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
Learning in graphical models
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
A Factorized Distribution Algorithm Using Single Connected Bayesian Networks
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
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
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
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
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
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
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
A matrix approach for finding extrema: problems with modularity, hierarchy, and overlap
A matrix approach for finding extrema: problems with modularity, hierarchy, and overlap
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
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
EDNA: Estimation of Dependency Networks Algorithm
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
EDA-RL: estimation of distribution algorithms for reinforcement learning problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Structure learning and optimisation in a Markov-network based estimation of distribution algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A fully multivariate DEUM algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Learning factorizations in estimation of distribution algorithms using affinity propagation
Evolutionary Computation
Combinatorial optimization by learning and simulation of Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Mixtures of kikuchi approximations
ECML'06 Proceedings of the 17th European conference on Machine Learning
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
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Several Estimation of Distribution Algorithms (EDAs) based on Markov networks have been recently proposed. The key idea behind these EDAs was to factorise the joint probability distribution of solution variables in terms of cliques in the undirected graph. As such, they made use of the global Markov property of the Markov network in one form or another. This paper presents a Markov Network based EDA that is based on the use of the local Markov property, the Markovianity, and does not directly model the joint distribution. We call it Markovianity based Optimisation Algorithm. The algorithm combines a novel method for extracting the neighbourhood structure from the mutual information between the variables, with a Gibbs sampler method to generate new points. We present an extensive empirical validation of the algorithm on problems with complex interactions, comparing its performance with other EDAs that use higher order interactions. We extend the analysis to other functions with discrete representation, where EDA results are scarce, comparing the algorithm with state of the art EDAs that use marginal product factorisations.