基于遗传算法的重复囚徒困境博弈策略在复杂网络中的演化  

Evolution of strategies based on genetic algorithm in the iterated prisoner's dilemma on complex networks

作　　者：林海[1] 吴晨旭[1] 

机构地区：[1]厦门大学物理系理论物理与天体物理研究所,厦门361005

出　　处：《物理学报》2007年第8期4313-4318,共6页Acta Physica Sinica

基　　金：国家杰出青年科学基金(批准号:10225420)资助的课题.~~

摘　　要：利用遗传算法研究重复囚徒困境博弈策略在复杂网络中的演化.研究结果表明:处于复杂网络中有记忆的个体通过基因的复制、重组、变异和选择能够进化出一种自组织的合作机制.这种合作机制既能够在群体中激发合作行为的产生,加强和维护持续的合作行为,同时又能对背叛的个体进行惩罚和报复,因此能够促使复杂网络中进化出具有很高合作率的群体.Using genetic algorithm, we studied the evolution of strategies in the iterated prisoner＇ s dilemma on complex networks. It is found that the agents located on complex networks can naturally develop some self-organization mechanics of cooperation by genome reproduction, recombination, mutation and selection, which can not only result in the emergence of cooperation, but also strengthen and sustain the persistent cooperation. At the same agents, leading to a high cooperation rate on complex networks time, such mechanics punishes and takes revenge on defective agents, leading to a high cooperation rate on complex networks.

关 键 词：复杂网络 遗传算法 进化博弈 合作 

分 类 号：N941.4[自然科学总论—系统科学]