研究人员推出了 BBO-Pile，这是一个包含超过 500,000 个优化轨迹的新型开源数据集，涵盖近 3,100 个黑盒。该数据集旨在解决以往工作中依赖非公开或合成数据而阻碍可复现性和现实世界泛化能力的局限性。通过使用 BBO-Pile，已经在各种规模上训练了黑盒优化基础模型，证明了大规模预训练在模仿优化方法方面的有效性。 AI
影响 通过提供大规模、开源的数据集，使黑盒优化领域的研究更具可复现性和泛化性。
排序理由 该集群包含多篇详细介绍黑盒优化新数据集和方法的学术论文。
在 arXiv cs.NE (Neural & Evolutionary) 阅读 →
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arXiv:2605.23417v1 Announce Type: new Abstract: Most black-box optimization methods require extensive hyperparameter tuning, often limiting their ability to generalize across different optimization domains. Foundation models for black-box optimization that learn optimization prin…
Most black-box optimization methods require extensive hyperparameter tuning, often limiting their ability to generalize across different optimization domains. Foundation models for black-box optimization that learn optimization principles from a large collection of optimization t…
arXiv:2604.12325v3 Announce Type: replace Abstract: We consider the problem of offline black-box optimization, where the goal is to discover optimal designs (e.g., molecules or materials) from past experimental data. A key challenge in this setting is data scarcity: in many scien…
Existing Meta-Black-Box Optimization (MetaBBO) methods focus on how to search when controlling optimizers, but largely overlook where to search. We propose MetaSG-SAEA, a bi-level MetaBBO framework for expensive constrained multi-objective optimization problems (ECMOPs), in which…