Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
On the Design of Optimization Strategies Based on Global Response Surface Approximation Models
Journal of Global Optimization
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Memetic algorithm using multi-surrogates for computationally expensive optimization problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Hierarchical Nonlinear Approximation for Experimental Design and Statistical Data Fitting
SIAM Journal on Scientific Computing
A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Comparison of surrogate models for the actual global optimization of a 2D turbomachinery flow
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Evolutionary Model Type Selection for Global Surrogate Modeling
The Journal of Machine Learning Research
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
The Journal of Machine Learning Research
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Nonlinear Least Square Regression by Adaptive Domain Method With Multiple Genetic Algorithms
IEEE Transactions on Evolutionary Computation
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The use of Surrogate Based Optimization (SBO) has become commonplace for optimizing expensive black-box simulation codes. A popular SBO method is the Efficient Global Optimization (EGO) approach. However, the performance of SBO methods critically depends on the quality of the guiding surrogate. In EGO the surrogate type is usually fixed to Kriging even though this may not be optimal for all problems. In this paper the authors propose to extend the well-known EGO method with an automatic surrogate model type selection framework that is able to dynamically select the best model type (including hybrid ensembles) depending on the data available so far. Hence, the expected improvement criterion will always be based on the best approximation available at each step of the optimization process. The approach is demonstrated on a structural optimization problem, i.e., reducing the stress on a truss-like structure. Results show that the proposed algorithm consequently finds better optimums than traditional kriging-based infill optimization.