Algorithm selection for solving educational timetabling problems
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Algorithm selection,Meta-learning,Educational timetabling,Meta-heuristic
论文评审过程：Received 12 March 2020, Revised 15 September 2020, Accepted 5 February 2021, Available online 12 February 2021, Version of Record 13 March 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114694
