Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games. However, in more complex games (e.g. those with a high branching factor or real-time ones) as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey was published in 2012. Contributions that appeared since its release are of particular interest for this review.

Evolutionary multitasking has recently emerged as an effective means of facilitating implicit genetic transfer across different optimization tasks, thereby potentially accelerating convergence characteristics for multiple tasks at once. A natural application of the paradigm is found to arise in the area of bi-level programming wherein an upper level optimization problem must take into consideration a nested lower level problem. Thus, while tackling instances of bi-level optimization, a significant challenge surfaces from the fact that multiple upper level candidate solutions are to be analyzed at the same time by inferring the corresponding optimum response from the lower level. Thus, the process of bi-level optimization often becomes exorbitantly time consuming, especially in the case of real-world instances involving expensive objective function evaluations. Accordingly, the significance of this paper lies in showcasing that the practicality of population-based bi-level optimization can be considerably enhanced by simply incorporating the novel concept of evolutionary multitasking into the search process. As a result, it becomes possible to process multiple lower level optimization tasks concurrently, thereby facilitating the exploitation of underlying commonalities among them. To demonstrate the implications of our proposal, we present computational experiments on some synthetic benchmark B Yew-Soon Ong

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