Dual-State-Driven Evolutionary Optimization for Expensive Optimization Problems with Continuous and Categorical Variables

The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for addressing expensive continuous or combinatorial optimization problems. However, it encounters significant challenges in expensive mixed-variable optimization problems (EMVOPs). To overcome this limitati...

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Veröffentlicht in:	2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS) S. 1 - 7
Hauptverfasser:	Xie, Lindong, Li, Genghui, Lin, Kangnian, Wang, Zhenkun
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 22.09.2023
Schlagworte:	Benchmark testing Complex systems continuous and categorical variables dual-state-driven Evolutionary computation expensive mixed-variable optimization Measurement Prediction algorithms Predictive models Search problems Surrogate-assisted evolutionary algorithm
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