Genetic Programming Discovers Efficient Learning Rules for the Hidden and Output Layers of Feedforward Neural Networks

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

@InProceedings{radi:1999:GPdelrholffNN,
  author =       "Amr Radi and Riccardo Poli",
  title =        "Genetic Programming Discovers Efficient Learning Rules
                 for the Hidden and Output Layers of Feedforward Neural
                 Networks",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and 
                 William B. Langdon and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "120--134",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  URL =          "http://www.cs.essex.ac.uk/staff/poli/papers/Radi-EUROGP1999.pdf",
  URL =          "http://citeseer.ist.psu.edu/335813.html",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=120",
  DOI =          "doi:10.1007/3-540-48885-5_10",
  abstract =     "The learning method is critical for obtaining good
                 generalisation in neural networks with limited training
                 data. The Standard BackPropagation (SBP ) training
                 algorithm suffers from several problems such as
                 sensitivity to the initial conditions and very slow
                 convergence. The aim of this work is to use Genetic
                 Programming (GP) to discover new supervised learning
                 algorithms which can overcome some of these problems.
                 In previous research a new learning algorithm for the
                 output layer has been discovered using GP. By comparing
                 this with SBP on different problems better performance
                 was demonstrated. This paper shows that GP can also
                 discover better learning algorithms for the hidden
                 layers to be used in conjunction with the algorithm
                 previously discovered. Comparing these with SBP on
                 different problems we show they provide better
                 performance. This study indicates that there exist many
                 supervised learning algorithms better than SBP and that
                 GP can be used to discover them.",
  notes =        "EuroGP'99, part of \cite{poli:1999:GP}",
}

Genetic Programming entries for Amr Mohamed Mahmoud Khairat Radi Riccardo Poli

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