A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories
This work provides insights for designing better optimization heuristics by understanding algorithm biases, though it is incremental as it builds on existing modular frameworks and bias detection tools.
The study investigated how structural bias in the modular CMA-ES algorithm affects performance on affine-transformed functions, identifying key modules through analysis of 435,456 configurations and showing their interplay with landscape features.
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.