Cooperative Selection: Improving Tournament Selection via Altruism

  • Juan Luis Jiménez Laredo
  • Sune S. Nielsen
  • Grégoire Danoy
  • Pascal Bouvry
  • Carlos M. Fernandes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8600)

Abstract

This paper analyzes the dynamics of a new selection scheme based on altruistic cooperation between individuals. The scheme, which we refer to as cooperative selection, extends from tournament selection and imposes a stringent restriction on the mating chances of an individual during its lifespan: winning a tournament entails a depreciation of its fitness value. We show that altruism minimizes the loss of genetic diversity while increasing the selection frequency of the fittest individuals. An additional contribution of this paper is the formulation of a new combinatorial problem for maximizing the similarity of proteins based on their secondary structure. We conduct experiments on this problem in order to validate cooperative selection. The new selection scheme outperforms tournament selection for any setting of the parameters and is the best trade-off, maximizing genetic diversity and minimizing computational efforts.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans. Evolutionary Computation 9(2), 126–142 (2005)CrossRefGoogle Scholar
  2. 2.
    Brünger, A.T., Adams, P.D., Clore, G.M., DeLano, W.L., Gros, P., Grosse-Kunstleve, R.W., Jiang, J., Kuszewski, J., Nilges, M., Pannu, N.S., Read, R.J., Rice, L.M., Simonson, T., Warren, G.L.: Crystallography and NMR System: A New Software Suite for Macromolecular Structure Determination. Acta Crystallographica Section D 54(5), 905–921 (1998)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer (2003)Google Scholar
  5. 5.
    Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey. Trans. Evol. Comp. 9(3), 303–317 (2005)CrossRefGoogle Scholar
  6. 6.
    Laredo, J.L.J., Dorronsoro, B., Fernandes, C., Merelo, J.J., Bouvry, P.: Oversized populations and cooperative selection: Dealing with massive resources in parallel infrastructures. In: Nicosia, G., Pardalos, P. (eds.) LION 7. LNCS, vol. 7997, pp. 444–449. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Laredo, J.L.J., Eiben, A.E., van Steen, M., Merelo, J.J.: On the run-time dynamics of a peer-to-peer evolutionary algorithm. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN X. LNCS, vol. 5199, pp. 236–245. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Miller, B.L., Goldberg, D.E.: Genetic algorithms, selection schemes, and the varying effects of noise. Evol. Comput. 4(2), 113–131 (1996)CrossRefGoogle Scholar
  9. 9.
    Poli, R.: Tournament selection, iterated coupon-collection problem, and backward-chaining evolutionary algorithms. In: Wright, A.H., Vose, M.D., De Jong, K.A., Schmitt, L.M. (eds.) FOGA 2005. LNCS, vol. 3469, pp. 132–155. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Rost, B., Sander, C.: Combining evolutionary information and neural networks to predict protein secondary structure. Proteins 19(1), 55–72 (1994)CrossRefGoogle Scholar
  11. 11.
    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 (1991)Google Scholar
  12. 12.
    Sokolov, A., Whitley, D.: Unbiased tournament selection. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 1131–1138. ACM, New York (2005)Google Scholar
  13. 13.
    Xie, H., Zhang, M.: Impacts of sampling strategies in tournament selection for genetic programming. Soft Comput. 16(4), 615–633 (2012)CrossRefGoogle Scholar
  14. 14.
    Xie, H., Zhang, M., Andreae, P.: Another investigation on tournament selection: modelling and visualisation. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO 2007, pp. 1468–1475. ACM, New York (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Juan Luis Jiménez Laredo
    • 1
  • Sune S. Nielsen
    • 1
  • Grégoire Danoy
    • 1
  • Pascal Bouvry
    • 1
  • Carlos M. Fernandes
    • 2
  1. 1.FSTC-CSC/SnTUniversity of LuxembourgLuxembourgLuxembourg
  2. 2.Laseeb: Evolutionary Systems and Biomedical EngineeringTechnical University of LisbonLisbonPortugal

Personalised recommendations