Simulated annealing is an extension of hill climbing, which uses randomness to avoid getting stuck in local maxima and plateaux.

1. Simulated annealing returns the current state when the end of the annealing schedule is reached and if the annealing schedule is slow enough. Given that we know the value (measure of goodness) of each state we visit, is there anything smarter we could do?

2. Simulated annealing requires a very small amount of memory, just enough to store two states: the current state and

   the proposed next state. Suppose we had enough memory to hold two million states. Propose a modification to

   simulated annealing that makes productive use of the additional memory. In particular, suggest something that will

   likely perform better than just running simulated annealing a million times consecutively with random restarts.

This is an artificial intelligence question