The nature of statistical learning theory
The nature of statistical learning theory
Genetic Programming and Evolvable Machines
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Extending Operator Equalisation: Fitness Based Self Adaptive Length Distribution for Bloat Free GP
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
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
Genetic programming, validation sets, and parsimony pressure
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on Evolutionary Computation
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Covariant parsimony pressure is a theoretically motivated method primarily aimed to control bloat. In this contribution we describe an adaptive method to control covariant parsimony pressure that is aimed to reduce overfitting in symbolic regression. The method is based on the assumption that overfitting can be reduced by controlling the evolution of program length. Additionally, we propose an overfitting detection criterion that is based on the correlation of the fitness values on the training set and a validation set of all models in the population. The proposed method uses covariant parsimony pressure to decrease the average program length when overfitting occurs and allows an increase of the average program length in the absence of overfitting. The proposed approach is applied on two real world datasets. The experimental results show that the correlation of training and validation fitness can be used as an indicator for overfitting and that the proposed method of covariant parsimony pressure adaption alleviates overfitting in symbolic regression experiments with the two datasets.