To address the inherent stochastic nature of genetic algorithms which can result in diverse outcomes due to their random selection and mutation processes, we have implemented a strategy that involves running multiple independent genetic algorithms in parallel. By conducting these algorithms separately, we can harness the variability in their search patterns, thereby mitigating the risk of converging to local optima that might be mistaken for global solutions. Upon completion of these independent runs, we aggregate their results, systematically comparing and combining the distinct solutions to identify the most optimal outcomes. This approach not only leverages the exploratory strengths of genetic algorithms but also significantly enhances the robustness of the solution set, ensuring that the best possible solutions are identified amidst the randomness.