Automatic Design of Modular Neural Networks Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{conf/icann/NourAshrafoddinVE07,
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author = "Naser NourAshrafoddin and Ali R. Vahdat and
Mohammad Mehdi Ebadzadeh",
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title = "Automatic Design of Modular Neural Networks Using
Genetic Programming",
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booktitle = "Proceedings of the 17th International Conference on
Artificial Neural Networks, ICANN 2007, Part {I}",
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year = "2007",
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editor = "Joaquim Marques de S{\'a} and
Lu{\'i}s A. Alexandre and Wlodzislaw Duch and Danilo P. Mandic",
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volume = "4668",
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series = "Lecture Notes in Computer Science",
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pages = "788--798",
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address = "Porto, Portugal",
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month = sep # " 9-13",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Modular
neural networks, evolutionary computing, automatic
design",
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isbn13 = "978-3-540-74689-8",
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DOI = "doi:10.1007/978-3-540-74690-4_80",
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size = "11 pages",
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abstract = "Traditional trial-and-error approach to design neural
networks is time consuming and does not guarantee
yielding the best neural network feasible for a
specific application. Therefore automatic approaches
have gained more importance and popularity. In
addition, traditional (non-modular) neural networks can
not solve complex problems since these problems
introduce wide range of overlap which, in turn, causes
a wide range of deviations from efficient learning in
different regions of the input space, whereas a modular
neural network attempts to reduce the effect of these
problems via a divide and conquer approach. In this
paper we are going to introduce a different approach to
autonomous design of modular neural networks. Here we
use genetic programming for automatic modular neural
networks design; their architectures, transfer
functions and connection weights. Our approach offers
important advantages over existing methods for
automated neural network design. First it prefers
smaller modules to bigger modules, second it allows
neurons even in the same layer to use different
transfer functions, and third it is not necessary to
convert each individual into a neural network to obtain
the fitness value during the evolution process. Several
tests were performed with problems based on some of the
most popular test databases. Results show that using
genetic programming for automatic design of neural
networks is an efficient method and is comparable with
the already existing techniques",
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bibdate = "2007-09-17",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icann/icann2007-1.html#NourAshrafoddinVE07",
- }
Genetic Programming entries for
Naser NourAshrafoddin
Ali Vahdat
Mohammad Mehdi Ebadzadeh
Citations