This research concerns the comparison of three different artificial evolution approaches for the design of cooperative behavior in a group of simulated mobile robots. The first and second approaches, termed: single pools and plasticity, are characterized by robots that share a single genotype, though the plasticity approach includes a learning mechanism. The third approach, termed: multiple pools, is characterized by robots that use different genotypes. The application domain is a pursuit-evasion game in which a team of robots, termed: predators, collectively work to capture or slow a single robot, termed: the prey. Results indicate that the multiple pools approach is superior comparative to the other two approaches in deriving robust and consistently effective prey-capture strategies, given that this approach facilitates the evolution of specialized behavioral roles in the predator team.