Generation of neural networks using a genetic algorithm approach

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

@Article{Trujillo-Romero:2013:IJBIC,
  author =       "Felipe Trujillo-Romero",
  title =        "Generation of neural networks using a genetic
                 algorithm approach",
  journal =      "International Journal of Bio-Inspired Computation",
  year =         "2013",
  month =        oct # "~17",
  volume =       "5",
  number =       "5",
  pages =        "289--302",
  keywords =     "genetic algorithms, genetic programming, GP, neural
                 networks, evolutionary algorithms, evolutionary entity,
                 alpha-numeric character recognition, classification.",
  ISSN =         "1758-0374",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  language =     "eng",
  publisher =    "Inderscience Publishers",
  URL =          "http://www.inderscience.com/link.php?id=57183",
  DOI =          "DOI:10.1504/IJBIC.2013.057183",
  abstract =     "This paper discusses the generation of neural networks
                 that are obtained from the evolution of individual's
                 population in a genetic algorithm. For achieving this,
                 the population of individuals for the genetic algorithm
                 is formed of structural elements which constitute the
                 neural networks. These elements include the number of
                 layers, neurons per layer, transfer functions and the
                 connections between neurons in the network, among
                 others. These individuals as can be seen a structure
                 which has the ability to evolve rather than a standard
                 genotype. Furthermore, the size of the individuals is
                 not defined and depends mainly on the neural network
                 which in turn depends on the problem to be solved. This
                 structure considered as an evolutionary entity, is able
                 to evolve until convergence towards a suitable
                 structure is achieved. The fitness function is
                 specified with the features of the problem to be solved
                 by the neural network. This algorithm has been tested
                 successfully in solving classification problems, as in
                 the case of alpha-numerical character recognition, and
                 has been compared against a neural network obtained by
                 conventional means. Better results were obtained with
                 the neural network generated by using genetic
                 programming of this type of evolutionary entities.",
}

Genetic Programming entries for Felipe Trujillo-Romero

Citations