Advertisement

Journal of Computer Science and Technology

, Volume 23, Issue 1, pp 35–43 | Cite as

Histogram-Based Estimation of Distribution Algorithm: A Competent Method for Continuous Optimization

  • Nan DingEmail author
  • Shu-De Zhou
  • Zeng-Qi Sun
Regular Paper

Abstract

Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. This paper utilizes histogram probabilistic model to describe the distribution of population and to generate promising solutions. The advantage of histogram model, its intrinsic multimodality, makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make histogram model more efficiently explore and exploit the search space, several strategies are brought into the algorithms: the surrounding effect reduces the population size in estimating the model with a certain number of the bins and the shrinking strategy guarantees the accuracy of optimal solutions. Furthermore, this paper shows that histogram-based EDA (Estimation of distribution algorithm) can give comparable or even much better performance than those predominant EDAs based on Gaussian models.

Keywords

evolutionary algorithm estimation of distribution algorithm histogram probabilistic model surrounding effect shrinking strategy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

11390_2008_Article_9108_ESM.pdf (85 kb)
(PDF 86 kb)

References

  1. [1]
    Larrañaga P, Lozano J A. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, 2002.Google Scholar
  2. [2]
    Pelikan M. Hierarchical Bayesian Optimization Algorithm: Toward A New Generation of Evolutionary Algorithms. Springer-Verlag, 2005.Google Scholar
  3. [3]
    Očenášek J. Parallel estimation of distribution algorithms [Dissertation]. Brno University of Technology, 2002.Google Scholar
  4. [4]
    Larrañaga P, Etxeberria R, Lozano J A, Peña J M. Optimization by learning and simulation of Bayesian and Gaussian networks. Technical Report EHU-KZAA-IK-4/99, University of the Basque Country, 1999.Google Scholar
  5. [5]
    Larrañaga P, Etxeberria R, Lozano J A, Peña J M. Optimization in continuous domains by learning and simulation of Gaussian networks. In Proc. the Genetic and Evolutionary Computation Conference, Las Vegas, Nevada, 2000, pp.201–204.Google Scholar
  6. [6]
    Sebag M, Ducolombier A. Extending Population-Based Incremental Learning to Continuous Search Spaces. Parallel Problem Solving from Nature– PPSN V, Springer-Verlag, 1998, pp.418–427.Google Scholar
  7. [7]
    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
  8. [8]
    Bosman P, Thierens D. Expanding from Discrete to Continuous Estimation of Distribution Algorithms: IDEA. Parallel Problem Solving From Nature– PPSN VI, 2000, pp.767–776.Google Scholar
  9. [9]
    Ahn C W. Real-coded Bayesian optimization algorithm. In Proc. Advances in Evolutionary Algorithms: Theory, Design and Practice, 2006, pp.85–124.Google Scholar
  10. [10]
    Tsutsui S, Pelikan M, Goldberg D E. Evolutionary algorithm using marginal histogram models in continuous domain. In Proc. the 2001 Genetic and Evolutionary Computation Conference Workshop, San Francisco, CA, 2001, pp.230–233.Google Scholar
  11. [11]
    Petri K, Petri M. Information-theoretically optimal histogram density estimation. HIIT Technical Reports 2006–2, Helsinki Institute for Information Technology, 2006.Google Scholar
  12. [12]
    Pelikan M, David E G, Tsutsui S. Combining the strengths of the Bayesian optimization algorithm and adaptive evolution strategies. IlliGAL Report No. 2001023, 2001.Google Scholar
  13. [13]
    Yuan B, Gallagher M. Playing in continuous spaces: Some analysis and extension of population-based incremental learning. In Proc. IEEE Congress on Evolutionary Computation, 2003, pp.443–450.Google Scholar
  14. [14]
    Ding N, Zhou S, Sun Z. Optimizing continuous problems using estimation of distribution algorithms based on histogram model. In Proc. the 6th Conference of Simulated Evolution and Learning, Hefei, China, 2006, pp.545–552.Google Scholar
  15. [15]
    Bosman P, Thierens D. Continuous iterated density estimation evolutionary algorithms within the IDEA framework. In Proc. the Optimization by Building and Using Probabilistic Models OBUPM Workshop at GECCO-2000, San Francisco, California, 2000, pp.197–200.Google Scholar
  16. [16]
    Lu Q, Yao X. Clustering and learning Gaussian distribution for continuous optimization. IEEE Transactions on Systems, Man and Cybernetics-Part C, 2005, 35(2): 195–204.CrossRefGoogle Scholar

Copyright information

© Science Press, Beijing, China and Springer Science + Business Media, LLC, USA 2008

Authors and Affiliations

  1. 1.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

Personalised recommendations