  Monte Carlo Tree Search (MCTS) is a best-first sampling method employed in
the search for optimal decisions. The effectiveness of MCTS relies on the
construction of its statistical tree, with the selection policy playing a
crucial role. A selection policy that works particularly well in MCTS is the
Upper Confidence Bounds for Trees, referred to as UCT. The research community
has also put forth more sophisticated bounds aimed at enhancing MCTS
performance on specific problem domains. Thus, while MCTS UCT generally
performs well, there may be variants that outperform it. This has led to
various efforts to evolve selection policies for use in MCTS. While all of
these previous works are inspiring, none have undertaken an in-depth analysis
to shed light on the circumstances in which an evolved alternative to MCTS UCT
might prove advantageous. Most of these studies have focused on a single type
of problem. In sharp contrast, this work explores the use of five functions of
different natures, ranging from unimodal to multimodal and deceptive functions.
We illustrate how the evolution of MCTS UCT can yield benefits in multimodal
and deceptive scenarios, whereas MCTS UCT is robust in all of the functions
used in this work.
