Early stopping criteria to counteract overfitting in genetic programming

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Title: Early stopping criteria to counteract overfitting in genetic programming
Authors: Tuite, Clíodhna
Agapitos, Alexandros
O'Neill, Michael
Brabazon, Anthony
Permanent link: http://hdl.handle.net/10197/3538
Date: 2011
Abstract: Early stopping typically stops training the first time validation fitness disimproves. This may not be the best strategy given that validation fitness can subsequently increase or decrease. We examine the effects of stopping subsequent to the first disimprovement in validation fitness, on symbolic regression problems. Stopping points are determined using criteria which measure generalisation loss and training progress. Results suggest that these criteria can improve the generalistion ability of symbolic regression functions evolved using Grammar-based GP.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2011 The authors
Keywords: Grammatical evolutionSymbolic regressionOverfitting
Subject LCSH: Evolutionary computation
Genetic programming (Computer science)
DOI: 10.1145/2001858.2001971
Language: en
Status of Item: Peer reviewed
Is part of: GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, Dublin, Ireland, 12-16, July 2011
Conference Details: Presented at GECCO '11, the 13th annual conference companion on Genetic and evolutionary computation, Dublin, Ireland, 12-16, July 2011
Appears in Collections:FMC² Research Collection

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