Most metaheuristic methods rely on strategies that are generic and fixed. Consequently, these methods often struggle to adapt their performance to different scenarios or challenges encountered during the search process. This chapter presents a metaheuristic algorithm designed to dynamically adjust its search strategy throughout the optimization process, improving overall efficiency. The algorithm is built upon Evolutionary Strategies (ES) due to their remarkable self-adaptive capabilities. We have integrated concepts from Evolutionary Game Theory (EGT) to enhance its performance further. This combination enables a more robust strategy adaptation process by considering not only the specific agent’s information but also insights from other members of the population. Furthermore, our approach modifies the traditional EGT mechanism by incorporating, in addition to pairwise evaluations, data from the top-performing individuals in the population based on their results. This expanded adaptation approach facilitates quicker convergence to the most effective dominant strategy. To validate the effectiveness of our method, we benchmarked it against several established metaheuristic algorithms using 28 diverse test functions. The results demonstrate that our approach achieves competitive outcomes, delivering higher-quality solutions and faster convergence speeds.