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Some Improvements of Genetic Programming in Data Fitting

Journal Advanced Materials Research (Volumes 201 - 203)
Volume Advanced Manufacturing Systems
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 Hu Jie et al., 2011, Advanced Materials Research, 201-203, 2536
Online since February, 2011
Authors Hu Jie, Jia Quan Feng, Da Lin Chen
Keywords Data Fitting, Genetic Algorithm (GA), Genetic Programming, Prediction
Abstract

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.

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