Implementation of Algorithm Recommendation Models for Timetabling Instances
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The Curriculum-Based Course Timetabling (CB-CTT) is a problem periodically solved in educational institutions, still, because of the diversity of conditions that define it within different educational contexts, selecting the solution approach that best suits the particular requirements of an instance is a complex task that can be properly formulated as an algorithm selection problem. In this paper, we analyze four selection mechanisms that could be used as algorithms recommendation models. From this analysis, it is concluded that the proposed regression approach exhibited the highest performance. Therefore, it could be applied for algorithm recommendation to solve CB-CTT instances. © Springer Nature Switzerland AG 2019.
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Algorithm selection; Educational timetabling; Supervised learning Artificial intelligence; Recommender systems; Scheduling; Soft computing; Supervised learning; Algorithm selection; Complex task; Course timetabling; Educational context; Educational institutions; Selection mechanism; Solution approach; Timetabling; Genetic algorithms
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