Constrained efficient global optimization with support vector machines

@inproceedings{Basudhar2012ConstrainedEG,
  title={Constrained efficient global optimization with support vector machines},
  author={Anirban Basudhar and Christoph Dribusch and Sylvain Lacaze and Samy Missoum},
  year={2012}
}
This paper presents a methodology for constrained efficient global optimization (EGO) using support vector machines (SVMs). While the objective function is approximated using Kriging, as in the original EGO formulation, the boundary of the feasible domain is approximated explicitly as a function of the design variables using an SVM. Because SVM is a classification approach and does not involve response approximations, this approach alleviates issues due to discontinuous or binary responses… CONTINUE READING
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