Journal of Central South University

, Volume 19, Issue 2, pp 443–452 | Cite as

Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally expensive problems

  • Na Luo (罗娜)
  • Feng Qian (钱锋)
  • Liang Zhao (赵亮)
  • Wei-min Zhong (钟伟民)
Article
First Online:
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Accepted:

Abstract

In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.

Key words

estimation of distribution algorithm fitness function modeling Gaussian process surrogate approach 
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Copyright information

© Central South University Press and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Na Luo (罗娜)
    • 1
  • Feng Qian (钱锋)
    • 1
  • Liang Zhao (赵亮)
    • 1
  • Wei-min Zhong (钟伟民)
    • 1
  1. 1.Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of EducationEast China University of Science and TechnologyShanghaiChina

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