Using Genetic Programming for Artificial Neural Network Development and Simplification

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

@InProceedings{Rivero:2006:WSEAS,
  author =       "Daniel Rivero and Julian Dorado and Juan Rabunal and 
                 Alejandro Pazos",
  title =        "Using Genetic Programming for Artificial Neural
                 Network Development and Simplification",
  booktitle =    "5th WSEAS International Conference on Computational
                 Intelligence, Man-Machine Systems and Cybernetics
                 (CIMMACS '06)",
  year =         "2006",
  editor =       "Nikos Mastorakis",
  pages =        "65--71",
  address =      "Venice",
  month =        nov # " 20-22",
  keywords =     "genetic algorithms, genetic programming, artificial
                 neural networks, evolutionary computation, data
                 mining",
  isbn13 =       "960-8457-56-4",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.558.7104",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.558.7104",
  URL =          "http://www.wseas.us/e-library/conferences/2006venice/papers/539-232.pdf",
  size =         "7 pages",
  abstract =     "The creation process of Artificial Neural Networks
                 (ANNs) used to be quite slow and the human expert had
                 to test several architectures until finding the one
                 that achieves the best results for the solution of a
                 certain problem. This work presents a new technique
                 that uses Genetic Programming (GP) for automatically
                 creating ANNs. This technique also allows the obtaining
                 of simplified networks with few neurons for solving the
                 problem. In order to measure the performance of the
                 system and to compare the results with other ANN
                 generation and training methods with Evolutionary
                 Computation (EC) techniques, several tests were
                 performed with problems based on some of the most used
                 test databases. The results of those comparisons showed
                 that the system achieved good results comparable with
                 already existing techniques and, in most of the cases,
                 they worked better than those techniques.",
  notes =        "Breast cancer, Iris Flower, Heart Cleveland,
                 Ionosphere

                 http://www.wseas.us/e-library/conferences/2006venice/cimmacs/index.htm",
}

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

Citations