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On Parallel Evolutionary Algorithms on the Computational Grid

  • N. Melab
  • E-G. Talbi
  • S. Cahon
Part of the Studies in Computational Intelligence book series (SCI, volume 22)

Abstract

In this chapter, we analyze the major traditional parallel models of evolutionary algorithms. The analysis is a contribution in parallel evolutionary computation as unlike previously published studies it is placed within the context of grid computing1. The objective is to visit again the parallel models in order to allow their adaptation to grids taking into account the characteristics of such execution environments in terms of volatility, heterogeneity, large scale and multi-administrative domain. The proposed study is a part of a methodological approach for the development of frameworks dedicated to the reusable design of parallel EAs transparently deployable on computational grids. We give an overview of such frameworks and present a case study related to ParadisEO-CMW which is a porting of ParadisEO onto Condor and MW allowing a transparent deployment of the parallel EAs on grids.

Keywords

Pareto Front Computational Grid Parallel Model Island Model Parallel Evaluation 
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.

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References

  1. 1.
    I. Foster and C. Kesselman. The Grid: Blueprint for a New Computing Infrastructure, chapter Chapter 2: Computational Grids. Morgan-Kaufman, San Francisco, CA, 1999.Google Scholar
  2. 2.
    S. Cahon, N. Melab, and E-G. Talbi. ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics. Journal of Heuristics, Vol. 10:353–376, May 2004. Kluwer Academic Publishers http://www.lif1.fr/~cahon/paradisEO/.CrossRefGoogle Scholar
  3. 3.
    S. Cahon, N. Melab, and E-G. Talbi. An Enabling Framework for Parallel Optimization on the Computational Grid. In the Proc. of the 5th IEEE/ACM Intl. Symp. on Cluster Computing and the Grid (CCGRID ′2005), Cardiff, UK, 9–12 May, 2005.Google Scholar
  4. 4.
    Miron Livny, Jim Basney, Rajesh Raman, and Todd Tannenbaum. Mechanisms for High Throughput Computing. SPEEDUP Journal, 11(1), June 1997. http://www.cs.wisc.edu/condor/.Google Scholar
  5. 5.
    J. Linderoth, S. Kulkarni, J.P. Goux, and M. Yoder. An Enabling Framework for Master-Worker Applications on the Computational Grid. In Proc. of the 9 th IEEE Symposium on High Performance Distributed Computing (HPDC9), pages 43–50, Pittsburgh, PA, Aug. 2000. http://www.cs.wisc.edu/condor/mw/.Google Scholar
  6. 6.
    J.H. Holland. Adaptation in natural and artificial systems. Ann Arbor, MI, USA, The University of Michigan Press, 1975.Google Scholar
  7. 7.
    I. Foster, C. Kesselman, and S. Tuecke. The anatomy of the Grid: Enabling Scalable Virtual Organizations. Int. J. High Perform. Comput. Appl., 15(3):200–222, 2001.CrossRefGoogle Scholar
  8. 8.
    K. Krauter, R. Buyya, and M. Maheswaran. A taxonomy and survey of grid resource management systems for distributed computing. Software - Practice Experience, 32(2):135–164, 2002.zbMATHCrossRefGoogle Scholar
  9. 9.
    Miguel L. Bote-Lorenzo, Yannis A. Dimitriadis, and Eduardo Gómez-Sánchez. Grid Characteristics and Uses: A Grid Definition. In European Across Grids Conference, LNCS 2970, Lecture Notes in Computer Science, pages 291–298, 2003.Google Scholar
  10. 10.
    Ian Foster. What is the Grid? A Three Point Checklist. Grid Today, 1(6), July 22 2002.Google Scholar
  11. 11.
    I. Foster and C. Kesselman. Globus: A Metacomputing Infrastructure Toolkit. Intl. J. of Supercomputer Applications, 11(2):115–128, 1997.CrossRefGoogle Scholar
  12. 12.
    Gilles Fedak. XtremWeb: une plate-forme pour l’étude expérimentale du calcul global pair-à-pair PhD thesis, Université Paris XI, 2003.Google Scholar
  13. 13.
    J.P. Cohoon, S.U. Hedge, W.N. Martin, and D. Richards. Punctuated equilibria: A parallel genetic algorithm. In Proc. of the Second Intl. Conf. on Genetic Algorithms, page 148. Lawrence Erlbaum Associates, 1987.Google Scholar
  14. 14.
    T. Starkweather, D. Whitley, and K. Mathias. Optimization using distributed genetic algorithms. In H.-P. Schwefel and R. Manner, editors, Parallel Problem Solving from Nature, Volume 496, page 176. LNCS, Springer-Verlag, 1991.Google Scholar
  15. 15.
    T. Belding. The distributed genetic algorithm revisited. In D. Eshelmann editor, editor, Sixth Int. Conf. on Genetic Algorithms, San Mateo, CA, 1995. Morgan Kaufmann.Google Scholar
  16. 16.
    N. Melab. Contributions la rśolution de problèmes d’optimisation combinatoire sur grilles de calcul. Thèse d’Habilitation à Diriger des Recherches (HDR,), Université de Lillel, Novembre, 2005.Google Scholar
  17. 17.
    E. Cantú-Paz. A Survey of Parallel Genetic Algorithms, 1997.Google Scholar
  18. 18.
    H. Meunier, El-Ghazali Talbi, and P. Reininger. A Multiobjective Genetic Algorithm for Radio Network Optimization. In Congress on Evolutionary Computation, Volume 1, pages 317–324. IEEE Service Center, July 2000.Google Scholar
  19. 19.
    M.G. Arenas, P. Collet, A.E. Eiben, M. Jelasity, J.J. Merelo, B. Paechter, M. Preuss, and M. Schoenauer. A framework for distributed evolutionary algorithms. In Proceedings of PPSN VII, September 2002.Google Scholar
  20. 20.
    S. Luke, L. Panait, J. Bassett, R. Hubley, C. Balan, and A. Chicop. ECJ: A Java-based Evolutionary Computation and Genetic Programming Research system, 2002. http://www.cs.umd.edu/projects/plus/ec/ecj/.Google Scholar
  21. 21.
    J. Costa, N. Lopes, and P. Silva. Jdeal: The Java Distributed Evolutionary Algorithms Library. 2000.Google Scholar
  22. 22.
    C. Gagné, M. Parizeau, and M. Dubreuil. Distributed BEAGLE: An Environment for Parallel and Distributed Evolutionary Computations. In Proc. of the 17 th Annual Intl. Symp. on High Performance Computing Systems and Applications (HPCS) 2003, May 11–14 2003.Google Scholar
  23. 23.
    M. Keijzer, J.J. Morelo, G. Romero, and M. Schoenauer. Evolving Objects: A General Purpose Evolutionary Computation Library. Proc. of the 5 th Intl. Conf. on Artificial Evolution (EA ′01), Le Creusot, France, Oct. 2001.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • N. Melab
    • 1
  • E-G. Talbi
    • 1
  • S. Cahon
    • 1
  1. 1.Laboratoire d’Informatique Fondamentale de Lille UMR CNRS 8022INRIA Futurs - DOLPHIN Project Cité scientifique - 59655France

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