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
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
A case study of memetic algorithms for constraint optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Stable solving of CVRPs using hyperheuristics
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Examination timetabling using late acceptance hyper-heuristics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An experimental study on hyper-heuristics and exam timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Scheduling English football fixtures over the holiday period using hyper-heuristics
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A hyper-heuristic for solving one and two-dimensional bin packing problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
An investigation of selection hyper-heuristics in dynamic environments
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Hill climbers and mutational heuristics in hyperheuristics
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Choosing the fittest subset of low level heuristics in a hyperheuristic framework
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
HyFlex: a benchmark framework for cross-domain heuristic search
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
A graph coloring constructive hyper-heuristic for examination timetabling problems
Applied Intelligence
Evaluation of a family of reinforcement learning cross-domain optimization heuristics
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
A hyper-heuristic with a round robin neighbourhood selection
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
An ant-based selection hyper-heuristic for dynamic environments
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Hyper-heuristics are a class of high-level search technologies to solve computationally difficult problems which operate on a search space of low-level heuristics rather than solutions directly. A iterative selection hyper-heuristic framework based on single-point search relies on two key components, a heuristic selection method and a move acceptance criteria. The Choice Function is an elegant heuristic selection method which scores heuristics based on a combination of three different measures and applies the heuristic with the highest rank at each given step. Each measure is weighted appropriately to provide balance between intensification and diversification during the heuristic search process. Choosing the right parameter values to weight these measures is not a trivial process and a small number of methods have been proposed in the literature. In this study we describe a new method, inspired by reinforcement learning, which controls these parameters automatically. The proposed method is tested and compared to previous approaches over a standard benchmark across six problem domains.