Symbolic Regression on Noisy Data with Stepwise Genetic Programming Algorithm

Hong Rui Zhang; Jian Hao; Yan Gang Lv; Li Zhao

doi:10.4028/www.scientific.net/AMM.530-531.625

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The Influence of Training Step on Price Forecasting Based on Least Squares Support Vector Machine
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Symbolic Regression on Noisy Data with Stepwise Genetic Programming Algorithm
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A Security Authentication Scheme in Wireless LAN for Portable Terminal
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A Wu‘s Method Based Parallelization Design and Implementation for Algebraic Cryptanalysis
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 530-531Symbolic Regression on Noisy Data with Stepwise...

Symbolic Regression on Noisy Data with Stepwise Genetic Programming Algorithm

Abstract:

In this paper, we present a stepwise genetic programming algorithm to perform regression on a large number of noisy data, the purpose of which is to find a mathematical model for samples. To obtain an accurate statistical model of noisy sample points, discrete cosine transform was inserted into a standard GP algorithm. The energy-compaction property of DCT makes it very suitable for accelerating the implement of the standard GP algorithm and dealing with the noisy data samples. We tested the proposed algorithm with benchmark instances and compared it with several popular other algorithms. The experimental results have shown that the proposed algorithm is a powerful tool in finding optimal solutions.