On the Model–Building Issue of Multi–Objective Estimation of Distribution Algorithms

  • Luis Martí
  • Jesús García
  • Antonio Berlanga
  • José M. Molina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5572)


It has been claimed that perhaps a paradigm shift is necessary in order to be able to deal with this scalability issue of multi–objective optimization evolutionary algorithms. Estimation of distribution algorithms are viable candidates for such task because of their adaptation and learning abilities and simplified algorithmics. Nevertheless, the extension of EDAs to the multi–objective domain have not provided a significant improvement over MOEAs.

In this paper we analyze the possible causes of this underachievement and propose a set of measures that should be taken in order to overcome the current situation.


Evolutionary Computation Multiobjective Optimization Objective Optimization Objective Space Distribution Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds.): Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  2. 2.
    Bäck, T.: Evolutionary algorithms in theory and practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, New York (1996)zbMATHGoogle Scholar
  3. 3.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)zbMATHGoogle Scholar
  4. 4.
    Khare, V., Yao, X., Deb, K.: Performance Scaling of Multi-objective Evolutionary Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 376–390. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Purshouse, R.C., Fleming, P.J.: On the evolutionary optimization of many conflicting objectives. IEEE Transactions on Evolutionary Computation 11(6), 770–784 (2007)CrossRefGoogle Scholar
  6. 6.
    Pelikan, M., Sastry, K., Goldberg, D.E.: Multiobjective estimation of distribution algorithms. In: Pelikan, M., Sastry, K., Cantú-Paz, E. (eds.) Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Studies in Computational Intelligence, pp. 223–248. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Pelikan, M., Goldberg, D.E., Lobo, F.: A survey of optimization by building and using probabilistic models. IlliGAL Report No. 99018, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL (1999)Google Scholar
  8. 8.
    Ahn, C.W., Ramakrishna, R.S.: Multiobjective real-coded bayesian optimization algorithm revisited: diversity preservation. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 593–600. ACM Press, New York (2007)Google Scholar
  9. 9.
    Shapiro, J.: Diversity loss in general estimation of distribution algorithms. In: Parallel Problem Solving from Nature - PPSN IX, pp. 92–101 (2006)Google Scholar
  10. 10.
    Yuan, B., Gallagher, M.: On the importance of diversity maintenance in estimation of distribution algorithms. In: GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 719–726. ACM, New York (2005)Google Scholar
  11. 11.
    Purshouse, R.C.: On the Evolutionary Optimisation of Many Objectives. PhD thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK (September 2003)Google Scholar
  12. 12.
    Zitzler, E., Künzli, S.: Indicator-based Selection in Multiobjective Search. 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. 832–842. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Brockhoff, D., Zitzler, E.: Improving hypervolume–based multiobjective evolutionary algorithms by using objective reduction methods. In: Congress on Evolutionary Computation (CEC 2007), pp. 2086–2093. IEEE Press, Los Alamitos (2007)Google Scholar
  14. 14.
    Zitzler, E., Brockhoff, D., Thiele, L.: The hypervolume indicator revisited: On the design of pareto-compliant indicators via weighted integration. In: Obayashi, S., et al. (eds.) EMO 2007. LNCS, vol. 4403, pp. 862–876. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Deb, K., Saxena, D.K.: On finding Pareto–optimal solutions through dimensionality reduction for certain large–dimensional multi–objective optimization problems. Technical Report 2005011, KanGAL (December 2005)Google Scholar
  16. 16.
    Brockhoff, D., Zitzler, E.: Dimensionality reduction in multiobjective optimization: The minimum objective subset problem. In: Waldmann, K.H., Stocker, U.M. (eds.) Operations Research Proceedings 2006, pp. 423–429. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Brockhoff, D., Saxena, D.K., Deb, K., Zitzler, E.: On handling a large number of objectives a posteriori and during optimization. In: Knowles, J., Corne, D., Deb, K. (eds.) Multi–Objective Problem Solving from Nature: From Concepts to Applications. Natural Computing Series, pp. 377–403. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Martí, L., García, J., Berlanga, A., Molina, J.M.: Model-building algorithms for multiobjective EDAs: Directions for improvement. In: Michalewicz, Z. (ed.) 2008 IEEE Conference on Evolutionary Computation (CEC), part of 2008 IEEE World Congress on Computational Intelligence (WCCI 2008), pp. 2848–2855. IEEE Press, Los Alamitos (2008)Google Scholar
  19. 19.
    Mart, L., Garca, J., Berlanga, A., Molina, J.M.: Introducing MONEDA: Scalable multiobjective optimization with a neural estimation of distribution algorithm. In: Thierens, D., Deb, K., Pelikan, M., Beyer, H.G., Doerr, B., Poli, R., Bittari, M. (eds.) GECCO 2008: 10th Annual Conference on Genetic and Evolutionary Computation, pp. 689–696. ACM Press, New York (2008); EMO Track Best Paper NomineeGoogle Scholar
  20. 20.
    Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 625–632. MIT Press, Cambridge (1995)Google Scholar
  21. 21.
    Martí, L., García, J., Berlanga, A., Molina, J.M.: On the computational properties of the multi-objective neural estimation of distribution algorithms. In: Pelta, D.A., Krasnogor, N. (eds.) International Workshop on Nature Inspired Cooperative Strategies for Optimization. Studies in Computational Intelligence. Springer, Heidelberg (2008) (in press)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luis Martí
    • 1
  • Jesús García
    • 1
  • Antonio Berlanga
    • 1
  • José M. Molina
    • 1
  1. 1.GIAA, Dept. of InformaticsUniversidad Carlos III de MadridMadridSpain

Personalised recommendations