2024/04/26 by Bas van Stein, van Stein, Niki, Sarah L. Thomson +3
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Blind Source Separation Techniques #FOS: Computer and information sciences #Fault Detection and Control Systems #Infrastructure Maintenance and Monitoring #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2404.17323
openalex publication_date 2024/04/26 · openalex created_date 2024/04/30 · openalex updated_date 2026/07/28
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.