Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Robust growing neural gas algorithm with application in cluster analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Multiobjective real-coded bayesian optimization algorithmrevisited: diversity preservation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Real-World Applications of Multiobjective Optimization
Multiobjective Optimization
Clustering
Diversity loss in general estimation of distribution algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
Annals of Mathematics and Artificial Intelligence
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We examine the model-building issue related to multi-objective estimation of distribution algorithms (MOEDAs) and show that some of their, as yet overlooked, characteristics render most current MOEDAs unviable when addressing optimization problems with many objectives. We propose a novel model-building growing neural gas (MB-GNG) network that is specially devised for properly dealing with that issue and therefore yields a better performance. Experiments are conducted in order to show from an empirical point of view the advantages of the new algorithm.