To guide the design of better iterative optimisation heuristics, it is imperative to understand how inherent structural biases within algorithm components affect the performance on a wide variety of search landscapes. This study explores the impact of structural bias in the modular Covariance Matrix Adaptation Evolution Strategy (modCMA), focusing on the roles of various modulars within the algorithm. Through an extensive investigation involving 435,456 configurations of modCMA, we identified key modules that significantly influence structural bias of various classes. Our analysis utilized the Deep-BIAS toolbox for structural bias detection and classification, complemented by SHAP analysis for quantifying module contributions. The performance of these configurations was tested on a sequence of affine-recombined functions, maintaining fixed optimum locations while gradually varying the landscape features. Our results demonstrate an interplay between module-induced structural bias and algorithm performance across different landscape characteristics.

翻译：为引导设计更优的迭代式优化启发式算法，理解算法组件中固有的结构偏差如何影响其在多样化搜索地形上的表现至关重要。本研究探究了模块化协方差矩阵自适应进化策略（modCMA）中结构偏差的影响，聚焦于算法内各模块的作用。通过对435,456种modCMA配置的广泛分析，我们识别出显著影响各类结构偏差的关键模块。分析过程采用Deep-BIAS工具箱进行结构偏差检测与分类，并辅以SHAP分析量化模块贡献。这些配置的性能在一系列仿射重组函数序列上进行了测试，函数在保持最优位置固定的同时逐步改变地形特征。结果表明，在不同地形特征下，模块引发的结构偏差与算法性能之间存在交互作用。