Optimization Letters

, Volume 2, Issue 3, pp 433–443 | Cite as

Extending a class of continuous estimation of distribution algorithms to dynamic problems

Original Paper

Abstract

In this paper, a class of continuous Estimation of Distribution Algorithms (EDAs) based on Gaussian models is analyzed to investigate their potential for solving dynamic optimization problems where the global optima may change dramatically during time. Experimental results on a number of dynamic problems show that the proposed strategy for dynamic optimization can significantly improve the performance of the original EDAs and the optimal solutions can be consistently located.

Keywords

Evolutionary algorithms Global optimization Estimation of distribution algorithms Dynamic optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bäck, T., Fogel, D.B., Michalewicz, Z.: Handbook of Evolutionary Computation. IOP Publishing Ltd and Oxford University Press, New York (1997)MATHGoogle Scholar
  2. 2.
    Baluja, S., Davies, S.: Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space. In: Fourteenth International Conference on Machine Learning, pp. 30–38 (1997)Google Scholar
  3. 3.
    De Bonet, J.S., Isbell, C.L., Viola, P.: MIMIC: Finding Optima by Estimating Probability Densities. In: Advances in Neural Information Processing Systems, vol. 9. MIT, Cambridge, pp. 424–430 (1997)Google Scholar
  4. 4.
    Eiben, A.E., Jelasity, M.: A critical note on experimental research methodology in EC. In: Congress on Evolutionary Computation, pp. 582–587 (2002)Google Scholar
  5. 5.
    Gallagher, M., Yuan, B.: A general-purpose tunable landscape generator. IEEE Trans. Evol. Comput. 10(5), 590–603 (2006)CrossRefGoogle Scholar
  6. 6.
    Larrañaga, P., Etxeberria, R., Lozano, J.A., Pena, J.M.: Optimization by learning and simulation of Bayesian and Gaussian networks. Research Report EHU-kZAA-IK-4/99, University of the Basque Country (1999)Google Scholar
  7. 7.
    Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer, Dordrecht (2001)Google Scholar
  8. 8.
    Larrañaga, P., Lozano, J.A., Bengoetxea, E.: Estimation of Distribution Algorithms based on multivariate normal and Gaussian networks. Technical Report KZZA-IK-1-01, University of the Basque Country (2001)Google Scholar
  9. 9.
    Pelikan, M.: Bayesian optimization algorithm: from single level to hierarchy. Ph.D. Thesis, University of Illinois at Urbana-Champaign (2002)Google Scholar
  10. 10.
    Pelikan, M.: Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms. Springer, Heidelberg (2005)MATHGoogle Scholar
  11. 11.
    Whitley, D., Mathias, K., Rana, S., Dzubera, J.: Evaluating evolutionary algorithms. Artif. Intell. 85(1–2), 245–276 (1996)CrossRefGoogle Scholar
  12. 12.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRefGoogle Scholar
  13. 13.
    Yang, S.: Memory-based immigrants for genetic algorithms in dynamic environments. In: The 2005 Genetic and Evolutionary Computation Conference, pp. 1115–1122 (2005)Google Scholar
  14. 14.
    Yang, S., Ong, Y., Jin, Y.: Evolutionary computation in dynamic and uncertain environments. In: Studies in Computational Intelligence, vol. 51. Springer, Heidelberg (2007)Google Scholar
  15. 15.
    Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput. 9(11), 815–834 (2005)CrossRefMATHGoogle Scholar
  16. 16.
    Yuan, B., Gallagher, M.: Experimental results for the special session on real-parameter optimization at CEC 2005: a simple, continuous EDA. In: Congress on Evolutionary Computation 2005, pp.~1792–1799 (2005)Google Scholar
  17. 17.
    Yuan, B., Gallagher, M.: A mathematical modelling technique for the analysis of the dynamics of a simple continuous EDA. In: The 2006 Congress on Evolutionary Computation, pp. 1585–1591 (2006)Google Scholar
  18. 18.
    Yuan, B., Gallagher, M.: On the importance of diversity maintenance in estimation of distribution algorithms. In: The 2005 Genetic and Evolutionary Computation Conference, pp. 719–726 (2005)Google Scholar
  19. 19.
    Yuan, B., Orlowska, M., Sadiq, S.: Finding the optimal path in 3D spaces using EDAs–the wireless sensor networks scenario. In: The 8th International Conference on Adaptive and Natural Computing Algorithms, pp. 536–545 (2007)Google Scholar
  20. 20.
    Yuan, B., Orlowska, M., Sadiq, S.: On the optimal robot routing problem in wireless sensor networks. IEEE Trans. Knowl. Data Eng. 19(9), 1252–1261 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Division of Informatics, Graduate School at ShenzhenTsinghua UniversityShenzhenPeople’s Republic of China
  2. 2.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

Personalised recommendations