Penalty Functions for Genetic Programming Algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{conf/iccsa/MontanaABD11,
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author = "Jose L. Montana and Cesar Luis Alonso and
Cruz Enrique Borges and Javier {de la Dehesa}",
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title = "Penalty Functions for Genetic Programming Algorithms",
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booktitle = "Proceedings of the International Conference on
Computational Science and Its Applications (ICCSA 2011)
Part {I}",
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year = "2011",
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editor = "Beniamino Murgante and Osvaldo Gervasi and
Andres Iglesias and David Taniar and Bernady O. Apduhan",
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volume = "6782",
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pages = "550--562",
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series = "Lecture Notes in Computer Science",
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address = "Santander, Spain",
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month = jun # " 20-23",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, symbolic
regression, inductive learning, regression model
selection",
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isbn13 = "978-3-642-21927-6",
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DOI = "doi:10.1007/978-3-642-21928-3_40",
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size = "13 pages",
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abstract = "Very often symbolic regression, as addressed in
Genetic Programming (GP), is equivalent to approximate
interpolation. This means that, in general, GP
algorithms try to fit the sample as better as possible
but no notion of generalisation error is considered. As
a consequence, overfitting, code-bloat and noisy data
are problems which are not satisfactorily solved under
this approach. Motivated by this situation we review
the problem of Symbolic Regression under the
perspective of Machine Learning, a well founded
mathematical toolbox for predictive learning. We
perform empirical comparisons between classical
statistical methods (AIC and BIC) and methods based on
Vapnik-Chrevonenkis (VC) theory for regression problems
under genetic training. Empirical comparisons of the
different methods suggest practical advantages of
VC-based model selection. We conclude that VC theory
provides methodological framework for complexity
control in Genetic Programming even when its technical
results seems not be directly applicable. As main
practical advantage, precise penalty functions founded
on the notion of generalisation error are proposed for
evolving GP-trees.",
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affiliation = "Departamento de Matematicas, Estadistica y
Computacion, Universidad de Cantabria, 39005 Santander,
Spain",
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bibdate = "2011-06-20",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/iccsa/iccsa2011-1.html#MontanaABD11",
- }
Genetic Programming entries for
Jose Luis Montana Arnaiz
Cesar Luis Alonso
Cruz Enrique Borges
Javier de la Dehesa
Citations