Metaheuristic Algorithms for Feature Selection (2014-2024)

Feature selection is a process used during machine learning and data analysis, aimed at selecting the best features to increase model efficiency, decrease complexity, and increase readability. Metaheuristic algorithms are suited to provide solutions to feature selection problems because these problems are combinatorial and require an effective and efficient search through large solution spaces. Over the last decade (2014–2024), numerous approaches have been explored, each with its own optimization strengths and constraints. Swarm intelligence and evolutionary algorithms—including genetic algorithms, particle swarm optimization, and the zebra optimization algorithm—have operated effectively in this area. This study seeks to discuss the metaheuristic algorithms for feature selection and responds to questions in the process. In this study, a case study is provided using datasets from the University of California, Irvine repository, where various metaheuristic algorithms are applied to identify optimal feature subsets.