Skip to main content
Log in

The EvoSpace Model for Pool-Based Evolutionary Algorithms

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

This work presents the EvoSpace model for the development of pool-based evolutionary algorithms (Pool-EA). Conceptually, the EvoSpace model is built around a central repository or population store, incorporating some of the principles of the tuple-space model and adding additional features to tackle some of the issues associated with Pool-EAs; such as, work redundancy, starvation of the population pool, unreliability of connected clients or workers, and a large parameter space. The model is intended as a platform to develop search algorithms that take an opportunistic approach to computing, allowing the exploitation of freely available services over the Internet or volunteer computing resources within a local network. A comprehensive analysis of the model at both the conceptual and implementation levels is provided, evaluating performance based on efficiency, optima found and speedup, while providing a comparison with a standard EA and an island-based model. The issues of lost connections and system parametrization are studied and validated experimentally with encouraging results, that suggest how EvoSpace can be used to develop and implement different Pool-EAs for search and optimization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. John Wiley & Sons (2005)

  2. Allcock, B., Bester, J., Bresnahan, J., Chervenak, A.L., Foster, I., Kesselman, C., Meder, S., Nefedova, V., Quesnel, D., Tuecke, S.: Data management and transfer in high-performance computational grid environments. Parallel Comput. 28(5), 749–771 (2002)

    Article  Google Scholar 

  3. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)

    Article  Google Scholar 

  4. Baxevanidis, K., Davies, H., Foster, I., Gagliardi, F.: Grids and research networks as drivers and enablers of future internet architectures. Comput. Netw. 40(1), 5–17 (2002)

    Article  Google Scholar 

  5. Bollini, A., Piastra, M.: Distributed and persistent evolutionary algorithms: A design pattern. In: Proceedings of the Second European Workshop on Genetic Programming, pp. 173–183. Springer-Verlag, London, UK, UK (1999)

  6. Cahon, S., Melab, N., Talbi, E.G.: ParadisEO: A framework for the reusable design of parallel and distributed metaheuristics. J. Heuristics 10(3), 357–380 (2004)

    Article  Google Scholar 

  7. Cantú-Paz, E.: Parameter setting in parallel genetic algorithms. In: Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms, volume 54 Studies in Computational Intelligence, pp 259–276. Springer (2007)

  8. Cole, N., Desell, T.J., Gonzalez, D.L, de Vega, F.F., Magdon-Ismail, M., Newberg, H.J., Szymanski, B.K., Varela, C.A.: Evolutionary algorithms on volunteer computing platforms: The milkyway@ home project, pp 63–90. Springer (2010)

  9. Cotillon, A., Valencia, P., Jurdak, R.: Android genetic programming framework Proceedings of the 15th European conference on Genetic Programming, EuroGP’12, pp 13–24. Springer, Berlin, Heidelberg (2012)

    Google Scholar 

  10. Curbera, F., Duftler, M., Khalaf, R., Nagy, W., Mukhi, N., Weerawarana, S.: Unraveling the web services web: An introduction to SOAP, WSDL, and UDDI. IEEE Internet Computing 6(2), 86–93 (2002)

    Article  Google Scholar 

  11. De Jong, K.A., Potter, M.A., Spears, W.M.: Using problem generators to explore the effects of epistasis. In: Bäck T. (ed.) Proceedings of the 7th International Conference on Genetic Algorithms, 338–345. Morgan Kauffman (1997)

  12. De Jong, K.A., Spears, W.M.: An analysis of the interacting roles of population size and crossover in genetic algorithms Proceedings of the 1st Workshop on Parallel Problem Solving from Nature, PPSN I, pp 38–47. Springer, London (1991)

    Google Scholar 

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

  14. Fazenda, P., McDermott, J., O’Reilly, U.M.: A library to run evolutionary algorithms in the cloud using mapreduce. In: di Chio, C., et al. (eds.) Applications of Evolutionary Computation, volume 7248 LNCS, pp. 416–425. Springer, Berlin Heidelberg (2012)

