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Genetic Programming and Evolvable Machines

, Volume 11, Issue 2, pp 227–246 | Cite as

EvAg: a scalable peer-to-peer evolutionary algorithm

  • J. L. J. Laredo
  • A. E. Eiben
  • M. van Steen
  • J. J. Merelo
Original Paper

Abstract

This paper studies the scalability of an Evolutionary Algorithm (EA) whose population is structured by means of a gossiping protocol and where the evolutionary operators act exclusively within the local neighborhoods. This makes the algorithm inherently suited for parallel execution in a peer-to-peer fashion which, in turn, offers great advantages when dealing with computationally expensive problems because distributed execution implies massive scalability. In this paper we show another advantage of this algorithm: We experimentally demonstrate that it scales up better than traditional alternatives even when executed in a sequential fashion. In particular, we analyze the behavior of several EAs on well-known deceptive trap functions with varying sizes and levels of deceptiveness. The results show that the new EA requires smaller optimal population sizes and fewer fitness evaluations to reach solutions. The relative advantage of the new EA is more outstanding as problem hardness and size increase. In some cases the new algorithm reduces the computational efforts of the traditional EAs by several orders of magnitude.

Keywords

Peer-to-peer computing Evolutionary algorithms Scalability analysis Diversity 

Notes

Acknowledgments

This work has been supported by the Spanish MICYT project TIN2007-68083-C02-01, the Junta de Andalucia CICE project P06-TIC-02025 and the Granada University PIUGR 9/11/06 project.

