Advertisement

Validating Evolutionary Algorithms on Volunteer Computing Grids

  • Travis Desell
  • Malik Magdon-Ismail
  • Boleslaw Szymanski
  • Carlos A. Varela
  • Heidi Newberg
  • David P. Anderson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6115)

Abstract

Computational science is placing new demands on distributed computing systems as the rate of data acquisition is far outpacing the improvements in processor speed. Evolutionary algorithms provide efficient means of optimizing the increasingly complex models required by different scientific projects, which can have very complex search spaces with many local minima. This work describes different validation strategies used by MilkyWay@Home, a volunteer computing project created to address the extreme computational demands of 3-dimensionally modeling the Milky Way galaxy, which currently consists of over 27,000 highly heterogeneous and volatile computing hosts, which provide a combined computing power of over 1.55 petaflops. The validation strategies presented form a foundation for efficiently validating evolutionary algorithms on unreliable or even partially malicious computing systems, and have significantly reduced the time taken to obtain good fits of MilkyWay@Home’s astronomical models.

Keywords

Volunteer Computing Evolutionary Algorithms Validation 

References

  1. 1.
    Anderson, D.P., Korpela, E., Walton, R.: High-performance task distribution for volunteer computing. In: e-Science, pp. 196–203. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  2. 2.
    Bánhelyi, B., Biazzini, M., Montresor, A., Jelasity, M.: Peer-to-peer optimization in large unreliable networks with branch-and-bound and particle swarms. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoCOMNET. LNCS, vol. 5484, pp. 87–92. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Biazzini, M., Banhelyi, B., Montresor, A., Jelasity, M.: Distributed hyper-heuristics for real parameter optimization. In: GECCO 2009: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp. 1339–1346. ACM, New York (2009)Google Scholar
  4. 4.
    Cole, N.: Maximum Likelihood Fitting of Tidal Streams with Application to the Sagittarius Dwarf Tidal Tails. PhD thesis, Rensselaer Polytechnic Institute (2009)Google Scholar
  5. 5.
    Cole, N., Newberg, H., Magdon-Ismail, M., Desell, T., Dawsey, K., Hayashi, W., Purnell, J., Szymanski, B., Varela, C.A., Willett, B., Wisniewski, J.: Maximum likelihood fitting of tidal streams with application to the sagittarius dwarf tidal tails. Astrophysical Journal 683, 750–766 (2008)CrossRefGoogle Scholar
  6. 6.
    Desell, T.: Asynchronous Global Optimization for Massive Scale Computing. PhD thesis, Rensselaer Polytechnic Institute (2009)Google Scholar
  7. 7.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Sixth International Symposium on Micromachine and Human Science, pp. 33–43 (1995)Google Scholar
  8. 8.
    Golle, P., Mironov, I.: Uncheatable distributed computations. In: Naccache, D. (ed.) CT-RSA 2001. LNCS, vol. 2020, pp. 425–440. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  10. 10.
    Mezura-Montes, E., Velzquez-Reyes, J., Coello, C.A.C.: A comparative study of differential evolution variants for global optimization. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 485–492 (2006)Google Scholar
  11. 11.
    Schwefel, H.-P.: Evolution and Optimization Seeking. John Wiley & Sons, New York (1995)Google Scholar
  12. 12.
    Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: IEEE World Congress on Computational Intelligence, May 1998, pp. 69–73 (1998)Google Scholar
  13. 13.
    Storn, R., Price, K.: Minimizing the real functions of the ICEC1996 contest by differential evolution. In: Proceedings of the IEEE International Conference on Evolutionary Computation, Nagoya, Japan, pp. 842–844 (1996)Google Scholar
  14. 14.
    Szajda, D., Lawson, B., Owen, J.: Hardening functions for large scale distributed computations. In: IEEE Symposium on Security and Privacy, vol. 0, p. 216 (2003)Google Scholar
  15. 15.
    Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Congress on Evolutionary Computation 2004 (CEC 2004), June 2004, vol. 2, pp. 1980–1987 (2004)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2010

Authors and Affiliations

  • Travis Desell
    • 1
  • Malik Magdon-Ismail
    • 1
  • Boleslaw Szymanski
    • 1
  • Carlos A. Varela
    • 1
  • Heidi Newberg
    • 2
  • David P. Anderson
    • 3
  1. 1.Department of Computer ScienceRensselaer Polytechnic InstituteTroyUSA
  2. 2.Department of Physics, Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyUSA
  3. 3.U.C. Berkeley Space Sciences LaboratoryUniversity of CaliforniaBerkeleyUSA

Personalised recommendations