关键词: 量子计算, 适应度函数, 量子遗传偏最小二乘法, 特征选择, 轴向柱塞泵

Abstract: In order to solve the problems of inaccurate population initial and complicated fitness function in feature selection, this paper proposes a novel feature selection algorithm—Improved Quantum Genetic Algorithm-Partial Least Square（IQGAPLS）. In IQGAPLS algorithm, according to the fact of problem, population is given initial value. Meanwhile, a new fitness function is designed for reducing computation amount. Based on previous fitness function, the formula for updating rotation angle is proposed. IQGAPLS is applied to feature selection for fault signal of axial piston pump. The experimental results indicate that IQGAPLS has less computation amount and shorter execution time. The selected features contain more information of fault states, which can enhance classification accuracy.

Key words: quantum computation, fitness function, Quantum Genetic Algorithm Partial Least Square（QGAPLS）, feature selection, axial piston pump