Implementing Linear Models in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{yeun_2004_tec,
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author = "Yun-Seog Yeun and Won-Sun Ruy and Young-Soon Yang and
Nam-Joon Kim",
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title = "Implementing Linear Models in Genetic Programming",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2004",
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volume = "8",
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number = "6",
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pages = "542--566",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Directional
derivative-based smoothing (DDBS), linear model,
minimum description length (MDL) principle, polynomial,
symbolic processing",
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URL = "http://members.kr.inter.net/yyshuj/paper/pre-lm-gp.pdf",
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DOI = "doi:10.1109/TEVC.2004.836818",
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size = "25 pages",
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abstract = "We deal with linear models of genetic programming (GP)
for regression or approximation problems when given
learning samples are not sufficient. The linear model,
which is a function of unknown parameters, is built
through extracting all possible base functions from the
standard GP tree by a symbolic processing algorithm.
The major advantage of a linear model in GP is that its
parameters can be estimated by the ordinary least
square (OLS) method and a good model can be selected by
applying the modern minimum description length (MDL)
principle, while the nonlinearity necessary to handle
the given problem is effectively maintained by
indirectly evolving and finding various forms of base
functions. In addition to a standard linear model
consisting of mathematical functions, one variant of a
linear model, which can be built using low-order Taylor
series and can be converted into the standard form of a
polynomial, is considered in this paper. With small
samples, GP frequently shows the abnormal behaviors
such as extreme large peaks or odd-looking
discontinuities at the points away from sample points.
To overcome this problem, a directional
derivative-based smoothing (DDBS) method, which is
incorporated into the OLS method, is introduced
together with the fitness function that is based on
MDL, reflecting the effects of DDBS. Also, two
illustrative examples and three engineering
applications are presented.",
- }
Genetic Programming entries for
Yun Seog Yeun
Won-Sun Ruy
Young-Soon Yang
Nam-Joon Kim
Citations