Monte Carlo Methods
A comparative study of four evolutionary optimization algorithms (MCTS, PUCT, Parallel MCTS, and GNRPA) within the context of a simulated dynamic ecosystem. The aim is to analyze their ability to maximize species survival through genetic adaptation, resource management, and population balance. * The results show that algorithm combinations (PUCT + Logistic Growth + Adaptive Feedbacks) offer the best trade-offs between stability and efficiency, with performance varying depending on environmental constraints. An interactive implementation is available online to visualize the evolutionary dynamics.