MetaSG-SAEA is a bi-level meta-BBO framework that uses a meta-policy for search guidance via the MM-CCI constraint abstraction and diffusion-based population initialization to outperform baselines on expensive constrained multi-objective optimization problems.
arXiv preprint arXiv:2509.15810 , year=
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AdaE-SAEA uses meta-RL to adaptively select infill criteria and ensemble surrogate strategies (bagging/boosting) that balance robustness and accuracy across search phases in SAEAs.
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Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization
MetaSG-SAEA is a bi-level meta-BBO framework that uses a meta-policy for search guidance via the MM-CCI constraint abstraction and diffusion-based population initialization to outperform baselines on expensive constrained multi-objective optimization problems.
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Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA
AdaE-SAEA uses meta-RL to adaptively select infill criteria and ensemble surrogate strategies (bagging/boosting) that balance robustness and accuracy across search phases in SAEAs.