Reference : Supervised learning to tune simulated annealing for in silico protein structure prediction
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/117699
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 >]
Maes, Francis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
25-Apr-2012
ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Verleysen, Michel
Ciaco
49-54
Yes
No
International
978-2-87419-047-6
Louvain-la-Neuve
Belgium
ESANN 2012, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
from 25-04-2012 to 27-04-2012
UCL, Université Catholique de Louvain, Louvain-la-Neuve
KULeuven, Katholiek Universiteit Leuven, Leuven
Bruges
Belgium
[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 distribu- tion 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.
Systèmes et Modélisation, GIGA-Research
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/117699

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