@InProceedings{KarapetyanVernitski2017,
  author    = {Daniel Karapetyan and Alexei Vernitski},
  title     = {Efficient Adaptive Implementation of the Serial Schedule Generation Scheme using Preprocessing and Bloom Filters},
  booktitle = {Learning and Intelligent Optimization Conference 11},
  year      = {2017},
  volume    = {10556},
  series    = {LNCS},
  pages     = {124-138},
  abstract  = {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.},
  arxivid   = {1708.07786},
  comment   = {[<a href="http://www.cs.nott.ac.uk/~pszdk/rcpsp-ssgs.zip">Source codes (17 Kb)</a>]},
  doi       = {10.1007/978-3-319-69404-7_9},
}