NeuroEvolution: Evolving Heterogeneous Artificial Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Andrew James Turner and Julian Francis Miller",
  title =        "NeuroEvolution: Evolving Heterogeneous Artificial
                 Neural Networks",
  journal =      "Evolutionary Intelligence",
  year =         "2014",
  volume =       "7",
  number =       "3",
  pages =        "135--154",
  month =        nov,
  note =         "Special Issue: Evolution in UK 20",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, ANN, Heterogeneous Artificial
                 Neural Networks, NeuroEvolution, Evolutionary
                 Algorithms, Artificial Neural Networks, Computational
  publisher =    "Springer",
  ISSN =         "1864-5909",
  URL =          "",
  DOI =          "doi:10.1007/s12065-014-0115-5",
  size =         "20 pages",
  abstract =     "NeuroEvolution is the application of Evolutionary
                 Algorithms to the training of Artificial Neural
                 Networks. Currently the vast majority of
                 NeuroEvolutionary methods create homogeneous networks
                 of user defined transfer functions. This is despite
                 NeuroEvolution being capable of creating heterogeneous
                 networks where each neuron's transfer function is not
                 chosen by the user, but selected or optimised during
                 evolution. This paper demonstrates how NeuroEvolution
                 can be used to select or optimise each neuron's
                 transfer function and empirically shows that doing so
                 significantly aids training. This result is important
                 as the majority of NeuroEvolutionary methods are
                 capable of creating heterogeneous networks using the
                 methods described.",

Genetic Programming entries for Andrew James Turner Julian F Miller