Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming
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- @InProceedings{Bautu:2005:SYNASC,
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author = "Elena Bautu and Andrei Bautu and Henri Luchian",
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title = "Symbolic Regression on Noisy Data with Genetic and
Gene Expression Programming",
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booktitle = "Seventh International Symposium on Symbolic and
Numeric Algorithms for Scientific Computing
(SYNASC'05)",
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year = "2005",
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pages = "321--324",
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keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
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DOI = "doi:10.1109/SYNASC.2005.70",
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abstract = "regression on a finite sample of noisy data. The
purpose is to obtain a mathematical model for data
which is both reliable and valid, yet the analytical
expression is not restricted to any particular form. To
obtain a statistical model of the noisy data set we use
symbolic regression with pseudo-random number
generators. We begin by describing symbolic regression
and our implementation of this technique using genetic
programming (GP) and gene expression programming (GEP).
We present some results for symbolic regression on
computer generated and real financial data sets in the
final part of this paper.",
- }
Genetic Programming entries for
Elena Bautu
Andrei Bautu
Henri Luchian
Citations