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A Proof That Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation., , , and . AAAI, page 12390-12398. AAAI Press, (2023)Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter., and . GECCO, page 1151-1159. ACM, (2021)Measuring and Maintaining Population Diversity in Search-Based Unit Test Generation., , and . SSBSE, volume 12420 of Lecture Notes in Computer Science, page 153-168. Springer, (2020)Fast Perturbative Algorithm Configurators., , and . PPSN (1), volume 12269 of Lecture Notes in Computer Science, page 19-32. Springer, (2020)A tight lower bound on the expected runtime of standard steady state genetic algorithms., , and . GECCO, page 1323-1331. ACM, (2020)On the Equivalence Between Stochastic Tournament and Power-Law Ranking Selection and How to Implement Them Efficiently., , and . PPSN (3), volume 15150 of Lecture Notes in Computer Science, page 230-245. Springer, (2024)Level-Based Theorems for Runtime Analysis of Multi-objective Evolutionary Algorithms., , and . PPSN (3), volume 15150 of Lecture Notes in Computer Science, page 246-263. Springer, (2024)Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis., , and . GECCO, ACM, (2024)Evolutionary Algorithms for One-Sided Bipartite Crossing Minimisation., , and . CoRR, (2024)Evolutionary Algorithms for One-Sided Bipartite Crossing Minimisation (Poster Abstract)., , and . GD, volume 320 of LIPIcs, page 51:1-51:3. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2024)