Incorporating a Metropolis method in a distribution estimation using Markov random field algorithm

Abstract- 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.

• Type: Conference
• Year: 2005
• Where: CEC
• Authors: Siddhartha Shakya, John A. W. McCall, Deryck F. Brown
• Keywords: Artificial Intelligence, CEC 2005, DEUM, Markov Random Fields, Univariate