Simulated Annealing

By Peter Rossmanith. 2008

683 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

The Non-IID Data Quagmire of Decentralized Machine Learning (2019, methodology): “We can employ various auto-tuning algorithms with Equation 2 to select θ t such as hill climbing, stochastic hill climbing (Russell & Norvig, 2016), and simulated annealing (Van Laarhoven & Aarts, 1987).”
Structured Learning and Prediction in Computer Vision (2011, methodology): “Simulated annealing [Kirkpatrick et al., 1983; Aarts et al., 1997] is a well-known method to approximately solve problems of the form (4.”
Submodular maximization by simulated annealing (2010, background): “309, while a better than 1/2-approximation is impossible [33], and there is 1 2 (1 − 1 ν − o(1))-approximation subject to a matroid base constraint for matroids of fractional base packing number at least ν ∈ [1, 2], while a better than (1 − 1 ν )approximation in this setting is impossible [33].”
Quantum computing for energy systems optimization: Challenges and opportunities (2019, methodology): “Instead of thermal fluctuations [7], quantum fluctuations are introduced in quantum annealing for faster convergence to optimal state [34].”
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining (2020, background): “Another promising idea is the use of a weighting schedule instead of a ﬁxed weighting, which may allow balancing exploration vs. exploitation similar to simulated annealing [58].”
A comprehensive review of firefly algorithms (2013, methodology): “Three well-known heuristics were taken into account: simulated annealing [56], particle swarm optimization [57], and the ﬁreﬂy algorithm.”
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors (2020, methodology): “…an auxiliary temperature parameter to enhance the transition probability between modes; these methods include annealing (Kirkpatrick et al., 1983; Bertsimas and Tsitsiklis, 1993), parallel tempering (Hansmann, 1997; Earl and Deem, 2005), simulated tempering (Marinari and Parisi, 1992; Neal,…”
ASAP: Architecture Search, Anneal and Prune (2019, background): “This definition is closely related to the Gibbs-Boltzmann distribution, where the weights α o correspond to negative energies, and operations o to mixed system states [37].”
Simulation optimization: a review of algorithms and applications (2014, methodology): “Simulated Annealing uses a probabilistic method that is derived from the annealing process in which the material is slowly cooled so that, while its structure freezes, it reaches a minimum energy state [113, 23].”
Relief-Based Feature Selection: Introduction and Review (2017, methodology): “Each iteration of the wrapper, the feature subset is generated based on the selected search strategy, e.g. forward or backward selection (Kittler, 1978; Langley, 1994) or a heuristic feature subset selection (Van Laarhoven and Aarts, 1987; Holland, 1992).”
A survey of simulated annealing as a tool for single and multiobjective optimization (2006, background): “Grover (1986), Greene and Supowit (1986) and Tovey (1988) have suggested using an approximate value of d rather than the exact value.”
Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview (2018, methodology): “The docking pipeline of the software Glide (Friesner et al., 2004; Halgren et al., 2004) involves a stage of the exhaustive search. b. Fragmentation - the ﬁrst implementation of ligand ﬂexibility into docking was introduced by DesJarlais et al. (1986), who proposed a method made of fragmentation of…”
Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. (2019, methodology): “The proposed comparative study employs a 105 promising feature selection (FS) method (Kira and Rendell, 1992, Liu and Motoda, 2007), namely 106 simulated annealing (SA) (Van Laarhoven and Aarts, 1987) to eliminate redundant variables from 107 the modeling process.”
Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm (2016, methodology): “EAs differ from some optimization methods, such as Simulated-Annealing [2] and Tabu search [3], because they evolve a population of solutions to reach an approximate optimal solution instead of one solution [4].”