# Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

arXiv: https://arxiv.org/abs/2010.11523 · HTML: https://mlsanity.com/paper/2010.11523/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Simulation-guided Beam Search for Neural Combinatorial Optimization](https://mlsanity.com/paper/2207.06190/) (2022)
- [On Monte Carlo Tree Search for Weighted Vertex Coloring](https://mlsanity.com/paper/2202.01665/) (2022)
- [Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring](https://mlsanity.com/paper/2304.12146/) (2023)
- [Geometry-Aware MCTS for Extremal Problems in Combinatorial Geometry](https://mlsanity.com/paper/2606.26399/) (2026)

## Abstract (arXiv)

In this article we propose a heuristic algorithm to explore search space trees associated with instances of combinatorial optimization problems. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees and represents the state-of-the-art algorithm for a number of games. Several enhancements to Monte Carlo tree search are proposed that make the algorithm more suitable in a combinatorial optimization context. These enhancements exploit the combinatorial structure of the problem and aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domains of variables by eliminating dominated value assignments and using a beam width. The algorithm was implemented with its components specifically tailored to two combinatorial optimization problems: the quay crane scheduling problem with non-crossing constraints and the 0-1 knapsack problem. For the first problem our algorithm surpasses the state-of-the-art results and several new best solutions are found for a benchmark set of instances. For the second problem our algorithm typically produces near-optimal solutions that are slightly worse than the state-of-the-art results, but it needs only a small fraction of the time to do so. These results indicate that the algorithm is competitive with the state-of-the-art for two entirely different combinatorial optimization problems.