Variable Selection in Industrial Datasets using Pareto Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{smits:2005:GPTP,
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author = "Guido Smits and Arthur Kordon and
Katherine Vladislavleva and Elsa Jordaan and Mark Kotanchek",
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title = "Variable Selection in Industrial Datasets using Pareto
Genetic Programming",
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booktitle = "Genetic Programming Theory and Practice {III}",
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year = "2005",
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editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
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volume = "9",
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series = "Genetic Programming",
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chapter = "6",
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pages = "79--92",
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address = "Ann Arbor",
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month = "12-14 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Symbolic
Regression, Variable Selection, Pareto GP",
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ISBN = "0-387-28110-X",
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DOI = "doi:10.1007/0-387-28111-8_6",
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size = "14 pages",
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abstract = "This chapter gives an overview, based on the
experience from the Dow Chemical Company, of the
importance of variable selection to build robust models
from industrial datasets. A quick review of variable
selection schemes based on linear techniques is given.
A relatively simple fitness inheritance scheme is
proposed to do nonlinear sensitivity analysis that is
especially effective when combined with Pareto GP. The
method is applied to two industrial datasets with good
results.",
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notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after
the workshop",
- }
Genetic Programming entries for
Guido F Smits
Arthur K Kordon
Ekaterina (Katya) Vladislavleva
Elsa Jordaan
Mark Kotanchek
Citations