The Importance of Topology Evolution in NeuroEvolution: A Case Study Using Cartesian Genetic Programming of Artificial Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Turner2013a,
  author =       "Andrew James Turner and Julian Francis Miller",
  title =        "The Importance of Topology Evolution in
                 NeuroEvolution: A Case Study Using Cartesian Genetic
                 Programming of Artificial Neural Networks",
  booktitle =    "Research and Development in Intelligent Systems XXX",
  year =         "2013",
  editor =       "Max Bramer and Miltos Petridis",
  pages =        "213--226",
  address =      "Cambridge",
  month =        "10-12 " # dec,
  organisation = "British Computer Society's Specialist Group on
                 Artificial Intelligence",
  publisher =    "Springer International Publishing",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-02620-6",
  URL =          "http://dx.doi.org/10.1007/978-3-319-02621-3_15",
  DOI =          "doi:10.1007/978-3-319-02621-3_15",
  abstract =     "NeuroEvolution (NE) is the application of evolutionary
                 algorithms to Artificial Neural Networks (ANN). This
                 paper reports on an investigation into the relative
                 importance of weight evolution and topology evolution
                 when training ANN using NE. This investigation used the
                 NE technique Cartesian Genetic Programming of
                 Artificial Neural Networks (CGPANN). The results
                 presented show that the choice of topology has a
                 dramatic impact on the effectiveness of NE when only
                 evolving weights; an issue not faced when manipulating
                 both weights and topology. This paper also presents the
                 surprising result that topology evolution alone is far
                 more effective when training ANN than weight evolution
                 alone. This is a significant result as many methods
                 which train ANN manipulate only weights.",
  notes =        "http://www.bcs-sgai.org/ai2013/ Incorporating
                 Applications and Innovations in Intelligent Systems XXI
                 Proceedings of AI-2013, The Thirty-third SGAI
                 International Conference on Innovative Techniques and
                 Applications of Artificial Intelligence",
}

Genetic Programming entries for Andrew James Turner Julian F Miller

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