用大模型自动设计可调控特性的优化问题，解决测试集结构单一难题。
LLM Driven Design of Continuous Optimization Problems with Controllable High-level Properties
- 大模型根据自然语言描述生成具有特定结构特性的优化函数。
- 新方法生成的问题在多模态、吸引盆大小一致性等特性上符合预期。
- 适合研究优化算法性能或需要可控测试基准的科研人员。
连续黑箱优化的基准测试受限于现有测试集（如BBOB）的结构多样性不足。本文探索将大语言模型嵌入进化循环中，以生成具有明确高层景观特征的优化问题。通过LLaMEA框架，引导大模型从目标属性的自然语言描述生成问题代码，包括多模态性、可分离性、吸引盆大小同质性、搜索空间同质性及全局-局部最优值对比度。在循环中利用基于ELA的属性预测器对候选函数评分，并引入ELA空间的适应度共享机制以提升种群多样性并避免重复景观。结合吸引盆分析、统计检验和可视化验证，结果表明多数生成函数确实具备预期结构特征。t-SNE嵌入显示这些函数扩展了BBOB实例空间，而非形成无关聚类。最终构建的基准库具有广泛性、可解释性和可复现性，适用于景观分析及自动算法选择等下游任务。
原文摘要 · Abstract (English)
Benchmarking in continuous black-box optimisation is hindered by the limited structural diversity of existing test suites such as BBOB. We explore whether large language models embedded in an evolutionary loop can be used to design optimisation problems with clearly defined high-level landscape characteristics. Using the LLaMEA framework, we guide an LLM to generate problem code from natural-language descriptions of target properties, including multimodality, separability, basin-size homogeneity, search-space homogeneity and globallocal optima contrast. Inside the loop we score candidates through ELA-based property predictors. We introduce an ELA-space fitness-sharing mechanism that increases population diversity and steers the generator away from redundant landscapes. A complementary basin-of-attraction analysis, statistical testing and visual inspection, verifies that many of the generated functions indeed exhibit the intended structural traits. In addition, a t-SNE embedding shows that they expand the BBOB instance space rather than forming an unrelated cluster. The resulting library provides a broad, interpretable, and reproducible set of benchmark problems for landscape analysis and downstream tasks such as automated algorithm selection.
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