Work

Recent advances in genetic algorithm-based feature selection: A comprehensive survey

Year: 2026

Type: article

Abstract: Feature selection (FS) is vital for high-dimensional machine learning datasets. Genetic Algorithms (GAs) offer a powerful, flexible approach to FS due to their global search. This survey reviews GA-based FS advancements over the past five years, focusing on variations enhancing selection efficiency, convergence, and interpretability. We categorize population-level, selection/crossover-based, and mutation-based strategies, highlighting their impact across diverse domains like network security, bioinformatics, and medical diagnostics. The review also identifies limitations and proposes future research directions, including integration into AutoML and Explainable AI, providing a comprehensive overview for researchers and practitioners.

Source: Swarm and Evolutionary Computation

DOI: 10.1016/j.swevo.2026.102318

Authors: Raghad Aldamani, Meriem Aoudia, Diaa Addeen Abuhani, Omar Arif

Institution: American University of Sharjah

Language: English

Topic: Evolutionary Algorithms and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,690

Funder: American University of Sharjah