Sensitiveness of Evolutionary Algorithms to the Random Number Generator

  • Miguel Cárdenas-Montes
  • Miguel A. Vega-Rodríguez
  • Antonio Gómez-Iglesias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)

Abstract

This article presents an empirical study of the impact of the change of the Random Number Generator over the performance of four Evolutionary Algorithms: Particle Swarm Optimisation, Differential Evolution, Genetic Algorithm and Firefly Algorithm. Random Number Generators are a key piece in the production of e-science, including optimisation problems by Evolutionary Algorithms. However, Random Number Generator ought to be carefully selected taking into account the quality of the generator. In order to analyse the impact over the performance of an evolutionary algorithm due to the change of Random Number Generator, a huge production of simulated data is necessary as well as the use of statistical techniques to extract relevant information from large data set. To support this production, a grid computing infrastructure has been employed. In this study, the most frequently employed high-quality Random Number Generators and Evolutionary Algorithms are coupled in order to cover the widest portfolio of cases. As consequence of this study, an evaluation about the impact of the use of different Random Number Generators over the final performance of the Evolutionary Algorithm is stated.

Keywords

Performance Analysis Evolutionary Algorithm Random Number Generator 

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References

  1. 1.
    Kesselman, C., Foster, I.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers, San Francisco (November 1998)Google Scholar
  2. 2.
    Meysenburg, M.M., Foster, J., Saghi, G., Dickinson, J., Jacobsen, R.T., Shreeve, J.M.: The effect of pseudo-random number generator quality on the performance of a simple genetic algorithm. Master’s thesis (1997)Google Scholar
  3. 3.
    Meysenburg, M.M., Foster, J.A.: The quality of pseudo-random number generations and simple genetic algorithm performance. In: ICGA, pp. 276–282 (1997)Google Scholar
  4. 4.
    Meysenburg, M.M., Foster, J.A.: Randomness and GA performance, revisited. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, July 13-17, vol. 1, pp. 425–432. Morgan Kaufmann, San Francisco (1999)Google Scholar
  5. 5.
    Cantú-Paz, E.: On random numbers and the performance of genetic algorithms. In: GECCO, pp. 311–318 (2002)Google Scholar
  6. 6.
    Matsumoto, M., Nishimura, T.: Mersenne twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Transactions on Modeling and Computer Simulation 8(1), 3–30 (1999)CrossRefMATHGoogle Scholar
  7. 7.
    Press, W., Flannery, B., Teukolsky, S., Vetterling, W.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge (1992)MATHGoogle Scholar
  8. 8.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)Google Scholar
  9. 9.
    Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence, 1st edn. The Morgan Kaufmann Series in Artificial Intelligence. Morgan Kaufmann, San Francisco (April 2001)Google Scholar
  10. 10.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory, 39–43 (1995)Google Scholar
  11. 11.
    Price, K.V., Storn, R., Lampinen, J.: Differential Evolution: A practical Approach to Global Optimization. Springer, Berlin (2005)MATHGoogle Scholar
  12. 12.
    Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. of Global Optimization 11(4), 341–359 (1997)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Inc., New York (1994)CrossRefMATHGoogle Scholar
  14. 14.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)MATHGoogle Scholar
  15. 15.
    Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Yang, X.-S., Deb, S.: Eagle strategy using lévy walk and firefly algorithms for stochastic optimization. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. Studies in Computational Intelligence, vol. 284, pp. 101–111. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. Evolutionary Computation 6(5), 443–462 (2002)CrossRefGoogle Scholar
  18. 18.
    Montgomery, D., Runger, G.: Applied Statistics and Probability for Engineers. John Wiley and Sons Ltd, New York (2002)MATHGoogle Scholar
  19. 19.
    García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the cec’2005 special session on real parameter optimization. J. Heuristics 15(6), 617–644 (2009)CrossRefMATHGoogle Scholar
  20. 20.
    Sheskin, D.: Handbook of parametric and nonparametric statistical procedures. Chapman Hall CFC, London (2003)CrossRefMATHGoogle Scholar
  21. 21.
    Zar, J.: Biostatistical Analysis. Prentice-Hall, Inc., Upper Saddle River (2007)Google Scholar
  22. 22.
    García, S., Fernández, A., Luengo, J., Herrera, F.: A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput. 13(10), 959–977 (2009)CrossRefGoogle Scholar
  23. 23.
    Tang, K., Li, X., Suganthan, P.N., Yang, Z., Weise, T.: Benchmark functions for the cec’2010 special session and competition on large-scale global optimization. Technical report, Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China (USTC), Electric Building No. 2, Room 504, West Campus, Huangshan Road, Hefei 230027, Anhui, China (2009)Google Scholar
  24. 24.
    Tang, K., Yao, X., Suganthan, P.N., MacNish, C., Chen, Y.P., Chen, C.M., Yang, Z.: Benchmark functions for the CEC 2008 special session and competition on large scale global optimization. Technical report, Nature Inspired Computation and Applications Laboratory, USTC, China (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Miguel Cárdenas-Montes
    • 1
  • Miguel A. Vega-Rodríguez
    • 2
  • Antonio Gómez-Iglesias
    • 3
  1. 1.Department of Fundamental ResearchCentro de Investigaciones Energéticas Medioambientales y TecnológicasMadridSpain
  2. 2.ARCO Research Group, Dept. Technologies of Computers and CommunicationsUniversity of ExtremaduraCáceresSpain
  3. 3.National Laboratory of FusionCentro de Investigaciones Energéticas Medioambientales y TecnológicasMadridSpain

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