Neuro-evolution versus Particle Swarm Optimization for competitive co-evolution of pursuit-evasion behaviors

This paper presents a study that compares the efficacy of Neuro-Evolution (NE) versus Particle Swarm Optimization (PSO) for evolving Artificial Neural Network (ANN) controllers in an unsupervised adaptation process. The research objective is to ascertain which adaptive method is most appropriate for deriving agent behaviors in a competitive co-evolution pursuit-evasion task. This task requires one predator agent to capture one prey agent in a simulation where behavior adaptation is guided by an arms race of competitive co-evolution. Results indicate that NE was overall more effective at deriving pursuit and evasion behaviors according to the task performance measures defined for this study.