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k-Bit Mutation with Self-Adjusting k Outperforms Standard Bit Mutation.

, , and . PPSN, volume 9921 of Lecture Notes in Computer Science, page 824-834. Springer, (2016)

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Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings., and . GECCO, page 1335-1342. ACM, (2015)OneMax in Black-Box Models with Several Restrictions., and . GECCO, page 1431-1438. ACM, (2015)Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm., , and . PPSN (2), volume 12270 of Lecture Notes in Computer Science, page 485-499. Springer, (2020)High Dimensional Bayesian Optimization Assisted by Principal Component Analysis., , , , and . PPSN (1), volume 12269 of Lecture Notes in Computer Science, page 169-183. Springer, (2020)Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy., , , and . PPSN (2), volume 12270 of Lecture Notes in Computer Science, page 139-153. Springer, (2020)Fixed-target runtime analysis., , , and . GECCO, page 1295-1303. ACM, (2020)Self-Adjusting Weighted Expected Improvement for Bayesian Optimization., , , , and . AutoML, volume 224 of Proceedings of Machine Learning Research, page 6/1-50. PMLR, (2023)Sensitivity Analysis of RF+clust for Leave-One-Problem-Out Performance Prediction., , , , and . CEC, page 1-8. IEEE, (2023)Self-adjusting mutation rates with provably optimal success rules., , and . GECCO, page 1479-1487. ACM, (2019)Elitist Black-Box Models: Analyzing the Impact of Elitist Selection on the Performance of Evolutionary Algorithms., and . GECCO, page 839-846. ACM, (2015)