A symbolic data-driven technique based on evolutionary polynomial regression
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- @Article{Giustolisi:2006:JH,
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author = "Orazio Giustolisi and Dragan A. Savic",
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title = "A symbolic data-driven technique based on evolutionary
polynomial regression",
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journal = "Journal of Hydroinformatics",
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year = "2006",
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volume = "8",
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number = "3",
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pages = "207--222",
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month = jul,
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keywords = "genetic algorithms, genetic programming, EPR, Chezy
resistance coefficient, Colebrook-White formula,
data-driven modelling, evolutionary computing,
regression",
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ISSN = "1464-7141",
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URL = "http://www.iwaponline.com/jh/008/0207/0080207.pdf",
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DOI = "doi:10.2166/hydro.2006.020b",
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size = "16 pages",
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abstract = "This paper describes a new hybrid regression method
that combines the best features of conventional
numerical regression techniques with the genetic
programming symbolic regression technique. The key idea
is to employ an evolutionary computing methodology to
search for a model of the system/process being modelled
and to employ parameter estimation to obtain constants
using least squares. The new technique, termed
Evolutionary Polynomial Regression (EPR) overcomes
shortcomings in the GP process, such as computational
performance; number of evolutionary parameters to tune
and complexity of the symbolic models. Similarly, it
alleviates issues arising from numerical regression,
including difficulties in using physical insight and
over-fitting problems. This paper demonstrates that EPR
is good, both in interpolating data and in scientific
knowledge discovery. As an illustration, EPR is used to
identify polynomial formulae with progressively
increasing levels of noise, to interpolate the
Colebrook-White formula for a pipe resistance
coefficient and to discover a formula for a resistance
coefficient from experimental data.",
- }
Genetic Programming entries for
Orazio Giustolisi
Dragan Savic
Citations