Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Hyper-heuristics and classifier systems for solving 2D-regular cutting stock problems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
How slow is the k-means method?
Proceedings of the twenty-second annual symposium on Computational geometry
A GA-based method to produce generalized hyper-heuristics for the 2D-regular cutting stock problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A hyper-heuristic for solving one and two-dimensional bin packing problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
The impact of the bin packing problem structure in hyper-heuristic performance
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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In various approaches for combinatorial optimization, a problem instance is represented by a numerical vector that summarizes to some extent the actual solution state of such instance. Such representation intends to include the most relevant features related to the instance and the problem domain. The proper selection of these features has a direct impact on the performance of a hyper-heuristic. Previous approaches for hyper-heuristics have been relying on intuitive ways to determine the feature set, usually based on the domain knowledge of a particular problem. In this paper, a more general methodology for establishing an adequate problem-state representation is proposed. We chose the irregular case of the two-dimensional Bin Packing Problem (2D irregular BPP) and a GA-based hyper-heuristic model to test the methodology. As far as we know, this is the only hyper-heuristic model applied to the 2D irregular BPP and it has been successful when solving a wide range of instances. Our developed representation shows a significant improvement in performance with respect to a more conventional representation for the 2D irregular BPP.