PSO Algorithm for IPD Game
Mechanisms promoting the evolution of cooperation in two-player, two-strategy evolutionary games have been discussed in great detail over the past decades. Understanding the effects of repeated interactions in multi-player with multi-choice is a formidable challenge. This paper presents and investigates the application of co-evolutionary training techniques based on Particle Swarm Optimization (PSO) to evolve cooperation for the Iterated Prisoner's Dilemma (IPD) game with multiple choices in noisy environment. Several issues will be addressed, which include the evolution of cooperation and the evolutionary stability in the presence of multiple choices and noise. First is using PSO approach to evolve cooperation. The second is the impact of noise on the evolution of cooperation is examined.