A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
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
Numerical Optimization Using Computer Experiments
Numerical Optimization Using Computer Experiments
Computer experiments and global optimization
Computer experiments and global optimization
Design and Analysis of Experiments
Design and Analysis of Experiments
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Ordinal regression in evolutionary computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Numerical assessment of metamodelling strategies in computationally intensive optimization
Environmental Modelling & Software
Automatic surrogate model type selection during the optimization of expensive black-box problems
Proceedings of the Winter Simulation Conference
Setting targets for surrogate-based optimization
Journal of Global Optimization
Efficient global optimization algorithm assisted by multiple surrogate techniques
Journal of Global Optimization
Aspects of adaptive hierarchical RBF metamodels for optimization
Journal of Computational Methods in Sciences and Engineering - Special issue on Advances in Simulation-Driven Optimization and Modeling
Enhancing intill sampling criteria for surrogate-based constrained optimization
Journal of Computational Methods in Sciences and Engineering - Special issue on Advances in Simulation-Driven Optimization and Modeling
The use of partially converged simulations in building surrogate models
Advances in Engineering Software
International Journal of Bio-Inspired Computation
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Striking the correct balance between global exploration of search spaces and local exploitation of promising basins of attraction is one of the principal concerns in the design of global optimization algorithms. This is true in the case of techniques based on global response surface approximation models as well. After constructing such a model using some initial database of designs it is far from obvious how to select further points to examine so that the appropriate mix of exploration and exploitation is achieved. In this paper we propose a selection criterion based on the expected improvement measure, which allows relatively precise control of the scope of the search. We investigate its behavior through a set of artificial test functions and two structural optimization problems. We also look at another aspect of setting up search heuristics of this type: the choice of the size of the database that the initial approximation is built upon.