Using Discrete PSO Algorithm to Evolve Multi-player Games on Spatial Structure Environment

1. Chong, S.Y., Yao, X.: Behavioral Diversity, Choices and Noise in the Iterated Prisoner’s Dilemma. IEEE Transactions on evolutionary computation 9(6), 540–551 (2005)

   Article  Google Scholar 

2. Chong, S.Y., Yao, X.: Multiple Choices and Reputation in Multiagent Transactions. IEEE Transactions on evolutionary computation 11(6), 689–711 (2007)

   Article  Google Scholar 

3. Chong, S.Y., Tiño, P., Yao, X.: Measuring Generalization Performance in Coevolutionary Learning. IEEE Transactions on evolutionary computation 12(4), 479–505 (2008)

   Article  Google Scholar 

4. Chong, S.Y., Tiño, P., Yao, X.: Relationship Between Generalization and Diversityin Coevolutionary Learning. IEEE Transactions on computational intelligence and AI in games 1(3), 214–232 (2009)

   Article  Google Scholar 

5. Chong, S.Y., Tiño, P., Ku, D.C., Yao, X.: Improving Generalization Performance in Co-Evolutionary Learning. IEEE Transactions on evolutionary computation 16(1), 70–85 (2012)

   Article  Google Scholar 

6. Ishibuchi, H., Takahashi, K., Hoshino, K., Maeda, J., Nojima, Y.: Effects of configuration of agents with different strategy representations on the evolution of cooperative behaviour in a spatial IPD game. In: IEEE Conference on Computational Intelligence and Games (2011)

   Google Scholar 

7. Axelrod, R.: The evolution of cooperation. Basic Books, New York (1984)

   Google Scholar 

8. Nowak, M.A., May, R.M.: Evolutionary games and spatial chaos. Nature 359(6398), 826–829 (1992)

   Article  Google Scholar 

9. David, B.F.: On the relationship between the duration of an encounter and the evolution of cooperation in the iterated prisoner’s dilemma. Evolution of computation 3(3), 349–363 (1996)

   Google Scholar 

10. Hauert, C., Doebeli, M.: Spatial structure often inhibits the evolution of cooperation in the snowdrift game. Nature 428(6983), 643–646 (2004)

    Article  Google Scholar 

11. Wang, X.Y., Chang, H.Y., Yi, Y., Lin, Y.B.: Co-evolutionary learning in the N-choice iterated prisoner’s dilemma with PSO algorithm in a spatial environment. In: 2013 IEEE Symposium Series on Computational Intelligence, pp. 47–53. IEEE press, Singapore (2013)

    Google Scholar 

12. Darwen, P.J., Yao, X.: Co-evolution in iterated prisoner’s dilemma with intermediate levels of cooperative: Application to missile defense. International Journal of Computational Intelligence and Applications 2(1), 83–107 (2002)

    Article  Google Scholar 

13. Ishibuchi, H., Namikawa, N.: Evolution of iterated prisoner’s dilemma game strategies in structured demes under random pairing in game playing. IEEE Transactions on evolutionary computation 9(6), 552–561 (2005)

    Article  Google Scholar 

14. Zheng, Y., Ma, L., Qian, I.: On the convergence analysis and parameter selection in particle swarm optimization. In: Processing of International Conference of Machine Learning Cybern., pp. 1802–1807 (2003)

    Google Scholar 

15. Franken, N., Engelbrecht, A.P.: Comparing PSO structures to learn the game of checkers from zero knowledge. In: The 2003 Congress on Evolutionary Computation, pp. 234–241(2003)

    Google Scholar 

16. Franken, N., Engelbrecht, A.P.: Particle swarm optimization approaches to coevolve strategies for the iterated prisoner’s dilemma. IEEE Transactions on evolutionary computation 9(6), 562–579 (2005)

    Article  Google Scholar 

17. Di Chio, C., Di Chio, P., Giacobini, M.: An evolutionary game-theoretical approach to particle swarm optimisation. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., McCormack, J., O’Neill, M., Romero, J., Rothlauf, F., Squillero, G., Uyar, A., Yang, S. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 575–584. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

18. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE International Conference of Neural Network, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

19. Ishibuchi, H., Takahashi, K., Hoshino, K., Maeda, J., Nojima, Y.: Effects of configuration of agents with different strategy representations on the evolution of cooperative behaviour in a spatial IPD game. In: IEEE Conference on Computational Intelligence and Games (2011)

    Google Scholar 

20. Zheng, D.F., Yin, H.P., Chan, C.H., Hui, P.M.: Cooperative behavior in a model of evolutionary snowdrift games with N-person interactions. Europhys. Lett. 80(1), 18002 (2007)

    Article  MathSciNet  Google Scholar 

21. Moriyama, K.: Utility based Q-learning to facilitate cooperation in Prisoner’s Dilemma games. Web Intelligence and Agent Systems: An International Journal, IOS Press 7, 233–242 (2009)

    Google Scholar 

22. Chen, B., Zhang, B., Zhu, W.D.: Combined trust model based on evidence theory in iterated prisoner’s dilemma game. International Journal of Systems Science 42(1), 63–80 (2011)

    Article  MATH  MathSciNet  Google Scholar 

23. Chiong, R., Kirley, M.: Effects of Iterated Interactions in Multi-player Spatial Evolutionary Games. IEEE Transactions on evolutionary computation (2013). doi:10.1109/TEVC.2011.2167682

24. Nowark, M.A.: Five rules of the evolution of cooperation. Science 314, 1560–1563 (2006)

    Article  Google Scholar 

25. Watts, D., Stogatz, S.H.: Collective dynamics of small-world networks. Natrue 393, 440–442 (1998)

    Article  Google Scholar 

26. Chiong, R., Kirley, M.: Iterated N-Player Games on Small-World Networks. In: GECCO 2011 (2011)

    Google Scholar 

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