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Incorporating a metropolis method in a distribution estimation using Markov random field algorithm.

Shakya, S.K.; McCall, J.A.W.; Brown, D.F.


S.K. Shakya

D.F. Brown


Markov Random Field (MRF) modelling techniques have been recently proposed as a novel approach to probabilistic modelling for Estimation of Distribution Algorithms (EDAs)[34, 4]. An EDA using this technique, presented in [34], was called Distribution Estimation using Markov Random Fields (DEUM). DEUM was later extended to DEUMd [32, 33]. DEUM and DEUMd use a univariate model of probability distribution, and have been shown to perform better than other univariate EDAs for a range of optimization problems. This paper extends DEUMd to incorporate a simple Metropolis method and empirically shows that for linear univariate problems the proposed univariate MRF models are very effective. In particular, the proposed DEUMd algorithm can find the solution in O(n) fitness evaluations. Furthermore, we suggest that the Metropolis method can also be used to extend the DEUM approach to multivariate problems.


SHAKYA, S.K., MCCALL, J.A.W. and BROWN, D.F. 2005. Incorporating a metropolis method in a distribution estimation using Markov random field algorithm. In Proceedings of the 2005 IEEE congress on evolutionary computation (CEC 2005), 2-5 September 2005, Edinburgh, UK. New York: IEEE [online], volume 3, article number 1555017, pages 2576-2583. Available from:

Conference Name 2005 IEEE congress on evolutionary computation (CEC 2005)
Conference Location Edinburgh, UK
Start Date Sep 2, 2005
End Date Sep 5, 2005
Acceptance Date Sep 30, 2005
Online Publication Date Sep 30, 2005
Publication Date Dec 31, 2005
Deposit Date Oct 20, 2009
Publicly Available Date Oct 20, 2009
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Article Number 1555017
Pages 2576-2583
Series Title IEEE transactions on evolutionary computation
ISBN 0780393635
Keywords Markov random fields; Electronic design automation and methodology; Probability distribution; Biological cells; Sampling methods; Random variables; Distributed computing; Evolutionary computation; Genetic algorithms; Genetic mutations
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