Evolving Swarm-Robotic Behavioral Allocations

This study investigates comparative methods for two-step collective behavior evolution (evolving group behaviors from pre-evolved behaviors), to encourage the evolution of behavioral diversity in swarm-robotic applications. Specifically, we investigate behavioral diversity evolution given pre-evolved behaviors in collective behaviors that are effective across increasingly complex and difficult collective herding task environments. Results indicate that specific complements of pre-evolved (lower task-performance) collective herding behaviors was suitable for achieving high task performance across all environments and task difficulty levels. Results support the efficacy of the two-step approach for evolving behaviorally heterogeneous groups in collective behavior tasks that benefit from groups comprising various complementary behaviors.