Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
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
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Protein Folding in Simplified Models With Estimation of Distribution Algorithms
IEEE Transactions on Evolutionary Computation
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
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In this paper we quantitatively analyze the probability distributions generated by an EDA during the search. In particular, we record the probabilities to the optimal solution, the solution with the highest probability and that of the best individual of the population, when the EDA is solving a trap function. By using different structures in the probabilistic models we can analyze the influence of the structural model accuracy on the aforementioned probability values. In addition, the objective function values of these solutions are contrasted with their probability values in order to study the connection between the function and the probabilistic model. The results provide new information about the behavior of the EDAs and they open a discussion regarding which are the minimum (in)dependences necessary to reach the optimum.