This work presents a hyper-heuristic approach to online learning, which combines Monte Carlo Tree Search with multiple local search operators selected on the fly during the search. The impacts of different operator policies, including proportional bias, one-armed bandit, and neural network, are investigated. Experiments on well-known benchmarks of the Weighted Vertex Coloring Problem are conducted to highlight the advantages and limitations of each dynamic selection strategy.