This paper investigates an emerging class of search algorithms, in which high-level domain independent heuristics, called hyper-heuristics, iteratively select and execute a set of application specific but simple search moves, called low-level heuristics, working toward achieving improved or even optimal solutions. Parallel architectures have been designed and evaluated. Results based on a university timetabling problem show an important relationship between performance, algorithm software and hardware implementation. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Rattadilok, P., Gaw, A., & Kwan, R. S. K. (2005). Distributed choice function hyper-heuristics for timetabling and scheduling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3616 LNCS, pp. 51–67). https://doi.org/10.1007/11593577_4
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