Genetic Team Composition and Level of Selection in the Evolution of Cooperation
Genetic Team Composition and Level of Selection in the Evolution of Cooperation
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DOI：
10.1109/tevc.2008.2011741
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发表时间：
2009-06
影响因子：
14.3
通讯作者：
M. Waibel;L. Keller;D. Floreano
中科院分区：
文献类型：
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作者：
M. Waibel;L. Keller;D. Floreano
在协作多智能体系统中，智能体通过交互来解决任务。多智能体团队的全局动态是由局部智能体交互产生的，并且复杂且难以预测。进化计算已被证明是一种很有前途的方法来设计这样的团队。目前的大多数研究使用的团队组成的代理具有相同的控制规则（ldquogenetically同质teamsrd）和选择行为在团队层面（ldquoteam-level selectionrd）。在这里，我们扩展目前的方法，包括四个组合的遗传团队的组成和水平的选择。我们比较了基因同质团队进化与个人层面的选择，基因同质团队进化与团队层面的选择，基因异质团队进化与个人层面的选择，和基因异质团队进化与团队层面的选择。我们使用一个模拟觅食任务表明，最佳组合取决于任务所需的合作量。因此，我们区分三种类型的合作任务，并建议遗传团队组成和选择水平的最佳选择的指导方针。
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 (ldquogenetically homogeneous teamsrdquo) and select behavior at the team level (ldquoteam-level selectionrdquo). 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.