on gradient and based on clustering,
                 have been implemented and tested. The results have been
                 compared with other data mining and mathematical
                 methods usually used in regression problems.",
  DOI =          "doi:10.1109/IJCNN.2010.5596330",
  notes =        "WCCI 2010. Also known as \cite{5596330}",
}

Genetic Programming entries for Maria Dolores Perez-Godoy Antonio Jesus Rivera Rivas Cristobal Jose Carmona del Jesus Maria Jose del Jesus

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