Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Properties of fitness functions and search landscapes
Theoretical aspects of evolutionary computing
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
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th 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
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Blocked stochastic sampling versus Estimation of Distribution Algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The equation for response to selection and its use for prediction
Evolutionary Computation
Approximate factorizations of distributions and the minimum relative entropy principle
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Sporadic model building for efficiency enhancement of the hierarchical BOA
Genetic Programming and Evolvable Machines
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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
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
Analyzing probabilistic models in hierarchical BOA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Rule acquisition for cognitive agents by using estimation of distribution algorithms
International Journal of Knowledge Engineering and Soft Data Paradigms
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Learning factorizations in estimation of distribution algorithms using affinity propagation
Evolutionary Computation
Towards the geometry of estimation of distribution algorithms based on the exponential family
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Hierarchical BOA, cluster exact approximation, and ising spin glasses
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Bayesian learning of markov network structure
ECML'06 Proceedings of the 17th European conference on Machine Learning
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
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
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The question of finding feasible ways for estimating probability distributions is one of the main challenges for Estimation of Distribution Algorithms (EDAs). To estimate the distribution of the selected solutions, EDAs use factorizations constructed according to graphical models. The class of factorizations that can be obtained from these probability models is highly constrained. Expanding the class of factorizations that could be employed for probability approximation is a necessary step for the conception of more robust EDAs. In this paper we introduce a method for learning a more general class of probability factorizations. The method combines a reformulation of a probability approximation procedure known in statistical physics as the Kikuchi approximation of energy, with a novel approach for finding graph decompositions. We present the Markov Network Estimation of Distribution Algorithm (MN-EDA), an EDA that uses Kikuchi approximations to estimate the distribution, and Gibbs Sampling (GS) to generate new points. A systematic empirical evaluation of MN-EDA is done in comparison with different Bayesian network based EDAs. From our experiments we conclude that the algorithm can outperform other EDAs that use traditional methods of probability approximation in the optimization of functions with strong interactions among their variables.