In this lecture, I will cover the basics of stochastic global optimization methods, such as simulated annealing, Monte Carlo with minimization, genetic algorithm, and generalized ensemble methods. The advantages and disadvantages of the existing methods will be discussed. The concept of annealing in the phase space (i.e., conformational space) is introduced to illustrate the philosophy/advantage of a new method, conformational space annealing (CSA). Examples of successful applications of CSA to various hard combinatorial optimization problems including traveling salesman problems, structural optimization of atomic clusters, model protein studies, and peptide/protein structure optimizations will be presented. The intrinsic difficulty of protein folding studies will be discussed in the light of the interplay between the accuracy of a score function and the difficulty of global optimization. Finally, a guideline to implement CSA method to outstanding problems of your interests will be provided. If time allowed, the parallel implementation of CSA will be also discussed.

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1. High-accuracy protein structure prediction by global optimization