An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Computer experiments and global optimization
Computer experiments and global optimization
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
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In many practical engineering design problems, the form of objective function is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective function is obtained by some analysis such as structural analysis, fluidmechanic analysis, thermodynamic analysis, and so on. Usually, these analyses are considerably time consuming to obtain a value of objective function. In order to make the number of analyses as few as possible, approximate optimization methods using computational intelligence have been developed. In those methods, optimization is performed in parallel with predicting the form of objective function. In this paper, radial basis function networks (RBFN) are employed in predicting the form of objective function, and genetic algorithms (GA) in searching the optimal value of the predicted objective function. One of the most important tasks in this approach is to allocate sample data moderately in order to make the number of experiments as small as possible. The effectiveness of the suggested method will be shown through some numerical examples along with an application to seismic design in reinforcement of cable-stayed bridges.