Neuro-Evolution approaches to collective behavior

This paper is a preliminary study of the types of collective behavior tasks that are best solved by neuro-evolution (NE). This research tests a hypothesis that for a multi-rover task, the best approach (for deriving effective collective behaviors) is to evolve complete artificial neural network (ANN) controllers, and then combine controller behaviors in a collective behavior context. Such methods are called multi-agent conventional neuro-evolution (multi-agent CNE). This is opposed to methods such as enforced sub-populations (ESP) which evolves individual neurons and then combines them to form complete ANN controllers. Single and multi-agent CNE and ESP approaches to evolving collective behavior solutions are tested comparatively in the multi-rover task. The multi-rover task requires that teams of rovers (controllers) cooperate in order to detect features of interest in a virtual environment. Results indicate that a multi-agent CNE approach derives rover teams with a higher task performance and genotype diversity, comparative to ESP.