Abstract
In this paper, we propose a variation on the fitness function in Genetic Programming based on Bias-Variance Genetic Programming (BVGP) [2], called BVGP*. In order to evaluate the effectiveness of this variation, we compare it with Genetic Programming [1] and Bias-Variance Genetic Programming (BVGP) [2]. The experimental results shown that the learned model by BVGP* is better than that of GP and BVGP in ability to generalize, model complexity and evaluation time.
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Thi, T.P., Nguyen, X.H., Nguyen, T.T. (2017). A Study on Fitness Representation in Genetic Programming. In: Akagi, M., Nguyen, TT., Vu, DT., Phung, TN., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2016. Advances in Intelligent Systems and Computing, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-49073-1_13
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DOI: https://doi.org/10.1007/978-3-319-49073-1_13
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