Supervised learning to tune simulated annealing for in silico protein structure prediction

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.