特征选择的多准则融合差分遗传算法及其应用

为了全面评价特征子集的好坏，提高特征子集作为最佳子集的可靠性，以及更快找到最佳子集，提出了一种用于特征选择的多准则融合差分遗传算法。引入多个评价准则对特征子集进行评价，并对遗传算法的选择算子进行改进，有利于选出适应度高且具有重要特征的个体；同时，引入差分策略改进变异算子，提高种群多样性和算法搜索能力；最后通过仿真实验和滚动轴承实例验证了该方法的有效性。

Feature selection method based on differential evolution and genetic algorithm with multi-criteria evaluation and its applications

In order to make a whole evaluation to the selected feature subset, which improves the reliability of the best subset and the speed of its searching, the paper presents a novel feature method based on differential evolution and genetic algorithm with multi-criteria evaluation. This algorithm is used to evaluate the feature subset by the multi-criteria evaluation. Meanwhile, the improved genetic operators were proposed, which improves the selection operator and the mutation operator. Designing the selection operator with a combination of feature weight values and fitness is beneficial to selecting the individuals which contain the high fitness and important features from the population. In addition, it introduces differential strategy to improve mutation operator, which improves the diversity of evolution population and searching efficiency. Finally, simulation example tests the validity of the proposed algorithm. The validity of the proposed method is also verified with rolling bearing fault diagnosis.