Artificial Neural Network Development by means of Genetic Programming with Graph Codification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Rivero:2005:ijamcs,
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author = "Daniel Rivero and Julian Dorado and
Juan R. Rabunal and Alejandro Pazos and Javier Pereira",
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title = "Artificial Neural Network Development by means of
Genetic Programming with Graph Codification",
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journal = "International Journal of Applied Mathematics and
Computer Sciences",
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year = "2005",
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volume = "1",
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number = "1",
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pages = "41--46",
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month = "Winter",
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keywords = "genetic algorithms, genetic programming, Artificial
Neural Networks, Evolutionary Computation",
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ISSN = "2070-3902",
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URL = "http://www.waset.org/journals/ijamcs/v1/v1-1-8.pdf",
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size = "6 pages",
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abstract = "The development of Artificial Neural Networks (ANNs)
is usually a slow process in which the human expert has
to test several architectures until he finds the one
that achieves best results to solve a certain problem.
This work presents a new technique that uses Genetic
Programming (GP) for automatically generating ANNs. To
do this, the GP algorithm had to be changed in order to
work with graph structures, so ANNs can be developed.
This technique also allows the obtaining of simplified
networks that solve the problem with a small group of
neurons. In order to measure the performance of the
system and to compare the results with other ANN
development methods by means of Evolutionary
Computation (EC) techniques, several tests were
performed with problems based on some of the most used
test databases. The results of those comparisons show
that the system achieves good results comparable with
the already existing techniques and, in most of the
cases, they worked better than those techniques.",
- }
Genetic Programming entries for
Daniel Rivero Cebrian
Julian Dorado
Juan Ramon Rabunal Dopico
Alejandro Pazos Sierra
Javier Pereira
Citations