Using Self-Adaptable Probes for Dynamic Parameter Control of Parallel Evolutionary Algorithms

  • Xavier Bonnaire
  • María-Cristina Riff
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3488)


Controlling parameters during execution of parallel evolutionary algorithms is an open research area. Some recent research have already shown good results applying self-calibrating strategies. The motivation of this work is to improve the search of parallel genetic algorithms using monitoring techniques. Monitoring results guides the algorithm to take some actions based on both the search state and the values of its parameters. In this paper, we propose a parameter control architecture for parallel evolutionary algorithms, based on self-adaptable monitoring techniques. Our approach provides an efficient and low cost monitoring technique to design parameters control strategies. Moreover, it is completely independant of the implementation of the evolutionary algorithm.


Genetic Algorithm Evolutionary Algorithm Monitoring Condition Parallel Genetic Algorithm Slave Processor 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Boutros, C., Bonnaire, X.: Cluster Monitoring Platform Based on Self-Adaptable Probes. In: Proceedings of the 12th Symposium on Computer Architecture and High Performance Computing (2000)Google Scholar
  2. 2.
    Boutros, C., Bonnaire, X., Folliot, B.: Flexible Monitoring Platform to Build Cluster Management Services. In: Proceedings of the IEEE International Conference on Cluster Computing- CLUSTER 2000 (2000)Google Scholar
  3. 3.
    Cantu-Paz, E.: Dessigning efficient and accurate parallel genetic algorithms. PhD Thesis, University of Illinois at Urbana Champaign (1999)Google Scholar
  4. 4.
    Davis, L.: Adapting Operator Probabilities in Genetic Algorithms. In: Proceedings of 3rd. International Conf. on Genetic Algorithms and their Applications (1989)Google Scholar
  5. 5.
    Deb, K., Agrawal, S.: Understanding Interactions among Genetic Algorithms Parameters. Foundations of Genetic Algorithms 5, 265–286 (1998)Google Scholar
  6. 6.
    Eiben, A., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions on evolutionary computation 3(2), 124–141 (1999)CrossRefGoogle Scholar
  7. 7.
    Eiben, A.E., Marchiori, E., Valko, V.A.: Evolutionary Algorithms with on-the-fly Population Size Adjustment. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 41–50. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Hinterding, R., Michalewicz, Z., Eiben, A.: Adaptation in Evolutionary Computation: A Survey. In: Proceedings of 4th. IEEE International Conf. on Evolutionary Computation (1997)Google Scholar
  9. 9.
    Lis, J.: Parallel Genetic Algorithm with the Dynamic Control Parameter. In: Proceedings of 3rd. IEEE International Conf. on Evolutionary Computation (1996)Google Scholar
  10. 10.
    Lis, J., Lis, M.: Self-adapting Parallel Genetic Algorithm with the Dynamic Mutation Probability, Crossover Rate and Population Size. In: Proceedings of 1st. Polish Nat. Conf. Evolutionary Computation (1996)Google Scholar
  11. 11.
    Lobo, F., Lima, C., Mártires, H.: An architecture for massive parallelization of the compact genetic algorithm. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 412–413. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Nuñez, A., Riff, M.-C.: Evaluating Migration Strategies for a graph-based evolutionary algorithm for CSP. In: Ohsuga, S., Raś, Z.W. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, pp. 196–204. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Pettinger, J., Everson, R.: Controlling Genetic Algorithms with Reinforcement Learning. In: Proceedings of the GECCO 2002 (2002 )Google Scholar
  14. 14.
    Riff, M.-C., Bonnaire, X.: Inheriting Parents Operators: A New Dynamic Strategy to improve Evolutionary Algorithms. In: Hacid, M.-S., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds.) ISMIS 2002. LNCS (LNAI), vol. 2366, pp. 333–341. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Smith, J., Fogarty, T.C.: Operator and parameter adaptation in genetic algorithms. Soft Computing 1(2), 81–87 (1997)CrossRefGoogle Scholar
  16. 16.
    Tongchim, S., Chongstitvatana, P.: Parallel genetic algorithm with parameter adaptation. Information Processing Letters 82(1), 47–54 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Tuson, A., Ross, P.: Adapting Operator Settings in Genetic Algorithms. Evolutionary Computation 2(6), 161–184 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xavier Bonnaire
    • 1
  • María-Cristina Riff
    • 1
  1. 1.Department of Computer ScienceUniversidad Técnica Federico Santa MaríaValparaísoChile

Personalised recommendations