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ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Open BEAGLE: A New C++ Evolutionary Computation Framework
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Backwarding: An Overfitting Control for Genetic Programming in a Remote Sensing Application
Selected Papers from the 5th European Conference on Artificial Evolution
Preventing overfitting in GP with canary functions
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On Improving Generalisation in Genetic Programming
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Measuring bloat, overfitting and functional complexity in genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Abstract functions and lifetime learning in genetic programming for symbolic regression
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Variance based selection to improve test set performance in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Drawing boundaries: using individual evolved class boundaries for binary classification problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Genetic programming, validation sets, and parsimony pressure
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Genetic programming and evolutionary generalization
IEEE Transactions on Evolutionary Computation
A bootstrapping approach to reduce over-fitting in genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper investigates the leveraging of a validation data set with Genetic Programming (GP) to counteract over-fitting. It considers fitness on both training and validation fitness, combined with with an early stopping mechanism to improve generalisation while significantly reducing run times. The method is tested on six benchmark binary classification data sets. Results of this preliminary investigation suggest that the strategy can deliver equivalent or improved results on test data.