PSO approaches to coevolve IPD strategies

This paper investigates two different approaches using particle swarm optimisation (PSO) to evolve strategies for iterated prisoner´s dilemma (IPD). Strategies evolved by the lesser known binary PSO algorithm are compared to strategies evolved by neural networks that were trained using PSO. Evolved strategies are compared against well-known game theory strategies, with positive results. The presence of noise during IPD interactions are also investigated, and evolved strategies are compared against the same well-known game theory strategies in a noisy environment.