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EFE repository

Authors: Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, and Ian Miguel

Abstract

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Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, Ian Miguel. EFE repository (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24086,
   title = {{EFE repository}}, 
   author = {Pellegrino, Alessio and Akg\"{u}n, \"{O}zg\"{u}r and Dang, Nguyen and Kiziltan, Zeynep and Miguel, Ian},
   note = {Software, version 1.0., swhId: \href{https://archive.softwareheritage.org/swh:1:dir:0d5708bbc3b0395ddcd80b52bbb6ed8da6ffe252;origin=https://github.com/SeppiaBrilla/EFE_project;visit=swh:1:snp:d6c381103db3c1b63eb2574073e4466639a93ef3;anchor=swh:1:rev:5124050c380534eb9c0dcb49763e034a844aef1b}{\texttt{swh:1:dir:0d5708bbc3b0395ddcd80b52bbb6ed8da6ffe252}} (visited on 2025-08-08)},
   url = {https://github.com/SeppiaBrilla/EFE_project},
   doi = {10.4230/artifacts.24086},
}

Document

DOI: 10.4230/LIPIcs.CP.2025.31

Transformer-Based Feature Learning for Algorithm Selection in Combinatorial Optimisation

Authors: Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, and Ian Miguel

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract

Given a combinatorial optimisation problem, there are typically multiple ways of modelling it for presentation to an automated solver. Choosing the right combination of model and target solver can have a significant impact on the effectiveness of the solving process. The best combination of model and solver can also be instance-dependent: there may not exist a single combination that works best for all instances of the same problem. We consider the task of building machine learning models to automatically select the best combination for a problem instance. Critical to the learning process is to define instance features, which serve as input to the selection model. Our contribution is the automatic learning of instance features directly from the high-level representation of a problem instance using a transformer encoder. We evaluate the performance of our approach using the Essence modelling language via a case study of three problem classes.

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Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, and Ian Miguel. Transformer-Based Feature Learning for Algorithm Selection in Combinatorial Optimisation. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 31:1-31:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{pellegrino_et_al:LIPIcs.CP.2025.31,
  author =	{Pellegrino, Alessio and Akg\"{u}n, \"{O}zg\"{u}r and Dang, Nguyen and Kiziltan, Zeynep and Miguel, Ian},
  title =	{{Transformer-Based Feature Learning for Algorithm Selection in Combinatorial Optimisation}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{31:1--31:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.31},
  URN =		{urn:nbn:de:0030-drops-238928},
  doi =		{10.4230/LIPIcs.CP.2025.31},
  annote =	{Keywords: Constraint modelling, algorithm selection, feature extraction, machine learning, transformer architecture}
}

@InProceedings{pellegrino_et_al:LIPIcs.CP.2025.31,
  author =	{Pellegrino, Alessio and Akg\"{u}n, \"{O}zg\"{u}r and Dang, Nguyen and Kiziltan, Zeynep and Miguel, Ian},
  title =	{{Transformer-Based Feature Learning for Algorithm Selection in Combinatorial Optimisation}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{31:1--31:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.31},
  URN =		{urn:nbn:de:0030-drops-238928},
  doi =		{10.4230/LIPIcs.CP.2025.31},
  annote =	{Keywords: Constraint modelling, algorithm selection, feature extraction, machine learning, transformer architecture}
}

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Documents authored by Pellegrino, Alessio

EFE repository

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Transformer-Based Feature Learning for Algorithm Selection in Combinatorial Optimisation

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