发表机构
Czech Academy of Sciences; Institute of Computer Science（捷克科学院; 计算机科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对黑箱优化评估昂贵的问题，本文提出基于人工神经网络的代理模型AFN-CMA-ES，通过选择性评估目标函数，在有限预算下高效引导搜索，实验验证其性能优于现有方法。
AI 中文摘要
黑箱优化（BBO）常应用于多个工程领域，并可利用数值测量和模拟技术的进步。它处理的是无法获得解析描述的目标函数优化问题。它依赖于仅需搜索空间中的输入点及其对应的目标函数值的方法，这些值通过非解析手段（如传感器、实验或模拟）获得。常见方法包括进化优化和其他元启发式算法。由于BBO方法仅依赖目标函数值，它们通常需要大量评估，当目标函数评估耗时或昂贵时，这会成为问题。这导致使用基于代理的优化，即评估选定的真实目标值，并训练回归模型以在搜索空间中近似目标函数。代理辅助的黑箱优化是一个小数据学习问题，因为优化器必须从有限的评估中近似昂贵的函数。代理模型作为数据高效的回归器，在有限的评估预算下引导搜索朝向有希望或信息丰富的点。本文提出了一种使用人工神经网络的新代理模型，称为自适应保真度联结协方差矩阵自适应进化策略（AFN-CMA-ES），用于目标函数的选择性评估。实验结果表明，与最先进的代理辅助BBO方法相比，其性能具有竞争力。
英文摘要
Black-Box Optimization (BBO) is often applied in several engineering fields and can utilize an advancement of numerical measure- ments and simulation technologies. It deals with the optimization func- tions, where an analytical description is unavailable. It relies on meth- ods that require only an input point in the search space, paired with its corresponding objective function value, obtained through non-analytical means, e.g., sensors, experiments, or simulations. Common approaches include evolutionary optimization and other metaheuristics. Since BBO methods rely solely on objective function values, they typically require many evaluations, which becomes problematic when evaluating the ob- jective function is time-consuming or expensive. This leads to using surrogate-based optimization which evaluates selected true objective val- ues and trains a regression model to approximate the objective function across the search space. Surrogate-assisted black-box optimization is a small-data learning problem because the optimizer must approximate an expensive objective function from limited evaluations. Surrogate models act as data-efficient regressors, guiding the search toward promising or informative points under a restricted evaluation budget. In this paper, a new surrogate model using artificial neural networks, called Adaptive- Fidelity Nexus Covariance Matrix Adaptation Evolution Strategy (AFN- CMA-ES), is proposed for the selective evaluation of objective functions. The experimental results show its competitive performance compared to state-of-the-art surrogate-assisted BBO methods.
