Integration of Specific Local Search Methods in Metaheuristic Algorithms for Optimizing the Feature Selection Process: A Survey Authors

Abstract

Metaheuristic algorithms have proven to be quite effective at solving global optimization issues, particularly feature selection difficulties. This class of algorithms often uses a specialized local search technique as an inner component or as a post-processing mechanism to improve the performance of their search process. This paper presents a comprehensive survey of the use of local search methods integrated into metaheuristic algorithms for optimizing the feature selection process. Based on the manner of operation, the local search methods examined in this study were classed as one-way or two-way. In addition, practical suggestions were also discussed to point out possible future directions.