Incorporating a Metropolis method in a Distribution Estimation using Markov Random Field Algorithm

2006 IEEE International Conference on Evolutionary Computation

Brown

Self Cite

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 DEUM d . DEUM and DEUM d 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.

Section: B Deum With Gibbs Samplermentioning

confidence: 99%

“…This has also been illustrated in[26] for the Onemax problem, where a Zero-Temperature Metropolis algorithm was able to find the solution in single generation3 Though it is straightforward to form a child population once we know how to sample from the MRFs (see[26] for an example)…”

mentioning

confidence: 97%

Solving the Ising Spin Glass Problem using a Bivariate EDA based on Markov Random Fields

2006 IEEE International Conference on Evolutionary Computation

Brown

Self Cite

“…The aim here is to provide an overview of some of the working example of DEUM. More complete description of these and other DEUM algorithms together with detail experimental results can be found in [9,10,[22][23][24][25].…”

Section: Instances Of Deummentioning

confidence: 99%

Optimization by estimation of distribution with DEUM framework based on Markov random fields

2007

Int J Automat Comput

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.

“…Within DEUM, direct sampling is used to generate new solutions with a high probability of being high in fitness [54,53,5]. This direct sampling of the fitness model rather than the fitness function has the benefit that the model can make the problem easier for the search part of the algorithm -the smoothing effect described in [63].…”

Section: Applicationsmentioning

confidence: 99%

The Markov Network Fitness Model

Brownlee

Adaptation, Learning, and Optimization

2012

Fitness modelling is an area of research which has recently received much interest among the evolutionary computing community. Fitness models can improve the efficiency of optimisation through direct sampling to generate new solutions, guiding of traditional genetic operators or as surrogates for a noisy or long-running fitness functions. In this chapter we discuss the application of Markov networks to fitness modelling of black-box functions within evolutionary computation, accompanied by discussion on the relationship between Markov networks and Walsh analysis of fitness functions. We review alternative fitness modelling and approximation techniques and draw comparisons with the Markov network approach. We discuss the applicability of Markov networks as fitness surrogates which may be used for constructing guided operators or more general hybrid algorithms. We conclude with some observations and issues which arise from work conducted in this area so far.