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Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed.

, , , , , and . GECCO (Companion), page 1217-1223. ACM, (2016)

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Comparing black-box differential evolution and classic differential evolution., , and . GECCO (Companion), page 1537-1544. ACM, (2018)Visualization in Multiobjective Optimization., and . GECCO (Companion), page 735-751. ACM, (2016)Identifying Properties of Real-World Optimisation Problems through a Questionnaire., , , , , , , and . CoRR, (2020)Discovering Comfortable Driving Strategies Using Simulation-Based Multiobjective Optimization., , , and . Informatica (Slovenia), 36 (3): 319-326 (2012)Parameter tuning in an evolutionary algorithm for commodity transportation optimization., , and . IEEE Congress on Evolutionary Computation, page 1-8. IEEE, (2010)Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games., , , and . Appl. Soft Comput., (March 2023)Predicting Algorithm Performance in Constrained Multiobjective Optimization: A Tough Nut to Crack., , , , and . EvoApplications@EvoStar, volume 14635 of Lecture Notes in Computer Science, page 310-325. Springer, (2024)Towards realistic optimization benchmarks: a questionnaire on the properties of real-world problems., , , , , , , and . GECCO Companion, page 293-294. ACM, (2020)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)