Use of genetic programming for the search of a new learning rule for neutral networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Bengio:1994:GPslrNN,
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author = "Samy Bengio and Yoshua Bengio and Jocelyn Cloutier",
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title = "Use of genetic programming for the search of a new
learning rule for neutral networks",
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booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
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year = "1994",
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volume = "1",
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pages = "324--327",
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address = "Orlando, Florida, USA",
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month = "27-29 " # jun,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, ANN",
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size = "4 pages",
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URL = "http://www.idiap.ch/~bengio/cv/publications/ps/bengio_1994_wcci.ps.gz",
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URL = "http://citeseer.ist.psu.edu/465154.html",
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DOI = "doi:10.1109/ICEC.1994.349932",
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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",
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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,
",
- }
Genetic Programming entries for
Samy Bengio
Yoshua Bengio
Jocelyn Cloutier
Citations