English(EN) FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization

FoMEMO 提出面向昂贵多目标优化的基础模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-14 05:00

研究人员提出了 FoMEMO，一个旨在为昂贵多目标优化任务创建基础模型的新框架。该方法通过在海量合成数据集上进行可扩展的预训练来解决现有方法的局限性，从而避免了进行大量真实世界实验的需要。FoMEMO 将多目标优化转化为偏好聚合后验学习任务，使基础模型能够在测试时进行上下文后验预测并优化采集函数以高效生成候选集，而无需进一步的模型训练。 AI

影响通过利用预训练的基础模型，能够为复杂现实世界问题实现更高效、更可扩展的优化。

排序理由该集群包含一篇详细介绍优化领域基础模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yiming Yao, Fei Liu, Liang Zhao, Xi Lin, Yilu Liu, Qingfu Zhang · 2026-11-14 05:00

FoMEMO：迈向面向昂贵多目标优化的基础模型

arXiv:2509.03244v3 Announce Type: replace-cross Abstract: Expensive multi-objective optimization is a prevalent and crucial concern in many real-world scenarios, where sample-efficiency is vital due to the limited evaluations to recover the true Pareto front for decision making. …

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报道来源 [1]

FoMEMO：迈向面向昂贵多目标优化的基础模型

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