Stock, MichielMichielStockVan Hauwermeiren, DaanDaanVan HauwermeirenDe Baets, BernardBernardDe BaetsTaelman, SteffSteffTaelmanMarzougui, DriesDriesMarzouguiVan Haeverbeke, MaximeMaximeVan Haeverbeke2026-01-272026-01-2720251939-0068https://imec-publications.be/handle/20.500.12860/58747Unlike gradient-based methods, evolutionary algorithms use populations and exploit randomness to find novel and performant solutions. Quality-Diversity algorithms have recently emerged as a distinct paradigm designed to cultivate populations of simultaneously high-performing yet behaviorally diverse solutions. These algorithms show considerable success in challenging fields such as robotics and reinforcement learning. Despite their proven effectiveness and growing popularity, Quality-Diversity algorithms remain relatively underrecognized and underutilized in the broader data science landscape. This review aims to bridge this gap by providing a comprehensive introduction to the Quality-Diversity paradigm, elucidating its underlying philosophy, and synthesizing illustrative case studies for a general machine learning audience.engJournal article review10.1002/wics.70047WOS:001588483200001AUGMENTATIONOPTIMIZATIONSELECTION