Dirk Sudholt

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 113

Top Venues

Dortmund University of Technology (PHD)

Education History

Dortmund University of Technology

PHD

Publications

Andre Opris, Duc-Cuong Dang, Dirk Sudholt
Hot of the Press: Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation. GECCO Companion (2024)
Andre Opris, Duc-Cuong Dang, Frank Neumann, Dirk Sudholt
Runtime Analyses of NSGA-III on Many-Objective Problems. CoRR (2024)
Duc-Cuong Dang, Andre Opris, Dirk Sudholt
Crossover can guarantee exponential speed-ups in evolutionary multi-objective optimisation. Artif. Intell. 330 (2024)
Andre Opris, Johannes Lengler, Dirk Sudholt
for Realistic Crossover Probabilities. CoRR (2024)
Mario Alejandro Hevia Fajardo, Dirk Sudholt
Self-adjusting offspring population sizes outperform fixed parameters on the cliff function. Artif. Intell. 328 (2024)
Marcus Schmidbauer, Andre Opris, Jakob Bossek, Frank Neumann, Dirk Sudholt
Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean Conjunctions. GECCO (2024)
Andre Opris, Johannes Lengler, Dirk Sudholt
for Realistic Crossover Probabilities. GECCO (2024)
Andre Opris, Duc-Cuong Dang, Frank Neumann, Dirk Sudholt
Runtime Analyses of NSGA-III on Many-Objective Problems. GECCO (2024)
Mario Alejandro Hevia Fajardo, Dirk Sudholt
Self-adjusting Population Sizes for Non-elitist Evolutionary Algorithms: Why Success Rates Matter. Algorithmica 86 (2) (2024)
Jakob Baumann, Ignaz Rutter, Dirk Sudholt
Evolutionary Computation Meets Graph Drawing: Runtime Analysis for Crossing Minimisation on Layered Graph Drawings. GECCO (2024)
Duc-Cuong Dang, Andre Opris, Dirk Sudholt
Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis. GECCO (2024)
Duc-Cuong Dang, Andre Opris, Dirk Sudholt
Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis. CoRR (2024)
Johannes Lengler, Andre Opris, Dirk Sudholt
Analysing Equilibrium States for Population Diversity. Algorithmica 86 (7) (2024)
Dirk Sudholt, Giovanni Squillero
Theory and Practice of Population Diversity in Evolutionary Computation. GECCO Companion (2024)
Johannes Lengler, Andre Opris, Dirk Sudholt
Analysing Equilibrium States for Population Diversity. GECCO (2023)
Duc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt
A Proof That Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation. AAAI (2023)
Jakob Bossek, Dirk Sudholt
Runtime Analysis of Quality Diversity Algorithms. GECCO (2023)
Jakob Bossek, Dirk Sudholt
Runtime Analysis of Quality Diversity Algorithms. CoRR (2023)
Duc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt
Analysing the Robustness of NSGA-II under Noise. CoRR (2023)
Jakob Bossek, Dirk Sudholt
Do additional target points speed up evolutionary algorithms? Theor. Comput. Sci. 950 (2023)
Johannes Lengler, Andre Opris, Dirk Sudholt
Analysing Equilibrium States for Population Diversity. CoRR (2023)
Joost Jorritsma, Johannes Lengler, Dirk Sudholt
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima. GECCO (2023)
Carlo Kneissl, Dirk Sudholt
The Cost of Randomness in Evolutionary Algorithms: Crossover can Save Random Bits. EvoCOP (2023)
Duc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt
A Proof that Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation. CoRR (2023)
Duc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt
Analysing the Robustness of NSGA-II under Noise. GECCO (2023)
Joost Jorritsma, Johannes Lengler, Dirk Sudholt
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima. CoRR (2023)
Dirk Sudholt, Giovanni Squillero
Theory and Practice of Population Diversity in Evolutionary Computation. GECCO Companion (2023)
Mario Alejandro Hevia Fajardo, Dirk Sudholt
Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (λ, λ)) Genetic Algorithm. ACM Trans. Evol. Learn. Optim. 2 (4) (2022)
