[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