Repeated-Evaluation Genetic Algorithm for Simulating Social Security In the Artificial Society

摘要：

In order to find optimal policy to govern agents society, artificial agents have been deployed in simulating social or economic phenomena. However, with an increase of the complexity of agents internal behaviors as well as their social interactions, modeling social behaviors and tracking down optimal policies in mathematical form become intractable. In this paper, the repeated evaluation genetic algorithm is used to find optimal solutions to deter criminals in order to reduce the social cost caused by the crimes in the artificial society. The society is characterized by multiple equilibria and noisy parameters. Sampling evaluation is used to evaluate every candidate. The results of experiments show that genetic algorithms can quickly find the optimal solutions.

关键词： Repeated-Evaluation Artificial agents Genetic Algorithm Artificial society Social security Simulation

作者: ZiqingYe XiaoyiYu

作者单位: Institute of Computing and Information Engineering, Central South University of Forestry &Technology Institute of Electronic and Information Engineering, Central South University, Changsha 410075,China

会议类型: 国际会议

会议名称: The 3th International Conference on Precision Instrumentation and Measurement 2011(CPIM2011)(第三届精密仪器与测量国际学术会议)

会议地点: 湘潭

会议语种:英文

页码: 513-517

在线出版日期: 2011-07-19（万方平台首次上网日期，不代表论文的发表时间）