Integration of Evolutionary Game Theory and Cooperative Algorithms

The paper introduces a novel algorithm that integrates evolutionary game theory with cooperative algorithms. Evolutionary game theory solves game-theoretic problems through evolutionary mechanisms, while cooperative algorithms achieve superior outcomes through collaborative strategies. This hybrid algorithm implements fitness-based strategy evolution using population dynamics, coupled with cooperative mechanisms that enable information sharing and joint optimization among agents. The implementation typically involves defining payoff matrices, strategy update rules using replicator dynamics, and cooperative decision-making modules that can be coded using probabilistic selection functions. This algorithmic fusion demonstrates enhanced performance in broader application scenarios such as information diffusion in social networks and collective intelligence decision-making systems. We anticipate this algorithm will deliver significant innovations in multi-agent systems and complex network optimization.