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On the Complexity of Computing the Hypervolume Indicator.

, , , , and . IEEE Trans. Evolutionary Computation, 13 (5): 1075-1082 (2009)

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Preference Articulation in Evolutionary Multiobjective Optimisation.. HIS, page 4. IEEE Computer Society, (2007)An Improved Dimension-Sweep Algorithm for the Hypervolume Indicator., , and . IEEE Congress on Evolutionary Computation, page 1157-1163. IEEE, (2006)A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms., , , and . PPSN, volume 8672 of Lecture Notes in Computer Science, page 672-681. Springer, (2014)Hypervolume Subset Selection in Two Dimensions: Formulations and Algorithms., , , , , and . Evolutionary Computation, 24 (3): 411-425 (2016)Greedy Hypervolume Subset Selection in Low Dimensions., , and . Evolutionary Computation, 24 (3): 521-544 (2016)Multi-objective Evolutionary Algorithms for Resource Allocation Problems., , and . EMO, volume 4403 of Lecture Notes in Computer Science, page 401-416. Springer, (2006)Methodology to select solutions from the pareto-optimal set: a comparative study., , and . GECCO, page 789-796. ACM, (2007)Graph partitioning through a multi-objective evolutionary algorithm: a preliminary study., , , and . GECCO, page 625-632. ACM, (2008)Hypervolume Sharpe-Ratio Indicator: Formalization and First Theoretical Results., and . PPSN, volume 9921 of Lecture Notes in Computer Science, page 814-823. Springer, (2016)Assessing the quality of the relation between scalarizing function parameters and solutions in multiobjective optimization., , and . IEEE Congress on Evolutionary Computation, page 1131-1136. IEEE, (2009)