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

Abstract

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 coevolution 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 coevolution. Results indicate that NE was overall more effective at deriving pursuit and evasion behaviors according to the task performance measures defined for this study.

Lifetimes of migration

“…ABM is an analogical system that aids ethologists in constructing novel hypotheses, and allows the investigation of emergent phenomena in experiments that could not be conducted in nature [15], [2], [12], [11]. Many studies in ethology have formalized mathematical models of collective migration behavior [1], but few have examined the impact of phenotypic traits (such as lifetime length) on the learning and evolution of collective migration behavior [9], [4].…”