Optimizing Continuous Problems Using Estimation of Distribution Algorithm Based on Histogram Model

  • Nan Ding
  • Shude Zhou
  • Zengqi Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


In the field of estimation of distribution algorithms, choosing probabilistic model for optimizing continuous problems is still a challenging task. This paper proposes an improved estimation of distribution algorithm (HEDA) based on histogram probabilistic model. By utilizing both historical and current population information, a novel learning method – accumulation strategy – is introduced to update the histogram model. In the sampling phase, mutation strategy is used to increase the diversity of population. In solving some well-known hard continuous problems, experimental results support that HEDA behaves much better than the conventional histogram-based implementation both in convergence speed and scalability. Compared with UMDA-Gaussian, SGA and CMA-ES, the proposed algorithms exhibit excellent performance in the test functions.


Continuous Problem Accumulation Strategy Mutation Strategy Distribution Algorithm Elitist Strategy 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nan Ding
    • 1
  • Shude Zhou
    • 2
  • Zengqi Sun
    • 2
  1. 1.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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