Markov random field modeling in computer vision
Markov random field modeling in computer vision
Selected papers from the 2nd Scottish Functional Programming Workshop (SFP00)
Using a Markov network model in a univariate EDA: an empirical cost-benefit analysis
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Solving the MAXSAT problem using a multivariate EDA based on Markov networks
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A fully multivariate DEUM algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Optimization by ℓ1-constrained Markov fitness modelling
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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Markov Random Fields (MRFs) [5] are a class of probabalistic models that have been applied for many years to the analysis of visual patterns or textures. In this paper, our objective is to establish MRFs as an interesting approach to modelling genetic algorithms. Our approach bears strong similarities to recent work on the Bayesian Optimisation Algorithm [9], but there are also some significant differences. We establish a theoretical result that every genetic algorithm problem can be characterised in terms of a MRF model. This allows us to construct an explicit probabilistic model of the GA fitness function. The model can be used to generate chromosomes, and derive a MRF fitness measure for the population. We then use a specific MRF model to analyse two Royal Road problems, relating our analysis to that of Mitchell et al. [7].