Scaled Symbolic Regression
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- @Article{keijzer:2004:GPEM,
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author = "Maarten Keijzer",
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title = "Scaled Symbolic Regression",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2004",
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volume = "5",
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number = "3",
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pages = "259--269",
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month = sep,
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keywords = "genetic algorithms, genetic programming, linear
regression, symbolic regression",
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ISSN = "1389-2576",
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DOI = "doi:10.1023/B:GENP.0000030195.77571.f9",
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abstract = "Performing a linear regression on the outputs of
arbitrary symbolic expressions has empirically been
found to provide great benefits. Here some basic
theoretical results of linear regression are reviewed
on their applicability for use in symbolic regression.
It will be proven that the use of a scaled error
measure, in which the error is calculated after
scaling, is expected to perform better than its
unscaled counterpart on all possible symbolic
regression problems. As the method (i) does not
introduce additional parameters to a symbolic
regression run, (ii) is guaranteed to improve results
on most symbolic regression problems (and is not worse
on any other problem), and (iii) has a well-defined
upper bound on the error, scaled squared error is an
ideal candidate to become the standard error measure
for practical applications of symbolic regression.",
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notes = "Article ID: 5272971",
- }
Genetic Programming entries for
Maarten Keijzer
Citations