Computer and Modernization ›› 2025, Vol. 0 ›› Issue (02): 33-43.doi: 10.3969/j.issn.1006-2475.2025.02.05

Previous Articles     Next Articles

Survey on Intelligent Optimization Algorithm for Feature Selection

  

  1. (School of Management, Guangdong University of Technology, Guangzhou 510520, China)
  • Online:2025-02-28 Published:2025-02-28

Abstract:  Feature selection, as one of the main techniques in data preprocessing, can effectively identify key features, thereby reducing dimensionality and effectively addressing the issue of “curse of dimensionality”. Feature selection is a typical NP-hard problem, and intelligent optimization algorithm have been widely employed in feature selection due to their remarkable global search ability. Firstly, this paper summarizes methods for evaluating feature importance and parameters updating. The former is used for evaluating the relevance and redundancy of features, while the latter is used for updating algorithm parameters. These two methodologies are both applicable to various crucial steps of intelligent optimization algorithm for feature selection. Then, the strategic design of three core steps in the process, namely algorithm initialization, population search, and objective function design, is introduced. The initialization strategy is summarized from the perspectives of decision space initialization and population initialization, with an analysis of the advantages and limitations of different strategies. Based on the population quantity, a detailed classification of search strategies for single population and multiple population is provided. According to the different metrics applied in the objective function, a categorization of objective function design can be summarized. Finally, it discusses future work for intelligent optimization algorithm to feature selection.

Key words: feature selection, intelligent optimization algorithm, initialization strategy, search strategy, objective function

CLC Number: