Quality-diversity Search
Quality-Diversity Search is a computational optimization paradigm that, unlike traditional search methods focused solely on maximizing performance, explicitly seeks to generate a diverse collection of high-performing solutions by simultaneously optimizing for both quality and behavioral or structural novelty within a single population or archive.
39
Publications
1.8K
Citations
81
Authors
33
Institutions
Publications per year
2017–2026
30
Variants
Qd OptimizationQd SearchQuality-diversity Optimization
Parents
39
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such et al. · Neural Information Processing Systems · 2018 · 148 citations
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such et al. · arXiv (Cornell University) · 2017 · 116 citations · Full text
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33
University of Southern California
Los Angeles, United States
6 h-index
19 publications
373 citations

Imperial College London
London, United Kingdom
5 h-index
10 publications
255 citations
Université de Lorraine
Nancy, France
3 h-index
5 publications
207 citations
New York University
New York, United States
3 h-index
9 publications
273 citations
Hochschule Bonn-Rhein-Sieg
Sankt Augustin, Germany
3 h-index
6 publications
150 citations
California Southern University
Chandler, United States
3 h-index
7 publications
183 citations
Iscte – Instituto Universitário de Lisboa
Lisbon, Portugal
2 h-index
7 publications
317 citations

National Taiwan University
Taipei, Taiwan
2 h-index
7 publications
99 citations
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Conference · 14 publications · 361 citations
12
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3 publications · 105 citations
3
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OR · 2 publications · 97 citations
2
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2 publications · 495 citations
2
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Repository · 2 publications · 146 citations
2
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