Literature Review on Hybrid Evolutionary Approaches for Feature Selection

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), crucial preprocessing step in that seeks out ideal set characteristics with maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers concentrating on variety metaheuristic (or evolutionary) algorithms trying suggest cutting-edge hybrid techniques handle FS issues. use approaches for has thus been subject numerous research works. purpose this paper is critically assess existing give thorough literature review hybridization different metaheuristic/evolutionary strategies have employed supporting FS. This article reviews pertinent documents frameworks were published period from 2009 2022 offers analysis used classifiers, datasets, applications, assessment metrics, schemes hybridization. Additionally, new open issues challenges identified pinpoint areas be further explored additional study.