Neural networks with multiple general neuron models: A hybrid computational intelligence approach using Genetic Programming

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@Article{Barton2009614,
  author =       "Alan J. Barton and Julio J. Valdes and 
                 Robert Orchard",
  title =        "Neural networks with multiple general neuron models: A
                 hybrid computational intelligence approach using
                 Genetic Programming",
  journal =      "Neural Networks",
  volume =       "22",
  number =       "5-6",
  pages =        "614--622",
  year =         "2009",
  note =         "Advances in Neural Networks Research: IJCNN2009, 2009
                 International Joint Conference on Neural Networks",
  editor =       "S. Bressler and R. Kozma and L. Perlovsky and 
                 Venayagamoorthy",
  keywords =     "genetic algorithms, genetic programming, General
                 neuron model, Evolutionary Computation, Hybrid
                 algorithm, Machine learning, Parameter space,
                 Visualization",
  ISSN =         "0893-6080",
  DOI =          "doi:10.1016/j.neunet.2009.06.043",
  URL =          "http://www.sciencedirect.com/science/article/B6T08-4WNRK15-3/2/d8803b07859caa7efcd99475af7005ae",
  abstract =     "Classical neural networks are composed of neurons
                 whose nature is determined by a certain function (the
                 neuron model), usually pre-specified. In this paper, a
                 type of neural network (NN-GP) is presented in which:
                 (i) each neuron may have its own neuron model in the
                 form of a general function, (ii) any layout (i.e
                 network interconnection) is possible, and (iii) no bias
                 nodes or weights are associated to the connections,
                 neurons or layers. The general functions associated to
                 a neuron are learned by searching a function space.
                 They are not provided a priori, but are rather built as
                 part of an Evolutionary Computation process based on
                 Genetic Programming. The resulting network solutions
                 are evaluated based on a fitness measure, which may,
                 for example, be based on classification or regression
                 errors. Two real-world examples are presented to
                 illustrate the promising behaviour on classification
                 problems via construction of a low-dimensional
                 representation of a high-dimensional parameter space
                 associated to the set of all network solutions.",
}

Genetic Programming entries for Alan J Barton Julio J Valdes Robert Orchard

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