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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kesselman, C., Foster, I.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers, San Francisco (November 1998)
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)
Meysenburg, M.M., Foster, J.A.: The quality of pseudo-random number generations and simple genetic algorithm performance. In: ICGA, pp. 276–282 (1997)
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)
Cantú-Paz, E.: On random numbers and the performance of genetic algorithms. In: GECCO, pp. 311–318 (2002)
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)
Press, W., Flannery, B., Teukolsky, S., Vetterling, W.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge (1992)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)
Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence, 1st edn. The Morgan Kaufmann Series in Artificial Intelligence. Morgan Kaufmann, San Francisco (April 2001)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory, 39–43 (1995)
Price, K.V., Storn, R., Lampinen, J.: Differential Evolution: A practical Approach to Global Optimization. Springer, Berlin (2005)
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)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Inc., New York (1994)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)
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)
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)
Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. Evolutionary Computation 6(5), 443–462 (2002)
Montgomery, D., Runger, G.: Applied Statistics and Probability for Engineers. John Wiley and Sons Ltd, New York (2002)
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)
Sheskin, D.: Handbook of parametric and nonparametric statistical procedures. Chapman Hall CFC, London (2003)
Zar, J.: Biostatistical Analysis. Prentice-Hall, Inc., Upper Saddle River (2007)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cárdenas-Montes, M., Vega-Rodríguez, M.A., Gómez-Iglesias, A. (2011). Sensitiveness of Evolutionary Algorithms to the Random Number Generator. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-20282-7_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20281-0
Online ISBN: 978-3-642-20282-7
eBook Packages: Computer ScienceComputer Science (R0)