Monte Carlo Tree Search with Adaptive Simulation: A Case Study on Weighted Vertex Coloring

Grelier, Cyril; Goudet, Olivier; Hao, Jin‐Kao

doi:10.1007/978-3-031-30035-6_7

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-30035-6_7

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Monte Carlo Tree Search with Adaptive Simulation: A Case Study on Weighted Vertex Coloring

Cyril Grelier¹,

Olivier Goudet²,

Jin‐Kao Hao³

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2024

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A Memetic Algorithm with Adaptive Operator Selection for Graph Coloring

Grelier,

Goudet,

Hao

2024

Lecture Notes in Computer Science

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We present a memetic algorithm with adaptive operator selection for k-coloring and weighted vertex coloring. Our method uses online selection to adaptively determine the couple of crossover and local search operators to apply during the search to improve the efficiency of the algorithm. This leads to better results than without the operator selection and allows us to find a new coloring with 404 colors for C2000.9, one of the largest and densest instances of the classical DIMACS coloring benchmarks. The proposed method also finds three new best solutions for the weighted vertex coloring problem. We investigate the impacts of the different algorithmic variants on both problems.

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A Memetic Algorithm with Adaptive Operator Selection for Graph Coloring

Grelier,

Goudet,

Hao

2024

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

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Monte Carlo Tree Search with Adaptive Simulation: A Case Study on Weighted Vertex Coloring

Cited by 1 publication

References 22 publications

A Memetic Algorithm with Adaptive Operator Selection for Graph Coloring

A Memetic Algorithm with Adaptive Operator Selection for Graph Coloring

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