  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.
