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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Controlling Overfitting in Symbolic Regression Based on a Bias/Variance Error Decomposition
verfasst von : Alexandros Agapitos, Anthony Brabazon, Michael O’Neill
Erschienen in: Parallel Problem Solving from Nature - PPSN XII
Verlag: Springer Berlin Heidelberg
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We consider the fundamental property of generalisation of data-driven models evolved by means of Genetic Programming (GP). The statistical treatment of decomposing the regression error into bias and variance terms provides insight into the generalisation capability of this modelling method. The error decomposition is used as a source of inspiration to design a fitness function that relaxes the sensitivity of an evolved model to a particular training dataset. Results on eight symbolic regression problems show that new method is capable on inducing better-generalising models than standard GP for most of the problems.