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Evolving Cooperating Agents Using Team-Based Fitness Assessment

One application of evolutionary computation is developing agents which play in team-based games. In such games, it is often beneficial for each agent in a team to specialise in a different role within the team. We present several methods of evolving such specialisation by assessing agents as members of teams, and compare their performance in evolving solutions to a simulation of an immune system. We show that by evolving a population of teams, rather than a population of individuals, we can significantly improve the performance of evolutionary algorithms in evolving teams of diverse, cooperating agents.