Abstract
Solvers for constraint optimisation problems exploit variable and value ordering heuristics. Numerous expert-designed heuristics exist, while recent research uses machine learning to learn novel heuristics. We introduce the concept of deep heuristics, a data-driven approach to learn extended versions of a given variable ordering heuristic. We demonstrate deep variable ordering heuristics based on the smallest, anti first-fail, and maximum regret heuristics. The results show that deep heuristics solve 20% more problem instances than classical ‘shallow’ heuristics.
| Original language | English |
|---|---|
| Title of host publication | BNAIC/BeneLearn 2021 |
| Subtitle of host publication | 33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning |
| Editors | Edit Luis A. Leiva, Cédric Pruski, Réka Markovich, Amro Najjar, Christoph Schommer |
| Pages | 789-791 |
| Publication status | Published - 2021 |
| Event | 33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning - Esch-sur-Alzette, Luxembourg Duration: 10 Nov 2021 → 12 Nov 2021 |
Conference
| Conference | 33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning |
|---|---|
| Abbreviated title | BNAIC/BeneLearn 2021 |
| Country/Territory | Luxembourg |
| City | Esch-sur-Alzette |
| Period | 10/11/21 → 12/11/21 |
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Online COP heuristic learning code: "Online Learning of Variable Ordering Heuristics for Constraint Optimisation Problems"
Yorke-Smith, N. (Creator) & Doolaard, F. P. (Creator), TU Delft - 4TU.ResearchData, 24 Nov 2022
DOI: 10.4121/17081021
Dataset/Software: Software