Advertisement

Dynamic Allocation of Data-Objects in the Web, Using Self-tuning Genetic Algorithms

  • Joaquín Pérez O.
  • Rodolfo A. Pazos R.
  • Graciela Mora O.
  • Guadalupe Castilla V.
  • José A. Martínez
  • Vanesa Landero N.
  • Héctor Fraire H.
  • Juan J. González B.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3171)

Abstract

In this paper, a new mechanism for automatically obtaining some control parameter values for Genetic Algorithms is presented, which is independent of problem domain and size. This approach differs from the traditional methods which require knowing the problem domain first, and then knowing how to select the parameter values for solving specific problem instances. The proposed method uses a sample of problem instances, whose solution allows to characterize the problem and to obtain the parameter values. To test the method, a combinatorial optimization model for data-object allocation in the Web (known as DFAR) was solved using Genetic Algorithms. We show how the proposed mechanism allows to develop a set of mathematical expressions that relates the problem instance size to the control parameters of the algorithm. The expressions are then used, in on-line process, to control the parameter values. We show the last experimental results with the self-tuning mechanism applied to solve a sample of random instances that simulates a typical Web workload. We consider that the proposed method principles must be extended to the self-tuning of control parameters for other heuristic algorithms.

Keywords

Genetic Algorithm Problem Instance Problem Size Random Instance Genetic Algorithm Parameter 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fogel, D., Ghozeil, A.: Using Fitness Distributions to Design More Efficient Evolutionary Computations. In: Proceedings of the 1996 IEEE Conference on Evolutionary Computation, Nagoya, Japan, pp. 11–19. IEEE Press, Piscataway (1996)CrossRefGoogle Scholar
  2. 2.
    Pérez, J., Pazos, R.A., Velez, L., Rodriguez, G.: Automatic Generation of Control Parameters for the Threshold Accepting Algorithm. In: Coello Coello, C.A., de Albornoz, Á., Sucar, L.E., Battistutti, O.C. (eds.) MICAI 2002. LNCS (LNAI), vol. 2313, pp. 119–127. Springer, Heidelberg (2002)Google Scholar
  3. 3.
    Back, T., Schwefel, H.P.: Evolution Strategies I: Variants and their computational implementation. In: Winter, G., Périaux, J., Galán, M., Cuesta, P. (eds.) Genetic Algorithms in Engineering and Computer Science, vol. 6, pp. 111–126. John Wiley and Sons, Chichester (1995)Google Scholar
  4. 4.
    Mercer, R.E., Sampson, J.R.: Adaptive Search Using a Reproductive Meta-plan. Kybernets 7, 215–228 (1978)CrossRefGoogle Scholar
  5. 5.
    Grefenstette, J.J.: Optimization of Control Parameters for Genetic Algorithms. In: Sage, A.P. (ed.) IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-16(1), pp. 122–128. IEEE, New York (1986)Google Scholar
  6. 6.
    Smith, R.E., Smuda, E.: Adaptively Resizing Population: Algorithm Analysis and First Results. Complex Systems 9, 47–72 (1995)Google Scholar
  7. 7.
    Harik, G.R., Lobo, F.G.: A parameter-less Genetic Algorithm. 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 GECCO 1999, pp. 258–267. Morgan Kaufmann, San Francisco (1999)Google Scholar
  8. 8.
    Pérez, J., Pazos, R.A., Romero, D., Santaolaya, R., Rodríguez, G., Sosa, V.: Adaptive and Scalable Allocation of Data-Objects in the Web. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003. LNCS, vol. 2667, pp. 134–143. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Joaquín Pérez O.
    • 1
  • Rodolfo A. Pazos R.
    • 1
  • Graciela Mora O.
    • 2
  • Guadalupe Castilla V.
    • 2
  • José A. Martínez
    • 2
  • Vanesa Landero N.
    • 2
  • Héctor Fraire H.
    • 2
  • Juan J. González B.
    • 2
  1. 1.Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET)CuernavacaMéxico
  2. 2.Instituto Tecnológico de Ciudad MaderoMéxico

Personalised recommendations