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Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Peckish Initialisation Strategies for Evolutionary Timetabling
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
A Hyperheuristic Approach to Scheduling a Sales Summit
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Solving a real-world problem using an evolving heuristically driven schedule builder
Evolutionary Computation
Adaptive problem-solving for large-scale scheduling problems: a case study
Journal of Artificial Intelligence Research
Binary Exponential Back Off for Tabu Tenure in Hyperheuristics
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Cost-benefit investigation of a genetic-programming hyperheuristic
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Frequency distribution based hyper-heuristic for the bin-packing problem
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
An improved choice function heuristic selection for cross domain heuristic search
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
The effect of the set of low-level heuristics on the performance of selection hyper-heuristics
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
A new hyper-heuristic as a general problem solver: an implementation in HyFlex
Journal of Scheduling
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A hyperheuristic is a high level procedure which searches over a space of low level heuristics rather than directly over the space of problem solutions. The sequence of low level heuristics, applied in an order which is intelligently determined by the hyperheuristic, form a solution method for the problem. In this paper, we consider a hyperheuristic-based methodology where a large set of low level heuristics is constructed by combining simple selection rules. Given sufficient time, this approach is able to achieve high quality results for a real-world personnel scheduling problem. However, some low level heuristics in the set do not make valuable contributions to the search and only slow down the solution process. We introduce learning strategies into hyperheuristics in order to select a fit subset of low level heuristics tailored to a particular problem instance. We compare a range of selection approaches applied to a varied collection of real-world personnel scheduling problem instances.