| 引用本文: | 张葛祥, 金炜东, 胡来招.基于量子遗传算法的特征选择算法[J].控制理论与应用,2005,22(5):810~813.[点击复制] |
| ZHANG Ge-xiang,JIN Wei-dong,HU Lai-zhao.Feature selection algorithm based on quantum genetic algorithm[J].Control Theory and Technology,2005,22(5):810~813.[点击复制] |
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| 基于量子遗传算法的特征选择算法 |
| Feature selection algorithm based on quantum genetic algorithm |
| 摘要点击 2150 全文点击 1901 投稿时间：2003-06-24 修订日期：2004-02-23 |
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| DOI编号 |
| 2005,22(5):810-813 |
| 中文关键词 遗传算法 特征选择 量子理论 量子遗传算法 |
| 英文关键词 genetic algorithm feature selection quantum theory quantum genetic algorithm |
| 基金项目 电子对抗技术预研基金项目(NEWL51435QT220401);国家自然科学基金资助项目(69574026);西南交通大学博士生创新基金资助项目(2003) |
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| 中文摘要 |
| 特征选择是模式识别和机器学习等领域中重要而困难的研究课题.提出一种最优特征子集评价准则和实现特征选择的一种新量子遗传算法(NQGA).NQGA采用量子门旋转角更新新方法和增强算法寻优能力及防止早熟收敛的移民和灾变策略.定性分析了NQGA的高效性.典型复杂函数测试和雷达辐射源信号特征选择的应用表明,NQGA寻优能力强、收敛速度快和能有效防止早熟现象.采用提出的准则函数和搜索策略实现特征选择,大大降低了特征维数,获得了更高的正确识别率. |
| 英文摘要 |
| Feature selection is always an important and difficult issue in pattern recognition and machine learning.This paper proposed a criterion function for selecting the optimal feature subset and a search strategy called novel quantum genetic algorithm(NQGA).NQGA adopted a novel update approach of rotation angles of quantum gates,and immigration and catastrophe operations to enhance search capability and to avoid premature convergence.Besides,high efficiency of NQGA was analyzed qualitatively.Testing results of typically complex functions and experimental results of feature selection in radar emitter signal recognition show that NQGA has good characteristics of strong search capability,rapid convergence and no premature convergence.The proposed feature selection algorithm reduces greatly the dimensions of original feature set and heightens accurate recognition rate of radar emitter signals. |
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