Validating a Peer-to-Peer Evolutionary Algorithm

  • Juan Luis Jiménez Laredo
  • Pascal Bouvry
  • Sanaz Mostaghim
  • Juan-Julián Merelo-Guervós
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

Abstract

This paper proposes a simple experiment for validating a Peer-to-Peer Evolutionary Algorithm in a real computing infrastructure in order to verify that results meet those obtained by simulations. The validation method consists of conducting a well-characterized experiment in a large computer cluster of up to a number of processors equal to the population estimated by the simulator. We argue that the validation stage is usually missing in the design of large-scale distributed meta-heuristics given the difficulty of harnessing a large number of computing resources. That way, most of the approaches in the literature focus on studying the model viability throughout a simulation-driven experimentation. However, simulations assume idealistic conditions that can influence the algorithmic performance and bias results when conducted in a real platform. Therefore, we aim at validating simulations by running a real version of the algorithm. Results show that the algorithmic performance is rather accurate to the predicted one whilst times-to-solutions can be drastically decreased when compared to the estimation of a sequential run.

Keywords

Evolutionary Algorithm Parallel Version Large Problem Instance Medium Size Instance Homogeneous Node 
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.
    Ackley, D.H.: A connectionist machine for genetic hillclimbing. Kluwer Academic Publishers, Norwell (1987)CrossRefGoogle Scholar
  2. 2.
    Anderson, D.P.: Boinc: A system for public-resource computing and storage. In: 5th IEEE/ACM International Workshop on Grid Computing, pp. 4–10 (2004)Google Scholar
  3. 3.
    Biazzini, M., Montresor, A.: Gossiping de: A decentralized heuristic for function optimization in p2p networks. In: ICPADS 2010, pp. 468–475 (2010)Google Scholar
  4. 4.
    Eibenand, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)Google Scholar
  5. 5.
    Guo, Y., Cheng, J., Cao, Y., Lin, Y.: A novel multi-population cultural algorithm adopting knowledge migration. Soft Comput. 15(5), 897–905 (2011)CrossRefGoogle Scholar
  6. 6.
    Jelasity, M., van Steen, M.: Large-scale newscast computing on the Internet. Technical Report IR-503, Vrije Universiteit Amsterdam, Department of Computer Science, Amsterdam, The Netherlands (October 2002)Google Scholar
  7. 7.
    Laredo, J.L.J., Castillo, P.A., Mora, A.M., Merelo, J.J.: Exploring population structures for locally concurrent and massively parallel evolutionary algorithms. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC2008), WCCI 2008, pp. 2610–2617. IEEE Press, Hong Kong (2008)Google Scholar
  8. 8.
    Laredo, J.L.J., Eiben, A.E., van Steen, M., Julián Merelo Guervós, J.: Evag: a scalable peer-to-peer evolutionary algorithm. Genetic Programming and Evolvable Machines 11(2), 227–246 (2010)CrossRefGoogle Scholar
  9. 9.
    Laredo, J.L.J., Lombraña, D., de Vega, F.F., Arenas, M.G., Merelo, J.J.: A Peer-to-Peer Approach to Genetic Programming. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds.) EuroGP 2011. LNCS, vol. 6621, pp. 108–117. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    McNairy, C., Bhatia, R.: Montecito: a dual-core, dual-thread itanium processor. IEEE Micro. 25(2), 10–20 (2005)CrossRefGoogle Scholar
  11. 11.
    Ruiz, P., Dorronsoro, B., Valentini, G., Pinel, F., Bouvry, P.: Optimisation of the enhanced distance based broadcasting protocol for manets. J. of Supercomputing. Special Issue on Green Networks, 1–28 (February 23, 2011), Online FirstTM Google Scholar
  12. 12.
    Sastry, K.: Evaluation-relaxation schemes for genetic and evolutionary algorithms. Technical Report 2002004, University of Illinois at Urbana-Champaign, Urbana, IL (2001)Google Scholar
  13. 13.
    Scriven, I., Ireland, D., Lewis, A., Mostaghim, S., Branke, J.: Asynchronous multiple objective particle swarm optimisation in unreliable distributed environments. In: IEEE Congress on Evolutionary Computation, CEC 2008 (2008)Google Scholar
  14. 14.
    Steinmetz, R., Wehrle, K.: What is this Peer-to-Peer About? In: Steinmetz, R., Wehrle, K. (eds.) Peer-to-Peer Systems and Applications. LNCS, vol. 3485, pp. 9–16. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ”small-world” networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  16. 16.
    Wickramasinghe, W.R.M.U.K., van Steen, M., Eiben, A.E.: Peer-to-peer evolutionary algorithms with adaptive autonomous selection. In: GECCO 2007, pp. 1460–1467. ACM Press, New York (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Juan Luis Jiménez Laredo
    • 1
  • Pascal Bouvry
    • 1
  • Sanaz Mostaghim
    • 2
  • Juan-Julián Merelo-Guervós
    • 3
  1. 1.Faculty of Sciences, Technology and CommunicationUniversity of LuxembourgLuxembourg CityLuxembourg
  2. 2.Karlsruhe Institute of TechnologieKarlsruheGermany
  3. 3.ATC-ETSIITUniversity of GranadaGranadaSpain

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