Modifying genetic programming for artificial neural network development for data mining
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{journals/soco/RiveroDRP09,
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title = "Modifying genetic programming for artificial neural
network development for data mining",
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author = "Daniel Rivero and Julian Dorado and
Juan R. Rabunal and Alejandro Pazos",
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journal = "Soft Computing - A Fusion of Foundations,
Methodologies and Applications",
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year = "2009",
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number = "3",
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volume = "13",
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pages = "291--305",
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month = feb,
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keywords = "genetic algorithms, genetic programming, Artificial
neural networks, Evolutionary computation, Data mining,
Soft computing",
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DOI = "doi:10.1007/s00500-008-0317-9",
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bibdate = "2008-12-01",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/soco/soco13.html#RiveroDRP09",
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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.
However, there are some tools that provide the ability
of automatically developing ANNs, many of them using
evolutionary computation (EC) tools. One of the main
problems of these techniques is that ANNs have a very
complex structure, which makes them very difficult to
be represented and developed by these tools. This work
presents a new technique that modifies genetic
programming (GP) so as to correctly and efficiently
work with graph structures in order to develop ANNs.
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 in the Data Mining domain. These
comparisons show that the system achieves good results
that are not only comparable to those of the already
existing techniques but, in most cases, improve them.",
- }
Genetic Programming entries for
Daniel Rivero Cebrian
Julian Dorado
Juan Ramon Rabunal Dopico
Alejandro Pazos Sierra
Citations