Learning Bayesian networks with local structure
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Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
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Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
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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
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multiobjective real-coded bayesian optimization algorithmrevisited: diversity preservation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Estimation of distribution algorithm based on archimedean copulas
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
A diversity preserving selection in multiobjective evolutionary algorithms
Applied Intelligence
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Multi-objective phylogenetic algorithm: solving multi-objective decomposable deceptive problems
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
A framework for multi-model EDAs with model recombination
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Proceedings of the 14th annual conference on Genetic and evolutionary computation
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Multi-objective optimization with estimation of distribution algorithm in a noisy environment
Evolutionary Computation
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This paper describes a scalable algorithm for solving multiobjective decomposable problems by combining the hierarchical Bayesian optimization algorithm (hBOA) with the nondominated sorting genetic algorithm (NSGA-II) and clustering in the objective space. It is first argued that for good scalability, clustering or some other form of niching in the objective space is necessary and the size of each niche should be approximately equal. Multiobjective hBOA (mohBOA) is then described that combines hBOA, NSGA-II and clustering in the objective space. The algorithm mohBOA differs from the multiobjective variants of BOA and hBOA proposed in the past by including clustering in the objective space and allocating an approximately equally sized portion of the population to each cluster. The algorithm mohBOA is shown to scale up well on a number of problems on which standard multiobjective evolutionary algorithms perform poorly.