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.