Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic Programming, Ensemble Methods and the Bias/Variance Tradeoff - Introductory Investigations
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Overfitting avoidance in genetic programming of polynomials
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Effects of code growth and parsimony pressure on populations in genetic programming
Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On Improving Generalisation in Genetic Programming
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
GECCO '96 Proceedings of the 1st 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
Regularization approach to inductive genetic programming
IEEE Transactions on Evolutionary Computation
Validation sets for evolutionary curtailment with improved generalisation
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
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 proposes to improve the performance of Genetic Programming (GP) over unseen data by minimizing the variance of the output values of evolving models alongwith reducing error on the training data. Variance is a well understood, simple and inexpensive statistical measure; it is easy to integrate into a GP implementation and can be computed over arbitrary input values even when the target output is not known. Moreover, we propose a simple variance based selection scheme to decide between two models (individuals). The scheme is simple because, although it uses bi-objective criteria to differentiate between two competing models, it does not rely on a multi-objective optimisation algorithm. In fact, standard multi-objective algorithms can also employ this scheme to identify good trade-offs such as those located around the knee of the Pareto Front. The results indicate that, despite some limitations, these proposals significantly improve the performance of GP over a selection of high dimensional (multi-variate) problems from the domain of symbolic regression. This improvement is manifested by superior results over test sets in three out of four problems, and by the fact that performance over the test sets does not degrade as often witnessed with standard GP; neither is this performance ever inferior to that on the training set. As with some earlier studies, these results do not find a link between expressions of small sizes and their ability to generalise to unseen data.