Symbolic Regression via Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{sbrn2000meta029,
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author = "Douglas A. Augusto and Helio J. C. Barbosa",
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title = "Symbolic Regression via Genetic Programming",
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booktitle = "{VI} Brazilian Symposium on Neural Networks
(SBRN'00)",
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year = "2000",
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pages = "173",
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address = "Rio de Janeiro, RJ, Brazil",
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month = jan # " 22-25",
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publisher = "IEEE",
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note = "VI Simposio Brasileiro de Redes Neurais",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-7695-0856-1",
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identifier = "sbrn2000article029",
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language = "eng",
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source = "sbrn2000",
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broken = "http://csdl.computer.org/comp/proceedings/sbrn/2000/0856/00/08560173abs.htm",
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DOI = "doi:10.1109/SBRN.2000.889734",
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abstract = "In this work, we present an implementation of symbolic
regression, which is based on genetic programming (GP).
Unfortunately, standard implementations of GP in
compiled languages are not usually the most efficient
ones. The present approach employs a simple
representation for tree-like structures by making use
of Read's linear code, leading to more simplicity and
better performance when compared with traditional GP
implementations. Creation, crossover and mutation of
individuals are formalized. An extension allowing for
the creation of random coefficients is presented. The
efficiency of the proposed implementation was confirmed
in computational experiments, which are summarized in
this paper.",
- }
Genetic Programming entries for
Douglas A Augusto
Helio J C Barbosa
Citations