Some Improvements of Genetic Programming in Data Fitting

Abstract:

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This paper proposed some improvement measures of Genetic Programming (GP) in data fitting, including developed new ways of crossover and mutation, improved the calculation efficiency greatly, and avoided the problem of parse tree expansion. The new adopted mutation method improved the problem of constant modification to some extent. Numerical simulation obtained a considerable good fitting and prediction precision.

Info:

Periodical:

Advanced Materials Research (Volumes 201-203)

Edited by:

Daoguo Yang, Tianlong Gu, Huaiying Zhou, Jianmin Zeng and Zhengyi Jiang

Pages:

2536-2539

DOI:

10.4028/www.scientific.net/AMR.201-203.2536

Citation:

H. Jie et al., "Some Improvements of Genetic Programming in Data Fitting", Advanced Materials Research, Vols. 201-203, pp. 2536-2539, 2011

Online since:

February 2011

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Price:

$35.00

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