A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
The State of the Art of Nurse Rostering
Journal of Scheduling
GRASP with path relinking for the weighted MAXSAT problem
Journal of Experimental Algorithmics (JEA)
Binary Exponential Back Off for Tabu Tenure in Hyperheuristics
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Analyzing the landscape of a graph based hyper-heuristic for timetabling problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Ant based hyper heuristics with space reduction: a case study of the p-median problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Iterated local search in nurse rostering problem
Proceedings of the Fourth Symposium on Information and Communication Technology
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The goal of hyper-heuristics is to design and choose heuristics to solve complex problems. The primary motivation behind the hyper-heuristics is to generalize the solving ability of the heuristics. In this paper, the authors propose a Hyper-heuristic using GRASP with Path-Relinking HyGrasPr. HyGrasPr generates heuristic sequences to produce solutions within an iterative procedure. The procedure of HyGrasPr consists of three phases, namely the construction phase, the local search phase, and the path-relinking phase. To show the performance of the HyGrasPr, the authors use the nurse rostering problem as a case study. The authors use an existing simulated annealing based hyper-heuristic as a baseline. The experimental results indicate that HyGrasPr can achieve better solutions than SAHH within the same running time and the path-relinking phase is effective for the framework of HyGrasPr.