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

An Analysis on the Effects of Evolving the Monte Carlo Tree Search Upper  Confidence for Trees Selection Policy on Unimodal, Multimodal and Deceptive  Landscapes