Abstract
This paper introduces a CGP (Cartesian Genetic Programming) based optimization and prediction techniques. In order to provide a superior search for optimization and a robust model for prediction, a nonlinear and symbolic regression method using CGP is suggested. CGP uses as genotype a linear string of integers that are mapped to a directed graph. Therefore, some evolved modules for regression polynomials in CGP network can be shared and reused among multiple outputs for prediction of neighborhood precipitation. To investigate the effectiveness of the proposed approach, experiments on gait generation for quadruped robots and prediction of heavy precipitation for local area of Korean Peninsular were executed.
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Seo, K., Hyeon, B. (2014). Cartesian Genetic Programming Based Optimization and Prediction. In: Rocha, Á., Correia, A., Tan, F., Stroetmann, K. (eds) New Perspectives in Information Systems and Technologies, Volume 1. Advances in Intelligent Systems and Computing, vol 275. Springer, Cham. https://doi.org/10.1007/978-3-319-05951-8_47
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DOI: https://doi.org/10.1007/978-3-319-05951-8_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05950-1
Online ISBN: 978-3-319-05951-8
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