Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3583 2019 2019 Department of Computer Science - College of Science University of Diyala, Iraq Ismael Ismael Department of Computer Science - College of Science University of Diyala, Iraq Dheyab Salman Ibrahim Department of Computer Science - College of Science University of Diyala, Iraq Bashar Talib AL AL-Nuaimi Feature selection (FS) is a crucial preprocessing step in data mining to eliminate redundant or irrelevant features from high-dimensional data. Many optimization algorithms for FS often lack balance in their search processes. This paper proposes a hybrid algorithm, the Artificial Hummingbird Algorithm based on the Genetic Algorithm (AHA-GA), to address this imbalance and solve the FS problem. The main goal of AHA-GA is to select the most crucial characteristics to improve overall model categorization. The UCI datasets are used to assess the performance of the proposed FS method. The proposed feature selection algorithm was compared with five feature selection optimization algorithms: BWOAHHO, HSGW, WOA-CM, BDA-SA, and ASGW. AHA-GA achieved a classification accuracy of 96% across 18 datasets, which was higher than BWOAHHO (93.2%), HSGW (92.5%), WOA-CM (94.4%), BDA-SA (93%), and ASGW (91.6%). When comparing the proposed AHA-GA algorithm to the results obtained by the other five algorithms in terms of selected attribute size, the average feature sizes were as follows: AHA-GA (15.10889), BWOAHHO (16.74222), HSGW (19.43111), WOA-CM (17.05389), BDA-SA (17.275), and ASGW (19.7585). The statistical and experimental tests demonstrated that the proposed AHA-GA performs better than competitive algorithms in selecting effective features. 2025 2025 86 101 10.54216/JISIoT.160108 https://www.americaspg.com/articleinfo/18/show/3583