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.

Abstract

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.

Citation

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: https://doi.org/10.1109/CEC.2006.1688408

• 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
• DOI: https://doi.org/10.1109/CEC.2006.1688408
• 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
• Public URL: http://hdl.handle.net/10059/430