基于预处理和Bloom过滤器的串行调度生成方案的自适应实现
Efficient Adaptive Implementation of the Serial Schedule Generation Scheme using Preprocessing and Bloom Filters
摘要：
大多数调度元算法使用解的间接表示作为有效探索搜索空间的一种方式。因此，这种元分析的一个关键部分是一个“时间表生成方案”-将间接解决方案表示转换为时间表的过程。每次需要评估新的候选解决方案时，都使用调度生成方案。由于速度相对较慢，它消耗了元启发式算法的大部分运行时间，因此，它的速度对元启发式算法的性能起着重要作用。尽管它的重要性，很少有人注意到在文献中的有效实施的时间表生成计划。我们给出了详细的描述串行调度生成方案，包括新的改进，并提出了一种新的方法来加快它，通过使用布隆过滤器。通过参数的自动控制，结果得到进一步加强。最后，我们采用在线算法选择来动态选择使用这两种实现。这种混合方法在广泛的实例上显著优于传统的实现。
英文摘要：
The majority of scheduling metaheuristics use indirect representation of solutions as a way to efficiently explore the search space. Thus, a crucial part of such metaheuristics is a "schedule generation scheme" -- procedure translating the indirect solution representation into a schedule. Schedule generation scheme is used every time a new candidate solution needs to be evaluated. Being relatively slow, it eats up most of the running time of the metaheuristic and, thus, its speed plays significant role in performance of the metaheuristic. Despite its importance, little attention has been paid in the literature to efficient implementation of schedule generation schemes. We give detailed description of serial schedule generation scheme, including new improvements, and propose a new approach for speeding it up, by using Bloom filters. The results are further strengthened by automated control of parameters. Finally, we employ online algorithm selection to dynamically choose which of the two implementations to use. This hybrid approach significantly outperforms conventional implementation on a wide range of instances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
