Evolution of Artificial Neural Networks Using a Two-dimensional Representation

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

@PhdThesis{pujol:thesis,
  author =       "Joao Carlos Figueira Pujol",
  title =        "Evolution of Artificial Neural Networks Using a
                 Two-dimensional Representation",
  school =       "School of Computer Science, University of Birmingham",
  year =         "1999",
  address =      "UK",
  month =        apr,
  email =        "pujol@urano.cdtn.br",
  keywords =     "genetic algorithms, genetic programming, PDGP",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/PhD_Thesis_pujol.pdf",
  size =         "178 pages",
  abstract =     "The design of artificial neural networks is still
                 largely performed by an expert, with only a few
                 heuristics to guide a trial-and-error search. Recently,
                 new methods based on evolutionary computation (EC) have
                 been applied to the synthesis of artificial neural
                 networks with modest results. The basic limitation of
                 EC-based methods is that they do not take into account
                 the fact that artificial neural networks are
                 two-dimensional structures, and do not use specialized
                 evolutionary operators. In this work, a new method
                 based on a special form of evolutionary computation
                 called genetic algorithms is proposed for the evolution
                 of artificial neural networks. The method is a general
                 purpose procedure able to evolve feedforward and
                 recurrent architectures. It is based on a
                 two-dimensional representation, and includes operators
                 to evolve the architecture and the connection weights
                 simultaneously. The new approach has shown promising
                 results, and has fared better than previous methods in
                 a number of applications, including: binary
                 classification problems, design of neural controllers
                 and a complex navigation task of traversing a trail. An
                 extension of the two-dimensional representation is also
                 presented, which can be combined with other methods,
                 providing them with an alternative procedure to evolve
                 the weights of the connections.",
}

Genetic Programming entries for Joao Carlos Figueira Pujol

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