非支配遗传算法(NSGA-Ⅱ)对双目标的特征筛选可以取得很好的效果，但该算法在优化过程中会出现局部收敛和早熟问题． 为此，文中提出了改进的 NSGA-Ⅱ特征筛选算法:先对父代种群运行第 1 次精英策略，从中筛选出父代精英种群;然后将筛选后的父代精英种群与子代种群构成联合种群，并对联合种群运行第 2 次精英策略获得下一父代种群． 在利用文中算法对三维人脸表情的候选特征进行筛选后，通过概率神经网络算法对筛选特征进行分类识别． 结果表明，文中算法可以在很大程度上解决传统 NSGA-Ⅱ的局部收敛和早熟问题，并能有效地提升表情识别的准确性．

Non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) works effectively in selecting bi-objective fea-tures，but may result in local convergence and prematurity in the process of optimization． In order to solve this problem，an improved NSGA-Ⅱ feature selection algorithm is proposed． In this algorithm，firstly，the first elite strategy is operated to select the elite population from parent population． Secondly，the selected parent elite popu-lation is combined with the offspring population to form a combined population． Finally，the second elite strategy is executed to obtain the next parent population． After the selection of 3D face expression candidate features，the selected features are classified by means of probabilistic neural network． Experimental results show that the pro-posed algorithm improves the performance of NSGA-Ⅱ with local convergence and prematurity problems greatly and increases the accuracy of facial expression recognition effectively．