Model-driven regularization approach to straight line program genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Montana:2016:ESA,
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author = "Jose L. Montana and Cesar L. Alonso and
Cruz E. Borges and Cristina Tirnauca",
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title = "Model-driven regularization approach to straight line
program genetic programming",
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journal = "Expert Systems with Applications",
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volume = "57",
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pages = "76--90",
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year = "2016",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2016.03.003",
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URL = "http://www.sciencedirect.com/science/article/pii/S095741741630094X",
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abstract = "This paper presents a regularization method for
program complexity control of linear genetic
programming tuned for transcendental elementary
functions. Our goal is to improve the performance of
evolutionary methods when solving symbolic regression
tasks involving Pfaffian functions such as polynomials,
analytic algebraic and transcendental operations like
sigmoid, inverse trigonometric and radial basis
functions. We propose the use of straight line programs
as the underlying structure for representing symbolic
expressions. Our main result is a sharp upper bound for
the Vapnik Chervonenkis dimension of families of
straight line programs containing transcendental
elementary functions. This bound leads to a
penalization criterion for the mean square error based
fitness function often used in genetic programming for
solving inductive learning problems. Our experiments
show that the new fitness function gives very good
results when compared with classical statistical
regularization methods (such as Akaike and Bayesian
Information Criteria) in almost all studied situations,
including some benchmark real-world regression
problems.",
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keywords = "genetic algorithms, genetic programming, Straight line
program, Pfaffian operator, Symbolic regression",
- }
Genetic Programming entries for
Jose Luis Montana Arnaiz
Cesar Luis Alonso
Cruz Enrique Borges
Cristina Tirnauca
Citations