Parallel Evolutionary Computations pp 97-115

Part of the Studies in Computational Intelligence book series (SCI, volume 22) | Cite as

Performance of Distributed GAs on DNA Fragment Assembly

  • Enrique Alba
  • Gabriel Luque

Abstract

In this work, we present results on analyzing the behavior of a parallel distributed genetic algorithm over different LAN technologies. Our goal is to offer a study on the potential impact in the search mechanics when shifting between LANs. We will address three LANs: a Fast Ethernet network, a Gigabit Ethernet network, and a Myrinet network. We also study the importance of several parameters of the migration policy. The whole analysis will use the DNA fragment assembly problem to show the actual power and utility of the proposed distributed technique.

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References

  1. 1.
    E. Alba. Parallel evolutionary algorithms can achieve super-linear performace. Information Processing Letters, 82:7–13, 2002.MATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    E. Alba. Parallel Metaheuristics: A New Class of Algorithms. Wiley, 2005.Google Scholar
  3. 3.
    E. Alba, C. Cotta, M. Díaz, E. Soler, and J.M. Troya. MALLBA: Middleware for a geographically distributed optimization system. Technical report, Dpto. Lenguajes y Ciencias de la Computatión, Universidad de Malága (internal report), 2000.Google Scholar
  4. 4.
    E. Alba, F. Luna, and A. J. Nebro. Advances in parallel heterogeneous genetic algorithms for continuous optimization. International Journal of Applied Mathematics and Computer Science, 14(3):317–333, 2004.MATHMathSciNetGoogle Scholar
  5. 5.
    E. Alba and the MALLBA Group. MALLBA: A library of skeletons for combinatorial optimisation. In R. Feldmann B. Monien, editor, Proceedings of the Euro-Par, volume 2400 of LNCS, pages 927–932, Paderborn (GE), 2002. Springer–Verlag.Google Scholar
  6. 6.
    E. Alba and M. Tomassini. Parallelism and Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation, 6(5):443–462, October 2002.CrossRefGoogle Scholar
  7. 7.
    C.F. Allex. Computational Methods for Fast and Accurate DNA Fragment Assembly. UW technical report CS-TR-99–1406, Department of Computer Sciences, University of Wisconsin-Madison, 1999.Google Scholar
  8. 8.
    N.J. Boden, D. Cohen, R.E. Felderman, A.E. Kulawik, C.L. Seitz, J. Seizovic, and W. Su. Myrinet A Gigabit Per Second Local Area Network. IEEE Micro, pages 29–36, 1995.Google Scholar
  9. 9.
    E. Cantú-Paz. Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Press, 2000.Google Scholar
  10. 10.
    M.L. Engle and C. Burks. Artificially generated data sets for testing DNA fragment assembly algorithms. Genomics, 16, 1993.Google Scholar
  11. 11.
    Message Passing Interface Forum. MPI: A message–passing interface standard. Technical Report UT-CS-94–230, 1994.Google Scholar
  12. 12.
    D.E. Goldberg and K. Deb. A comparative analysis of selection schemes used in genetic algorithms. In G.J.E. Rawlins, editor, Foundations of Genetic Algorithms, pages 69–93. Morgan Kaufmann, 1991.Google Scholar
  13. 13.
    S. Kim. A structured Pattern Matching Approach to Shotgun Sequence Assembly. PhD thesis, Computer Science Department, The University of Iowa, 1997.Google Scholar
  14. 14.
    C. Notredame and D.G. Higgins. SAGA: sequence alignment by genetic algorithm. Nucleic Acids Research, 24:1515–1524, 1996.CrossRefGoogle Scholar
  15. 15.
    R. Parsons, S. Forrest, and C. Burks. Genetic algorithms, operators, and DNA fragment assembly. Machine Learning, 21:11–33, 1995.Google Scholar
  16. 16.
    R. Parsons and M.E. Johnson. A case study in experimental design applied to genetic algorithms with applications to DNA sequence assembly. American Journal of Mathematical and Management Sciences, 17:369–396, 1995.Google Scholar
  17. 17.
    P.A. Pevzner. Computational molecular biology: An algorithmic approach. The MIT Press, London, 2000.Google Scholar
  18. 18.
    J. Setubal and J. Meidanis. Introduction to Computational Molecular Biology, chapter 4 - Fragment Assembly of DNA, pages 105–139. University of Campinas, Brazil, 1997.Google Scholar
  19. 19.
    D. Whitely. The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In J.D. Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 116–121. Morgan Kaufmann, 1989.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Enrique Alba
    • 1
  • Gabriel Luque
    • 2
  1. 1.Departamento de Lenguajes y Ciencias de la Computación E.T.S.I. InformáticaUniversity of MálagaSpain
  2. 2.Departamento de Lenguajes y Ciencias de la Computación E.T.S.I. InformáticaUniversity of Málaga29071Spain

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