    Google Scholar 

  15. Fernández De Vega, F., Olague, G., Trujillo, L., Lombraña González, D.: Customizable Execution Environments for Evolutionary Computation Using BOINC + Virtualization. Nat. Comput. 12(2), 163–177 (2013)

    Article  MathSciNet  Google Scholar 

  16. Fortin, F.A., Rainville, F.M.D., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: Evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)

    MathSciNet  MATH  Google Scholar 

  17. Foster, I., Kesselman, C. (eds.): The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers Inc., San Francisco (1999)

  18. Garcia-Arenas, M., Merelo, J.J., Mora, A.M., Castillo, P., Romero, G., Laredo, J.: Assessing speed-ups in commodity cloud storage services for distributed evolutionary algorithms. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 304–311. IEEE (2011)

  19. Garcia-Valdez, M., Mancilla, A., Trujillo, L., Merelo, J.J., Fernandez-de Vega, F.: Is there a free lunch for cloud-based evolutionary algorithms?. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 1255–1262 (2013)

  20. Garcia-Valdez, M., Trujillo, L., Fernández de Vega, F., Merelo Guervós, J., Olague, G.: Evospace-interactive: A framework to develop distributed collaborative-interactive evolutionary algorithms for artistic design. In: Machado, P., et al. (eds.) Evolutionary and Biologically Inspired Music, Sound, Art and Design, LNCS, vol. 7834, pp. 121–130. Springer, Berlin Heidelberg (2013)

    Chapter  Google Scholar 

  21. García-Valdez, M., Trujillo, L., Fernández de Vega, F., Merelo Guervós, J.J., Olague, G.: EvoSpace: A Distributed Evolutionary Platform Based on the Tuple Space Model. In: Esparcia-Alcázar, A., et al. (eds.) Applications of Evolutionary Computation, LNCS, vol. 7835, pp. 499–508. Springer, Berlin Heidelberg (2013)

    Google Scholar 

  22. Gelernter, D.: Generative communication in linda. ACM Trans. Program. Lang. Syst. 7 (1), 80–112 (1985)

    Article  MATH  Google Scholar 

  23. Gong, Y., Fukunaga, A.: Distributed island-model genetic algorithms using heterogeneous parameter settings. In: IEEE Congress on Evolutionary Computation, pp. 820–827. IEEE (2011)

  24. Klein, J., Spector, L.: Unwitting distributed genetic programming via asynchronous JavaScript and XML. Proceedings of the 9th annual conference on Genetic and evolutionary computation, GECCO ’07, pp. 1628–1635. ACM, New York (2007)

  25. Kramer, O.: Self-Adaptive Heuristics for Evolutionary Computation, Studies in Computational Intelligence, vol. 147. Springer (2008)

  26. Langdon, W.B. In: Keijzer, M., O’Reilly, U.M., Lucas, S.M., Costa, E., Soule, T. (eds.) : Global distributed evolution of l-systems fractals, pp 349–358. Springer (2004)

  27. Lobo, F.G., Lima, C.F., Michalewicz, Z.: Parameter Setting in Evolutionary Algorithms. Springer Publishing Company, Incorporated (2007)

  28. Merelo, J.J., Castillo, P., Mora, A., Esparcia-Alcázar, A., Rivas-Santos, V.: NodEO, a multi-paradigm distributed evolutionary algorithm platform in javascript. Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion, pp. 1155–1162. ACM (2014)

  29. Merelo, J.J., Fernandes, C.M., Mora, A.M., Esparcia, A.I.: Sofea: A pool-based framework for evolutionary algorithms using couchdb Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO ’12, pp 109–116. ACM, New York (2012)

    Google Scholar 

  30. Merelo, J.J., Mora, A., Fernandes, C., Esparcia-Alcazar, A., Laredo, J.: Pool vs. island based evolutionary algorithms: An initial exploration. In: P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2012 Seventh International Conference on, pp. 19–24 (2012)

