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Evaluating solutions to optimization problems is arguably the most importantstep for heuristic algorithms, as it is used to guide the algorithms towardsthe optimal solution in the solution search space. Research has shownevaluation functions to some optimization problems to be impractical to computeand have thus found surrogate less expensive evaluation functions to thoseproblems. This study investigates the extent to which supervised learningalgorithms can be used to find approximations to evaluation functions for theuniversity course timetabling problem. Up to 97 percent of the time, thetraditional evaluation function agreed with the supervised learning regressionmodel on the result of comparison of the quality of pair of solutions to theuniversity course timetabling problem, suggesting that supervised learningregression models can be suitable alternatives for optimization problems'evaluation functions.

Incorporating Machine Learning to Evaluate Solutions to the University Course Timetabling Problem