Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Journal of Global Optimization
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Global Optimization by Multilevel Coordinate Search
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
MAPO: using a committee of algorithm-experts for parallel optimization of costly functions
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
A Pseudo-Global Optimization Approach with Application to the Design of Containerships
Journal of Global Optimization
TRIOPT: a triangulation-based partitioning algorithm for global optimization
Journal of Computational and Applied Mathematics
Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions
Journal of Global Optimization
On the Design of Optimization Strategies Based on Global Response Surface Approximation Models
Journal of Global Optimization
Multi-funnel optimization using Gaussian underestimation
Journal of Global Optimization
Evolving neural networks for fractured domains
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization
Journal of Global Optimization
Improved scatter search for the global optimization of computationally expensive dynamic models
Journal of Global Optimization
Journal of Global Optimization
An informational approach to the global optimization of expensive-to-evaluate functions
Journal of Global Optimization
Parallel Stochastic Global Optimization Using Radial Basis Functions
INFORMS Journal on Computing
A review of recent advances in global optimization
Journal of Global Optimization
Rational radial basis function interpolation with applications to antenna design
Journal of Computational and Applied Mathematics
TRIOPT: a triangulation-based partitioning algorithm for global optimization
Journal of Computational and Applied Mathematics
Journal of Global Optimization
Journal of Computational and Applied Mathematics
A metamodel-assisted evolutionary algorithm for expensive optimization
Journal of Computational and Applied Mathematics
Surrogate modeling in the evolutionary optimization of catalytic materials
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An experimental methodology for response surface optimization methods
Journal of Global Optimization
Registrar: a complete-memory operator to enhance performance of genetic algorithms
Journal of Global Optimization
Structural and Multidisciplinary Optimization
Computers and Operations Research
Setting targets for surrogate-based optimization
Journal of Global Optimization
Flicker: a dynamically adaptive architecture for power limited multicore systems
Proceedings of the 40th Annual International Symposium on Computer Architecture
Examples of inconsistency in optimization by expected improvement
Journal of Global Optimization
Journal of Global Optimization
Efficient global optimization algorithm assisted by multiple surrogate techniques
Journal of Global Optimization
Global optimization of expensive black box problems with a known lower bound
Journal of Global Optimization
Proceedings of the International Conference on Computer-Aided Design
Determination of realistic worst imperfection for cylindrical shells using surrogate model
Structural and Multidisciplinary Optimization
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We introduce a method that aims to find the global minimum of a continuous nonconvex function on a compact subset of \dRd. It is assumed that function evaluations are expensive and that no additional information is available. Radial basis function interpolation is used to define a utility function. The maximizer of this function is the next point where the objective function is evaluated. We show that, for most types of radial basis functions that are considered in this paper, convergence can be achieved without further assumptions on the objective function. Besides, it turns out that our method is closely related to a statistical global optimization method, the P-algorithm. A general framework for both methods is presented. Finally, a few numerical examples show that on the set of Dixon-Szegö test functions our method yields favourable results in comparison to other global optimization methods.