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Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation.

, , и . CEC, стр. 2292-2299. IEEE, (2019)

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Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings., и . GECCO, стр. 1335-1342. ACM, (2015)OneMax in Black-Box Models with Several Restrictions., и . GECCO, стр. 1431-1438. ACM, (2015)k-Bit Mutation with Self-Adjusting k Outperforms Standard Bit Mutation., , и . PPSN, том 9921 из Lecture Notes in Computer Science, стр. 824-834. Springer, (2016)Optimal static mutation strength distributions for the (1 + λ) evolutionary algorithm on OneMax., и . GECCO, стр. 660-668. ACM, (2021)Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions., , , и . EvoApplications, том 12694 из Lecture Notes in Computer Science, стр. 17-33. Springer, (2021)MATE: A Model-Based Algorithm Tuning Engine - A Proof of Concept Towards Transparent Feature-Dependent Parameter Tuning Using Symbolic Regression., , , , , и . EvoCOP, том 12692 из Lecture Notes in Computer Science, стр. 51-67. Springer, (2021)Automated configuration of genetic algorithms by tuning for anytime performance: hot-off-the-press track at GECCCO 2022., , , и . GECCO Companion, стр. 51-52. ACM, (2022)Black-box complexity: from complexity theory to playing mastermind., и . GECCO (Companion), стр. 617-640. ACM, (2013)High Dimensional Bayesian Optimization Assisted by Principal Component Analysis., , , , и . PPSN (1), том 12269 из Lecture Notes in Computer Science, стр. 169-183. Springer, (2020)Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm., , и . PPSN (2), том 12270 из Lecture Notes in Computer Science, стр. 485-499. Springer, (2020)