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How to compare many-objective algorithms under different settings of population and archive sizes.

, , , and . CEC, page 1149-1156. IEEE, (2016)

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Hypervolume Subset Selection for Triangular and Inverted Triangular Pareto Fronts of Three-Objective Problems., , , and . FOGA, page 95-110. ACM, (2017)Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes., , , and . IEEE Trans. Evol. Comput., 21 (2): 169-190 (2017)Algorithm structure optimization by choosing operators in multiobjective genetic local search., , , , and . CEC, page 854-861. IEEE, (2015)A Knee-Based EMO Algorithm with an Efficient Method to Update Mobile Reference Points., , and . EMO (1), volume 9018 of Lecture Notes in Computer Science, page 202-217. Springer, (2015)Reference point specification in hypervolume calculation for fair comparison and efficient search., , , and . GECCO, page 585-592. ACM, (2017)Performance comparison of NSGA-II and NSGA-III on various many-objective test problems., , , and . CEC, page 3045-3052. IEEE, (2016)How to compare many-objective algorithms under different settings of population and archive sizes., , , and . CEC, page 1149-1156. IEEE, (2016)How to Specify a Reference Point in Hypervolume Calculation for Fair Performance Comparison., , , and . Evol. Comput., (2018)Reference Point Specification in Inverted Generational Distance for Triangular Linear Pareto Front., , , and . IEEE Trans. Evol. Comput., 22 (6): 961-975 (2018)