Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems. However, their reliance on rigid and manually designed components limits their flexibility and generalization across tasks. Meta-black-box optimization (MetaBBO) provides a promising paradigm for adaptively configuring algorithmic components. Nevertheless, existing MetaBBO methods usually control only a single component, and few studies have investigated the unified control of multi-component optimizers such as SAEAs. Moreover, the robustness-accuracy trade-off in surrogate modeling, which is crucial for stable early-stage exploration and accurate late-stage exploitation, has rarely been explicitly considered. To address these issues, we propose AdaE-SAEA, an adaptive ensemble surrogate-assisted evolutionary algorithm for expensive multi-objective optimization. AdaE-SAEA embeds SAEA as the low-level optimizer within the MetaBBO framework and jointly controls the infill criterion and ensemble-based surrogate modeling. Specifically, bagging and boosting are designed as surrogate modeling modules to adaptively balance robustness and accuracy across different search phases, while the meta-policy simultaneously selects the infill criterion to enable adaptive sampling decisions. The meta-policy is trained through reinforcement learning with parallel sampling and centralized training, improving both training efficiency and transferability. Experiments on synthetic and real-world problems demonstrate that AdaE-SAEA outperforms state-of-the-art baselines and MetaBBO-based methods. We further verify the effectiveness of TabPFN as the base surrogate model for ensemble learning. To the best of our knowledge, this is the first work to unify the control of surrogate modeling and infill criteria in SAEAs while explicitly addressing the robustness--accuracy trade-off.

翻译：摘要：代理辅助进化算法（SAEAs）已广泛用于求解昂贵的黑箱优化问题。然而，它们对固定且人工设计组件的依赖限制了其在任务间的灵活性和泛化能力。元黑箱优化（MetaBBO）为自适应配置算法组件提供了有前景的范式。但现有MetaBBO方法通常仅控制单一组件，鲜有研究探讨对SAEA等多组件优化器的统一控制。此外，代理建模中鲁棒性与精度的权衡——这对早期稳定探索和后期精确利用至关重要——鲜少被明确考虑。针对这些问题，我们提出AdaE-SAEA，一种面向昂贵多目标优化的自适应集成代理辅助进化算法。AdaE-SAEA将SAEA嵌入MetaBBO框架作为底层优化器，并联合控制填充准则与基于集成方法的代理建模。具体而言，设计bagging与boosting作为代理建模模块，以自适应平衡不同搜索阶段的鲁棒性与精度，同时元策略同步选择填充准则以实现自适应采样决策。元策略通过并行采样和集中式训练的强化学习进行训练，从而提升训练效率与可迁移性。在合成问题与真实世界问题上的实验表明，AdaE-SAEA优于最先进的基线方法和基于MetaBBO的方法。我们进一步验证了TabPFN作为基础代理模型用于集成学习的有效性。据我们所知，这是首个在SAEA中统一控制代理建模与填充准则，同时显式处理鲁棒性-精度权衡的工作。