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A Proof That Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation., , , и . AAAI, стр. 12390-12398. AAAI Press, (2023)Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter., и . GECCO, стр. 1151-1159. ACM, (2021)Fast Perturbative Algorithm Configurators., , и . PPSN (1), том 12269 из Lecture Notes in Computer Science, стр. 19-32. Springer, (2020)Measuring and Maintaining Population Diversity in Search-Based Unit Test Generation., , и . SSBSE, том 12420 из Lecture Notes in Computer Science, стр. 153-168. Springer, (2020)A tight lower bound on the expected runtime of standard steady state genetic algorithms., , и . GECCO, стр. 1323-1331. ACM, (2020)Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis., , и . GECCO, ACM, (2024)Analysis of speedups in parallel evolutionary algorithms and (1+λ) EAs for combinatorial optimization., и . Theor. Comput. Sci., (2014)Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem., , , и . CoRR, (2021)Preface to the Special Issue on Theory of Genetic and Evolutionary Computation., и . Algorithmica, 81 (2): 589-592 (2019)The Cost of Randomness in Evolutionary Algorithms: Crossover can Save Random Bits., и . EvoCOP, том 13987 из Lecture Notes in Computer Science, стр. 179-194. Springer, (2023)