This article presents results from an evaluation
of the collective neuro-evolution (CONE)controller design method. CONE solves collective behavior tasks, and increases task performance via facilitating emergent behavioral specialization. Emergent specialization is guided
by genotype and behavioral specialization difference metrics that regulate genotype recombination. CONE is comparatively tested and evaluated with similar neuro-evolution methods in an extension of the multi-rover task, where
behavioral specialization is known to benefit task performance. The task is for multiple simulated autonomous vehicles (rovers) to maximize the detection of points of interest (red rocks) in a virtual environment. Results indicate
that CONE is appropriate for deriving sets of specialized rover behaviors that complement each other such that a higher task performance, comparative to related controller design methods, is attained in the multi-rover task.