发表机构
China University of Geosciences（中国地质大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对昂贵黑箱优化中传统方法的局限，提出NeurGO框架，用基于注意力的编码器捕捉搜索趋势，解码器生成候选解，设计质量-多样性损失，经测试在相同预算下优化性能更好且收敛更快。
AI 中文摘要
昂贵的黑箱优化在科学与工程中普遍存在，函数评估成本高昂且评估预算有限。传统进化算法和元黑箱优化方法通常在候选解选择上消耗大量评估，常将宝贵预算浪费在劣质解上。代理辅助进化和贝叶斯优化虽旨在通过代理模型减少评估，但从有限数据构建准确全局模型仍具挑战，模型偏差易使搜索陷入局部最优。为克服这些局限，我们提出NeurGO，一个从历史种群状态直接合成精英候选解的生成式元黑箱优化框架。具体而言，我们使用基于注意力的编码器捕捉种群级搜索趋势，并基于此表示对解码器进行条件设定以生成高质量候选解，避免对大量后代池进行昂贵评估。然后，我们设计了一个质量-多样性损失，以在整个搜索过程中保持解的质量和种群多样性。通过在CEC 2008和COCO BBOB测试套件上的广泛基准测试，我们的方法在相同评估预算下实现了更好的优化性能，并展现出更快的收敛速度。
英文摘要
Expensive black-box optimization is ubiquitous in science and engineering, where function evaluations are costly and the evaluation budget is limited. Traditional evolutionary algorithms and Meta-BlackBox Optimization (MetaBBO) approaches typically consume most evaluations on candidate selection, often wasting precious budget on inferior solutions. Although surrogate-assisted evolution and Bayesian optimization aim to reduce evaluations through surrogate models, constructing an accurate global model from limited data remains challenging, and model bias can easily trap the search in local optima. To overcome these limitations, we propose NeurGO, a generative MetaBBO framework that directly synthesizes elite candidates from historical population states. Specifically, we employ an attention-based encoder to capture the population-level search trend and condition a decoder on this representation to generate high-quality candidates, avoiding the expensive evaluation of large offspring pools. We then design a quality-diversity loss to maintain solution quality and population diversity throughout the search. Through extensive benchmarking on CEC 2008 and the COCO BBOB test suites, our method achieves better optimization performance under the same evaluation budget and exhibits faster convergence.
Comments18 pages, and 5 figures
