Feature selection algorithm based on quantum genetic algorithm
基于量子遗传算法的特征选择算法

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.