Cartesian genetic programming encoded artificial neural networks: a comparison using three benchmarks

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Andrew James Turner and Julian Francis Miller",
  title =        "Cartesian genetic programming encoded artificial
                 neural networks: a comparison using three benchmarks",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "1005--1012",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463484",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Neuroevolution, the application of evolutionary
                 algorithms to artificial neural networks (ANNs), is
                 well-established in machine learning. Cartesian Genetic
                 Programming (CGP) is a graph-based form of Genetic
                 Programming which can easily represent ANNs. Cartesian
                 Genetic Programming encoded ANNs (CGPANNs) can evolve
                 every aspect of an ANN: weights, topology, arity and
                 node transfer functions. This makes CGPANNs very suited
                 to situations where appropriate configurations are not
                 known in advance. The effectiveness of CGPANNs is
                 compared with a large number of previous methods on
                 three benchmark problems. The results show that CGPANNs
                 perform as well as or better than many other
                 approaches. We also discuss the strength and weaknesses
                 of each of the three benchmarks.",
  notes =        "Also known as \cite{2463484} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",

Genetic Programming entries for Andrew James Turner Julian F Miller