A hybrid learning algorithm for evolving Flexible Beta Basis Function Neural Tree Model

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@Article{Bouaziz:2013:Neurocomputing,
  author =       "Souhir Bouaziz and Habib Dhahri and Adel M. Alimi and 
                 Ajith Abraham",
  title =        "A hybrid learning algorithm for evolving Flexible Beta
                 Basis Function Neural Tree Model",
  journal =      "Neurocomputing",
  volume =       "117",
  pages =        "107--117",
  year =         "2013",
  keywords =     "genetic algorithms, genetic programming, Flexible Beta
                 Basis Function Neural Tree Model, Opposite-based
                 particle swarm optimization algorithm, Time-series
                 forecasting, Control system",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2013.01.024",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231213001975",
  abstract =     "In this paper, a tree-based encoding method is
                 introduced to represent the Beta basis function neural
                 network. The proposed model called Flexible Beta Basis
                 Function Neural Tree (FBBFNT) can be created and
                 optimised based on the predefined Beta operator sets. A
                 hybrid learning algorithm is used to evolving FBBFNT
                 Model: the structure is developed using the Extended
                 Genetic Programming (EGP) and the Beta parameters and
                 connected weights are optimized by the Opposite-based
                 Particle Swarm Optimisation algorithm (OPSO). The
                 performance of the proposed method is evaluated for
                 benchmark problems drawn from control system and time
                 series prediction area and is compared with those of
                 related methods.",
}

Genetic Programming entries for Souhir Bouaziz Habib Dhahri Adel M Alimi Ajith Abraham

Citations