[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.
Research center :
Systems and Modeling
Disciplines :
Computer science
Author, co-author :
Marcos Alvarez, Alejandro ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Supervised learning to tune simulated annealing for in silico protein structure prediction
Publication date :
21 February 2012
Number of pages :
A0
Event name :
Bridging statistical physics and optimization, inference and learning
Event organizer :
Ecole de Physique des Houches
Event place :
Les Houches, France
Event date :
from 19-02-2012 to 24-02-2012
Audience :
International
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
