Reference : Supervised learning to tune simulated annealing for in silico protein structure prediction
Scientific congresses and symposiums : Poster
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/117671
Supervised learning to tune simulated annealing for in silico protein structure prediction
English
Marcos Alvarez, Alejandro mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
21-Feb-2012
A0
No
No
International
Bridging statistical physics and optimization, inference and learning
from 19-02-2012 to 24-02-2012
Ecole de Physique des Houches
Les Houches
France
[en] Optimization ; Machine learning ; Simulated annealing ; Protein ; Structure prediction
[en] Simulated annealing is a widely used stochastic optimization algorithm whose efficiency essentially depends on the proposal distribution used to generate the next search state at each step. We propose to adapt this distribution to a family of parametric optimization problems by using supervised machine learning on a sample of search states derived from a set of typical runs of the algorithm over this family. We apply this idea in the context of in silico protein structure prediction.
Systems and Modeling
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/117671

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