Simulated annealing search
| SIMULATED-ANNEALING allows
the agent to take steps with decreases in the objective function with some
probability. The probability depends
on the schedule AND on the degree of “badness” of the move. In this sense all bad moves are not created
equal. As more steps are taken in the
algorithm, the probability of a backward move decreases and converges to only
taking moves with increasing utility.
If the rate of annealing is correctly set, the algorithm is guaranteed
to find a solution if one exists. |
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| Note that it picks a move at
random. The probability transition is
only used for moves with negative value.
Any positive valued move (which increases the objective function’s value)
is taken with probability 1 if it is chosen, no matter how big or small the
gain is. |
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| To think about: what types
of problems would this algorithm work well on? Work poorly on? |