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Communication Dans Un Congrès Année : 2011

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

Résumé

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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Dates et versions

hal-00642530 , version 1 (18-11-2011)

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Leonardo Trujillo, Sara Silva, Pierrick Legrand, Leonardo Vanneschi. An empirical study of functional complexity as an indicator of overfitting in Genetic Programming. EuroGP, Apr 2011, Turin, Italy. pp.262-273, ⟨10.1007/978-3-642-20407-4_23⟩. ⟨hal-00642530⟩
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