Early stopping criteria to counteract overfitting in genetic programming

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.