Characterizing Fault-Tolerance in Evolutionary Algorithms

  • Daniel Lombraña González
  • Juan Luis Jiménez Laredo
  • Francisco Fernández de Vega
  • Juan Julián Merelo Guervós
Part of the Studies in Computational Intelligence book series (SCI, volume 415)


This chapter presents a study of the fault-tolerant nature of some of the best known Evolutionary Algorithms, namely Genetic Algorithms (GAs) and Genetic Programming (GP), on a real-world Desktop Grid System. We study the situation when no fault-tolerance mechanisms is employed. The results show that when parallel GAs and GPs are run on non-reliable distributed infrastructures -thus suffering degradation of available hardware- they can achieve results of a similar quality when compared with a failure-free platform in three of the six scenarios under study. Additionally, we show that increasing the initial population size is a successful method to provide resilience to system failures in five of the scenarios. Such results suggest that Parallel GAs and GPs are inherently and naturally fault-tolerant.


genetic programming genetic algorithms evolutionary algorithms fault tolerance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ackley, D.H.: A connectionist machine for genetic hillclimbing. Kluwer Academic Publishers, Norwell (1987)CrossRefGoogle Scholar
  2. 2.
    Agbaria, A., Friedman, R.: Starfish: Fault-tolerant dynamic mpi programs on clusters of workstations. In: HPDC 1999: Proceedings of the The Eighth IEEE International Symposium on High Performance Distributed Computing, vol. 31, IEEE Computer Society, Washington, DC (1999)Google Scholar
  3. 3.
    Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation 6(5), 443–462 (2002)CrossRefGoogle Scholar
  4. 4.
    Anderson, D.P.: Boinc: a system for public-resource computing and storage. In: Proceedings of Fifth IEEE/ACM International Workshop on Grid Computing, 2004, pp. 4–10 (2004)Google Scholar
  5. 5.
    Anderson, D.P., Fedak, G.: The Computational and Storage Potential of Volunteer Computing. In: Proceedings of the IEEE International Symposium on Cluster Computing and the Grid, CCGRID 2006 (2006)Google Scholar
  6. 6.
    Andre, D., Koza, J.R.: Parallel genetic programming: a scalable implementation using the transputer network architecture, pp. 317–337 (1996)Google Scholar
  7. 7.
    Arenas, M., Collet, P., Eiben, A.E., Jelasity, M., Merelo, J.J., Paechter, B., Preuß, M., Schoenauer, M.: A Framework for Distributed Evolutionary Algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 665–675. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Cahon, S., Melab, N., Talbi, E.G.: Building with paradisEO reusable parallel and distributed evolutionary algorithms. Parallel Computing 30(5-6), 677–697 (2004)CrossRefGoogle Scholar
  9. 9.
    Cantu-Paz, E.: A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis 10(2), 141–171 (1998)Google Scholar
  10. 10.
    Crawley, M.J.: In: Statistics, An Introduction using R. Wiley (2007)Google Scholar
  11. 11.
    Francisco Chávez de la, O., Guisado, J.L., Lombraña, D., Fernández, F.: Una herramienta de programación genética paralela que aprovecha recursos públicos de computación. In: V Congreso Español sobre Metaheuŕsticas, Algoritmos Evolutivos y Bioinspirados, Tenerife, Spain, vol. 1, pp. 167–173 (February 2007)Google Scholar
  12. 12.
    Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Darrell Whitley, L. (ed.) FOGA, pp. 93–108. Morgan Kaufmann (1992)Google Scholar
  13. 13.
    Desell, T., Szymanski, B., Varela, C.: An asynchronous hybrid genetic-simplex search for modeling the Milky Way galaxy using volunteer computing. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 921–928. ACM (2008)Google Scholar
  14. 14.
    Douglas Zongker Dr. Bill Punch. Lil-gp.:
  15. 15.
    Elnozahy, E.N.M., Alvisi, L., Wang, Y.M., Johnson, D.B.: A survey of rollback-recovery protocols in message-passing systems. ACM Computing Surveys (CSUR) 34(3), 375–408 (2002)CrossRefGoogle Scholar
  16. 16.
    Vanneschi, L., Fernández, F., Tomassini, M.: Saving computational effort in genetic programming by means of plagues. In: The 2003 Congress on Evolutionary Computation, CEC 2003 (2003)Google Scholar
  17. 17.
    Fernandez, F., Spezzano, G., Tomassini, M., Vanneschi, L.: Parallel genetic programming. In: Alba, E. (ed.) Parallel Metaheuristics, Parallel and Distributed Computing, ch. 6, pp. 127–153. Wiley-Interscience, Hoboken (2005)Google Scholar
  18. 18.
