Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System
By Handing Wang, Yaochu Jin, Jan O. Jansen. 2016
226 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics
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In Computational Theory and Mathematics
- Yaochu Jin
- Handing Wang
- Mengjie Zhang
- Bing Xue
- Yanan Sun
- Gary G. Yen
- Tinkle Chugh
- John James Doherty
Citations in the corpus
- Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, methodology): “According to [42], SAEAs can be divided into online and offline algorithms, which depend on whether the”
- A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem (2017, methodology): “[7] have also categorized data-driven optimization into two types: online and offline.”
- A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, methodology): “According to [5], data-driven surrogate-assisted optimization can be divided into offline and online data-driven, depending on whether a certain amount of new data can be made available during the optimization process.”
- Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, methodology): “For example, some of the objective and constraint functions in the trauma system design problem [6] can only be calculated using emergency incident records.”
- Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems (2017, background): “strategy considerably varies depending on whether the amount of given data is big [10] or small [20].”
- Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, methodology): “While most SAEAs focus on developing model management strategies for online data-driven optimization, relatively little effort has been dedicated to offline data-driven optimization with a few exceptions [6], [37], [38], where no new data can be made available for managing the surrogates.”