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.

翻译：本文针对无容量考试时间表问题，提供了两种局部搜索混合遗传算法的实验经验。所提出的两种混合算法分别采用基于分区和优先级的解表示方法，其灵感分别源于图着色和项目调度问题中成功的遗传算法。算法采用参数化的饱和度启发式混合交叉方案。在实验中，算法首先通过实验设计方法进行校准，然后在著名的多伦多基准实例上进行测试。校准表明，混合算法倾向于使用密集型局部搜索方法。实验揭示了局部搜索在本文提出的遗传算法中的关键作用，同时实验也表明混合算法对局部搜索本身也有益处。有趣的是，尽管两种算法结构不同，但它们的性能非常相似，并且与文献中提出的其他最新遗传类算法性能也高度一致。