User-guided Evolutionary Search
User-Guided Evolutionary Search is an evolutionary search framework in which human users iteratively evaluate and select candidate solutions to guide the optimization process, distinguishing it from fully automated evolutionary algorithms by integrating human judgment as a central mechanism for fitness assessment and search direction.
25
Publications
683
Citations
56
Authors
36
Institutions
Publications per year
2017–2026
2
Parents
25
1–5 of 25
56
3
h-index
3
h-index
2
h-index
2
h-index
1
h-index
1–5 of 56
36

University of Vermont
Burlington, United States
3 h-index
4 publications
65 citations
University of California, Santa Cruz
Santa Cruz, United States
2 h-index
2 publications
25 citations

Ames Research Center
Mountain View, United States
2 h-index
2 publications
40 citations
ETH Zurich
Zurich, Switzerland
2 h-index
4 publications
196 citations

Aletheia University
Taipei, Taiwan
1 h-index
1 publications
18 citations

Monash University
Melbourne, Australia
1 h-index
2 publications
118 citations
UCL Australia
Adelaide, Australia
1 h-index
1 publications
10 citations
China University of Mining and Technology
Xuzhou, China
1 h-index
4 publications
168 citations
1–8 of 36
2 publications · 101 citations
2
h-index
Repository · 1 publications · 10 citations
1
h-index
1 publications · 18 citations
1
h-index
1 publications · 13 citations
1
h-index
Repository · 1 publications · 14 citations
1
h-index
1–5