Stochastic Methods for Global Optimization and Problem Solving

Stochastic algorithms provide an effective framework to solve complex optimization problems, when derivative information is not available. Simulated Annealing and Genetic Algorithms are stochastic optimization methods that can identify putative optimal solutions without using derivative information. This article presents the mathematical foundations and the algorithmic framework of these two methods.

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