arXiv Artificial Intelligence

Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation

1 hour ago 09.10.2026 07:00 👁 1 views
Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation

Quick summary

arXiv:2610.12119v1 Announce Type: cross Abstract: Algorithm Selection is essential for efficient Constraint Programming. Over the years, many algorithm selectors based on machine learning methods have been successfully applied, yet traditional feature extraction methods often rely on manually decided instance-level statistics that fail to capture the underlying problem structure. In this paper we aim to bridge this gap by introducing a novel, automated feature extraction methodology that integrates graph conversion and Weisfeiler-Lehman graph kernels to generate robust structural representatio

Key takeaways

  • arXiv:2610.12119v1 Announce Type: cross Abstract: Algorithm Selection is essential for efficient Constraint Programming.
  • Over the years, many algorithm selectors based on machine learning methods have been successfully applied, yet traditional feature extraction methods often rely on manually decided instance-level statistics that fail to capture the underlying problem structure.
  • In this paper we aim to bridge this gap by introducing a novel, automated feature extraction methodology that integrates graph conversion and Weisfeiler-Lehman graph kernels to generate robust structural representatio

Why it matters

The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