Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches

Boisvert, Léo; Verhaeghe, Hélène; Cappart, Quentin

doi:10.1007/978-3-031-60597-0_7

Lecture Notes in Computer Science

2024

DOI: 10.1007/978-3-031-60597-0_7

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Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches

Léo Boisvert,

Hélène Verhaeghe,

Quentin Cappart

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Learning and fine-tuning a generic value-selection heuristic inside a constraint programming solver

Marty,

Boisvert,

François

et al. 2024

Constraints

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Constraint programming is known for being an efficient approach to solving combinatorial problems. Important design choices in a solver are the branching heuristics, designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process that requires problem-specific expertise. This observation has motivated many efforts to use machine learning to automatically learn efficient heuristics without expert intervention. Although several generic variable-selection heuristics are available in the literature, the options for value-selection heuristics are more scarce. We propose to tackle this issue by introducing a generic learning procedure that can be used to obtain a value-selection heuristic inside a constraint programming solver. This has been achieved thanks to the combination of a deep Q-learning algorithm, a tailored reward signal, and a heterogeneous graph neural network. Experiments on graph coloring, maximum independent set, maximum cut, and minimum vertex cover problems show that this framework competes with the well-known impact-based and activity-based search heuristics and can find solutions close to optimality without requiring a large number of backtracks. Additionally, we observe that fine-tuning a model with a different problem class can accelerate the learning process.

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Learning and fine-tuning a generic value-selection heuristic inside a constraint programming solver

Marty,

Boisvert,

François

et al. 2024

Constraints

View full text Add to dashboard Cite

show abstract

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Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches

Cited by 1 publication

References 16 publications

Learning and fine-tuning a generic value-selection heuristic inside a constraint programming solver

Learning and fine-tuning a generic value-selection heuristic inside a constraint programming solver

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