利用大语言模型自动设计优化测试问题
Automatic Design of Optimization Test Problems with Large Language Models
AI总结：
EoTF利用大语言模型自动设计优化测试问题，通过生成与目标ELA特征向量匹配的连续优化函数，提高基准测试的可扩展性和可解释性。
摘要：
黑箱优化算法的发展依赖于具有多样性和代表性的真实世界问题景观的基准套件。广泛使用的集合如BBOB和CEC仍然由手工设计的合成函数主导，对探索景观分析（ELA）特征的高维空间覆盖有限，从而偏移评估并阻碍元黑箱优化器的训练。我们引入了测试函数的进化（EoTF），这是一个能够自动生成连续优化测试函数的框架，其景观能够匹配指定的目标ELA特征向量。EoTF采用LLM驱动的进化搜索，最初是为启发式发现提出的，通过最小化生成候选者采样ELA特征与目标轮廓之间的距离，来进化可解释、自包含的numpy实现的目标函数。在24个无噪声BBOB函数和24个MA-BBOB混合函数的实验中，EoTF能够可靠地生成非平凡函数，其ELA特性与目标特征高度匹配，并在固定评估预算下保持优化器性能排名，支持其作为替代基准的有效性。虽然基线神经网络生成器在2D上实现了更高的准确性，但EoTF在3D中表现显著优于它，并且在维度增加时表现出稳定的解决方案质量，突显了良好的可扩展性。总体而言，EoTF提供了一条实用的路线，用于生成可扩展、可移植和可解释的基准生成，以针对期望的景观属性。
英文摘要：
The development of black-box optimization algorithms depends on the availability of benchmark suites that are both diverse and representative of real-world problem landscapes. Widely used collections such as BBOB and CEC remain dominated by hand-crafted synthetic functions and provide limited coverage of the high-dimensional space of Exploratory Landscape Analysis (ELA) features, which in turn biases evaluation and hinders training of meta-black-box optimizers. We introduce Evolution of Test Functions (EoTF), a framework that automatically generates continuous optimization test functions whose landscapes match a specified target ELA feature vector. EoTF adapts LLM-driven evolutionary search, originally proposed for heuristic discovery, to evolve interpretable, self-contained numpy implementations of objective functions by minimizing the distance between sampled ELA features of generated candidates and a target profile. In experiments on 24 noiseless BBOB functions and a contamination-mitigating suite of 24 MA-BBOB hybrid functions, EoTF reliably produces non-trivial functions with closely matching ELA characteristics and preserves optimizer performance rankings under fixed evaluation budgets, supporting their validity as surrogate benchmarks. While a baseline neural-network-based generator achieves higher accuracy in 2D, EoTF substantially outperforms it in 3D and exhibits stable solution quality as dimensionality increases, highlighting favorable scalability. Overall, EoTF offers a practical route to scalable, portable, and interpretable benchmark generation targeted to desired landscape properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
