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 comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
A genetic programming based hyper-heuristic approach for combinatorial optimisation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Adaptive iterated local search for cross-domain optimisation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Policy matrix evolution for generation of heuristics
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Hill climbers and mutational heuristics in hyperheuristics
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
The Interleaved Constructive Memetic Algorithm and its application to timetabling
Computers and Operations Research
HyFlex: a benchmark framework for cross-domain heuristic search
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Hyper-Heuristic based on iterated local search driven by evolutionary algorithm
EvoCOP'12 Proceedings of the 12th 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
Adaptive evolutionary algorithms and extensions to the hyflex hyper-heuristic framework
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Evaluation of a family of reinforcement learning cross-domain optimization heuristics
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyper-heuristic is implemented as an extension to a public software used for benchmarking of hyper-heuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains.