Oversized Populations and Cooperative Selection: Dealing with Massive Resources in Parallel Infrastructures

  • Juan Luis Jiménez Laredo
  • Bernabe Dorronsoro
  • Carlos Fernandes
  • Juan Julian Merelo
  • Pascal Bouvry
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7997)


This paper proposes a new selection scheme for Evolutionary Algorithms (EAs) based on altruistic cooperation between individuals. Cooperation takes place every time an individual undergoes selection: the individual decreases its own fitness in order to improve the mating chances of worse individuals. On the one hand, the selection scheme guarantees that the genetic material of fitter individuals passes to subsequent generations as to decrease their fitnesses individuals have to be firstly selected. On the other hand, the scheme restricts the number of times an individual can be selected not to take over the entire population. We conduct an empirical study for a parallel EA version where cooperative selection scheme is shown to outperform binary tournament: both selection schemes yield the same qualities of solutions but cooperative selection always improves the times to solutions.


Selection schemes Evolutionary algorithms Parallelization Execution times 



This work was supported by the Luxembourg FNR Green@Cloud project (INTER/CNRS/11/03) and by the Spanish Ministry of Science Project (TIN2011-28627-C04). B. Dorronsoro acknowledges the support by the Fonds National de la Recherche, Luxembourg (AFR contract no 4017742).


  1. 1.
    Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans. Evol. Comput. 9(2), 126–142 (2005)CrossRefGoogle Scholar
  2. 2.
    Eiben, A.E., Schut, M.C., De Wilde, A.R.: Boosting genetic algorithms with self-adaptive selection. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1584–1589 (2006)Google Scholar
  3. 3.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)CrossRefMATHGoogle Scholar
  4. 4.
    Lombraña González, D., Jiménez Laredo, J., Fernández de Vega, F., Merelo Guervós, J.: Characterizing fault-tolerance of genetic algorithms in desktop grid systems. In: Cowling, P., Merz, P. (eds.) EvoCOP 2010. LNCS, vol. 6022, pp. 131–142. Springer, Heidelberg (2010)Google Scholar
  5. 5.
    Laredo, J.L.J., Eiben, A.E., van Steen, M., Merelo Guervós, J.J.: Evag: a scalable peer-to-peer evolutionary algorithm. Genet. Program. Evolvable Mach. 11(2), 227–246 (2010)CrossRefGoogle Scholar
  6. 6.
    Lässig, J., Sudholt, D.: General scheme for analyzing running times of parallel evolutionary algorithms. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 234–243. Springer, Heidelberg (2010)Google Scholar
  7. 7.
    Lobo, F., Lima, C.: Adaptive population sizing schemes in genetic algorithms. In: Lobo, F., Lima, C., Zbigniew, M. (eds.) Parameter Setting in Evolutionary Algorithms, vol. 54, pp. 185–204. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Lobo, F.G., Goldberg, D.E.: The parameter-less genetic algorithm in practice. Inf. Sci. Inf. Comput. Sci. 167(1–4), 217–232 (2004)MATHGoogle Scholar
  9. 9.
    Potter, M.A., De Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN LNCS, vol. 866, pp. 249–267. Springer, Heidelberg (1994)Google Scholar
  10. 10.
    David Schaffer, J., Eshelman, L.J.: On crossover as an evolutionarily viable strategy. In: Belew, R.K., Booker, L.B. (eds.) ICGA, pp. 61–68. Morgan Kaufmann, San Francisco (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Juan Luis Jiménez Laredo
    • 1
  • Bernabe Dorronsoro
    • 3
  • Carlos Fernandes
    • 2
    • 4
  • Juan Julian Merelo
    • 4
  • Pascal Bouvry
    • 1
  1. 1.FSTC-CSC/SnTUniversity of LuxembourgWalferdangeLuxembourg
  2. 2.LaseebTechnical University of LisbonLisbonPortugal
  3. 3.Laboratoire d’Informatique Fondamentale de LilleUniversity of LilleLilleFrance
  4. 4.Geneura LabUniversity of GranadaGranadaSpain

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