Statistics Seminars: Parallel and Interacting Stochastic Approximation Annealing algorithms for global optimisation
20 June 2017 14:00 in CM221
We present the parallel and interacting stochastic approximation annealing algorithm, a stochastic simulation procedure for global optimisation. The proposed algorithm is suitable to address global optimisation problems in high dimensional or rugged scenarios, where standard optimization algorithms suffer from the so-called local trapping problem. Central to our methodology is the idea of simulating a population of Markov chains that interact each other in a manner able to overcome the local trapping problem. We demonstrate the good performance of the algorithm on a theoretical protein folding application, and compare it with the performance of other competitors.
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