Advertisement

A Framework of GRID Problem-Solving Environment Employing Robust Evolutionary Search

  • Masaharu Munetomo
  • Asim Munawar
  • Kiyoshi Akama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4739)

Abstract

This paper presents a problem-solving framework based on robust evolutionary search in GRID computing environment. Our problem-solving environment called virtual innovative laboratory performs simulator programs in parallel and optimize their input parameters employing a competent evolutionary algorithm with gene analysis. The objective of our project is to replace a part of human designer’s try-and-error processes by a parallel and robust evolutionary search on GRID computing systems.

Keywords

GRID computing problem-solving environment evolutionary algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Czyzyk, J., Mesnier, M., More, J.: The NEOS server. IEEE Journal on Computational Science and Engineering 5, 68–75 (1998)CrossRefGoogle Scholar
  2. 2.
    Dolan, E.: The NEOS server 4.0 administrative guide. Technical Memorandum ANL/MCS-TM-250, Mathematics and Computer Science Division, Argonne National Laboratory, May, Discusses the Server implementation and use in detail (2001)Google Scholar
  3. 3.
    Foster, I.: Globus toolkit version 4: Software for service-oriented systems. In: Jin, H., Reed, D., Jiang, W. (eds.) NPC 2005. LNCS, vol. 3779, pp. 2–13. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan-Kaufman, San Francisco (1999)Google Scholar
  5. 5.
    Gropp, W., Mor’e, J.: Optimization environments and the neos server (1997)Google Scholar
  6. 6.
    Hoos, H.H., Stützle, T.: SATLIB: An Online Resource for Research on SAT, pp. 283–292Google Scholar
  7. 7.
    Munetomo, M., Goldberg, D.E.: Identifying linkage groups by nonlinearity/non-monotonicity detection. 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, vol. 1, pp. 433–440. Morgan Kaufmann, Orlando, Florida, USA (1999)Google Scholar
  8. 8.
    Munetomo, M., Murao, N., Akama, K.: A parallel genetic algorithm based on linkage identification. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 1222–1233. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Tsuji, M.: Designing Genetic Algorithm Based on Exploration and Exploitation of Gene Linkage. PhD thesis, Hokkaido University, Sapporo, Japan (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Masaharu Munetomo
    • 1
  • Asim Munawar
    • 2
  • Kiyoshi Akama
    • 1
  1. 1.Information Initiative Center 
  2. 2.Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0811Japan

Personalised recommendations