Mario Alejandro Hevia Fajardo, Dirk Sudholt
Hard Problems are Easier for Success-based Parameter Control. CoRR (2022)
George T. Hall, Pietro S. Oliveto, Dirk Sudholt
On the impact of the performance metric on efficient algorithm configuration. Artif. Intell. 303 (2022)
Edgar Covantes Osuna, Dirk Sudholt
Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation. CoRR (2022)
Vijay Dhanjibhai Bhuva, Duc-Cuong Dang, Liam Huber, Dirk Sudholt
Evolutionary Algorithms for Cardinality-Constrained Ising Models. PPSN (2) (2022)
Pietro S. Oliveto, Dirk Sudholt, Carsten Witt
Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm. Algorithmica 84 (6) (2022)
Frank Neumann, Dirk Sudholt, Carsten Witt
The Compact Genetic Algorithm Struggles on Cliff Functions. CoRR (2022)
Edgar Covantes Osuna, Dirk Sudholt
Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation. Evol. Comput. 30 (1) (2022)
Dirk Sudholt, Giovanni Squillero
Theory and practice of population diversity in evolutionary computation. GECCO Companion (2022)
Mario Alejandro Hevia Fajardo, Dirk Sudholt
Hard problems are easier for success-based parameter control. GECCO (2022)
Frank Neumann, Dirk Sudholt, Carsten Witt
The compact genetic algorithm struggles on Cliff functions. GECCO (2022)
Johannes Lengler, Dirk Sudholt, Carsten Witt
The Complex Parameter Landscape of the Compact Genetic Algorithm. Algorithmica 83 (4) (2021)
Mario Alejandro Hevia Fajardo, Dirk Sudholt
Self-adjusting offspring population sizes outperform fixed parameters on the cliff function. FOGA (2021)
Mario Alejandro Hevia Fajardo, Dirk Sudholt
Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates Matter. CoRR (2021)
Mario Alejandro Hevia Fajardo, Dirk Sudholt
Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter. GECCO (2021)
Jakob Bossek, Dirk Sudholt
Do additional optima speed up evolutionary algorithms? FOGA (2021)
Dirk Sudholt
Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial. Algorithmica 83 (4) (2021)
Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica 83 (10) (2021)
Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. CoRR (2021)
Mario Alejandro Hevia Fajardo, Dirk Sudholt
)) genetic algorithm. GECCO (2020)
Pietro S. Oliveto, Dirk Sudholt, Carsten Witt
A tight lower bound on the expected runtime of standard steady state genetic algorithms. GECCO (2020)
George T. Hall, Pietro S. Oliveto, Dirk Sudholt
Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators. CoRR (2020)
George T. Hall, Pietro S. Oliveto, Dirk Sudholt
Fast Perturbative Algorithm Configurators. PPSN (1) (2020)
George T. Hall, Pietro Simone Oliveto, Dirk Sudholt
Fast Perturbative Algorithm Configurators. CoRR (2020)
Edgar Covantes Osuna, Dirk Sudholt
Runtime Analysis of Crowding Mechanisms for Multimodal Optimization. IEEE Trans. Evol. Comput. 24 (3) (2020)
Nasser M. Albunian, Gordon Fraser, Dirk Sudholt
Causes and effects of fitness landscapes in unit test generation. GECCO (2020)
Michael Foster, Matthew Hughes, George O'Brien, Pietro S. Oliveto, James Pyle, Dirk Sudholt, James Williams
Do sophisticated evolutionary algorithms perform better than simple ones? GECCO (2020)
Phan Trung Hai Nguyen, Dirk Sudholt
Memetic algorithms outperform evolutionary algorithms in multimodal optimisation. Artif. Intell. 287 (2020)
Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt
More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization. CoRR (2020)
Nasser M. Albunian, Gordon Fraser, Dirk Sudholt
Measuring and Maintaining Population Diversity in Search-Based Unit Test Generation. SSBSE (2020)
George T. Hall, Pietro S. Oliveto, Dirk Sudholt
Analysis of the performance of algorithm configurators for search heuristics with global mutation operators. GECCO (2020)
Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt
More effective randomized search heuristics for graph coloring through dynamic optimization. GECCO (2020)
Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt
Design and analysis of diversity-based parent selection schemes for speeding up evolutionary multi-objective optimisation. Theor. Comput. Sci. 832 (2020)
Dirk Sudholt, Giovanni Squillero
Theory and practice of population diversity in evolutionary computation. GECCO Companion (2020)
Per Kristian Lehre, Dirk Sudholt
Parallel Black-Box Complexity With Tail Bounds. IEEE Trans. Evol. Comput. 24 (6) (2020)
Per Kristian Lehre, Dirk Sudholt
Parallel Black-Box Complexity with Tail Bounds. CoRR (2019)
Jakob Bossek, Dirk Sudholt
Time complexity analysis of RLS and (1 + 1) EA for the edge coloring problem. FOGA (2019)
Pietro S. Oliveto, Dirk Sudholt, Christine Zarges
On the benefits and risks of using fitness sharing for multimodal optimisation. Theor. Comput. Sci. 773 (2019)
Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt
Runtime analysis of randomized search heuristics for dynamic graph coloring. GECCO (2019)
Samadhi Nallaperuma, Pietro S. Oliveto, Jorge Pérez Heredia, Dirk Sudholt
On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements? Algorithmica 81 (2) (2019)
George T. Hall, Pietro S. Oliveto, Dirk Sudholt
On the impact of the cutoff time on the performance of algorithm configurators. GECCO (2019)
Dirk Sudholt, Carsten Witt
On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization. Algorithmica 81 (4) (2019)
George T. Hall, Pietro S. Oliveto, Dirk Sudholt
On the Impact of the Cutoff Time on the Performance of Algorithm Configurators. CoRR (2019)
Carola Doerr, Dirk Sudholt
Preface to the Special Issue on Theory of Genetic and Evolutionary Computation. Algorithmica 81 (2) (2019)
Edgar Covantes Osuna, Dirk Sudholt
On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism. Evol. Comput. 27 (3) (2019)
Edgar Covantes Osuna, Dirk Sudholt
Empirical Analysis of Diversity-Preserving Mechanisms on Example Landscapes for Multimodal Optimisation. PPSN (2) (2018)
Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton
Escaping Local Optima Using Crossover With Emergent Diversity. IEEE Trans. Evol. Comput. 22 (3) (2018)
Phan Trung Hai Nguyen, Dirk Sudholt
Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems. CoRR (2018)
Johannes Lengler, Dirk Sudholt, Carsten Witt
Medium step sizes are harmful for the compact genetic algorithm. GECCO (2018)
Dirk Sudholt
On the robustness of evolutionary algorithms to noise: refined results and an example where noise helps. GECCO (2018)
Edgar Covantes Osuna, Dirk Sudholt
Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation. CoRR (2018)
Phan Trung Hai Nguyen, Dirk Sudholt
Memetic algorithms beat evolutionary algorithms on the class of hurdle problems. GECCO (2018)
Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt
Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation. CoRR (2018)
Dirk Sudholt
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses. CoRR (2018)
Edgar Covantes Osuna, Dirk Sudholt
Runtime analysis of probabilistic crowding and restricted tournament selection for bimodal optimisation. GECCO (2018)
Edgar Covantes Osuna, Dirk Sudholt
On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism. CoRR (2018)
Pietro S. Oliveto, Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Barbora Trubenová
How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism. Algorithmica 80 (5) (2018)
Dirk Sudholt
Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial. CoRR (2018)
Gisele Lobo Pappa, Michael T. M. Emmerich, Ana L. C. Bazzan, Will N. Browne, Kalyanmoy Deb, Carola Doerr, Marko Durasevic, Michael G. Epitropakis, Saemundur O. