Game evolutionary algorithm based on behavioral game theory

被引:0
|
作者
Yang G. [1 ,2 ,3 ]
Wang Y. [4 ]
Li S. [2 ,4 ]
Xie Q. [1 ]
机构
[1] Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang
[2] School of Mechanical Engineering, Guizhou University, Guiyang
[3] School of Electrical and Computer Engineering, Oklahoma State University, Stillwater
[4] Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu
来源
Li, Shaobo (lishaobo@gzu.edu.cn) | 1600年 / Huazhong University of Science and Technology卷 / 44期
关键词
Belief learning; Game evolutionary algorithm (GameEA); Game theory; Genetic algorithm; Imitation learning;
D O I
10.13245/j.hust.160714
中图分类号
学科分类号
摘要
Aimed at obtaining an optimization approach embodied the mechanism of behaviour game fully, a game evolutionary model used to calculate the payoffs expectation was established, and then the game evolutionary algorithm (GameEA) was put forward, and the individual learning frame of imitation and belief learning were designed. The individuals made decisions by checking the payoffs expectation, and the imitation operator was used to revise gene so as to learn from other competitor, and the belief learning operator was employed to mutate chromosome to improve competitiveness. The results on thirteen benchmark problems show that GameEA outperforms not only the standard StGA but also IMGA and DPGA on accuracy and exploration. © 2016, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
引用
收藏
页码:69 / 74
页数:5
相关论文
共 14 条
  • [1] Fulcher J., Computational intelligence: an introduction, Neurl Networks IEEE Transactions on, 16, 3, pp. 780-781, (2005)
  • [2] Kontogiannis S., Spirakis P., Evolutionary Games: An Algorithmic View, (2005)
  • [3] Ganesan T., Elamvazuthi I., Vasant P., Multiobjective design optimization of a nano-CMOS voltage-controlled oscillator using game theoretic-differential evolution, Applied Soft Computing, 32, pp. 293-299, (2015)
  • [4] Leboucher C., Shin H., Siarry P., Et al., Convergence proof of an enhanced particle swarm optimisation method integrated with evolutionary game theory, Information Sciences, 346-347, pp. 389-411, (2016)
  • [5] Koh A., An evolutionary algorithm based on Nash dominance for equilibrium problems with equilibrium constraints, Applied Soft Computing, 12, 1, pp. 161-173, (2012)
  • [6] Moradi M.H., Abedini M., Hosseinian S.M., A combination of evolutionary algorithm and game theory for optimal location and operation of DG from DG owner standpoints, IEEE Transactions on Smart Grid, 7, 2, pp. 608-616, (2016)
  • [7] Misra S., Sarkar S., Priority-based time-slot allocation in wireless body area networks during medical emergency situations: an evolutionary game-theoretic perspective, IEEE Journal of Biomedical and Health Informatics, 19, 2, pp. 541-548, (2015)
  • [8] Mejia M., Pena N., Munoz J.L., Et al., A game theoretic trust model for on-line distributed evolution of cooperation in MANETs, Journal of Network and Computer Applications, 34, 1, pp. 39-51, (2011)
  • [9] Myerson R.B., Game Theory: Analysis of Conflict, (1991)
  • [10] Hu H., Stuart H.W., An epistemic analysis of the Harsanyi transformation, International Journal of Game Theory, 30, 4, pp. 517-525, (2002)