论文标题
合并机器学习以评估大学课程时间表问题的解决方案
Incorporating Machine Learning to Evaluate Solutions to the University Course Timetabling Problem
论文作者
论文摘要
评估优化问题的解决方案可以说是启发式算法的最重要步骤，因为它用于指导算法在解决方案搜索空间中的最佳解决方案。研究表明，评估功能对一些优化问题进行计算不切实际，因此发现替代较便宜的评估功能对这些问题。这项研究调查了可以使用监督学习算法来找到用于评估大学课程时间表问题的近似值的程度。传统的评估功能多达97％的时间与监督学习回归模型一致，该模型是根据将解决方案的质量与大学课程时间表问题进行比较的结果，这表明监督的学习回归模型可以是优化问题的合适替代方法。
Evaluating solutions to optimization problems is arguably the most important step for heuristic algorithms, as it is used to guide the algorithms towards the optimal solution in the solution search space. Research has shown evaluation functions to some optimization problems to be impractical to compute and have thus found surrogate less expensive evaluation functions to those problems. This study investigates the extent to which supervised learning algorithms can be used to find approximations to evaluation functions for the university course timetabling problem. Up to 97 percent of the time, the traditional evaluation function agreed with the supervised learning regression model on the result of comparison of the quality of pair of solutions to the university course timetabling problem, suggesting that supervised learning regression models can be suitable alternatives for optimization problems' evaluation functions.
