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Hierarchical Bayesian Optimization Algorithm = Bayesian Optimization Algorithm + Niching + Local Structures
, 2001
"... The paper describes the hierarchical Bayesian optimization algorithm which combines the Bayesian optimization algorithm, local structures in Bayesian networks, and a powerful niching technique. The proposed algorithm is able to solve hierarchical traps and other difficult problems very efficiently. ..."
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Cited by 327 (70 self)
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The paper describes the hierarchical Bayesian optimization algorithm which combines the Bayesian optimization algorithm, local structures in Bayesian networks, and a powerful niching technique. The proposed algorithm is able to solve hierarchical traps and other difficult problems very efficiently.
BOA: The Bayesian Optimization Algorithm
 In
, 1999
"... In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions is proposed. The proposed algorithm is called the Bayesian optimization algorithm (BOA). To estimate the distr ..."
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Cited by 1 (0 self)
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In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions is proposed. The proposed algorithm is called the Bayesian optimization algorithm (BOA). To estimate
MultiObjective Bayesian Optimization Algorithm
 in Proceedings of the Genetic and Evolutionary Computation Conference
, 2002
"... This paper proposes a competent multiobjective genetic algorithm called the multiobjective Bayesian optimization algorithm (mBOA). mBOA incorporates the selection method of the nondominated sorting genetic algorithmII (NSGAII) into the Bayesian optimization algorithm (BOA). The proposed algorith ..."
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Cited by 18 (4 self)
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This paper proposes a competent multiobjective genetic algorithm called the multiobjective Bayesian optimization algorithm (mBOA). mBOA incorporates the selection method of the nondominated sorting genetic algorithmII (NSGAII) into the Bayesian optimization algorithm (BOA). The proposed
Fitness inheritance in the Bayesian optimization algorithm
, 2004
"... This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems whe ..."
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Cited by 32 (23 self)
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This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems
A Simple Implementation of Bayesian Optimization Algorithm
 in C++(Version1.0). Illigal Report 99011
, 1999
"... To be used with the source code of the Bayesian optimization algorithm with decision graphs, available at ftp://ftpilligal.ge.uiuc.edu ..."
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Cited by 13 (0 self)
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To be used with the source code of the Bayesian optimization algorithm with decision graphs, available at ftp://ftpilligal.ge.uiuc.edu
Loopy Substructural . . . Bayesian Optimization Algorithm
, 2010
"... This paper presents a local search method for the Bayesian optimization algorithm (BOA) based on the concepts of substructural neighborhoods and loopy belief propagation. The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the topology of ..."
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This paper presents a local search method for the Bayesian optimization algorithm (BOA) based on the concepts of substructural neighborhoods and loopy belief propagation. The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the topology
WITH THE BAYESIAN OPTIMIZATION ALGORITHM by Moshe Looks
, 2005
"... The hierarchical Bayesian Optimization Algorithm (hBOA) [24, 25] learns bitstrings by constructing explicit centralized models of a population and using them to generate new instances. This thesis is concerned with extending hBOA to learning openended program trees. The new system, BOA programming ..."
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The hierarchical Bayesian Optimization Algorithm (hBOA) [24, 25] learns bitstrings by constructing explicit centralized models of a population and using them to generate new instances. This thesis is concerned with extending hBOA to learning openended program trees. The new system, BOA programming
Model Accuracy in the Bayesian Optimization Algorithm
, 2010
"... Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new view to evolutiona ..."
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Cited by 7 (4 self)
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and consequently reducing model interpretability, increases as well. This paper investigates the relationship between the probabilistic models learned by the Bayesian optimization algorithm (BOA) and the underlying problem structure. The purpose of the paper is threefold. First, model building in BOA is analyzed
Hierarchical Problem Solving by the Bayesian Optimization Algorithm
 PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE 2000
, 2000
"... The paper discusses three major issues. First, it discusses why it makes sense to approach problems in a hierarchical fashion. It defines the class of hierarchically decomposable functions that can be used to test the algorithms that approach problems in this fashion. Finally, the Bayesian optimi ..."
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Cited by 35 (10 self)
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The paper discusses three major issues. First, it discusses why it makes sense to approach problems in a hierarchical fashion. It defines the class of hierarchically decomposable functions that can be used to test the algorithms that approach problems in this fashion. Finally, the Bayesian
Introducing Assignment Functions to Bayesian Optimization Algorithms
"... In this paper we improve Bayesian optimization algorithms by introducing proportionate and rankbased assignment functions. A Bayesian optimization algorithm builds a Bayesian network from a selected subpopulation of promising solutions, and this probabilistic model is employed to generate the offs ..."
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In this paper we improve Bayesian optimization algorithms by introducing proportionate and rankbased assignment functions. A Bayesian optimization algorithm builds a Bayesian network from a selected subpopulation of promising solutions, and this probabilistic model is employed to generate
Results 1  10
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