| Local Minority Game with Evolutionary Strategies |
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| 引用本文： | 杨伟松 李平 邹珊珊 汪秉宏. Local Minority Game with Evolutionary Strategies[J]. 中国物理快报, 2006, 23(8): 1961-1964 |
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| 作者姓名： | 杨伟松 李平 邹珊珊 汪秉宏 |
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| 作者单位： | [1]Department of Physics, diangxi Science and Technology Teacher＇s College, Nanchang 330013 [2]Department of Basic Sciences, Nanjing Institute of Technology, Nanjing 210013 [3]Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China Hefei 230026 |
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| 基金项目： | Supported by the Science and Technology Foundation of Nanjing Institute of Technology under Grant No KXJ06048. |
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| 摘 要： | We study a model of local minority game in the random Kauffman network with evolutionary strategies and propose three methods to update the strategy of poor agents, with lower points in a given generation： namely to update either the Boolean function of their strategies randomly, or their local information of randomly adjacent m agents, or the number m of randomly chosen adjacent agents. The results of extended numerical simulations show that the behaviour of strategies in the three methods may enhance significantly the entire coordination of agents in the system. It is also found that a poor agent tends to use both small m strategies and correlated strategies, and the strategies of agents will finally self-organize into a steady-state distribution for a long time playing of the game.
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| 关 键 词： | 进化策略 随机Kauffman网络 Boolean函数 非线性物理 |
| 收稿时间： | 2006-04-29 |
| 修稿时间： | 2006-04-29 |
Local Minority Game with Evolutionary Strategies |
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| YANG Wei-Song,LI Ping,ZOU Shan-Shan,WANG Bing-Hong. Local Minority Game with Evolutionary Strategies[J]. Chinese Physics Letters, 2006, 23(8): 1961-1964 |
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| Authors: | YANG Wei-Song LI Ping ZOU Shan-Shan WANG Bing-Hong |
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| Abstract: | We study a model of local minority game in the random Kauffman network with evolutionary strategies and propose three methods to update the strategy of poor agents, with lower points in a given generation: namely to update either the Boolean function of their strategies randomly, or their local information of randomly adjacent m agents, or the number m of randomly chosen adjacent agents. The results of extended numerical simulations show that the behaviour of strategies in the three methods may enhance significantly the entire coordination of agents in the system. It is also found that a poor agent tends to use both small m strategies and correlated strategies, and the strategies of agents will finally self-organize into a steady-state distribution for a long time playing of the game. |
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| Keywords: | 02.50.Le 87.23.Ge 89.65.Gh 89.75.Hc |
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