Tuite, ClíodhnaClíodhnaTuiteAgapitos, AlexandrosAlexandrosAgapitosO'Neill, MichaelMichaelO'NeillBrabazon, AnthonyAnthonyBrabazon2012-06-142012-06-142011 Sprin2011-04-27http://hdl.handle.net/10197/3655EvoFIN 2011, 5th European Event on Evolutionary and Natural Computation in Finance and Economics in EvoApplications, Torino, Italy, 27-29 April 2011This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early training stopping criterion is the moment within the learning process that an unbiased estimate of the performance of the model begins to decrease after a strictly monotonic increase through the earlier learning iterations. We are conducting an initial investigation on the effects of early stopping in the performance of Genetic Programming in symbolic regression and financial modelling. Empirical results suggest that early stopping using the above criterion increases the extrapolation abilities of symbolic regression models, but is by no means the optimal training-stopping criterion in the case of a real-world financial dataset.1212123 bytesapplication/pdfenThe final publication is available at springerlink.comGenetic programmingOverfittingFinancial modellingGeneralisationGenetic programming (Computer science)Evolutionary computationFinance--Computer simulationConference Publication10.1007/978-3-642-20520-0_13https://creativecommons.org/licenses/by-nc-sa/1.0/