Evolutionary optimization of cooperative heterogeneous teams

There is considerable interest in developing teams of autonomous, unmanned vehicles that can function in hostile environments without endangering human lives. However, heterogeneous teams, teams of units with specialized roles and/or specialized capabilities, have received relatively little attention. Specialized roles and capabilities can significantly increase team effectiveness and efficiency. Unfortunately, developing effective cooperation mechanisms is much more difficult in heterogeneous teams. Units with specialized roles or capabilities require specialized software that take into account the role and capabilities of both itself and its neighbors. Evolutionary algorithms, algorithms modeled on the principles of natural selection, have a proven track record in generating successful teams for a wide variety of problem domains. Using classification problems as a prototype, we have shown that typical evolutionary algorithms either generate highly effective teams members that cooperate poorly or poorly performing individuals that cooperate well. To overcome these weaknesses we have developed a novel class of evolutionary algorithms. In this paper we apply these algorithms to the problem of controlling simulated, heterogeneous teams of "scouts" and "investigators". Our test problem requires producing a map of an area and to further investigate "areas of interest". We compare several evolutionary algorithms for their ability to generate individually effective members and high levels of cooperation.