  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.
