Optimal Population

Optimal population size in optimization algorithms is a crucial research area focusing on finding the ideal number of solutions to maintain during the search process. Current research investigates the performance of various evolutionary algorithms, including those employing diverse offspring populations and self-play mechanisms, across different problem types and diversity measures. These studies aim to improve algorithm efficiency and solution quality by mitigating issues like genetic drift and premature convergence, ultimately impacting the effectiveness of optimization techniques in diverse fields such as procedural content generation and game AI. The findings contribute to a deeper understanding of algorithm behavior and guide the development of more robust and efficient optimization strategies.