  In many applications of computer algebra large expressions must be simplified
to make repeated numerical evaluations tractable. Previous works presented
heuristically guided improvements, e.g., for Horner schemes. The remaining
expression is then further reduced by common subexpression elimination. A
recent approach successfully applied a relatively new algorithm, Monte Carlo
Tree Search (MCTS) with UCT as the selection criterion, to find better variable
orderings. Yet, this approach is fit for further improvements since it is
sensitive to the so-called exploration-exploitation constant $C_p$ and the
number of tree updates $N$. In this paper we propose a new selection criterion
called Simulated Annealing UCT (SA-UCT) that has a dynamic
exploration-exploitation parameter, which decreases with the iteration number
$i$ and thus reduces the importance of exploration over time. First, we provide
an intuitive explanation in terms of the exploration-exploitation behavior of
the algorithm. Then, we test our algorithm on three large expressions of
different origins. We observe that SA-UCT widens the interval of good initial
values $C_p$ where best results are achieved. The improvement is large (more
than a tenfold) and facilitates the selection of an appropriate $C_p$.
