TRUJILLO, Leonardo; SILVA, Sara; LEGRAND, Pierrick; VANNESCHI, Leonardo

doi:10.1007/978-3-642-20407-4_23

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TRUJILLO, Leonardo
Instituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]

SILVA, Sara
Knowledge Discovery and Bioinformatics [KDBIO]
Evolutionary and Complex Systems Group [ECOS Group]

LEGRAND, Pierrick
Institut de Mathématiques de Bordeaux [IMB]
Advanced Learning Evolutionary Algorithms [ALEA]

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EuroGP, EuroGP, EuroGP, 2011-04-27, Turin. 2011p. 262-273

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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.< Réduire

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An empirical study of functional complexity as an indicator of overfitting in Genetic Programming

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