Advertisement

The Influence of Elitism Strategy on Migration Intervals of a Distributed Genetic Algorithm

  • Takeshi Uchida
  • Teruo Matsuzawa
  • Yasushi Inoguchi
Conference paper
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 2)

Abstract

A distributed genetic algorithm is an important technique on practical use. To parallelize distributed genetic algorithms, researchers have been discussing various advanced algorithms with reduced migrations. An interesting previous study shows that both too long migration interval and too short migration interval cause a degraded performance in finding solutions. This paper assumes that a cause degrading the performance is a behavior of elites and also discusses a modified elitist model. The experiments show that a modified elitist model improves the performance even if the migration intervals are not set appropriately. These results seem to be design guides for discussing distributed genetic algorithms with reduced migrations.

Keywords

evolutionary computation genetic algorithm island model multiple populations migration elitist model convergence 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison Wesley Longman Publishing, Boston (1989)Google Scholar
  2. 2.
    Gen, M., Cheng, R.: Genetic Algorithms and Engineering Optimization. Wiley-Interscience Publication, New York (1999)CrossRefGoogle Scholar
  3. 3.
    Tanese, R.: Distributed Genetic Algorithms. In: Schaffer, J.D. (ed.) Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 434–439. Morgan Kaufmann Publishers, Virginia (1989)Google Scholar
  4. 4.
    Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Springer, New York (2000)MATHGoogle Scholar
  5. 5.
    Skolicki, Z., De Jong, K.A.: The influence of migration sizes and intervals on island models. In: Beyer, H., O’Reilly, U. (eds.) Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 1295–1302. ACM, Washington DC (2005)Google Scholar
  6. 6.
    Skolicki, Z.: An analysis of island models in evolutionary computation. In: Beyer, H., O’Reilly, U. (eds.) Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 386–389. ACM, Washington DC (2005)Google Scholar
  7. 7.
    Gong, Y., Guan, S., Nakamura, M.: Migration Effects of Parallel Genetic Algorithms on Line Topologies of Heterogeneous Computing Resources. IEICE Transactions 91-A(4), 1121–1128 (2008)CrossRefGoogle Scholar
  8. 8.
    Miyagi, H., Tengan, T., Mohamed, S., Nakamura, M.: Migration Effects on Tree Topology of Parallel Evolutionary Computation. In: Proceedings of TENCON 2010 - 2010 IEEE Region 10 Conference, pp. 1601–1606. IEEE, Fukuoka (2010)CrossRefGoogle Scholar
  9. 9.
    Kojima, K., Ishigame, M., Chakraborty, G., Hatsuo, H., Makino, S.: Asynchronous Parallel Distributed Genetic Algorithm with Elite Migration. International Journal of Computational Intelligence 4(2), 105–111 (2008)MathSciNetGoogle Scholar
  10. 10.
    De Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D Thesis, University of Michigan (1975)Google Scholar
  11. 11.
    Hiroyasu, T., Miki, M., Negami, M.: Distributed Genetic Algorithms with Randomized Migration Rate. In: Proceedings of 1999 IEEE International Conference on Systems, Man, and Cybernetics Conference, vol. 1, pp. 689–694. IEEE, Tokyo (1999)Google Scholar
  12. 12.
    Munetomo, M., Takai, Y., Sato, Y.: An Efficient String Exchange Algorithm for a Subpopulation-Based Asynchronously Parallel Genetic Algorithm and Its Evaluation. Transactions of IPSJ 35(9), 1815–1827 (1994) (in Japanese)Google Scholar
  13. 13.
    Lässig, J., Sudholt, D.: Design and Analysis of Migration in Parallel Evolutionary Algorithms. Soft Computing 17(7), 1121–1144 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Takeshi Uchida
    • 1
  • Teruo Matsuzawa
    • 2
  • Yasushi Inoguchi
    • 2
  1. 1.Salesian PolytechnicTokyoJapan
  2. 2.Japan Advanced Institute of Science and TechnologyIshikawaJapan

Personalised recommendations