This paper introduces a novel hybrid genetic algorithm (HGA) for feature selection, which aims to improve the performance of traditional genetic algorithms (GAs) by incorporating local search operations. The local search operations, parameterized by a ripple factor, are designed to fine-tune solutions and enhance convergence. The HGA is compared with both simple GAs and sequential search algorithms using various standard datasets. Experimental results show that the HGA outperforms the simple GA and sequential search algorithms, particularly in large-scale problems. The HGA's effectiveness is attributed to its ability to improve overall performance and control subset size. A rigorous timing analysis is also conducted to compare the computational efficiency of different algorithms, demonstrating that the HGA can significantly reduce computation time. The study concludes that the HGA is superior to other feature selection algorithms in terms of both accuracy and computational efficiency.This paper introduces a novel hybrid genetic algorithm (HGA) for feature selection, which aims to improve the performance of traditional genetic algorithms (GAs) by incorporating local search operations. The local search operations, parameterized by a ripple factor, are designed to fine-tune solutions and enhance convergence. The HGA is compared with both simple GAs and sequential search algorithms using various standard datasets. Experimental results show that the HGA outperforms the simple GA and sequential search algorithms, particularly in large-scale problems. The HGA's effectiveness is attributed to its ability to improve overall performance and control subset size. A rigorous timing analysis is also conducted to compare the computational efficiency of different algorithms, demonstrating that the HGA can significantly reduce computation time. The study concludes that the HGA is superior to other feature selection algorithms in terms of both accuracy and computational efficiency.