Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Bayesian Optimization Algorithms for Multi-objective Optimization
PPSN VII Proceedings of the 7th 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
A taxonomy of glyph placement strategies for multidimensional data visualization
Information Visualization
Parallel Coordinates: Visual Multidimensional Geometry and Its Applications
Parallel Coordinates: Visual Multidimensional Geometry and Its Applications
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
IEEE Computer Graphics and Applications
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Proceedings of the 9th annual conference on Genetic and evolutionary computation
From mating pool distributions to model overfitting
Proceedings of the 10th annual conference on Genetic and evolutionary computation
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Estimation of distribution algorithms: from available implementations to potential developments
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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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One of the uses of the probabilistic models learned by estimation of distribution algorithms is to reveal previous unknown information about the problem structure. In this paper we investigate the mapping between the problem structure and the dependencies captured in the probabilistic models learned by EDAs for a set of multi-objective satisfiability problems. We present and discuss the application of different data mining and visualization techniques for processing and visualizing relevant information from the structure of the learned probabilistic models. We show that also in the case of multi-objective optimization problems, some features of the original problem structure can be translated to the probabilistic models and unveiled by using algorithms that mine the model structures.