Quality‐diversity methods for the modern data scientist
- Author
- Michiel Stock (UGent) , Daan Van Hauwermeiren (UGent) , Bernard De Baets (UGent) , Steff Taelman (UGent) , Dries Marzougui (UGent) and Maxime Van Haeverbeke (UGent)
- Organization
- Project
-
- Advanced impedimetric system characterization for engineering and the life sciences
- Kernel Mean Embedding as the grand unifying theory for working with distributional data in pharmaceutical applications
- Simultaneous Evolution of a Seahorse Tail based Robotic Manipulator and Controller.
- Harnessing computational evolution for biodesign
- 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.
- Keywords
- Quality-Diversity; Optimization; Diversity, Evolutionary Computation; Machine Learning; MAP-Elites; Novelty Search
Downloads
-
(...).pdf
- full text (Accepted manuscript)
- |
- UGent only (changes to open access on 2026-04-06)
- |
- |
- 1.81 MB
-
(...).pdf
- full text (Published version)
- |
- UGent only
- |
- |
- 1.80 MB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01K7KTXDCYD8463PBGAJNJCMG9
- MLA
- Stock, Michiel, et al. “Quality‐diversity Methods for the Modern Data Scientist.” WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, vol. 17, no. 4, 2025, doi:10.1002/wics.70047.
- APA
- Stock, M., Van Hauwermeiren, D., De Baets, B., Taelman, S., Marzougui, D., & Van Haeverbeke, M. (2025). Quality‐diversity methods for the modern data scientist. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 17(4). https://doi.org/10.1002/wics.70047
- Chicago author-date
- Stock, Michiel, Daan Van Hauwermeiren, Bernard De Baets, Steff Taelman, Dries Marzougui, and Maxime Van Haeverbeke. 2025. “Quality‐diversity Methods for the Modern Data Scientist.” WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 17 (4). https://doi.org/10.1002/wics.70047.
- Chicago author-date (all authors)
- Stock, Michiel, Daan Van Hauwermeiren, Bernard De Baets, Steff Taelman, Dries Marzougui, and Maxime Van Haeverbeke. 2025. “Quality‐diversity Methods for the Modern Data Scientist.” WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 17 (4). doi:10.1002/wics.70047.
- Vancouver
- 1.Stock M, Van Hauwermeiren D, De Baets B, Taelman S, Marzougui D, Van Haeverbeke M. Quality‐diversity methods for the modern data scientist. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS. 2025;17(4).
- IEEE
- [1]M. Stock, D. Van Hauwermeiren, B. De Baets, S. Taelman, D. Marzougui, and M. Van Haeverbeke, “Quality‐diversity methods for the modern data scientist,” WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, vol. 17, no. 4, 2025.
@article{01K7KTXDCYD8463PBGAJNJCMG9,
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.}},
articleno = {{e70047}},
author = {{Stock, Michiel and Van Hauwermeiren, Daan and De Baets, Bernard and Taelman, Steff and Marzougui, Dries and Van Haeverbeke, Maxime}},
issn = {{1939-5108}},
journal = {{WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS}},
keywords = {{Quality-Diversity; Optimization; Diversity, Evolutionary Computation; Machine Learning; MAP-Elites; Novelty Search}},
language = {{eng}},
number = {{4}},
pages = {{20}},
title = {{Quality‐diversity methods for the modern data scientist}},
url = {{http://doi.org/10.1002/wics.70047}},
volume = {{17}},
year = {{2025}},
}
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: