Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 13, Issue 3, Pages 648-660Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2008.2011741
Keywords
Altruism; artificial evolution; cooperation; evolutionary robotics; fitness allocation; multiagent systems (MAS); team composition
Funding
- Swiss National Science Foundation
- European Commission
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In cooperative multiagent systems, agents interact to solve tasks. Global dynamics of multiagent teams result from local agent interactions, and are complex and difficult to predict. Evolutionary computation has proven a promising approach to the design of such teams. The majority of current studies use teams composed of agents with identical control rules (genetically homogeneous teams) and select behavior at the team level (team-level selection). Here we extend current approaches to include four combinations of genetic team composition and level of selection. We compare the performance of genetically homogeneous teams evolved with individual-level selection, genetically homogeneous teams evolved with team-level selection, genetically heterogeneous teams evolved with individual-level selection, and genetically heterogeneous teams evolved with team-level selection. We use a simulated foraging task to show that the optimal combination depends on the amount of cooperation required by the task. Accordingly, we distinguish between three types of cooperative tasks and suggest guidelines for the optimal choice of genetic team composition and level of selection.
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