  The personnel scheduling problem is a well-known NP-hard combinatorial
problem. Due to the complexity of this problem and the size of the real-world
instances, it is not possible to use exact methods, and thus heuristics,
meta-heuristics, or hyper-heuristics must be employed. The majority of
heuristic approaches are based on iterative search, where the quality of
intermediate solutions must be calculated. Unfortunately, this is
computationally highly expensive because these problems have many constraints
and some are very complex. In this study, we propose a machine learning
technique as a tool to accelerate the evaluation phase in heuristic approaches.
The solution is based on a simple classifier, which is able to determine
whether the changed solution (more precisely, the changed part of the solution)
is better than the original or not. This decision is made much faster than a
standard cost-oriented evaluation process. However, the classification process
cannot guarantee 100% correctness. Therefore, our approach, which is
illustrated using a tabu search algorithm in this study, includes a filtering
mechanism, where the classifier rejects the majority of the potentially bad
solutions and the remaining solutions are then evaluated in a standard manner.
We also show how the boosting algorithms can improve the quality of the final
solution compared with a simple classifier. We verified our proposed approach
and premises, based on standard and real-world benchmark instances, to
demonstrate the significant speedup obtained with comparable solution quality.
