Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiobjective hBOA, clustering, and scalability
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A real-coded multi-objective estimation of distribution algorithm
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
Noisy Multiobjective Optimization on a Budget of 250 Evaluations
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Mining probabilistic models learned by EDAs in the optimization of multi-objective problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Design of multithreaded estimation of distribution algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Instance generators and test suites for the multiobjective quadratic assignment problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A diversity preserving selection in multiobjective evolutionary algorithms
Applied Intelligence
Multiobjective optimization on a budget of 250 evaluations
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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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In recent years, several researchers have concentrated on using probabilistic models in evolutionary algorithms. These Estimation Distribution Algorithms (EDA) incorporate methods for automated learning of correlations between variables of the encoded solutions. The process of sampling new individuals from a probabilistic model respects these mutual dependencies such that disruption of important building blocks is avoided, in comparison with classical recombination operators. The goal of this paper is to investigate the usefulness of this concept in multi-objective optimization, where the aim is to approximate the set of Pareto-optimal solutions. We integrate the model building and sampling techniques of a special EDA called Bayesian Optimization Algorithm, based on binary decision trees, into an evolutionary multi-objective optimizer using a special selection scheme. The behavior of the resulting Bayesian Multi-objective Optimization Algorithm (BMOA) is empirically investigated on the multi-objective knapsack problem.