Reactive Search Optimization
Reactive Search Optimization is a heuristic optimization methodology that integrates machine learning mechanisms for self-tuning of parameters and strategies during the search process, distinguishing itself from other metaheuristics by its adaptive, feedback-driven control of search dynamics in real time.
27
Publications
1.4K
Citations
70
Authors
32
Institutions
Publications per year
2017–2026
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Variants
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Parents
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70
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32
The University of Texas at Austin
Austin, United States
4 h-index
9 publications
912 citations
Fuji Electric (Japan)
Tokyo, Japan
3 h-index
7 publications
237 citations
University of Kent
Canterbury, United Kingdom
3 h-index
7 publications
462 citations

University of Debrecen
Debrecen, Hungary
3 h-index
3 publications
94 citations

University of Trento
Trento, Italy
2 h-index
4 publications
292 citations
Tokyo Metropolitan University
Tokyo, Japan
2 h-index
6 publications
122 citations
Excel Life Sciences (India)
Noida, India
1 h-index
2 publications
32 citations
University of Verona
Verona, Italy
1 h-index
4 publications
48 citations
1–8 of 32
2 publications · 146 citations
2
h-index
1 publications · 12 citations
1
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1 publications · 10 citations
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Repository · 1 publications · 25 citations
1
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1 publications · 58 citations
1
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