References

  1. 1.
    D.H. Ackley, A Connectionist Machine for Genetic Hillclimbing. (Kluwer, Norwell, MA, 1987)Google Scholar
  2. 2.
    E. Alba, M. Tomassini, Parallelism and evolutionary algorithms. IEEE Trans. Evol. Comput. 6(5), 443–462 (2002)CrossRefGoogle Scholar
  3. 3.
    M. Arenas, P. Collet, A.E. Eiben, M. Jelasity, J.J. Merelo, B. Paechter, M. Preuss, M. Schoenauer, A framework for distributed evolutionary algorithms. In Parallel Problem Solving from Nature—PPSN VII, Granada, Spain, No. 2439 in Lecture Notes in Computer Science, LNCS. (Springer, 2002), pp. 665–675Google Scholar
  4. 4.
    B. Bánhelyi, M. Biazzini, A. Montresor, M. Jelasity, Peer-to-peer optimization in large unreliable networks with branch-and-bound and particle swarms. In Applications of Evolutionary Computing, Lecture Notes in Computer Science, ed. by M. Giacobini, A. Brabazon, S. Cagnoni, G.A.D. Caro, A. Ekárt, A.I. Esparcia-Alcázar, M. Farooq, A. Fink, P. Machado (Springer, 2009), pp. 87–92Google Scholar
  5. 5.
    J. Berntsson, G2DGA: an adaptive framework for internet-based distributed genetic algorithms. In GECCO ’05: Proceedings of the 2005 workshops on Genetic and evolutionary computation, pp. 346–349Google Scholar
  6. 6.
    E. Cantú-Paz, Efficient and Accurate Parallel Genetic Algorithms. (Kluwer, Norwell, MA, 2000)MATHGoogle Scholar
  7. 7.
    K. Deb, D.E. Goldberg, Analyzing deception in trap functions. In Foundations of Genetic Algorithms. (Morgan Kaufmann, 1991), pp. 93–108Google Scholar
  8. 8.
    A.E. Eiben, J.E. Smith, Introduction to Evolutionary Computing. (Springer, Berlin, 2003)MATHGoogle Scholar
  9. 9.
    A.E. Eiben, A.R. Griffioen, E. Haasdijk, Population-based adaptive systems: concepts, issues, and the platform NEW TIES. In Proceedings of European Conference on Complex Systems, Dresden, Germany, http://www.cs.vu.nl/~gusz/papers/2007-ECCS-PAS.pdf
  10. 10.
    G. Folino, G. Spezzano, P-cage: an environment for evolutionary computation in peer-to-peer systems. In EuroGP, Lecture Notes in Computer Science, vol. 3905, ed. by P. Collet, M. Tomassini, M. Ebner, S. Gustafson, A. Ekárt (Springer, 2006), pp. 341–350Google Scholar
  11. 11.
    M. Giacobini, E. Alba, A. Tettamanzi, M. Tomassini, Modeling selection intensity for toroidal cellular evolutionary algorithms. In GECCO ’04: Proceedings of the 2004 conference on Genetic and Evolutionary Computation (Springer, Berlin/Heidelberg, LNCS, 2004), pp. 1138–1149Google Scholar
  12. 12.
    M. Giacobini, M. Tomassini, A. Tettamanzi, Takeover time curves in random and small-world structured populations. In GECCO ’05: Proceedings of the 2005 conference on Genetic and evolutionary computation (ACM, New York, NY, 2005a), pp. 1333–1340. http://www.doi.acm.org/10.1145/1068009.1068224
  13. 13.
    M. Giacobini, M. Tomassini, A. Tettamanzi, E. Alba, Selection intensity in cellular evolutionary algorithms for regular lattices. IEEE Trans. Evol. Comput. 9(5), 489–505 (2005b)CrossRefGoogle Scholar
  14. 14.
    M. Giacobini, M. Preuss, M. Tomassini, Effects of scale-free and small-world topologies on binary coded self-adaptive CEA. In Evolutionary Computation in Combinatorial Optimization—EvoCOP 2006, vol. 3906, ed. by J. Gottlieb, G.R. Raidl (Springer, Budapest, LNCS, 2006), pp. 85–96Google Scholar
  15. 15.
    D.E. Goldberg, The Design of Innovation—Lessons from and for Competent Genetic Algorithms (Kluwer, Norwell, MA, 2002)MATHGoogle Scholar
  16. 16.
    D.E. Goldberg, K. Deb, A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of Genetic Algorithms (Morgan Kaufmann, 1991), pp. 69–93Google Scholar
  17. 17.
    G. Harik, E. Cantú-Paz, D. Goldberg, B. Miller, The Gambler’s ruin problem, genetic algorithms, and the sizing of populations. Evol. Comput. 7(3), 231–253 (1999)CrossRefGoogle Scholar
  18. 18.
    I. Hidalgo, F. Fernández, Balancing the computation effort in genetic algorithms. In The 2005 IEEE Congress on Evolutionary Computation, vol. 2 (IEEE Press, 2005), pp. 1645–1652. doi: 10.1109/CEC.2005.1554886
  19. 19.
    M. Jelasity, M. van Steen, Large-scale Newscast Computing on the Internet. Tech. Rep. IR-503 (Vrije Universiteit Amsterdam, Department of Computer Science, Amsterdam, The Netherlands). http://www.cs.vu.nl/pub/papers/globe/IR-503.02.pdf
  20. 20.
    M. Jelasity, A. Montresor, O. Babaoglu, Gossip-based aggregation in large dynamic networks. ACM Trans. Comput. Syst. 23(3), 219–252 (2005)CrossRefGoogle Scholar
  21. 21.
