Genetic team composition and level of selection in the evolution of cooperation

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Publications
Institution
Title
Genetic team composition and level of selection in the evolution of cooperation
Journal
IEEE Transactions on Evolutionary Computation
Author(s)
Waibel M., Keller L., Floreano D.
ISSN
1089-778X
Publication state
Published
Issued date
2009
Peer-reviewed
Oui
Volume
13
Number
3
Pages
648-660
Language
english
Abstract
In cooperative multiagent systems, agents interac 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 ("geneti- cally 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 homo- geneous 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
Keywords
Altruism, artificial evolution , cooperation, evolutionary robotics, fitness allocation, multiagent systems (MAS), team composition
Web of science
Create date
06/11/2008 10:10
Last modification date
20/08/2019 14:28
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