    Message Passing Interface Forum. Mpi: a message-passing interface standard. International Journal Supercomput. Applic. 8(3-4), 165–414 (1994)Google Scholar
  19. 19.
    Gagné, C., Parizeau, M., Dubreuil, M.: Distributed beagle: An environment for parallel and distributed evolutionary computations. In: Proc. of the 17th Annual International Symposium on High Performance Computing Systems and Applications (HPCS 2003), May 11-14, pp. 201–208 (2003)Google Scholar
  20. 20.
    Gartner, F.C.: Fundamentals of fault-tolerant distributed computing in asynchronous environments. ACM Computing Surveys 31(1), 1–26 (1999)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Ghosh, S.: Distributed systems: an algorithmic approach. Chapman & Hall/CRC (2006)Google Scholar
  22. 22.
    Gonzáez, D.L., de Vega, F.F., Casanova, H.: Characterizing fault tolerance in genetic programming. In: Workshop on Bio-Inspired Algorithms for Distributed Systems, Barcelona, Spain, pp. 1–10 (June 2009)Google Scholar
  23. 23.
    González, D.L., de Vega, F.F., Casanova, H.: Characterizing fault tolerance in genetic programming. Future Generation Computer Systems 26(6), 847–856 (2010)CrossRefGoogle Scholar
  24. 24.
    González, D.L., de Vega, F.F., Trujillo, L., Olague, G., Araujo, L., Castillo, P., Merelo, J.J., Sharman, K.: Increasing gp computing power for free via desktop grid computing and virtualization. In: Proceedings of the 17th Euromicro Conference on Parallel, Distributed and Network-Based Processing, Weimar, Germany, pp. 419–423 (February 2009)Google Scholar
  25. 25.
    González, D.L., Laredo, J.L.J., de Vega, F.F., Guervós, J.J.M.: Characterizing fault-tolerance of genetic algorithms in desktop grid systems. In: 10th European Conference on Evolutionary Computation in Combinatorial Optimization, Istanbul, Turkey, pp. 131–142 (April 2010)Google Scholar
  26. 26.
    Guerraoui, R., Schiper, A.: Software-Based Replication for Fault Tolerance. IEEE Computer 30(4), 68–74 (1997)CrossRefGoogle Scholar
  27. 27.
    Hidalgo, I., Fernández, F., Lanchares, J., Lombraña, D.: Is the island model fault tolerant? In: Genetic and Evolutionary Computation Conference, London, England, vol. 2, p. 1519 (July 2007)Google Scholar
  28. 28.
    Jelasity, M., Preuß, M., van Steen, M., Paechter, B.: Maintaining connectivity in a scalable and robust distributed environment. In: Bal, H.E., Löhr, K.-P., Reinefeld, A. (eds.) Proceedings of the Second IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid2002), 2nd GP2PC Workshop, Berlin, Germany, pp. 389–394. IEEE Computer Society (2002)Google Scholar
  29. 29.
    Knispel, B., Allen, B., Cordes, J.M., Deneva, J.S., Anderson, D., Aulbert, C., Bhat, N.D.R., Bock, O., Bogdanov, S., Brazier, A., et al.: Pulsar Discovery by Global Volunteer Computing. Science 329(5997), 1305 (2010)CrossRefGoogle Scholar
  30. 30.
    Kondo, D., Fedak, G., Cappello, F., Chien, A.A., Casanova, H.: Characterizing resource availability in enterprise desktop grids, vol. 23, pp. 888–903. Elsevier (2007)Google Scholar
  31. 31.
    Kondo, D., Taufer, M., Brooks, C., Casanova, H., Chien, A.: Characterizing and evaluating desktop grids: An empirical study. In: Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS 2004). Citeseer (2004)Google Scholar
  32. 32.
    Kouchakpour, P., Zaknich, A., Bräunl, T.: Dynamic population variation in genetic programming. Information Sciences 179(8), 1078–1091 (2009)CrossRefGoogle Scholar
  33. 33.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  34. 34.
    Laredo, J.L.J., Castillo, P.A., Mora, A.M., Merelo, J.J., Fernandes, C.: Merelo, and Carlos Fernandes. Resilience to churn of a peer-to-peer evolutionary algorithm. Int. J. High Performance Systems Architecture 1(4), 260–268 (2008)CrossRefGoogle Scholar
  35. 35.
    Laredo, J.L.J., Eiben, A.E., van Steen, M., Merelo, J.J.: On the Run-Time Dynamics of a Peer-to-Peer Evolutionary Algorithm. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 236–245. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  36. 36.
    Lombraña, D., Fernández, F.: Analyzing fault tolerance on parallel genetic programming by means of dynamic-size populations. In: Congress on Evolutionary Computation, Singapore, vol. 1, pp. 4392–4398 (2007)Google Scholar
  37. 37.
