Agent Based Evolutionary Approach: An Introduction

  • Ruhul A. Sarker
  • Tapabrata Ray
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 5)


Agent based evolutionary approach is a new paradigm to efficiently solve a range of complex problems. The approach can be considered as a hybrid scheme which combines an agent system with an evolutionary algorithm. In this chapter, we provide an introduction to an evolutionary algorithm and an agent based system which leads to the foundation of the agent based evolutionary algorithm. The strengths and weaknesses of these algorithms are analyzed. In addition, the contributions in this book are also discussed.


Genetic Algorithm Evolutionary Algorithm Multiagent System Intelligent Agent Memetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Fischer, M., Leung, Y.: Geocomputational modelling techniques and applications. Springer, Berlin (2001)Google Scholar
  2. 2.
    Barnett, W., Chiarella, C., Keen, S., Marks, R., Schnabl, H.: Complexity and Evolution. Cambridge University Press, Cambridge (2000)MATHGoogle Scholar
  3. 3.
    Sarker, R., Kamruzzaman, J., Newton, C.: Evolutionary optimization (EvOpt): A brief review and analysis. International Journal of Computational Intelligence and Applications 3(4), 311–330 (2003)CrossRefGoogle Scholar
  4. 4.
    Sycara, K.: Multiagent systems. AI Magazine 19(2), 79–92 (1998)Google Scholar
  5. 5.
    Guo, Y.-N., Cheng, J., Gong, D.-W., Yang, D.-Q.: Knowledge-inducing interactive genetic algorithms based on multi-agent. In: Jiao, L., Wang, L., Gao, X.-b., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 759–768. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Wooldridge, M., Jannings, N.: Intelligent agents: theory and practice. Knowledge Engineering Review 10(2), 115–162 (1995)CrossRefGoogle Scholar
  7. 7.
    Franklin, S., Graesser, A.: Is it an agent, or just a program? A taxonomy for autonomous agents. In: Jennings, N.R., Wooldridge, M.J., Müller, J.P. (eds.) ECAI-WS 1996 and ATAL 1996. LNCS, vol. 1193, pp. 21–35. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  8. 8.
    Russell, S., Norvig, P.: Artificial intelligence: a modern approach. Prentice Hall, Upper Saddle River (2003)Google Scholar
  9. 9.
    Scheutz, M., Schermerhorn, P.: Steps towards a systematic investigation of possible evolutionary trajectories from reactive to deliberable control systems. In: Proc. of Artificial Life VIII, pp. 283–292. MIT Press, Cambridge (2002)Google Scholar
  10. 10.
    Wu, M., Cao, W.-H., Peng, J., She, J.-H., Chen, X.: Balanced reactive-deliberative architecture for multi-agent system for simulation league of robocup. International Journal of Control, Automation, and Systems 7(6), 945–955 (2009)CrossRefGoogle Scholar
  11. 11.
    Brooks, R.A.: Intelligence without representation. Artificial Intelligence 47, 349–355 (1991)CrossRefGoogle Scholar
  12. 12.
    Bratman, M., Isreal, D., Pollack, M.: Plans and resource-bound practical reasoning. Computational Intelligence 4, 349–355 (1988)CrossRefGoogle Scholar
  13. 13.
    Vacher, J.-P., Galinho, T., Lesage, F., Cardon, A.: Genetic algorithms in a multi-agent system. In: IEEE International Joint Symposia on Intelligence and Systems, pp. 17–26 (1998)Google Scholar
  14. 14.
    Nunes, L., Oliveira, E.: Learning from multiple sources. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 3, pp. 1106–1113 (2004)Google Scholar
  15. 15.
    Iantovics, B., Enăchescu, C.: Intelligent complex evolutionary agent-based systems. In: Proceedings of the 1st International Conference on Bio-Inspired Computational Methods used for Difficult Problems Solving, AIP, pp. 116–124 (2008)Google Scholar
  16. 16.
