Improving the Relevance of Artificial Instances for Curriculum-Based Course Timetabling through Feasibility Prediction-Reference-Cited by-同舟云学术

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Improving the Relevance of Artificial Instances for Curriculum-Based Course Timetabling through Feasibility Prediction

Published:2023-07-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Companion Conference on Genetic and Evolutionary Computation
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Author:

Feutrier Thomas¹^ORCID,Veerapen Nadarajen¹^ORCID,Kessaci Marie-Eleonore¹^ORCID

Affiliation:

1. CRIStAL, University of Lille, Lille, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583133.3590690

Reference10 articles.

1. An overview of curriculum-based course timetabling

2. Algorithm selection and instance space analysis for curriculum-based course timetabling

3. Investigating the landscape of a hybrid local search approach for a timetabling problem

4. Thomas Feutrier , Marie-Eleonore Kessaci , and Nadarajen Veerapen . 2022. Analysis of Search Landscape Samplers for Solver Performance Prediction on a University Timetabling Problem . In Parallel Problem Solving from Nature - PPSN XVII (2022) (LNCS) . Springer International Publishing , Cham , 548--561. Thomas Feutrier, Marie-Eleonore Kessaci, and Nadarajen Veerapen. 2022. Analysis of Search Landscape Samplers for Solver Performance Prediction on a University Timetabling Problem. In Parallel Problem Solving from Nature - PPSN XVII (2022) (LNCS). Springer International Publishing, Cham, 548--561.

5. Curriculum based course timetabling: new solutions to Udine benchmark instances

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