A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Nelder-Mead simplex modifications for simulation optimization
Management Science
A framework for Response Surface Methodology for simulation optimization
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Locally-Biased form of the DIRECT Algorithm
Journal of Global Optimization
A Revised Simplex Search Procedure for Stochastic Simulation Response Surface Optimization
INFORMS Journal on Computing
Sequential design of computer experiments to minimize integrated response functions
Sequential design of computer experiments to minimize integrated response functions
Recent advances in simulation optimization: response surface methodology revisited
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Lean optimization using supersaturated experimental design
Applied Numerical Mathematics
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
Journal of Global Optimization
Discrete stochastic optimization using linear interpolation
Proceedings of the 40th Conference on Winter Simulation
Kriging metamodel management in the design optimization of a CNG injection system
Mathematics and Computers in Simulation
Noisy Multiobjective Optimization on a Budget of 250 Evaluations
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
An informational approach to the global optimization of expensive-to-evaluate functions
Journal of Global Optimization
An experimental investigation of model-based parameter optimisation: SPO and beyond
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A review of recent advances in global optimization
Journal of Global Optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
On the performance of metamodel assisted MOEA/D
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Expensive multiobjective optimization by MOEA/D with Gaussian process model
IEEE Transactions on Evolutionary Computation
Optimal design of aeroengine turbine disc based on kriging surrogate models
Computers and Structures
Time-bounded sequential parameter optimization
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
A Bayesian interactive optimization approach to procedural animation design
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Simulation optimization based on Taylor Kriging and evolutionary algorithm
Applied Soft Computing
Simulation optimization using metamodels
Winter Simulation Conference
The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
INFORMS Journal on Computing
Structural and Multidisciplinary Optimization
Kriging metamodel with modified nugget-effect: The heteroscedastic variance case
Computers and Industrial Engineering
Hierarchical Knowledge Gradient for Sequential Sampling
The Journal of Machine Learning Research
Local meta-models for optimization using evolution strategies
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Application of SEUMRE global optimization algorithm in automotive magnetorheological brake design
Structural and Multidisciplinary Optimization
A framework for optimization under limited information
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Automated configuration of mixed integer programming solvers
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
On the effect of response transformations in sequential parameter optimization
Evolutionary Computation
Resampling methods for meta-model validation with recommendations for evolutionary computation
Evolutionary Computation
Bayesian Kriging Analysis and Design for Stochastic Simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
An adaptive hybrid surrogate model
Structural and Multidisciplinary Optimization
An experimental methodology for response surface optimization methods
Journal of Global Optimization
High-dimensional model-based optimization based on noisy evaluations of computer games
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Efficient discrete optimization via simulation using stochastic kriging
Proceedings of the Winter Simulation Conference
A Bayesian metamodeling approach for stochastic simulations
Proceedings of the Winter Simulation Conference
Calibrating simulation models using the knowledge gradient with continuous parameters
Proceedings of the Winter Simulation Conference
A framework for optimization under limited information
Journal of Global Optimization
Setting targets for surrogate-based optimization
Journal of Global Optimization
Worst-case global optimization of black-box functions through Kriging and relaxation
Journal of Global Optimization
Journal of Global Optimization
Efficient global optimization algorithm assisted by multiple surrogate techniques
Journal of Global Optimization
The use of partially converged simulations in building surrogate models
Advances in Engineering Software
Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package
Computational Statistics & Data Analysis
A benchmark of kriging-based infill criteria for noisy optimization
Structural and Multidisciplinary Optimization
Review: Structural design employing a sequential approximation optimization approach
Computers and Structures
Optimal learning for sequential sampling with non-parametric beliefs
Journal of Global Optimization
A beginner's guide to tuning methods
Applied Soft Computing
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This paper proposes a new method that extends the efficient global optimization to address stochastic black-box systems. The method is based on a kriging meta-model that provides a global prediction of the objective values and a measure of prediction uncertainty at every point. The criterion for the infill sample selection is an augmented expected improvement function with desirable properties for stochastic responses. The method is empirically compared with the revised simplex search, the simultaneous perturbation stochastic approximation, and the DIRECT methods using six test problems from the literature. An application case study on an inventory system is also documented. The results suggest that the proposed method has excellent consistency and efficiency in finding global optimal solutions, and is particularly useful for expensive systems.