Fuzzy Adaptive Search Method for Parallel Genetic Algorithm Tuned by Evolution Degree Based on Diversity Measure

  • Yoichiro Maeda
  • Qiang Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4529)


Generally, as for Genetic Algorithms (GAs), it is not always optimal search efficiency, because genetic parameters (crossover rate, mutation rate and so on) are fixed. For this problem, we have already proposed Fuzzy Adaptive Search Method for GA (FASGA) that is able to tune the genetic parameters according to the search stage by the fuzzy reasoning. On the other hand, in order to improve the solution quality of GA, Parallel Genetic Algorithm (PGA) based on the local evolution in plural sub-populations (islands) and the migration of individuals between islands has been researched.

In this research, Fuzzy Adaptive Search method for Parallel GA (FASPGA) combined FASGA with PGA is proposed. Moreover as the improvement method for FASPGA, Diversity Measure based Fuzzy Adaptive Search method for Parallel GA (DM-FASPGA) is also proposed. Computer simulation was carried out to confirm the efficiency of the proposed method and the simulation results are also reported in this paper.


Diversity Measure Fuzzy Rule Migration Rate Crossover Rate Gray Code 
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.
    Subbu, R., Bonissone, P.: A Retrospective View of Fuzzy Control of Evolutionary Algorithm Resources. In: Proc. FUZZ-IEEE 2003, pp. 143–148 (2003)Google Scholar
  2. 2.
    Holland, J.H.: Adaptation in Netural and Artifical System. University of Michigan Press, Ann Arbor (1992)Google Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  4. 4.
    Xu, H.Y., Vukovich, G.: A Fuzzy Genetic Algorithm with Effective Search and Optimization. In: Int’l J. Conf. on Neural Networks (IJCNN’93), pp. 2967–2970 (1993)Google Scholar
  5. 5.
    Lee, M.A., Takagi, H.: Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques. In: Proc. of 5th International Conference on Genetic Algorithms (ICGA’93), pp. 76–83 (1993)Google Scholar
  6. 6.
    Herrera, F., Lozamo, M.: Adaptive Genetic Algorithms Based on Fuzzy Tecniques. In: Proc. Sixth Int’l Conf. on Information Processing and Management of Uncertainty in Knowledge Based System (IPMU’96), pp. 775–780 (1996)Google Scholar
  7. 7.
    Maeda, Y.: A Method for Improving Search performance of GA with Fuzzy Rules (In Japanese). In: Proc. of the 6th Intelligent System symposium, vol. 3, pp. 27–30 (1996)Google Scholar
  8. 8.
    Maeda, Y.: Fuzzy Adaptive Search Method for Genetic Programming. International Journal of Advanced Computational Intelligence 3(2), 131–135 (1999)Google Scholar
  9. 9.
    Nang, J., Matsuo, K.: A Survey on the Parallel Genetic Algorithms. Journal of the Society of Instrument and Control Engineering 33(6), 500–509 (1994)Google Scholar
  10. 10.
    Starkweather, T., Whitley, D., Mathisa, K.: Optimization Using Distributed Genetic Algorithms. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, Springer, Heidelberg (1991)CrossRefGoogle Scholar
  11. 11.
    Tanese, R.: Distributed Genetic Algorithms. In: Proc. 3rd International Conf on Genetic Algorithms, pp. 434–439. Morgan Kaufmann, San Francisco (1989)Google Scholar
  12. 12.
    Cant’u-Paz, E.: A Survey on the Parallel Genetic Algorithms. Calculateurs Paralleles (1998)Google Scholar
  13. 13.
    Mühlenbein, H.: Evolution in Time and Space: The Parallel Genetic Algorithm. In: Rawlins, G. (ed.) FOGA-1, pp. 316–337. Morgan Kaufmann, San Francisco (1991)Google Scholar
  14. 14.
    Hiroyasu, T., Miki, M., Negami, M.: Distributed Genetic Algorithms with Randomized Migration Rate. In: IEEE Proceedings of Systems, Man and Cybernetics Conference (SMC’99), vol. 1, pp. 689–694 (1999)Google Scholar
  15. 15.
    Miki, M., Hiroyasu, T., Kaneco, O., Hatanaka, K.: A Parallel Genetic Algorithm with Distributed Environment Scheme. In: IEEE Proceedings of Systems, Man and Cybernetics Conference (SMC’99), pp. 695–700 (1999)Google Scholar
  16. 16.
    Li, Q., Maeda, Y.: Adaptive Search Method for Parallel Genetic Algorithms Used Fuzzy Reasoning. In: The 23rd Annual Conference of the Robotics Society of Japan, 2B15 (2004)Google Scholar
  17. 17.
    Li, Q., Maeda, Y.: Parallel Genetic Algorithms with Adaptive Migration Rate Tuned by Fuzzy Reasoning. In: Proceedings of the Fourth International Symposium on Human and Artificial Intelligence Systems (HART 2004), pp. 259–264 (2004)Google Scholar
  18. 18.
    Maeda, Y., Li, Q.: Parallel Genetic Algorithm with Adaptive Genetic Parameters Tuned by Fuzzy Reasoning. International Journal of Innovating Computing, Information and Control 1(1), 95–107 (2005)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Yoichiro Maeda
    • 1
  • Qiang Li
    • 1
  1. 1.Dept. of Human and Artificial Intelligent Systems, Graduate School of Engineering, Univ. of Fukui, 3-9-1 Bunkyo, Fukui 910-8507Japan

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