Randomized Search Algorithms
Randomized Search Algorithms is a computational method that belongs to the class of search algorithms, distinguished by its use of randomness as an intrinsic component to guide the exploration of the solution space in order to improve efficiency or avoid local optima.
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18.4K
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404
Authors
217
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2017–2026
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182
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
Boosting combinatorial search through randomization
Carla P. Gomes, Bart Selman, Henry Kautz · 1998 · 565 citations
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Technical University of Denmark
Kongens Lyngby, Denmark
10 h-index
12 publications
1.2K citations
University of California, Berkeley
Berkeley, United States
8 h-index
14 publications
3.4K citations
TU Dortmund University
Dortmund, Germany
6 h-index
10 publications
629 citations
AT&T (United States)
Bedminster, United States
6 h-index
6 publications
1.6K citations

Cornell University
Ithaca, United States
5 h-index
9 publications
1.3K citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
9 publications
1.8K citations

Centre National de la Recherche Scientifique
Paris, France
5 h-index
9 publications
444 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
5 publications
429 citations
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8 publications · 583 citations
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4 publications · 940 citations
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Repository · 4 publications · 70 citations
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4 publications · 303 citations
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Conference · 4 publications · 119 citations
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