Designing Neural Networks Using Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{ferreira:2004:wsc9,
  author =       "Candida Ferreira",
  title =        "Designing Neural Networks Using Gene Expression
                 Programming",
  booktitle =    "9th Online World Conference on Soft Computing in
                 Industrial Applications",
  year =         "2004",
  editor =       "Ajith Abraham and Bernard {de Baets} and 
                 Mario Koeppen and Bertram Nickolay",
  volume =       "34",
  series =       "Advances in Soft Computing",
  pages =        "517--535",
  address =      "On the World Wide Web",
  month =        "20 " # sep # " - 8 " # oct,
  organisation = "World Federation on Soft Computing (WFSC)",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  isbn13 =       "978-3-540-31649-7",
  URL =          "http://www.gene-expression-programming.com/webpapers/Ferreira-WSC9.pdf",
  URL =          "http://www.gene-expression-programming.com/webpapers/abstracts.asp#14",
  DOI =          "doi:10.1007/3-540-31662-0_40",
  abstract =     "An artificial neural network with all its elements is
                 a rather complex structure, not easily constructed
                 and/or trained to perform a particular task.
                 Consequently, several researchers used Genetic
                 Algorithms to evolve partial aspects of neural
                 networks, such as the weights, the thresholds, and the
                 network architecture. Indeed, over the last decade many
                 systems have been developed that perform total network
                 induction. In this work it is shown how the chromosomes
                 of Gene Expression Programming can be modified so that
                 a complete neural network, including the architecture,
                 the weights and thresholds, could be totally encoded in
                 a linear chromosome. It is also shown how this
                 chromosomal organization allows the training/adaptation
                 of the network using the evolutionary mechanisms of
                 selection and modification, thus providing an approach
                 to the automatic design of neural networks. The
                 workings and performance of this new algorithm are
                 tested on the 6-multiplexer and on the classical
                 exclusive-or problems.",
  notes =        "WSC9 genetic_programming@yahoogroups.com Wed, 15 Aug
                 2007 09:27:52 BST

                 This volume presents the proceedings of the 9th Online
                 World Conference on Soft Computing in Industrial
                 Applications (WSC9), September 20th - October 08th,
                 2004, held on the World Wide Web.",
}

Genetic Programming entries for Candida Ferreira

Citations