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Evolvable Agents: A Framework for Peer-to-Peer Evolutionary Algorithms

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Parallel and Distributed Computational Intelligence

Abstract

Distributed evolutionary computation programs often needs increasingly big amounts of computational power when tackling large instances of hard optimization problems, and Peer-to-Peer (P2P) systems could be an option for building the large virtual supercomputer in which they could be run. Even as distributed Evolutionary Algorithms (EA) do take advantage of parallel execution by simultaneously promoting diversity and reducing runtime, there are still many challenges on the parallelization of EAs in P2P systems. In this chapter we present a survey of the state of the art in P2P EAs and our solutions to the main P2P issues such as decentralization, massive scalability and fault tolerance.

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References

  1. Ackley, D.H.: A connectionist machine for genetic hillclimbing. Kluwer Academic Publishers, Norwell (1987)

    Google Scholar 

  2. Anderson, D.P., Cobb, J., Korpela, E., Lebofsky, M., Werthimer, D.: SETI@home: an experiment in public-resource computing. Commun. ACM 45(11), 56–61 (2002)

    Article  Google Scholar 

  3. Angeline, P.J.: Tracking extrema in dynamic environments. In: Angeline, P.J., McDonnell, J.R., Reynolds, R.G., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  4. Arenas, M.G., 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)

    Chapter  Google Scholar 

  5. Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Norwell (2000)

    MATH  Google Scholar 

  6. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  7. Giacobini, M., Tomassini, M., Tettamanzi, A., Alba, E.: Selection intensity in cellular evolutionary algorithms for regular lattices. IEEE Transactions on Evolutionary Computation 9(5), 489–505 (2005)

    Article  Google Scholar 

  8. Giacobini, M., Alba, E., Tettamanzi, A., Tomassini, M.: Modeling selection intensity for toroidal cellular evolutionary algorithms. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 1138–1149. Springer, Heidelberg (2004)

    Google Scholar 

  9. Giacobini, M., Preuss, M., Tomassini, M.: Effects of scale-free and small-world topologies on binary coded self-adaptive CEA. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2006. LNCS, vol. 3906, pp. 86–98. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Giacobini, M., Tomassini, M., Tettamanzi, A.: Takeover time curves in random and small-world structured populations. In: GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 1333–1340. ACM, New York (2005)

    Chapter  Google Scholar 

  11. Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of Genetic Algorithms, pp. 69–93. Morgan Kaufmann, San Francisco (1991)

    Google Scholar 

  12. Grefenstette, J.J.: Parallel adaptive algorithms for function optimization. Technical Report CS-81-19, Vanderbilt University, Computer Science Department, Nashville (1981)

    Google Scholar 

  13. Hidalgo, I., Fernández, F.: Balancing the computation effort in genetic algorithms. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1645–1652. IEEE Press, Los Alamitos (2005)

    Chapter  Google Scholar 

  14. 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 

  15. Laredo, J.L.J., Eiben, E.A., Schoenauer, M., Castillo, P.A., Mora, A.M., Merelo, J.J.: Exploring selection mechanisms for an agent-based distributed evolutionary algorithm. In: GECCO 2007, pp. 2801–2808. ACM Press, New York (2007)

    Google Scholar 

  16. 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: IEEE Congress on Evolutionary Computation (CEC 2008), WCCI 2008 Proceedings, pp. 2610–2617. IEEE Press, Hong Kong (2008)

    Google Scholar 

  17. Laredo, J.L.J., Eiben, A.E., van Steen, M., Castillo, P.A., Mora, A.M., Merelo, J.J.: P2P Evolutionary Algorithms: A suitable approach for tackling large instances in hard optimization problems. In: Luque, E., Margalef, T., Benítez, D. (eds.) Euro-Par 2008. LNCS, vol. 5168, pp. 622–631. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  18. Laredo, J.L.J., Castillo, P.A., Mora, A.M., Merelo, J.J., Fernandes, C.: Resilience to churn of a peer-to-peer evolutionary algorithm. Int. J. High Performance Systems Architecture 1(4), 260–268 (2008)

    Article  Google Scholar 

  19. Laredo, J.L.J., Castillo, P.A., Mora, A.M., Merelo, J.J.: Evolvable agents, a fine grained approach for distributed evolutionary computing: walking towards the peer-to-peer computing frontiers. Soft Computing - A Fusion of Foundations, Methodologies and Applications 12(12), 1145–1156 (2008)

    MATH  Google Scholar 

  20. Laredo, J.L.J., Castillo, P.A., Mora, A.M., Merelo, J.J., Rosa, A., Fernandes, C.: Evolvable agents in static and dynamic optimization problems. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 488–497. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  21. Laredo, J.L.J., Castillo, P.A., Paechter, B., Mora, A.M., Alfaro-Cid, E., Esparcia-Alcázar, A., Merelo, J.J.: Empirical validation of a gossiping communication mechanism for parallel EAs. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 129–136. Springer, Heidelberg (2007)

    Google Scholar 

  22. Merelo, J.J., Castillo, P.A., Laredo, J.L.J., Mora, A.M., Prieto, A.: Asynchronous distributed genetic algorithms with Javascript and JSON. In: IEEE Congress on Evolutionary Computation (CEC 2008), WCCI 2008 Proceedings, pp. 1372–1379. IEEE Press, Hong Kong (2008)

    Chapter  Google Scholar 

  23. Preuss, M., Lasarczyk, C.: On the importance of information speed in structured populations. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 91–100. Springer, Heidelberg (2004)

    Google Scholar 

  24. Sastry, K.: Evaluation-relaxation schemes for genetic and evolutionary algorithms. Technical Report 2002004, University of Illinois at Urbana-Champaign, Urbana, IL (2001)

    Google Scholar 

  25. 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)

    Chapter  Google Scholar 

  26. 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)

    Chapter  Google Scholar 

  27. Tinós, R., Yang, S.: A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines 8(3), 255–286 (2007)

    Article  Google Scholar 

  28. Tomassini, M.: Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Natural Computing Series. Springer, New York (2005)

    MATH  Google Scholar 

  29. Fernandez de Vega, F.: A fault tolerant optimization algorithm based on evolutionary computation. In: DEPCOS-RELCOMEX 2006: Proceedings of the International Conference on Dependability of Computer Systems, Washington, DC, USA, pp. 335–342. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  30. Voulgaris, S., Jelasity, M., van Steen, M.: A Robust and Scalable Peer-to-Peer Gossiping Protocol. In: Moro, G., Sartori, C., Singh, M.P. (eds.) AP2PC 2003. LNCS (LNAI), vol. 2872, pp. 47–58. Springer, Heidelberg (2004)

    Google Scholar 

  31. Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature 393, 440–442 (1998)

    Article  Google Scholar 

  32. 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)

    Chapter  Google Scholar 

  33. Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput. 9(11), 815–834 (2005)

    Article  MATH  Google Scholar 

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Jimenez Laredo, J.L., Merelo Guervos, J.J., Castillo Valdivieso, P.A. (2010). Evolvable Agents: A Framework for Peer-to-Peer Evolutionary Algorithms. In: de Vega, F.F., Cantú-Paz, E. (eds) Parallel and Distributed Computational Intelligence. Studies in Computational Intelligence, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10675-0_3

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  • DOI: https://doi.org/10.1007/978-3-642-10675-0_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10674-3

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