In this work we present a multiagent Fleet Coordination Problem (FCP). In this formulation, agents seek to minimize the fuel consumed to complete all deliveries while maintaining acceptable on-time delivery performance. Individual vehicles must both (i) bid on the rights to deliver a load of goods from origin to destination in a distributed, cooperative auction and (ii) choose the rate of travel between customer locations. We create two populations of adaptive agents, each to address one of these necessary functions. By training each agent population in separate source domains, we use transfer learning to boost initial performance in the target FCP. This boost removes the need for 300 generations of agent training in the target FCP, though the source problem computation time was less than the computation time for 5 generations in the FCP. / Graduation date: 2012