Evolution of Layer Based Neural Networks: Preliminary Report

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@InProceedings{Pantridge:2016:GECCOcomp,
  author =       "Edward R. Pantridge and Lee Spector",
  title =        "Evolution of Layer Based Neural Networks: Preliminary
                 Report",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  isbn13 =       "978-1-4503-4323-7",
  pages =        "1015--1022",
  address =      "Denver, Colorado, USA",
  month =        "20-24 " # jul,
  keywords =     "genetic algorithms, genetic programming, push",
  organisation = "SIGEVO",
  DOI =          "doi:10.1145/2908961.2931664",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Modern applications of Artificial Neural Networks
                 (ANNs)largely feature networks organized into layers of
                 nodes. Each layer contains an arbitrary number of
                 nodes, and these nodes only share edges with nodes in
                 certain other layers, as determined by the network's
                 topology. Topologies of ANNs are frequently designed by
                 human intuition, due to the lack of a versatile method
                 of determining the best topology for any given problem.
                 Previous attempts at creating a system to automate the
                 discovery of network topologies have used evolutionary
                 computing [6]. The evolution in these systems built
                 networks on a node-by-node basis, limiting the
                 probability of larger, layered topologies. This paper
                 provides on overview of Growth from Embryo of Layered
                 Neural Networks (GELNN), which attempts to evolve
                 topologies of neural networks in terms of layers, and
                 inter-layer connections, instead of individual nodes
                 and edges.",
  notes =        "Distributed at GECCO-2016.",
}

Genetic Programming entries for Edward R Pantridge Lee Spector

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