Quality‐Diversity Methods for the Modern Data Scientist
Michiel Stock, Daan Van Hauwermeiren, Bernard De Baets, Steff Taelman, Dries Marzougui, Maxime Van Haeverbeke · Wiley Interdisciplinary Reviews Computational Statistics · 2025
ABSTRACT Unlike 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. This article is categorized under: Algorithms and Computational Methods > Genetic Algorithms and Evolutionary Computing