Abstract
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.
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Rattadilok, P., Gaw, A., Kwan, R.S.K. (2005). Distributed Choice Function Hyper-heuristics for Timetabling and Scheduling. In: Burke, E., Trick, M. (eds) Practice and Theory of Automated Timetabling V. PATAT 2004. Lecture Notes in Computer Science, vol 3616. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11593577_4
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DOI: https://doi.org/10.1007/11593577_4
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