CINECA IRIS Institutional Research Information System
IRIS è la piattaforma adottata dall’Università degli Studi di Udine per gestire l’archivio aperto dei prodotti della ricerca. L’archivio contiene articoli, contributi in volume, monografie, interventi pubblicati in atti di convegno, Tesi di dottorato e altri materiali.
EnglishWe propose a simulated annealing approach for the examination timetabling problem, as formulated in the 2nd International Timetabling Competition. We apply a single-stage procedure in which infeasible solutions are included in the search space and dealt with using suitable penalties. Upon our approach, we perform a statistically-principled experimental analysis, in order to understand the effect of parameter selection on the performance of our algorithm, and to devise a feature-based parameter tuning strategy, which can achieve better generalization on unseen instances with respect to a one-fits-all parameter setting. The outcome of this work is that this rather straightforward search method, if properly tuned, is able to compete with all state-of-the-art specialized solvers on the available instances. As a byproduct of this analysis, we propose and publish a new, larger set of (artificial) instances that could be used for tuning and also as a ground for future comparisons.
| Titolo: | Feature-based tuning of single-stage simulated annealing for examination timetabling | |
| Autori: | BATTISTUTTA, MICHELE (Primo) SCHAERF, Andrea (Secondo) URLI, Tommaso (Ultimo) (Corresponding) | |
| Data di pubblicazione: | 2017 | |
| Rivista: | ||
| Abstract: | We propose a simulated annealing approach for the examination timetabling problem, as formulated in the 2nd International Timetabling Competition. We apply a single-stage procedure in which infeasible solutions are included in the search space and dealt with using suitable penalties. Upon our approach, we perform a statistically-principled experimental analysis, in order to understand the effect of parameter selection on the performance of our algorithm, and to devise a feature-based parameter tuning strategy, which can achieve better generalization on unseen instances with respect to a one-fits-all parameter setting. The outcome of this work is that this rather straightforward search method, if properly tuned, is able to compete with all state-of-the-art specialized solvers on the available instances. As a byproduct of this analysis, we propose and publish a new, larger set of (artificial) instances that could be used for tuning and also as a ground for future comparisons. | |
| Handle: | http://hdl.handle.net/11390/1069900 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| Battistutta2017_Article_Feature-basedTuningOfSingle-st.pdf | Articolo principale | Versione Editoriale (PDF) | Non pubblico | Accesso ristretto Richiedi una copia |
http://hdl.handle.net/11390/1069900
Copyright © 2021