No free lunch for early stopping
Neural Computation
Machine Learning
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
GEVA: grammatical evolution in Java
ACM SIGEVOlution
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
IEEE Transactions on Neural Networks
Where should we stop? an investigation on early stopping for GP learning
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Adaptive distance metrics for nearest neighbour classification based on genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Hi-index | 0.00 |
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.