Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Readings in computer vision: issues, problems, principles, and paradigms
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Markov Random Field Modelling of Royal Road Genetic Algorithms
Selected Papers from the 5th European Conference on Artificial Evolution
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Bayesian learning in undirected graphical models: approximate MCMC algorithms
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
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
Estimation of Distribution Algorithms with Kikuchi Approximations
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
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Marleda: effective distribution estimation through markov random fields
Marleda: effective distribution estimation through markov random fields
Optimisation and fitness modelling of bio-control in mushroom farming using a Markov network eda
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An EDA based on local markov property and gibbs sampling
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hierarchical BOA solves ising spin glasses and MAXSAT
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Mixtures of kikuchi approximations
ECML'06 Proceedings of the 17th European conference on Machine Learning
A Markovianity based optimisation algorithm
Genetic Programming and Evolvable Machines
Influence of selection on structure learning in markov network EDAs: an empirical study
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Variable transformations in estimation of distribution algorithms
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Optimization by ℓ1-constrained Markov fitness modelling
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Implicit model selection based on variable transformations in estimation of distribution
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Estimation of distribution algorithm based on hidden Markov models for combinatorial optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Distribution Estimation Using Markov network (DEUM) algorithm is a class of estimation of distribution algorithms that uses Markov networks to model and sample the distribution. Several different versions of this algorithm have been proposed and are shown to work well in a number of different optimisation problems. One of the key similarities between all of the DEUM algorithms proposed so far is that they all assume the interaction between variables in the problem to be pre given. In other words, they do not learn the structure of the problem and assume that it is known in advance. Therefore, they may not be classified as full estimation of distribution algorithms. This work presents a fully multivariate DEUM algorithm that can automatically learn the undirected structure of the problem, automatically find the cliques from the structure and automatically estimate a joint probability model of the Markov network. This model is then sampled using Monte Carlo samplers. The proposed DEUM algorithm can be applied to any general optimisation problem even when the structure is not known.