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Abstract
Systems that select an optimal or nearly optimal member from a specified search set are reviewed with special emphasis on stochastic approaches such as simulated annealing, genetic algorithms, as well as other probabilistic heuristics. Because of local minima, selecting a global optimum may require time that increases exponentially in the problem size. Stochastic search provides advantages in robustness, generality, and simplicity over other approaches and is more efficient than exhaustive deterministic search.
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Griff L. Bilbro "Stochastic search", Proc. SPIE 10277, Adaptive Computing: Mathematics, Electronics, and Optics: A Critical Review, 1027703 (1 March 1994); https://doi.org/10.1117/12.171192
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Stochastic processes
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