    Lombraña, D., Fernández, F., Trujillo, L., Olague, G., Cárdenas, M., Araujo, L., Castillo, P., Sharman, K., Silva, A.: Interpreted applications within boinc infrastructure. In: Ibergrid 2008, Porto, Portugal, pp. 261–272 (May 2008)Google Scholar
  38. 38.
    Lombraña, D., Fernández, F., Trujillo, L., Olague, G., Segal, B.: Customizable execution environments with virtual desktop grid computing. In: Parallel and Distributed Computing and Systems, PDCS (2007)Google Scholar
  39. 39.
    Luke, S., Balan, G.C., Panait, L.: Population Implosion in Genetic Programming. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, D., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Standish, R., Kendall, G., Wilson, S., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K., Jonoska, N., Miller, J. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1729–1739. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  40. 40.
    Luke, S., Panait, L., Balan, G., Paus, S., Skolicki, Z., Popovici, E., Harrison, J., Bassett, J., Hubley, R., Chircop, A.: Ecj a java-based evolutionary computation research system (2007),
  41. 41.
    Melab, N., Cahon, S., Talbi, E.-G.: Grid computing for parallel bioinspired algorithms. J. Parallel Distrib. Comput. 66(8), 1052–1061 (2006)MATHCrossRefGoogle Scholar
  42. 42.
    Pruyne, J., Livny, M.: Interfacing Condor and PVM to harness the cycles of workstation clusters. Future Generations Computer Systems FGCS 12(1), 67–85 (1996)CrossRefGoogle Scholar
  43. 43.
    Reed, D.A., Lu, C., Mendes, C.L.: Reliability challenges in large systems. Future Generation Computer Systems 22(3), 293–302 (2006)CrossRefGoogle Scholar
  44. 44.
    Schroeder, B., Gibson, G.A.: A Large-Scale Study of Failures in High-Performance Computing Systems. In: Proc. of the International Conference on Dependable Systems, pp. 249–258 (2006)Google Scholar
  45. 45.
    Shooman, M.L.: Reliability of computer systems and networks: fault tolerance, analysis and design. Wiley Interscience (2002)Google Scholar
  46. 46.
    Stutzbach, D., Rejaie, R.: Understanding churn in peer-to-peer networks. In: Proceedings of the 6th ACM SIGCOMM on Internet Measurement (IMC 2006), pp. 189–202. ACM Press, New York (2006)CrossRefGoogle Scholar
  47. 47.
    Sunderam, V.S.: Pvm: A framework for parallel distributed computing. Concurrency: Practice and Experience 2, 315–339 (1990)CrossRefGoogle Scholar
  48. 48.
    Tai, A.T., Tso, K.S.: A performability-oriented software rejuvenation framework for distributed applications. In: DSN 2005: Proceedings of the 2005 International Conference on Dependable Systems and Networks (DSN 2005), pp. 570–579. IEEE Computer Society, Washington, DC (2005)Google Scholar
  49. 49.
    Thierens, D.: Scalability problems of simple genetic algorithms. Evolutionary Computation 7(4), 331–352 (1999)CrossRefGoogle Scholar
  50. 50.
    Tomassini, M.: Parallel and distributed evolutionary algorithms: A review. In: Neittaanmäki, P., Miettinen, K., Mäkelä, M., Periaux, J. (eds.) Evolutionary Algorithms in Engineering and Computer Science, p. 113, 133. J. Wiley and Sons, Chichester (1999)Google Scholar
  51. 51.
    Tomassini, M.: Spatially Structured Evolutionary Algorithms. Springer (2005)Google Scholar
  52. 52.
    Tomassini, M., Vanneschi, L., Cuendet, J., Fernandez, F.: A new technique for dynamic size populations in genetic programming. In: Congress on Evolutionary Computation, CEC 2004, vol. 1 (2004)Google Scholar
  53. 53.
    Trujillo, L., Olague, G.: Automated Design of Image Operators that Detect Interest Points, vol. 16, pp. 483–507. MIT Press (2008)Google Scholar
  54. 54.
    Vargas, E.: High availability fundamentals. Sun Blueprints (2000)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Daniel Lombraña González
    • 1
  • Juan Luis Jiménez Laredo
    • 2
  • Francisco Fernández de Vega
    • 3
  • Juan Julián Merelo Guervós
    • 2
  1. 1.Citizen Cyberscience CentreCERNGenevaSwitzerland
  2. 2.University of GranadaGranadaSpain
  3. 3.Centro Universitario de MéridaUniversidad de ExtremaduraMéridaSpain

Personalised recommendations