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Performance of the DEMO Algorithm on the Bi-objective BBOB Test Suite., and . GECCO (Companion), page 1249-1256. ACM, (2016)Initial experiments in visualization of empirical attainment function differences using maximum intensity projection., and . GECCO (Companion), page 1099-1106. ACM, (2014)Benchmarking multiobjective optimizers 2.0., and . GECCO Companion, page 664-668. ACM, (2021)COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite., , , , , , and . CoRR, (2019)Characterization of Constrained Continuous Multiobjective Optimization Problems: A Performance Space Perspective., , and . CoRR, (2023)Towards Constructing a Suite of Multi-objective Optimization Problems with Diverse Landscapes., , , and . EvoApplications@EvoStar, volume 13989 of Lecture Notes in Computer Science, page 442-457. Springer, (2023)Evolutionary scheduling of flexible offers for balancing electricity supply and demand., , and . IEEE Congress on Evolutionary Computation, page 1-8. IEEE, (2012)COCO: a platform for comparing continuous optimizers in a black-box setting., , , , , and . Optim. Methods Softw., 36 (1): 114-144 (2021)The DU map: a visualization to gain insights into genotype-phenotype mapping and diversity., and . GECCO (Companion), page 1705-1712. ACM, (2017)Benchmarking algorithms from the platypus framework on the biobjective bbob-biobj testbed., and . GECCO (Companion), page 1905-1911. ACM, (2019)