通过算法足迹分析模块化CMA-ES在不同问题上的表现差异
Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes
- 用算法足迹量化配置与问题特征的交互关系
- 在5维和30维下测试6种配置，发现性能差异源于问题特性
- 揭示共性行为与配置特异性，助力算法选型
本文借助算法足迹概念，研究算法配置与问题特性之间的相互作用。对6种模块化CMA-ES（modCMA）变体在BBOB基准集的24个问题上进行评估，分别在5维和30维设置下计算性能足迹。这些足迹揭示了同一算法不同配置为何表现各异，并识别出影响结果的关键问题特征。分析显示，配置间存在因共同响应问题属性而产生的共享行为模式，同时在同一问题上也因问题特征差异产生独特行为。结果表明，算法足迹能有效提升算法可解释性，并指导配置选择。
原文摘要 · Abstract (English)
This paper leverages the recently introduced concept of algorithm footprints to investigate the interplay between algorithm configurations and problem characteristics. Performance footprints are calculated for six modular variants of the CMA-ES algorithm (modCMA), evaluated on 24 benchmark problems from the BBOB suite, across two-dimensional settings: 5-dimensional and 30-dimensional. These footprints provide insights into why different configurations of the same algorithm exhibit varying performance and identify the problem features influencing these outcomes. Our analysis uncovers shared behavioral patterns across configurations due to common interactions with problem properties, as well as distinct behaviors on the same problem driven by differing problem features. The results demonstrate the effectiveness of algorithm footprints in enhancing interpretability and guiding configuration choices.
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