Landscape feature representations play a central role in automated algorithm selection and meta-learning for black-box optimization, yet little is known about how different representations agree (or disagree) in the structures they impose on problem spaces. This paper presents a systematic unsupervised evaluation of four state-of-the-art representations (ELA, DeepELA, TransOptAS, and DoE2Vec) using a diverse set of affine combinations of BBOB functions (MA-BBOB). By applying extensive clustering analyses, coverage-based stability measures, and cross-representation similarity assessments, we show that each representation organizes the same problems in markedly different ways: ELA and TransOptAS form compact geometric structures, DeepELA provides a balanced intermediate view, and DoE2Vec achieves strong semantic alignment but with substantial fragmentation. Our results reveal that no single representation dominates; rather, they capture complementary aspects of the underlying landscapes. These findings highlight the importance of multi-view analyses for understanding representation behavior and offer guidance on selecting or combining representations in downstream meta-learning and algorithm selection tasks. In addition, across two different algorithm families (Differential Evolution and Particle Swarm Optimization), we show that landscape representations face an inherent trade-off in how well they align structural landscape descriptions with observed performance, indicating that no single representation can fully capture algorithm performance.

翻译：景观特征表示在自动算法选择和黑箱优化的元学习中扮演核心角色，然而关于不同表示在问题空间施加的结构之间如何一致（或不一致）的认知仍然匮乏。本文基于一组多样化的BBOB函数仿射组合（MA-BBOB），对四种前沿表示方法（ELA、DeepELA、TransOptAS、DoE2Vec）进行了系统的无监督评估。通过广泛聚类分析、基于覆盖率的稳定性度量及跨表示相似性评估，我们发现每种表示对相同问题的组织方式存在显著差异：ELA和TransOptAS形成紧凑的几何结构，DeepELA提供均衡的中间视图，而DoE2Vec虽实现强语义对齐却伴随显著碎片化。研究结果表明，不存在主导性的单一表示；相反，它们捕捉了底层景观的互补特征。这些发现揭示了多视角分析对理解表示行为的重要性，并为下游元学习与算法选择任务中选择或组合表示提供了指导。此外，在两种不同算法族（差分进化与粒子群优化）上，我们证实景观表示在结构描述与观测性能的对齐程度上面临固有折衷，表明任何单一表示均无法完整刻画算法性能。