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More effective randomized search heuristics for graph coloring through dynamic optimization.

, , , and . GECCO, page 1277-1285. ACM, (2020)

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The Complex Parameter Landscape of the Compact Genetic Algorithm., , and . Algorithmica, 83 (4): 1096-1137 (2021)Do additional target points speed up evolutionary algorithms?, and . Theor. Comput. Sci., (March 2023)Fast Perturbative Algorithm Configurators., , and . PPSN (1), volume 12269 of Lecture Notes in Computer Science, page 19-32. Springer, (2020)A Proof That Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation., , , and . AAAI, page 12390-12398. AAAI Press, (2023)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)A tight lower bound on the expected runtime of standard steady state genetic algorithms., , and . GECCO, page 1323-1331. ACM, (2020)Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis., , and . CoRR, (2024)Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter., and . GECCO, page 1151-1159. ACM, (2021)Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial.. CoRR, (2018)Preface to the Special Issue on Theory of Genetic and Evolutionary Computation., and . Algorithmica, 81 (2): 589-592 (2019)