Randomized Search
Randomized Search is a metaheuristic optimization method that belongs to the class of stochastic search algorithms, distinguished by its use of random sampling of parameter values from predefined distributions to explore the search space without relying on gradient information or deterministic rules.
310
Publications
34.4K
Citations
658
Authors
316
Institutions
Publications per year
2017–2026
61
Parents
310
Random search for hyper-parameter optimization
James Bergstra, Yoshua Bengio · 2012 · 7.9K citations
Greedy Randomized Adaptive Search Procedures
Thomas A. Feo, Maurício G. C. Resende · Journal of Global Optimization · 1995 · 2.7K citations
Efficient and Effective Clustering Methods for Spatial Data Mining
Raymond T. Ng, Jiawei Han · 1994 · 1.8K citations
Optimizing the success of random searches
G. M. Viswanathan, Sergey V. Buldyrev, Shlomo Havlin et al. · Nature · 1999 · 1.5K citations
Low-Density Parity-Check (LDPC) Codes Constructed from Protographs
Jeremy Thorpe · 2003 · 653 citations
1–5 of 310
658
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316
Technical University of Denmark
Kongens Lyngby, Denmark
13 h-index
19 publications
923 citations
TU Dortmund University
Dortmund, Germany
11 h-index
18 publications
1.1K citations
AT&T (United States)
Bedminster, United States
10 h-index
10 publications
1.7K citations
University of California, Berkeley
Berkeley, United States
9 h-index
18 publications
3.7K citations

The University of Adelaide
Adelaide, Australia
8 h-index
12 publications
268 citations
Max Planck Institute for Informatics
Saarbrücken, Germany
8 h-index
12 publications
823 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
7 h-index
12 publications
1.1K citations

Centre National de la Recherche Scientifique
Paris, France
7 h-index
14 publications
570 citations
1–8 of 316
Repository · 12 publications · 2K citations
12
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8 publications · 594 citations
8
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6 publications · 570 citations
6
h-index
6 publications · 208 citations
6
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6 publications · 409 citations
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1–5