Journal Article10.1007/S10898-018-0716-0
Conditional optimization of a noisy function using a kriging metamodel
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TL;DR: Testing this new method on test functions showed that, in the case of a high level of noise on the function, the PEQI criterion that is proposed is better than the PEI criterion usually implemented in such a situation.
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Abstract: The efficient global optimization method is popular for the global optimization of computer-intensive black-box functions. Extensions exist, either for the optimization of noisy functions, or for the conditional optimization of deterministic functions, i.e. the search for the values of a subset of parameters that optimize the function conditionally to the values taken by another subset, which are fixed. A metaphor for conditional optimization is the search for a crest line. No method has yet been developed for the conditional optimization of noisy functions: this is what we propose in this article. Testing this new method on test functions showed that, in the case of a high level of noise on the function, the PEQI criterion that we propose is better than the PEI criterion usually implemented in such a situation.
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Citations
Reliability updating and parameter inversion of micro-milling
TL;DR: In this paper , an adaptive Bayesian updating based on structural reliability analysis and adaptive-kriging method is developed to solve the actual distribution of parameters with uncertainty, and the actual cutting force data is measured by the micro-milling experiment with workpiece material Al6061.
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Practical Bayesian Optimization of Objectives with Conditioning Variables
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A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering
Rafael Castro-Amoedo,Julia Granacher,François Maréchal +2 more
TL;DR: A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering efficiently generates solutions without compromising accuracy or solution quality.
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Mechanistic model and probability characteristics of micro-milling force with a new parameter identification method
TL;DR: In this article , a flexible force model of micro-milling is established, which accounts for the influence of the actual cycloid tool path, tool runout, elastic recovery, tool deformation, and chip separation state.
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ConBO: Conditional Bayesian Optimization.
TL;DR: ConBO is proposed, a novel efficient algorithm that is based on a new hybrid Knowledge Gradient method, that outperforms recently published works on synthetic and real world problems, and is easily parallelized to collecting a batch of points.
2
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