Randomized Parameter Settings for Heterogeneous Workers in a Pool-Based Evolutionary Algorithm

  • Mario García-Valdez
  • Leonardo Trujillo
  • Juan Julián Merelo-Guérvos
  • Francisco Fernández-de-Vega
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8672)


Recently, several Pool-based Evolutionary Algorithms (PEAs) have been proposed, that asynchronously distribute an evolutionary search among heterogeneous devices, using controlled nodes and nodes outside the local network, through web browsers or cloud services. In PEAs, the population is stored in a shared pool, while distributed processes called workers execute the actual evolutionary search. This approach allows researchers to use low cost computational power that might not be available otherwise. On the other hand, it introduces the challenge of leveraging the computing power of heterogeneous and unreliable resources. The heterogeneity of the system suggests that using a heterogeneous parametrization might be a better option, so the goal of this work is to test such a scheme. In particular, this paper evaluates the strategy proposed by Gong and Fukunaga for the Island-Model, which assigns random control parameter values to each worker. Experiments were conducted to assess the viability of this strategy on pool-based EAs using benchmark problems and the EvoSpace framework. The results suggest that the approach can yield results which are competitive with other parametrization approaches, without requiring any form of experimental tuning.


Pool-based Evolutionary Algorithms Distributed Evolutionary Algorithms Algorithm Parametrization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. John Wiley & Sons (2005)Google Scholar
  2. 2.
    Cantú-Paz, E.: Parameter setting in parallel genetic algorithms. In: Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms. SCI, vol. 54, pp. 259–276. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    De Jong, K.A., Potter, M.A., Spears, W.M.: Using problem generators to explore the effects of epistasis. In: Bäck, T. (ed.) ICGA, pp. 338–345. Morgan Kaufmann (1997)Google Scholar
  4. 4.
    De Jong, K.A., Spears, W.M.: An analysis of the interacting roles of population size and crossover in genetic algorithms. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 38–47. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  5. 5.
    Di Martino, S., Ferrucci, F., Maggio, V., Sarro, F.: Towards migrating genetic algorithms for test data generation to the cloud. In: Software Testing in the Cloud: Perspectives on an Emerging Discipline, pp. 113–135. IGI Global (2013)Google Scholar
  6. 6.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer (2003)Google Scholar
  7. 7.
    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.) EvoApplications 2012. LNCS, vol. 7248, pp. 416–425. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Fernández De Vega, F., Olague, G., Trujillo, L., Lombraña González, D.: Customizable execution environments for evolutionary computation using boinc + virtualization. Natural Computing 12(2), 163–177 (2013)MathSciNetCrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    García-Valdez, M., Trujillo, L., de Vega, F.F., Merelo Guervós, J.J., Olague, G.: Evospace-interactive: A framework to develop distributed collaborative-interactive evolutionary algorithms for artistic design. In: Machado, P., McDermott, J., Carballal, A. (eds.) EvoMUSART 2013. LNCS, vol. 7834, pp. 121–132. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    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.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 499–508. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Gong, Y., Fukunaga, A.: Distributed island-model genetic algorithms using heterogeneous parameter settings. In: IEEE Congress on Evolutionary Computation, pp. 820–827. IEEE (2011)Google Scholar
  13. 13.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)Google Scholar
  14. 14.
    Kennedy, J., Spears, W.: Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence 1998, pp. 78–83 (May 1998)Google Scholar
  15. 15.
    Kramer, O.: Self-Adaptive Heuristics for Evolutionary Computation. SCI, vol. 147. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  16. 16.
    Lobo, F.G., Lima, C.F., Michalewicz, Z.: Parameter Setting in Evolutionary Algorithms. Springer Publishing Company, Incorporated (2007)Google Scholar
  17. 17.
    Merelo-Guervos, J., Castillo, P., Laredo, J.L.J., Mora Garcia, A., Prieto, A.: Asynchronous distributed genetic algorithms with Javascript and JSON. In: 2008 IEEE Congress on Evolutionary Computation (CEC), pp. 1372–1379 (June 2008)Google Scholar
  18. 18.
    Sherry, D., Veeramachaneni, K., McDermott, J., O’Reilly, U.-M.: Flex-GP: Genetic programming on the cloud. In: Di Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 477–486. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Smaoui Feki, M., Nguyen, H.V., Garbey, M.: Parallel genetic algorithm implementation for boinc. In: PARCO, pp. 212–219 (2009)Google Scholar
  20. 20.
    Tanabe, R., Fukunaga, A.: Evaluation of a randomized parameter setting strategy for island-model evolutionary algorithms. In: IEEE Congress on Evolutionary Computation, pp. 1263–1270. IEEE (2013)Google Scholar
  21. 21.
    Trujillo, L., Valdez, M.G., de Vega, F.F., Guervós, J.J.M.: Fireworks: Evolutionary art project based on evospace-interactive. In: IEEE Congress on Evolutionary Computation, pp. 2871–2878. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mario García-Valdez
    • 1
  • Leonardo Trujillo
    • 1
  • Juan Julián Merelo-Guérvos
    • 2
  • Francisco Fernández-de-Vega
    • 3
  1. 1.Instituto Tecnológico de TijuanaTijuanaMéxico
  2. 2.Universidad de GranadaGranadaSpain
  3. 3.Grupo de Evolución ArtificialUniversidad de ExtremaduraMéridaSpain

Personalised recommendations