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Solving the Ising spin glass problem using a bivariate EDA based on Markov random fields.

Shakya, Siddhartha K.; McCall, John A.W.; Brown, Deryck F.


Siddhartha K. Shakya

Deryck 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). An EDA using this technique was called Distribution Estimation using Markov Random Fields (DEUM). DEUM was later extended to DEUMd. 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 DEUM to use a bivariate model and applies it to the Ising spin glass problems. We propose two variants of DEUM that use different sampling techniques. Our experimental result show a noticeable gain in performance.


SHAKYA, S.K., MCCALL, J.A.W. and BROWN, D.F. 2006. Solving the Ising spin glass problem using a bivariate EDA based on Markov random fields. In Proceedings of the 2006 IEEE congress on evolutionary computation (CEC 2006), 16-21 July 2006, Vancouver, Canada. New York: IEEE [online], article number 1688408, pages 908-915. Available from:

Conference Name 2006 IEEE congress on evolutionary computation (CEC 2006)
Conference Location Vancouver, Canada
Start Date Jul 16, 2006
End Date Jul 21, 2006
Acceptance Date Jul 31, 2006
Online Publication Date Jul 31, 2006
Publication Date Dec 31, 2006
Deposit Date Oct 20, 2009
Publicly Available Date Oct 20, 2009
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Article Number 1688408
Pages 908-915
Series Title IEEE transactions on evolutionary computation
ISBN 0780394879
Keywords Glass; Electronic design automation and methodology ;Markov random fields; Sampling methods; Evolutionary computation; Lattices; Distributed computing; Probability distribution; Performance gain; Genetic mutations; Ising model; Markov processes; optimisat
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