OpenAIR @ RGU >
Design and Technology >
Computing >
Conference publications (Computing) >

Please use this identifier to cite or link to this item:
This item has been viewed 4 times in the last year. View Statistics

Files in This Item:

File Description SizeFormat
McCall CEC 2006.pdf228.59 kBAdobe PDFView/Open
Title: Solving the ising spin glass problem using a bivariate RDA based on Markov random fields.
Authors: Shakya, Siddhartha
McCall, John
Brown, Deryck
Editors: Yen, G. G.
Lucas, S. M.
Fogel, G.
Kendall, G.
Salomon, R.
Zhang, B.-T.
Coello, C. A.
Runarsson, T. P.
Issue Date: Jul-2006
Publisher: IEEE
Citation: SHAKYA, S., MCCALL, J. and BROWN, D., 2006. Solving the ising spin glass problem using a bivariate RDA based on Markov random fields. In: YEN, G., LUCAS, S., FOGEL, G., KENDALL, G., SALOMON, R., ZHANG, B.-T., COELLO, C. and RUNARSSON, T., eds. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2006). 16-21 July 2006. New York: IEEE. pp. 908-915.
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.
ISBN: 0780394879
Appears in Collections:Conference publications (Computing)

All items in OpenAIR are protected by copyright, with all rights reserved.


   Disclaimer | Freedom of Information | Privacy Statement |Copyright ©2012 Robert Gordon University, Garthdee House, Garthdee Road, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781