  31. Merelo-Guervós, J.J., Mora, A., Cruz, J.A., Esparcia, A.I.: Pool-based distributed evolutionary algorithms using an object database. Proceedings of the 2012 European conference on Applications of Evolutionary Computation, EvoApplications’12, pp. 446–455. Springer, Berlin, Heidelberg (2012)

    Chapter  Google Scholar 

  32. Merelo-Guervos, J.J., Mora, A., Cruz, J.A., Esparcia-Alcazar, A.I., Cotta, C.: Scaling in distributed evolutionary algorithms with persistent population 2012 IEEE Congress on Evolutionary Computation (CEC), pp 1–8. IEEE Comuter Society (2012)

  33. Merelo Guervos, J.J., Valdivieso, P.A.C., Laredo, J.L.J., García, A.M., Prieto, A.: Asynchronous distributed genetic algorithms with JavaScript and JSON. IEEE Congress on Evolutionary Computation, pp. 1372–1379. IEEE (2008)

  34. Oram, A. (ed.): Peer-to-Peer: Harnessing the Power of Disruptive Technologies. O’Reilly & Associates, Inc., Sebastopol (2001)

  35. Paechter, B., Back, T., Schoenauer, M., Sebag, M., Eiben, A., Merelo, J.J., Fogarty, T.: A distributed resource evolutionary algorithm machine (DREAM). In: Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, vol. 2, pp. 951–958 vol.2 (2000)

  36. Roy, G., Lee, H., Welch, J.L., Zhao, Y., Pandey, V., Thurston, D.: A distributed pool architecture for genetic algorithms Proceedings of the Eleventh conference on Congress on Evolutionary Computation, CEC’09, pp 1177–1184. IEEE Press, Piscataway, NJ, USA (2009)

    Google Scholar 

  37. Schmidt, M., Lipson, H.: Distilling free-form natural laws from experimental data. Science 324, 81–85 (2009)

    Article  Google Scholar 

  38. Secretan, J., Beato, N., D’Ambrosio, D.B., Rodriguez, A., Campbell, A., Folsom-Kovarik, J.T., Stanley, K.O.: Picbreeder: A case study in collaborative evolutionary exploration of design space. Evol. Comput. 19(3), 373–403 (2011)

    Article  Google Scholar 

  39. Sherry, D., Veeramachaneni, K., McDermott, J., O’Reilly, U.M.: Flex-gp: Genetic programming on the cloud. In: di Chio, C., et al. (eds.) Applications of Evolutionary Computation, LNCS, vol. 7248, pp 477–486. Springer, Berlin Heidelberg (2012)

    Google Scholar 

  40. Talukdar, S., Baerentzen, L., Gove, A., De Souza, P.: Asynchronous teams: Cooperation schemes for autonomous agents. J. Heuristics 4(4), 295–321 (1998)

    Article  Google Scholar 

  41. Tanabe, R., Fukunaga, A.: Evaluation of a randomized parameter setting strategy for island-model evolutionary algorithms IEEE Congress on Evolutionary Computation, pp. 1263–1270. IEEE (2013)

  42. Thierens, D.: Scalability problems of simple genetic algorithms. Evol. Comput. 7, 331–352 (1999)

    Article  Google Scholar 

  43. Trujillo, L., Valdez, M.G, de Vega, F.F., Merelo-Guervós, J.J.: Fireworks: Evolutionary art project based on EvoSpace-interactive IEEE Congress on Evolutionary Computation, pp. 2871–2878. IEEE (2013)

  44. Varia, J.: Cloud architectures. White Paper of Amazon (2008)

  45. Vecchiola, C., Kirley, M., Buyya, R.: Multi-objective problem solving with offspring on enterprise clouds. CoRR abs/0903.1386 (2009)

  46. Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1(1), 7–18 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leonardo Trujillo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

García-Valdez, M., Trujillo, L., Merelo, JJ. et al. The EvoSpace Model for Pool-Based Evolutionary Algorithms. J Grid Computing 13, 329–349 (2015). https://doi.org/10.1007/s10723-014-9319-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-014-9319-2

Keywords

Navigation