Accelerated Genetic Programming of Polynomials
Genetic Programming and Evolvable Machines
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Constant creation in grammatical evolution
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Information Sciences: an International Journal
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EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Variance based selection to improve test set performance in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Long memory time series forecasting by using genetic programming
Genetic Programming and Evolvable Machines
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This paper presents an approach to regularization of inductive genetic programming tuned for learning polynomials. The objective is to achieve optimal evolutionary performance when searching high-order multivariate polynomials represented as tree structures. We show how to improve the genetic programming of polynomials by balancing its statistical bias with its variance. Bias reduction is achieved by employing a set of basis polynomials in the tree nodes for better agreement with the examples. Since this often leads to over-fitting, such tendencies are counteracted by decreasing the variance through regularization of the fitness function. We demonstrate that this balance facilitates the search as well as enables discovery of parsimonious, accurate, and predictive polynomials. The experimental results given show that this regularization approach outperforms traditional genetic programming on benchmark data mining and practical time-series prediction tasks