SHC versus SA Sample Clauses

SHC versus SA. A Stochastic Hill Climbing procedure, also known as iterative improvement local search, has two parameters: (1) the number of iterations N, and (2) the neigh- bourhood structure, which defines the transition function [75]. We consider the res(7,5) with 14 variables 1e3 N=300 UCT N=300 SA-UCT 22 21 20 19 18 17 16 15 2 14 13 1e3 N=1000 UCT N=1000 SA-UCT 21 20 Number of operations 19 18 17 16 15 14 13 1e3 N=3000 UCT N=3000 SA-UCT 22 21 20 19 18 17 16 15 14 13 0.01 0.1 1 10 0.01 0.1 1 10 Cp Figure 6: res(7,5) polynomial with 14 variables: on the x-axis we show Cp and on the y-axis the number of operations. A lower number of operations is better. On the left, we show UCT with constant Cp and on the right we show SA-UCT where Cp is the starting value of T. Vertically, we increase the tree updates N from 300, to 1000, to 3000. As indicated by the dashed lines, an area with an operation count close to the global minimum appears, as soon as there are sufficient tree updates N. This area is wider for SA-UCT than for UCT. HEP(σ) with 15 variables‌
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