AdaMoR-DDMOEA: Adaptive Model Selection with a Reliable Individual-Based Model Management Framework for Offline Data-Driven Multi-Objective Optimization
Many real-world expensive industrial and engineering multi-objective optimization problems (MOPs) are driven by historical, experimental, or simulation data. In such scenarios, due to the expensive cost and time required, we are only left with a small amount of labeled data to perform the optimizati...
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Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
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Series: | Mathematics |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-7390/13/1/158 |
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