Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
A Heuristic Combination Method for Solving Job-Shop Scheduling Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Hyperheuristics: A Robust Optimisation Method Applied to Nurse Scheduling
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
On Permutation Representations for Scheduling Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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 fuzzy genetic algorithm for real-world job shop scheduling
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Design and Analysis of Experiments
Design and Analysis of Experiments
Computers and Industrial Engineering
How the landscape of random job shop scheduling instances depends on the ratio of jobs to machines
Journal of Artificial Intelligence Research
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Hyper-heuristics with low level parameter adaptation
Evolutionary Computation
Calibrating continuous multi-objective heuristics using mixture experiments
Journal of Heuristics
Computers and Industrial Engineering
Inventory based two-objective job shop scheduling model and its hybrid genetic algorithm
Applied Soft Computing
Computers and Operations Research
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Hyper-heuristics or "methodologies to choose heuristics" are becoming increasingly popular given their suitability to solve hard real world combinatorial optimisation problems. Their distinguishing feature is that they operate in the space of heuristics or heuristic components rather than in the solution space. In Dispatching Rule Based Genetic Algorithms (DRGA) solutions are represented as sequences of dispatching rules which are called one at a time and used to sequence a number of operations onto machines. The number of operations that each dispatching rule in the sequence handles is a parameter to which DRGA is notoriously sensitive. This paper proposes a new hybrid DRGA which searches simultaneously for the best sequence of dispatching rules and the number of operations to be handled by each dispatching rule. The investigated DRGA uses the selection mechanism of NSGA-II when handling multi-objective problems.The proposed representation was used to solve different variants of the multi-objective job shop problem as well as the single objective problem with the sum of weighted tardiness objective. Our results, supported by the statistical analysis, confirm that DRGAs that use the proposed representation obtained better results in both the single and multi-objective environment overall and on each particular set of instances than DRGAs using the conventional dispatching rule representation and a GA that uses the more common permutation representation.