Evolutionary Algorithm Parameter Tuning with Sensitivity Analysis

  • Frédéric Pinel
  • Grégoire Danoy
  • Pascal Bouvry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7053)


This article introduces a generic sensitivity analysis method to measure the influence and interdependencies of Evolutionary Algorithms parameters. The proposed work focuses on its application to a Parallel Asynchronous Cellular Genetic Algorithm (PA-CGA). Experimental results on two different instances of a scheduling problem have demonstrated that some metaheuristic parameters values have little influence on the solution quality. On the opposite, some local search parameter values have a strong impact on the obtained results for both instances. This study highlights the benefits of the method, which significantly reduces the parameter search space.


Evolutionary Algorithm Parameter Tuning Sensitivity Analysis 


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  1. 1.
    Alba, E., Dorronsoro, B.: Cellular Genetic Algorithms. Operations Research/Compuer Science Interfaces. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  2. 2.
    Ali, S., Siegel, H.J., Maheswaran, M., Hensgen, D., Ali, S.: Representing task and machine heterogeneities for heterogeneous. Journal of Science and Engineering, Special 50 th Anniversary Issue (3), 195–207 (2000)Google Scholar
  3. 3.
    Bartz-Beielstein, T., Lasarczyk, C.W.G., Preuss, M.: Sequential Parameter Optimization. In: IEEE Congress on Evolutionary Computation, vol. 1, pp. 773–780. IEEE (2005)Google Scholar
  4. 4.
    Blazewicz, J., Lenstra, J.K., Rinnooy Kan, A.H.G.: Scheduling subject to resource constraints: classification and complexity. Discrete Applied Mathematics 5, 11–24 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Braun, T.D., Siegel, H.J., Beck, N., Bölöni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hengsen, D., Freund, R.F.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing 61(6), 810–837 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Casanova, H., Legrand, A., Zagorodnov, D., Berman, F.: Heuristics for scheduling parameter sweep applications in grid environments. In: Heterogeneous Computing Workshop, pp. 349–363 (2000)Google Scholar
  7. 7.
    de Castro, L., Von Zuben, F.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)CrossRefGoogle Scholar
  8. 8.
    DeJong, K.: Parameter setting in eas: a 30 year perspective. In: Lobo, F.G., et al. (eds.) [18], pp. 1–18Google Scholar
  9. 9.
    Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evolutionary Computation 3(2), 124–141 (1999)CrossRefGoogle Scholar
  10. 10.
    Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter control in evolutionary algorithms. In: Lobo, F.G., et al. (eds.) [18], pp. 19–46Google Scholar
  11. 11.
    Geem, Z.: Harmony search algorithm for solving sudoku. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part I. LNCS (LNAI), vol. 4692, pp. 371–378. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Ghafoor, A., Yang, J.: Distributed heterogeneous supercomputing management system. IEEE Comput. 26(6), 78–86 (1993)CrossRefGoogle Scholar
  13. 13.
    Ho, S.Y., Chen, H.M., Ho, S.J., Chen, T.K.: Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(2), 1031–1044 (2004)CrossRefGoogle Scholar
  14. 14.
    Ibarra, O.H., Kim, C.E.: Heuristic algorithms for scheduling independent tasks on nonidentical processors. Journal of the ACM 24(2), 280–289 (1977)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    IEEE and The Open Group: Posix (ieee std 1003.1-2008, open group base specifications issue 7) (2008),
  16. 16.
    Kafil, M., Ahmad, I.: Optimal task assignment in heterogeneous distributed computing systems. IEEE Concurrency 6(3), 42–51 (1998)CrossRefGoogle Scholar
  17. 17.
    Kramer, O.: Evolutionary self-adaptation: a survey of operators and strategy parameters. Evolutionary Intelligence 3, 51–65 (2010)CrossRefzbMATHGoogle Scholar
  18. 18.
    Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. SCS, vol. 54. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  19. 19.
    Maturana, J., Lardeux, F., Saubion, F.: Autonomous operator management for evolutionary algorithms. Journal of Heuristics 16, 881–909 (2010)CrossRefzbMATHGoogle Scholar
  20. 20.
    Min, H., Ko, H.J., Ko, C.S.: A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns. Omega 34(1), 56–69 (2006)CrossRefGoogle Scholar
  21. 21.
    Nannen, V., Eiben, A.E.: Relevance estimation and value calibration of evolutionary algorithm parameters. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 975–980. Morgan Kaufmann Publishers Inc., San Francisco (2007)Google Scholar
  22. 22.
    Pinel, F., Dorronsoro, B., Bouvry, P.: A new parallel asynchronous cellular genetic algorithm for scheduling in grids. In: Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd. Forum, IPDPSW 2010 (2010)Google Scholar
  23. 23.
    Pinel, F., Dorronsoro, B., Bouvry, P.: A new parallel asynchronous cellular genetic algorithm for de novo genomic sequencing. In: Proceedings of the IEEE International Conference on Soft Computing and Pattern Recognition (SOCPAR 2009), pp. 178–183 (2009)Google Scholar
  24. 24.
    Pujol, G.: sensitivity: Sensitivity Analysis (2008), r package version 1.4-0Google Scholar
  25. 25.
    Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.: Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley (2004)Google Scholar
  26. 26.
    Saltelli, A., Tarantola, S., Chan, K.: A quantitative, model independent method for global sensitivity analysis of model output. Technometrics 41, 39–56 (1999)CrossRefGoogle Scholar
  27. 27.
    Smit, S.K., Eiben, A.E.: Comparing parameter tuning methods for evolutionary algorithms. In: Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, CEC 2009, pp. 399–406. IEEE Press, Piscataway (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Frédéric Pinel
    • 1
  • Grégoire Danoy
    • 1
  • Pascal Bouvry
    • 1
  1. 1.FSTC/CSC/ILIASUniversity of LuxembourgLuxembourg

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