Publikationen

D. Sudholt, "Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial" , Algorithmica , vol. 83 , no. 4 , pp. 976-1011 , 2021.
https://doi.org/10.1007/s00453-020-00671-0

J. Bossek and D. Sudholt, "Do additional optima speed up evolutionary algorithms?" in Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms , Finck, Steffen and Hellwig, Michael and Oliveto, Pietro S., Eds. New York, NY, USA : ACM , 2021. pp. 1--11.
DOI: 10.1145/3450218.3477309

G. T. Hall, D. Sudholt and P. S. Oliveto, "On the impact of the performance metric on efficient algorithm configuration" , Nov. 2021. http://1. https://www.sciencedirect.com/science/article/abs/pii/S0004370221001806 .

E. Covantes Osuna and D. Sudholt, "Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation" , Evolutionary Computation , 2021. MIT Press .

M. A. Hevia Fajardo and D. Sudholt, "Self-adjusting offspring population sizes outperform fixed parameters on the cliff function" in Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms , Finck, Steffen and Hellwig, Michael and Oliveto, Pietro S., Eds. New York, NY, USA : ACM , 2021. pp. 1--15.
DOI: 10.1145/3450218.3477306

M. A. Hevia Fajardo and D. Sudholt, "Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates Matter" in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021) , ACM , 2021.

J. Lengler, D. Sudholt and C. Witt, "The Complex Parameter Landscape of the Compact Genetic Algorithm" , Algorithmica , vol. 83 , no. 4 , pp. 1096-1137 , 2021. https://rdcu.be/b9KBR .

P. S. Oliveto, D. Sudholt and C. Witt, "Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm", Algorithmica" , 2021.

J. Bossek, F. Neumann, P. Peng and D. Sudholt, "Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem" , Algorithmica , 2021.
DOI: 10.1007/s00453-021-00838-3

P. S. Oliveto, D. Sudholt and C. Witt, "A Tight Lower Bound on the Expected Runtime of Standard Steady State Genetic Algorithms" in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2020) , ACM , 2020. pp. 1323-1331.
https://doi.org/10.1145/3377930.3390212

G. T. Hall, P. S. Oliveto and D. Sudholt, "Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators" in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2020) , ACM , 2020. pp. 823-831.
https://doi.org/10.1145/3377930.3390218

N. Albunian, G. Fraser and D. Sudholt, "Causes and Effects of Fitness Landscapes in Unit Test Generation" in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2020) , ACM , 2020. pp. 1204-1212.
https://doi.org/10.1145/3377930.3390194

E. Covantes Osuna, W. Gao, F. Neumann and D. Sudholt, "Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation" , Theoretical Computer Science , vol. 832 , pp. 123-142 , 2020.
https://authors.elsevier.com/c/1b6NN15DaHylEh

M. Foster, M. Hughes, G. O'Brien, P. S. Oliveto, J. Pyle, D. Sudholt and J. Williams, "Do Sophisticated Evolutionary Algorithms Perform Better than Simple Ones?" in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2020) , ACM , 2020. pp. 184-192.
https://doi.org/10.1145/3377930.3389830

G. T. Hall, P. S. Oliveto and D. Sudholt, "Fast Perturbative Algorithm Configurators" in Parallel Problem Solving from Nature (PPSN 2020) , 2020. pp. 19--32.
https://doi.org/10.1007/978-3-030-58112-1_2

N. Albunian, G. Fraser and D. Sudholt, "Measuring and Maintaining Population Diversity in Search-based Unit Test Generation" in Proceedings of the 12th Symposium on Search-Based Software Engineering (SSBSE 2020) , 2020. pp. 153--168.
https://doi.org/10.1007/978-3-030-59762-7_11

P. T. H. Nguyen and D. Sudholt, "Memetic Algorithms Outperform Evolutionary Algorithms in Multimodal Optimisation" , Artificial Intelligence , vol. 287 , pp. 103345 , 2020.
https://doi.org/10.1016/j.artint.2020.103345

J. Bossek, F. Neumann, P. Peng and D. Sudholt, "More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization" in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2020) , ACM , 2020. pp. 1277-1285.
https://doi.org/10.1145/3377930.3390174

M. A. Hevia Fajardo and D. Sudholt, "On the Choice of the Parameter Control Mechanism in the(1+(λ,λ)) Genetic Algorithm" in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2020) , ACM , 2020. pp. 832-840.
https://doi.org/10.1145/3377930.3390200

P. K. Lehre and D. Sudholt, "Parallel Black-Box Complexity with Tail Bounds" , IEEE Transactions on Evolutionary Computation , vol. 24 , no. 6 , pp. 1010-1024 , 2020.
https://doi.org/10.1109/TEVC.2019.2954234

E. Covantes Osuna and D. Sudholt, "Runtime Analysis of Crowding Mechanisms for Multimodal Optimisation" , IEEE Transactions on Evolutionary Computation , vol. 24 , no. 3 , pp. 581--592 , 2020.
https://doi.org/10.1109/TEVC.2019.2914606

D. Sudholt, "The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses" in Theory of Evolutionary Computation: Recent Developments in Discrete Optimization , Benjamin Doerr and Frank Neumann, Eds. Springer , 2020 .
https://link.springer.com/chapter/10.1007/978-3-030-29414-4_8

S. Nallaperuma, P. S. Oliveto, J. Pérez Heredia and D. Sudholt, "On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements?" , Algorithmica , vol. 81 , no. 2 , pp. 858--885 , 2019.
https://doi.org/10.1007/s00453-018-0462-1

P. S. Oliveto, D. Sudholt and C. Zarges, "On the Benefits and Risks of Using Fitness Sharing for Multimodal Optimisation" , Theoretical Computer Science , vol. 773 , pp. 53--70 , 2019.
https://doi.org/10.1016/j.tcs.2018.07.007

D. Sudholt and C. Witt, "On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization" , Algorithmica , vol. 81 , no. 4 , pp. 1450--1489 , 2019.
https://doi.org/10.1007/s00453-018-0480-z