
Monte Carlo Graph Coloring
Cazenave, Tristan; Negrevergne, Benjamin; Sikora, Florian (2020), Monte Carlo Graph Coloring, in Cazenave, Tristan; Teytaud, Olivier; Winands, Mark H. M., Monte Carlo Search, Springer, p. 100-115. 10.1007/978-3-030-89453-5_8
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Type
Communication / ConférenceDate
2020Conference title
First Workshop, MCS 2020, Held in Conjunction with IJCAI 2020Conference date
2021-01Conference city
virtuelBook title
Monte Carlo SearchBook author
Cazenave, Tristan; Teytaud, Olivier; Winands, Mark H. M.Publisher
Springer
ISBN
978-3-030-89453-5
Pages
100-115
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Show full item recordAuthor(s)
Cazenave, TristanLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Negrevergne, Benjamin
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Sikora, Florian

Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
Graph Coloring is probably one of the most studied and famous problem in graph algorithms. Exact methods fail to solve instances with more than few hundred vertices, therefore, a large number of heuristics have been proposed. Nested Monte Carlo Search (NMCS) and Nested Rollout Policy Adaptation (NRPA) are Monte Carlo search algorithms for single player games. Surprisingly, few work has been dedicated to evaluating Monte Carlo search algorithms to combinatorial graph problems. In this paper we expose how to efficiently apply Monte Carlo search to Graph Coloring and compare this approach to existing ones.Subjects / Keywords
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