    J.L.J. Laredo, P.A. Castillo, B. Paechter, A.M. Mora, E. Alfaro-Cid, A. Esparcia-Alcázar, J.J. Merelo, Empirical validation of a gossiping communication mechanism for parallel EAs. In EvoWorkshops, Lecture Notes in Computer Science, vol. 4448 (Springer, 2007), pp. 129–136Google Scholar
  22. 22.
    J.L.J. Laredo, P.A. Castillo, A.M. Mora, J.J. Merelo, Exploring population structures for locally concurrent and massively parallel evolutionary algorithms. In IEEE Congress on Evolutionary Computation (CEC2008), WCCI2008 Proceedings (IEEE Press, Hong Kong, 2008a), pp. 2610–2617Google Scholar
  23. 23.
    J.L.J. Laredo, P.A. Castillo, A.M. Mora, J.J. Merelo, C. Fernandes, Resilience to churn of a peer-to-peer evolutionary algorithm. Int. J. High Perform. Syst. Archit. 1(4), 260–268 (2008b). http://www.dx.doi.org/10.1504/IJHPSA.2008.024210
  24. 24.
    J.L.J. Laredo, A.E. Eiben, M. van Steen, J.J. Merelo, On the run-time dynamics of a peer-to-peer evolutionary algorithm. In Proceedings of the 10th international conference on Parallel Problem Solving from Nature (Springer, Berlin, Heidelberg, 2008c), pp. 236–245. http://www.dx.doi.org/10.1007/978-3-540-87700-4_24
  25. 25.
    J.L.J. Laredo, P.A. Castillo, A.M. Mora, J.J. Merelo, Evolvable agents, a fine grained approach for distributed evolutionary computing: walking towards the peer-to-peer computing frontiers. Soft Comput. Fusion Found. Methodol. Appl. 12(12), 1145–1156 (2008d)MATHGoogle Scholar
  26. 26.
    J.L.J. Laredo, C. Fernandes, A. Mora, P.A. Castillo, P. Garcia-Sanchez, J.J. Merelo, Studying the Cache Size in a Gossip-based Evolutionary Algorithm. In 3rd International Symposium on Intelligent Distributed Computing (Springer, Berlin/Heidelberg, 2009), pp. 131–140Google Scholar
  27. 27.
    W.P. Lee, Parallelizing evolutionary computation: a mobile agent-based approach. Expert Syst. Appl. 32(2), 318–328 (2007)CrossRefGoogle Scholar
  28. 28.
    F.G. Lobo, C.F. Lima, Adaptive population sizing schemes in genetic algorithms. In Parameter Setting in Evolutionary Algorithms, Studies in Computational Intelligence (Springer, Berlin/Heidelberg, 2007), pp. 185–204Google Scholar
  29. 29.
    J.J. Merelo, P.A. Castillo, J.L.J. Laredo, A.M. Mora, A. Prieto, Asynchronous distributed genetic algorithms with Javascript and JSON. In IEEE Congress on Evolutionary Computation (CEC2008), WCCI2008 Proceedings (IEEE Press, Hong Kong, 2008), pp. 1372–1379Google Scholar
  30. 30.
    N. Nedjah, L. de Macedo Mourelle, E. Alba (eds.), Parallel Evolutionary Computations, Studies in Computational Intelligence, vol. 22. (Springer, 2006)Google Scholar
  31. 31.
    M. Preuss, C. Lasarczyk, On the importance of information speed in structured populations. In PPSN, vol. 3242 (Springer, 2004), pp. 91–100Google Scholar
  32. 32.
    K. Sastry, Evaluation-relaxation Schemes for Genetic and Evolutionary Algorithms. Tech. Rep. 2002004 (University of Illinois at Urbana-Champaign, Urbana, IL, 2001)Google Scholar
  33. 33.
    R. Steinmetz, K. Wehrle, What is this peer-to-peer about? In Peer-to-Peer Systems and Applications, Lecture Notes in Computer Science, vol. 3485, ed. by R. Steinmetz, K. Wehrle (Springer, 2005), pp 9–16Google Scholar
  34. 34.
    D. Thierens, Scalability problems of simple genetic algorithms. Evol. Comput. 7(4), 331–352 (2005)CrossRefGoogle Scholar
  35. 35.
    M. Tomassini, Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series) (Springer New York, Inc., Secaucus, NJ, 2005)MATHGoogle Scholar
  36. 36.
    S. Voulgaris, M. Jelasity, M. van Steen, A Robust and Scalable Peer-to-Peer Gossiping Protocol, Lecture Notes in Computer Science (LNCS), vol. 2872 (Springer, Berlin/Heidelberg, 2004), pp. 47–58. doi: 10.1007/b104265
  37. 37.
    D. Watts, S. Strogatz, Collective dynamics of “small-world” networks. Nature 393, 440–442 (1998). http://www.dx.doi.org/10.1038/30918 Google Scholar
  38. 38.
    J. Whitacre, R. Sarker, Q. Pham, The self-organization of interaction networks for nature-inspired optimization. IEEE Trans. Evol. Comput. 12(2), 220–230 (2008). doi: 10.1109/TEVC.2007.900327 Google Scholar
  39. 39.
    W.R.M.U.K. Wickramasinghe, M. van Steen, A.E. Eiben, Peer-to-peer evolutionary algorithms with adaptive autonomous selection. In GECCO ’07 (ACM Press, New York, NY, 2007), pp. 1460–1467, http://www.doi.acm.org/10.1145/1276958.1277225

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • J. L. J. Laredo
    • 1
  • A. E. Eiben
    • 2
  • M. van Steen
    • 2
  • J. J. Merelo
    • 1
  1. 1.University of Granada, ATC-ETSITGranadaSpain
  2. 2.Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands

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