. "Parallel Problem Solving from Nature - PPSN VII" . "Bayesovsk\u00FD optimaliza\u010Dn\u00ED algoritmus pro multikriteri\u00E1ln\u00ED optimalizaci"@cs . . . . "2002-09-07+02:00"^^ . "639404" . "10"^^ . . "Bayesian Optimization Algorithms for Multi-Objective Optimization"@en . . . "298-307" . "O\u010Den\u00E1\u0161ek, Ji\u0159\u00ED" . "Bayesian Optimization Algorithms for Multi-Objective Optimization" . "3-540-444139-5" . "[8BB8315C1053]" . "Granada" . "Granada" . "Bayesian Optimization Algorithms for Multi-Objective Optimization"@en . . . . "Bayesian Optimization Algorithms for Multi-Objective Optimization" . "Bayesovsk\u00FD optimaliza\u010Dn\u00ED algoritmus pro multikriteri\u00E1ln\u00ED optimalizaci"@cs . . "C\u00EDlem \u010Dl\u00E1nku je ov\u011B\u0159t pou\u017Eitelnost Bayesovsk\u00FDch optimaliza\u010Dn\u00EDch algoritm\u016F pro multikriteri\u00E1ln\u00ED optimaliza\u010Dn\u00ED \u00FAlohy s aproximac\u00ED Paretovsk\u00E9 hranice. Navr\u017Een\u00FD optimaliza\u010Dn\u00ED algoritmus na b\u00E1zi bin\u00E1rn\u00EDch rozhodovac\u00EDch strom\u016F byl testov\u00E1n na bikriteri\u00E1ln\u00ED \u00FAloze o batohu"@cs . . . "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 among genes such that disruption of important building blocks is avoided, in comparison with classical recombination operators. The goal of thiis paper is to investigate the usefulness of this concept in multi-objective evolutionary 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 a general evolutionary multi-objective optimizer. A potential performance gain is empirically tested in comparison with other state-of-the-art multi-objective EA on the bi-objective 0/1 k" . . "probabilistic models,Estimation Distribution Algorithms, multi-objective evolutionary optimization, Pareto-optimal solutions, Bayesian Optimization Algorithm, binary decision trees, knapsack problem."@en . "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 among genes such that disruption of important building blocks is avoided, in comparison with classical recombination operators. The goal of thiis paper is to investigate the usefulness of this concept in multi-objective evolutionary 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 a general evolutionary multi-objective optimizer. A potential performance gain is empirically tested in comparison with other state-of-the-art multi-objective EA on the bi-objective 0/1 k"@en . "Springer-Verlag" . "2"^^ . "P(GA102/02/0503)" . . "1"^^ . . . "RIV/00216305:26230/02:PU36222!RIV/2005/GA0/262305/N" . . . . "RIV/00216305:26230/02:PU36222" . "26230" .