Abstract | ||
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Grouping problems represent a class of computationally hard to solve problems requiring optimal partitioning of a given set of items with respect to multiple criteria varying dependent on the domain. A recent work proposed a general-purpose selection hyper-heuristic search framework with reusable components, designed for rapid development of grouping hyper-heuristics to solve grouping problems. The framework was tested only on the graph colouring problem domain. Extending the previous work, this study compares the performance of selection hyper-heuristics implemented using the framework, pairing up various heuristic/operator selection and move acceptance methods for data clustering. The selection hyper-heuristic performs the search processing a single solution at any decision point and controls a fixed set of generic low level heuristics specifically designed for the grouping problems based on a bio-bjective formulation. An archive of high quality solutions, capturing the trade-off between the number of clusters and overall error of clustering, is maintained during the search process. The empirical results verify the effectiveness of a successful selection hyper-heuristic, winner of a recent hyper-heuristic challenge for data clustering on a set of benchmark problem instances. |
Year | DOI | Venue |
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2018 | 10.1007/978-3-319-77449-7_7 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Heuristic,Multiobjective optimisation,Reinforcement learning,Adaptive move acceptance | Heuristic,Multiple criteria,Problem domain,Computer science,Theoretical computer science,Pairing,Heuristics,Operator (computer programming),Cluster analysis,Reinforcement learning | Conference |
Volume | ISSN | Citations |
10782 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 16 | 2 |
Name | Order | Citations | PageRank |
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Anas Elhag | 1 | 0 | 0.68 |
Ender Özcan | 2 | 0 | 1.01 |