Advanced Equilibrium Analysis in Game-Theoretic Models: Extending Evolutionary Algorithms to Complex Strategic Scenarios

This article presents a comprehensive empirical and theoretical evaluation of an evolutionary optimization algorithm designed to locate multiple equilibria in game-theoretic models. Building upon a general-purpose framework established in [1], the algorithm is extended to complex strategic scenarios, including mixed strategies, dynamic environments, and high-dimensional strategy spaces. Its performance is rigorously analyzed across various models, examining the impact of parameters such as population size and mutation rate on convergence dynamics.