Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring

Fuente: arXiv
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Main Authors: Grelier, Cyril, Goudet, Olivier, Hao, Jin-Kao
Format: Preprint
Published: 2023
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author Grelier, Cyril
Goudet, Olivier
Hao, Jin-Kao
author_facet Grelier, Cyril
Goudet, Olivier
Hao, Jin-Kao
contents This work investigates the Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem. In addition to the basic MCTS algorithm, we study several MCTS variants where the conventional random simulation is replaced by other simulation strategies including greedy and local search heuristics. We conduct experiments on well-known benchmark instances to assess these combined MCTS variants. We provide empirical evidence to shed light on the advantages and limits of each simulation strategy. This is an extension of the work of Grelier and al. presented at EvoCOP2022.
format Preprint
id arxiv_https___arxiv_org_abs_2304_12146
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring
Grelier, Cyril
Goudet, Olivier
Hao, Jin-Kao
Artificial Intelligence
This work investigates the Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem. In addition to the basic MCTS algorithm, we study several MCTS variants where the conventional random simulation is replaced by other simulation strategies including greedy and local search heuristics. We conduct experiments on well-known benchmark instances to assess these combined MCTS variants. We provide empirical evidence to shed light on the advantages and limits of each simulation strategy. This is an extension of the work of Grelier and al. presented at EvoCOP2022.
title Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring
topic Artificial Intelligence
url https://arxiv.org/abs/2304.12146