International Journal of Electrical and Computer Engineering (IJECE) Vol. 14, No. 1, February 2024, pp. 944~959 ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp944-959  944 Journal homepage: http://ijece.iaescore.com Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques Salsabila Benghazouani 1 , Said Nouh 1 , Abdelali Zakrani 2 , Ihsane Haloum 3 , Mostafa Jebbar 4 1 Department of Mathematics and Computer Science, Faculty of Sciences Ben M’Sick, Hassan II University, Casablanca, Morocco 2 Department of Computer Science Engineering, ENSAM, Hassan II University, Casablanca, Morocco 3 Department of Immunogenetics and Human Pathologies, Faculty of Medicine and Pharmacy, Hassan II University, Casablanca, Morocco 4 Departement of Mathematics and Computer Science, EST, Hassan II University, Casablanca, Morocco Article Info ABSTRACT Article history: Received Oct 20, 2022 Revised Sep 2, 2023 Accepted Sep 15, 2023 Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance. Keywords: Feature selection Genetic algorithms Machine learning Multi-objective optimization Swarm intelligence This is an open access article under the CC BY-SA license. Corresponding Author: Salsabila Benghazouani Department of Mathematics and Computer Science, Faculty of Sciences Ben M’Sick, Hassan II University Casablanca, Morocco Email: benghazouani.salsabila239@gmail.com 1. INTRODUCTION Feature selection plays a crucial role in the preprocessing phase of machine learning, it eliminates irrelevant and redundant features (noisy attributes), which increases the performance of a classifier and reduces the computational complexity [1]. In the healthcare sector, feature identification and selection play a vital role in enhancing accuracy in prediction, classification, and detection systems. This crucial preprocessing step not only enables reduction of dimensionality but also permits a better understanding of pathologies [2]. In an exhaustive search space, the number of possible combinations to determine the most relevant and non-redundant features is 2 n , where n represents the number of features (NP-complete problem)