Back to Search
Start Over
Algorithm selection for solving educational timetabling problems.
- Source :
-
Expert Systems with Applications . Jul2021, Vol. 174, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- • Features of initial solutions predict the performance of perturbation algorithms. • Low performance variation within portfolios makes algorithm selection difficult. • Hybrid selection models are useful for portfolios with low performance variation. • Accuracy is not a fair measure to evaluate the performance of algorithm selectors. In this paper, we present the construction process of a per-instance algorithm selection model to improve the initial solutions of Curriculum-Based Course Timetabling (CB-CTT) instances. Following the meta-learning framework, we apply a hybrid approach that integrates the predictions of a classifier and linear regression models to estimate and compare the performance of four meta-heuristics across different problem sub-spaces described by seven types of features. Rather than reporting the average accuracy, we evaluate the model using the closed SBS-VBS gap, a performance measure used at international algorithm selection competitions. The experimental results show that our model obtains a performance of 0.386, within the range obtained by per-instance algorithm selection models in other combinatorial problems. As a result of the process, we conclude that the performance variation between the meta-heuristics has a significant role in the effectiveness of the model. Therefore, we introduce statistical analyses to evaluate this factor within per-instance algorithm portfolios. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 174
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 150231455
- Full Text :
- https://doi.org/10.1016/j.eswa.2021.114694