    Milano, M., Roli, A.: MAGMA: a multiagent architecture for metaheuristics. IEEE Transactions on Systems, Man, and Cybernetics, Part B 34(2), 925–941 (2004)CrossRefGoogle Scholar
  17. 17.
    Liu, J., Zhong, W., Jiao, L.: A multiagent evolutionary algorithm for combinatorial optimization problems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 40(1), 229–240 (2010)CrossRefGoogle Scholar
  18. 18.
    Barkat Ullah, A.S.S.M., Sarker, R., Cornforth, D., Lokan, C.: AMA: A new approach for solving constrained real-valued optimization problems. Soft Computing 13(8-9), 741–762 (2009)CrossRefGoogle Scholar
  19. 19.
    Zhang, J., Liang, C., Huang, Y., Wu, J., Yang, S.: An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization. Applied Mathematics and Computation 211, 392–416 (2009)CrossRefMathSciNetMATHGoogle Scholar
  20. 20.
    Drezewski, R., Siwik, L.: Agent-based co-operative co-evolutionary algorithm for multi-objective optimization. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 388–397. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Liu, B., Duan, T., Li, Y.: One improved agent genetic algorithm - ring-like agent genetic algorithm for global numerical optimization. Asia-Pacific Journal of Operational Research 26(4), 479–502 (2009)CrossRefMathSciNetMATHGoogle Scholar
  22. 22.
    Hippolyte, J.-L., Bloch, C., Chatonnay, P., Espanet, C., Chamagne, D.: A self-adaptive multiagent evolutionary algorithm for electrical machine design. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1250–1255 (2007)Google Scholar
  23. 23.
    Li, Q., Du, L.: Research on hybrid-genetic algorithm for mas based job-shop dynamic scheduling. In: 2009 Second International Conference on Intelligent Computation Technology and Automation, pp. 404–407. IEEE Press, Los Alamitos (2009)Google Scholar
  24. 24.
    Giardini, G., Kalmar-Nagy, T.: Genetic algorithm for multi-agent space exploration. In: 2007 AIAA InfoTech at Aerospace Conference, vol. 2, pp. 1146–1160 (2007)Google Scholar
  25. 25.
    Liu, H., Tang, M.: Evolutionary design in a multi-agent design environment. Applied Soft Computing 6(2), 207–220 (2005)CrossRefGoogle Scholar
  26. 26.
    Zhong, W., Liu, J., Jiao, L.: An agent model for binary constraint satisfaction problems. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 260–269. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  27. 27.
    Zhong, W., Liu, J., Jiao, L.: Job-shop scheduling based on multiagent evolutionary algorithm. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 925–933. Springer, Heidelberg (2005)Google Scholar
  28. 28.
    Zhong, W., Liu, J., Xue, M., Jiao, L.: A multiagent genetic algorithm for global numerical optimization. IEEE Transactions on Systems, Man and Cybernetics, Part B 34, 1128–1141 (2004)CrossRefGoogle Scholar
  29. 29.
    Davidsson, P., Persson, J., Holmgren, J.: On the integration of agent-based and mathematical optimization techniques. Agent and Multiagent Systems: Technologies and Applications, 1–10 (2007)Google Scholar
  30. 30.
    Barkat Ullah, A.S.S.M., Sarker, R., Cornforth, D., Lokan, C.: An agent-based memetic algorithm (ama) for solving constrained optimization problems. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 999–1006 (2007)Google Scholar
  31. 31.
    De Jong, K.A.: Evolving intelligent agents: A 50 year quest. IEEE Computational Intelligence Magazine 3, 12–17 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ruhul A. Sarker
    • 1
  • Tapabrata Ray
    • 1
  1. 1.School of Engineering and Information Technology (SEIT)University of New South Wales, Australian Defence Force Academy (UNSW@ADFA)Canberra ACTAustralia

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