LTR-HSS: A Learning-to-Rank Based Framework for Hypervolume Subset Selection
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70085-9_3
Reference34 articles.
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2. Basseur, M., Derbel, B., Goëffon, A., Liefooghe, A.: Experiments on greedy and local search heuristics for d dimensional hypervolume subset selection. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 541–548 (2016)
3. Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653–1669 (2007)
4. Lecture Notes in Computer Science;K Bringmann,2014
5. Bringmann, K., Friedrich, T., Klitzke, P.: Two-dimensional subset selection for hypervolume and epsilon-indicator. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 589–596 (2014)
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