Text-Based Feature-Free Automatic Algorithm Selection

Amanda Salinas-Pinto, Bryan Alvarado-Ulloa, Dorit Hochbaum, Matías Francia-Carramiñana, Ricardo Ñanculef, Roberto Asín-Achá

2024

Abstract

Automatic Algorithm Selection involves predicting which solver, among a portfolio, will perform best for a given problem instance. Traditionally, the design of algorithm selectors has relied on domain-specific features crafted by experts. However, an alternative approach involves designing selectors that do not depend on domain-specific features, but receive a raw representation of the problem’s instances and automatically learn the characteristics of that particular problem using Deep Learning techniques. Previously, such raw representation was a fixed-sized image, generated from the input text file specifying the instance, which was fed to a Convolutional Neural Network. Here we show that a better approach is to use text-based Deep Learning models that are fed directly with the input text files specifying the instances. Our approach improves on the image-based feature-free models by a significant margin and furthermore matches traditional Machine Learning models based on basic domain-specific features, known to be among the most informative features.

Paper Citation

in Harvard Style

Salinas-Pinto A., Alvarado-Ulloa B., Hochbaum D., Francia-Carramiñana M., Ñanculef R. and Asín-Achá R. (2024). Text-Based Feature-Free Automatic Algorithm Selection. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 267-274. DOI: 10.5220/0012913700003838

in Bibtex Style

@conference{kdir24,
author={Amanda Salinas-Pinto and Bryan Alvarado-Ulloa and Dorit Hochbaum and Matías Francia-Carramiñana and Ricardo Ñanculef and Roberto Asín-Achá},
title={Text-Based Feature-Free Automatic Algorithm Selection},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={267-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012913700003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Text-Based Feature-Free Automatic Algorithm Selection
SN - 978-989-758-716-0
AU - Salinas-Pinto A.
AU - Alvarado-Ulloa B.
AU - Hochbaum D.
AU - Francia-Carramiñana M.
AU - Ñanculef R.
AU - Asín-Achá R.
PY - 2024
SP - 267
EP - 274
DO - 10.5220/0012913700003838
PB - SciTePress