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An Information-Based Elite-Guided Evolutionary Algorithm for Multi-Objective Feature Selection

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摘要 Dear Editor, This letter is concerned with the evolution strategy for addressing multi-objective feature selection problems in classification. Previous methods suffer from limitations such as being trapped in local optima and lacking stability. To overcome them, we propose a novel eliteguided mechanism based on information theory. Firstly, an elite solution is generated through a dimension reduction strategy and incorporated to the initialization population.
出处 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期264-266,共3页 自动化学报(英文版)
基金 supported in part by the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP22H 03643) the Japan Science and Technology Agency (JST) (the establishment of university fellowships towards the creation of science technology innovation) (JPMJFS2115)。
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