Local search algorithms
By Stanisław Gawiejnowicz. 2008
1,280 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/817cc10430e6c8682c6ee0209f1160abf3a23217
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Walid Saad
- Merouane Debbah
- Mingzhe Chen
- Ayça Deniz
- V. Varadharajan
- U. Tupakula
- Tansel Dökeroğlu
- Changchuan Yin
In Signal Processing
- Ayça Deniz
- Merouane Debbah
- Walid Saad
- V. Varadharajan
- U. Tupakula
- Tansel Dökeroğlu
- Changchuan Yin
- Azlan Mohd Zain
Citations in the corpus
- A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “They have applied the combination of simulated annealing (SA) [162] and Support Vector Machine (SVM) [20] to improve the detection accuracy and reduce the false alarms.”
- A comprehensive survey on recent metaheuristics for feature selection (2022, methodology): “In this review, we focus on recent generation metaheuristics of the last two decades that have been proposed for the solution of feature selection other than the classical methods, Genetic Algorithm (GA) [19], Particle Swarm Optimization (PSO) [20, 21], Ant Colony Optimization (ACO) [22], Simulated ”
- Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, methodology): “Other global optimization algorithms may be used to seek a good set of weights, such as simulated annealing [90] or particle swarm optimization [91].”
- The Deep Learning Compiler: A Comprehensive Survey (2020, neutral): “3) Simulated annealing algorithm (SA) [59]: SA is also a meta-”
- Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007-2011) (2012, background): “SA was proposed by Kirkpatrick, Gelatt, and Vecchi (1983) to find the optimal global cost function that may possess several local optima (Bertsimas & Tsitsiklis, 1993; Cerny, 1985).”
- Antenna Subset Modulation for secure millimeter-wave wireless communication (2013, neutral): “Simulated annealing [50], [51] is a probabilistic iterative algorithm for finding approximate solutions to the global optimum of a function in a large search space.”