Penalty Functions for Genetic Programming Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{conf/iccsa/MontanaABD11,

• author = "Jose L. Montana and Cesar Luis Alonso and Cruz Enrique Borges and Javier {de la Dehesa}",
• title = "Penalty Functions for Genetic Programming Algorithms",
• booktitle = "Proceedings of the International Conference on Computational Science and Its Applications (ICCSA 2011) Part {I}",
• year = "2011",
• editor = "Beniamino Murgante and Osvaldo Gervasi and Andres Iglesias and David Taniar and Bernady O. Apduhan",
• volume = "6782",
• pages = "550--562",
• series = "Lecture Notes in Computer Science",
• address = "Santander, Spain",
• month = jun # " 20-23",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, symbolic regression, inductive learning, regression model selection",
• isbn13 = "978-3-642-21927-6",
• DOI = "doi:10.1007/978-3-642-21928-3_40",
• size = "13 pages",
• 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.",
• affiliation = "Departamento de Matematicas, Estadistica y Computacion, Universidad de Cantabria, 39005 Santander, Spain",
• bibdate = "2011-06-20",
• 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