15.7.2. Simulated Annealing
This is a global optimization algorithm. More specifically, under certain conditions, it guarantees, in probability, the computation of the globally optimal solution of the problem at hand via the minimization of a cost function J. This algorithm has been proposed by Kirkpatrick et al. [Kirk 83] (see also [Laar 87]) and it is inspired by the problem of condensed matter in physics.[11] In contrast to the algorithms that allow corrections of the unknown parameters only to directions that reduce the cost function J, simulated annealing allows moves that, temporarily, may increase the value of J. The rationale is that, by allowing such moves, we may escape from the region of attraction of a local minimum.
11 Also, it ...
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