Use of genetic programming for the search of a new learning rule for neutral networks

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

@InProceedings{Bengio:1994:GPslrNN,
  author =       "Samy Bengio and Yoshua Bengio and Jocelyn Cloutier",
  title =        "Use of genetic programming for the search of a new
                 learning rule for neutral networks",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "324--327",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  size =         "4 pages",
  URL =          "http://www.idiap.ch/~bengio/cv/publications/ps/bengio_1994_wcci.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/465154.html",
  DOI =          "doi:10.1109/ICEC.1994.349932",
  abstract =     "In previous work ([1, 2, 3]) we explained how to use
                 standard optimization methods such as simulated
                 annealing, gradient descent and genetic algorithms to
                 optimize a parametric function which could be used as a
                 learning rule for neural networks. To use these
                 methods, we had to choose a fixed number of parameters
                 and a rigid form for the learning rule. In this
                 article, we propose to use genetic programming to find
                 not only the values of rule parameters but also the
                 optimal number of parameters and the form of the rule.
                 Experiments on classification tasks suggest genetic
                 programming finds better learning rules than other
                 optimization methods. Furthermore, the best rule found
                 with genetic programming outperformed the well-known
                 backpropagation algorithm for a given set of tasks",
  notes =        "Uses GP to produce a learning rule for training a
                 neural network. Evolved rule like back-propergation but
                 better, differential is cubed. Says neural network is
                 fully connected,

                 IEEE Xplore link broken 16 Oct 2004",
}

Genetic Programming entries for Samy Bengio Yoshua Bengio Jocelyn Cloutier

Citations