An empirical study of functional complexity as an indicator of overfitting in Genetic Programming
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- @InProceedings{trujillo:2011:EuroGP,
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author = "Leonardo Trujillo and Sara Silva and
Pierrick Legrand and Leonardo Vanneschi",
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title = "An empirical study of functional complexity as an
indicator of overfitting in Genetic Programming",
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booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
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year = "2011",
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month = "27-29 " # apr,
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editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
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series = "LNCS",
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volume = "6621",
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publisher = "Springer Verlag",
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address = "Turin, Italy",
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pages = "262--273",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming: poster",
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isbn13 = "978-3-642-20406-7",
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DOI = "doi:10.1007/978-3-642-20407-4_23",
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abstract = "Recently, it has been stated that the complexity of a
solution is a good indicator of the amount of
overfitting it incurs. However, measuring the
complexity of a program, in Genetic Programming, is not
a trivial task. In this paper, we study the functional
complexity and how it relates with overfitting on
symbolic regression problems. We consider two measures
of complexity, Slope-based Functional Complexity,
inspired by the concept of curvature, and
Regularity-based Functional Complexity based on the
concept of Holderian regularity. In general, both
complexity measures appear to be poor indicators of
program overfitting. However, results suggest that
Regularity-based Functional Complexity could provide a
good indication of overfitting in extreme cases.",
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notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
- }
Genetic Programming entries for
Leonardo Trujillo
Sara Silva
Pierrick Legrand
Leonardo Vanneschi
Citations