特征选择的多准则融合差分遗传算法及其应用

doi:10.7527/S1000-6893.2016.0036

Abstract

Abstract:

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.

Key words: feature selection, multi-criteria, differential evolution, genetic algorithm, rolling bearing, fault diagnosis

CLC Number:

V263.6

GUAN Xiaoying, CHEN Guo, LIN Tong. Feature selection method based on differential evolution and genetic algorithm with multi-criteria evaluation and its applications[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(11): 3455-3465.

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

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