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• W3032937891 abstract "The great computational burden caused by complicated and unknown analysis restricts the use of simulation based optimization. In order to mitigate this challenge, surrogate-based global optimization methods have gained popularity for their capability in handling expensive black-box problems. This paper surveys the fundamental issues that arise in surrogate-based global optimization (SBGO) from a practitioner’s perspective, including highlighting concepts, methods, techniques as well as engineering applications. To provide a brief discussion on the issues involved, recent advances in design of experiments, surrogate modeling techniques, infill criteria and design space reduction are investigated. Future challenges and research is also analyzed and discussed." @default.
• W3032937891 created "2020-06-12" @default.
• W3032937891 creator A5059683046 @default.
• W3032937891 creator A5084872802 @default.
• W3032937891 date "2019-08-01" @default.
• W3032937891 modified "2023-09-24" @default.
• W3032937891 title "Surrogate-based Global Optimization Methods for Expensive Black-Box Problems: Recent Advances and Future Challenges" @default.
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• W3032937891 doi "https://doi.org/10.1109/irce.2019.00026" @default.
• W3032937891 hasPublicationYear "2019" @default.
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