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Benchmarking algorithms from the platypus framework on the biobjective bbob-biobj testbed.

, and . GECCO (Companion), page 1905-1911. ACM, (2019)

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On Bi-Objective convex-quadratic problems., , , and . CoRR, (2018)The impact of sample volume in random search on the bbob test suite., and . GECCO (Companion), page 1912-1919. ACM, (2019)On Handling a Large Number of Objectives A Posteriori and During Optimization., , , and . Multiobjective Problem Solving from Nature, Springer, (2008)Tutorials at PPSN 2016., , , , , , , , , and 19 other author(s). PPSN, volume 9921 of Lecture Notes in Computer Science, page 1012-1022. Springer, (2016)DMS and MultiGLODS: black-box optimization benchmarking of two direct search methods on the bbob-biobj test suite., , , and . GECCO Companion, page 1251-1258. ACM, (2021)Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point., , , and . FOGA, page 87-102. ACM, (2009)Benchmarking algorithms from the platypus framework on the biobjective bbob-biobj testbed., and . GECCO (Companion), page 1905-1911. ACM, (2019)GECCO 2023 Tutorial on Benchmarking Multiobjective Optimizers 2.0., and . GECCO Companion, page 1183-1212. ACM, (2023)GECCO 2022 tutorial on benchmarking multiobjective optimizers 2.0., and . GECCO Companion, page 1269-1309. ACM, (2022)The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration., , and . EMO, volume 4403 of Lecture Notes in Computer Science, page 862-876. Springer, (2007)