Volume 4 Number 3
July 2007
Article Contents
Siddhartha Shakya and John McCall. Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields. International Journal of Automation and Computing, vol. 4, no. 3, pp. 262-272, 2007. doi: 10.1007/s11633-007-0262-6
Cite as: Siddhartha Shakya and John McCall. Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields. International Journal of Automation and Computing, vol. 4, no. 3, pp. 262-272, 2007. doi: 10.1007/s11633-007-0262-6

Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields

  • Received: 2007-03-05
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields

Abstract: This paper presents a Markov random field (MRF) approach to estimating and sampling the probability distribution in populations of solutions.The approach is used to define a class of algorithms under the general heading distribution estimation using Markov random fields (DEUM).DEUM is a subclass of estimation of distribution algorithms (EDAs) where interaction between solution variables is represented as an undirected graph and the joint probability of a solution is factorized as a Gibbs distribution derived from the structure of the graph.The focus of this paper will be on describing the three main characteristics of DEUM framework,which distinguishes it from the traditional EDA.They are:1) use of MRF models,2) fitness modeling approach to estimating the parameter of the model and 3) Monte Carlo approach to sampling from the model.

Siddhartha Shakya and John McCall. Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields. International Journal of Automation and Computing, vol. 4, no. 3, pp. 262-272, 2007. doi: 10.1007/s11633-007-0262-6
Citation: Siddhartha Shakya and John McCall. Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields. International Journal of Automation and Computing, vol. 4, no. 3, pp. 262-272, 2007. doi: 10.1007/s11633-007-0262-6
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