Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Finding elliptic Fekete points sets: two numerical solution approaches
Journal of Computational and Applied Mathematics
Fast Global Optimization of Difficult Lennard-Jones Clusters
Computational Optimization and Applications
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Efficient Algorithms for Large Scale Global Optimization: Lennard-Jones Clusters
Computational Optimization and Applications
Application of Deterministic Low-Discrepancy Sequences in Global Optimization
Computational Optimization and Applications
A comparison of complete global optimization solvers
Mathematical Programming: Series A and B
Comparative Assessment of Algorithms and Software for Global Optimization
Journal of Global Optimization
Journal of Global Optimization
Improved Strategies for Radial basis Function Methods for Global Optimization
Journal of Global Optimization
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Maximin Latin Hypercube Designs in Two Dimensions
Operations Research
Nonlinear optimization with GAMS /LGO
Journal of Global Optimization
Kriging metamodeling in constrained simulation optimization: an explorative study
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
To be fair or efficient or a bit of both
Computers and Operations Research
An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization
Journal of Global Optimization
Journal of Global Optimization
Comparing designs for computer simulation experiments
Proceedings of the 40th Conference on Winter Simulation
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Bounds for Maximin Latin Hypercube Designs
Operations Research
A review of recent advances in global optimization
Journal of Global Optimization
A hybrid meta-heuristic for global optimisation using low-discrepancy sequences of points
Computers and Operations Research
One-dimensional nested maximin designs
Journal of Global Optimization
TRIOPT: a triangulation-based partitioning algorithm for global optimization
Journal of Computational and Applied Mathematics
Calibrating artificial neural networks by global optimization
Expert Systems with Applications: An International Journal
Design of computer experiments: space filling and beyond
Statistics and Computing
Development and calibration of a currency trading strategy using global optimization
Journal of Global Optimization
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In many real-world applications of optimization, the underlying descriptive system model is defined by computationally expensive functions: simulation modules, numerical models and other "black box" model components are typical examples. In such cases, the model development and optimization team often has to rely on optimization carried out under severe resource constraints. To address this important issue, recently a Regularly Spaced Sampling (RSS) module has been added to the Lipschitz Global Optimizer (LGO) solver suite. RSS generates non-collapsing space filling designs, and produces corresponding solution estimates: this information is passed along to LGO for refinement within the given resource (function evaluation and/or runtime) limitations. Obviously, the quality of the solution obtained will essentially depend both on model instance difficulty and on the admissible computational effort. In spite of this general caveat, our results based on solving a selection of non-trivial global optimization test problems suggest that even a moderate amount of well-placed sampling effort enhanced by limited optimization can lead at least to reasonable or even to high quality results. Our numerical tests also indicate that LGO's overall efficiency is often increased by using RSS as a presolver, both in resource-constrained and in completed LGO runs.