Evolving team members to act cohesively is a complex and challenging problem. To allow the greatest range of solutions in team problem solving, heterogeneous agents are desirable. To produce highly specialized agents, team members should be evolved in separate populations. Co-evolution in separate populations requires a system for selecting suitable partners for evaluation at trial time. Selecting too many partners for evaluation drives computation time to unreasonable levels, while selecting too few partners blinds the GA from recognizing highly fit individuals. In previous work, we employed a method based on punctuated anytime learning which periodically tests a number of partner combinations to select a single individual from each population to be used at trail time. We began testing our method in simulation using a two-agent box pushing task. We then expanded our research by simulating a predator-prey scenario in which all the agents had the exact same capabilities. In this paper, we report the expansion of our work by applying this method of team learning to five dissimilar robots.