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Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking.

, , , , , , , and . IEEE Trans. Evol. Comput., 26 (3): 490-500 (2022)

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Compiling a benchmarking test-suite for combinatorial black-box optimization: a position paper., , and . GECCO (Companion), page 1753-1760. ACM, (2018)Optimal Parameter Choices via Precise Black-Box Analysis., , and . GECCO, page 1123-1130. ACM, (2016)The Right Mutation Strength for Multi-Valued Decision Variables., , and . GECCO, page 1115-1122. ACM, (2016)Complexity Theory for Discrete Black-Box Optimization Heuristics.. CoRR, (2018)Maximizing drift is not optimal for solving OneMax., and . GECCO (Companion), page 425-426. ACM, (2019)The unbiased black-box complexity of partition is polynomial., , and . Artif. Intell., (2014)The Impact of Random Initialization on the Runtime of Randomized Search Heuristics., and . Algorithmica, 75 (3): 529-553 (2016)Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking., , , , , , , and . IEEE Trans. Evol. Comput., 26 (3): 490-500 (2022)Introducing Elitist Black-Box Models: When Does Elitist Selection Weaken the Performance of Evolutionary Algorithms?, and . CoRR, (2015)Sensitivity of Parameter Control Mechanisms with Respect to Their Initialization., and . PPSN (2), volume 11102 of Lecture Notes in Computer Science, page 360-372. Springer, (2018)