  This paper provides experimental experiences on two local search hybridized
genetic algorithms in solving the uncapacitated examination timetabling
problem. The proposed two hybrid algorithms use partition and priority based
solution representations which are inspired from successful genetic algorithms
proposed for graph coloring and project scheduling problems, respectively. The
algorithms use a parametrized saturation degree heuristic hybridized crossover
scheme. In the experiments, the algorithms firstly are calibrated with a Design
of Experiments approach and then tested on the well-known Toronto benchmark
instances. The calibration shows that the hybridization prefers an intensive
local search method. The experiments indicate the vitality of local search in
the proposed genetic algorithms, however, experiments also show that the
hybridization benefits local search as well. Interestingly, although the
structures of the two algorithms are not alike, their performances are quite
similar to each other and also to other state-of-the-art genetic-type
algorithms proposed in the literature.
