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摘要


A function f: {0,1,2,L,a}^n→R is said to be uncorrelated if Prob [f(x) ≦ u]= G(u). This paper studies the effectiveness of simulated annealing as a strategy for optimizing uncorrelated functions. A recurrence relation expressing the effectiveness of the algorithm in terms of the function G is derived. Surprising numerical results are obtained, to the effect that for certain parametdzed families of functions {G_c, ∈ R}, where c represents the "steepness" of the curve G'(u), the effectiveness of simulated annealing increases steadily with c These results suggest that on the average annealing is effective whenever most points have very small objective function values, but a few points have very large objective function values.

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