量子GA-PLS特征选择算法及其应用

J4 ›› 2014, Vol. 31 ›› Issue (2): 194-201.

量子GA-PLS特征选择算法及其应用

李胜,张培林,李兵,周云川

1 军械工程学院七系，河北石家庄，050003； 2 军械工程学院四系，河北石家庄，050003； 3 军械工程学院军械技术研究所，河北石家庄，050003

收稿日期:2013-05-27 修回日期:2013-07-10 出版日期:2014-03-28 发布日期:2014-03-20
通讯作者: 张培林（1955-）博士，教授，博士研究生导师。研究方向为车辆维修理论与技术。 E-mail:zpl1955@163.com
作者简介:李胜（1986-），博士生，研究领域为车辆系统信号处理与智能故障诊断，E-mail：bcako@163.com
基金资助:
国家自然科学基金（E51205405）

Quantum GA-PLS for Feature Selection Method and its Application

Li Sheng, Zhang Peilin, Li Bing, Zhou Yunchuan

1 Department Seventh, Ordnance Engineering College, Shijiazhuang 050003, China; 2 Department Fourth, Ordnance Engineering College, Shijiazhuang 050003, China; 3 Ordnance Technology Research Institute, Ordnance Engineering College, Shijiazhuang 050003, China

Received:2013-05-27 Revised:2013-07-10 Published:2014-03-28 Online:2014-03-20

摘要/Abstract

摘要： 为进一步提高遗传算法-偏最小二乘法的计算速度和计算效率，将量子算法融合到遗传算法-偏最小二乘法中，提出一种新的特征选择方法—量子遗传算法-偏最小二乘法（Quantum Genetic Algorithm-Partial Square Least，QGA-PLS）算法。该方法利用量子态和叠加态原理对染色体进行编码，采用量子旋转门进行遗传操作，以实现参数的更新和增强种群多样性，同时，用量子计算重新构建了偏最小二乘法回归模型来计算个体适应度，以充分发挥快速收敛和全局优化能力。将方法应用于函数极值优化和Iris数据集的特征选择，实验结果表明，QGA-PLS在特征选择、运算时间和分类准确率方面优于QGA和GA-PLS，从而验证了QGA-PLS算法的有效性。

关键词: 量子光学, 量子遗传算法-偏最小二乘法, 量子计算, 特征选择

Abstract: To improve computation speed and efficiency of Genetic Algorithm-Partial Square Least (GA-PLS), a novel feature selection algorithm which combines quantum computation and GA-PLS (QGA-PLS) is proposed. In QGA-PLS algorithm, qubits and superposition of states are used for chromosome code. Quantum rotation gate is used for genetic operation to update parameters and enhance population diversity. Meanwhile, with PLS model which is reconstructed by quantum computing, the value of individual adaptability is calculated. Rapid convergence and good global optimization capability characterize the performance of QGA-PLS. The proposed method is applied to two simulation experiments, extreme value of a function and feature selection for Iris dataset. The experimental results indicated that, compared with QGA and GA-PLS, QGA-PLS has better performance in feature selection, execution time and classification accuracy, which proves the efficient of proposed method.

Key words: quantum optics, quantum genetic algorithm-partial square least (QGA-PLS), quantum computation, feature selection

中图分类号:

TP18

李胜张培林李兵周云川. 量子GA-PLS特征选择算法及其应用[J]. J4, 2014, 31(2): 194-201.

Li Sheng, Zhang Peilin, Li Bing, Zhou Yunchuan. Quantum GA-PLS for Feature Selection Method and its Application[J]. J4, 2014, 31(2): 194-201.

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