LAMF-MCTS: Learning-Augmented Multi-Fidelity Monte Carlo Tree Search for Expensive Combinatorial Black-Box Optimization

Expensive stochastic simulations limit how broadly an optimizer can explore a combinatorial design space. We introduce LAMF-MCTS, a learning-augmented multi-fidelity Monte Carlo tree search framework that coordinates search and simulation effort. The key idea is to exploit information available before a simulation finishes: a partial trajectory can inform both a design's eventual performance and the direction of subsequent search. The framework combines observed partial returns with learned predictions of the remaining return, penalizing uncertain estimates. Repeatedly selected designs are progressively promoted to higher fidelities, while side information from the same trajectories provides action priors that guide tree expansion. Experiments on an application-derived combinatorial black-box optimization problem show that LAMF-MCTS attains the highest final full-fidelity objective among six application-specific planning baselines and, under the same simulator-call budget as full-fidelity MCTS, reduces cumulative simulation cost by 35.2% while attaining a higher running-best objective.