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- W2783192054 abstract "Due to the use of complex models in simulation, black-box optimization methods have been useful in the field of simulation optimization. Since black-box functions lack exogenous information about the structure of the function, optimization methods often employ random search to focus sampling inside a domain. This poster describes an optimization framework, Quantile Adaptive Search (QAS), for focusing sampling inside a nested set of quantile level sets on a continuous domain. The poster describes theoretical results regarding the complexity of QAS. In addition, methods for implementing the framework using Probabilistic Branch and Bound and Hit-and-Run are discussed." @default.
- W2783192054 created "2018-01-26" @default.
- W2783192054 creator A5034523672 @default.
- W2783192054 date "2017-12-01" @default.
- W2783192054 modified "2023-09-26" @default.
- W2783192054 title "A quantile adaptive search for black-box simulation optimization on continuous domains with practical implementations" @default.
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- W2783192054 doi "https://doi.org/10.1109/wsc.2017.8248231" @default.
- W2783192054 hasPublicationYear "2017" @default.
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