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.
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