Stochastic methods, including simulated annealing, genetic algorithms and evolutionary programs are being used for many types of practical global optimization problems. These problems include discrete and continuous variables, and the functions have many local optima. Yet the performance of these adaptive stochastic search methods is difficult to analyze. This tutorial will present theoretical results on a family of stochastic search methods, including pure random search, pure adaptive search, hesitant adaptive search, adaptive search, and most recently backtracking adaptive search to gain understanding of the convergence of adaptive random search techniques.
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