Artificial Neural Network Development by means of Genetic Programming with Graph Codification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Rivero:2005:ijamcs,
  author =       "Daniel Rivero and Julian Dorado and 
                 Juan R. Rabunal and Alejandro Pazos and Javier Pereira",
  title =        "Artificial Neural Network Development by means of
                 Genetic Programming with Graph Codification",
  journal =      "International Journal of Applied Mathematics and
                 Computer Sciences",
  year =         "2005",
  volume =       "1",
  number =       "1",
  pages =        "41--46",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 Neural Networks, Evolutionary Computation",
  ISSN =         "2070-3902",
  URL =          "http://www.waset.org/journals/ijamcs/v1/v1-1-8.pdf",
  size =         "6 pages",
  abstract =     "The development of Artificial Neural Networks (ANNs)
                 is usually a slow process in which the human expert has
                 to test several architectures until he finds the one
                 that achieves best results to solve a certain problem.
                 This work presents a new technique that uses Genetic
                 Programming (GP) for automatically generating ANNs. To
                 do this, the GP algorithm had to be changed in order to
                 work with graph structures, so ANNs can be developed.
                 This technique also allows the obtaining of simplified
                 networks that solve the problem with a small group of
                 neurons. In order to measure the performance of the
                 system and to compare the results with other ANN
                 development methods by means of Evolutionary
                 Computation (EC) techniques, several tests were
                 performed with problems based on some of the most used
                 test databases. The results of those comparisons show
                 that the system achieves good results comparable with
                 the already existing techniques and, in most of the
                 cases, they worked better than those techniques.",
}

Genetic Programming entries for Daniel Rivero Cebrian Julian Dorado Juan Ramon Rabunal Dopico Alejandro Pazos Sierra Javier Pereira

Citations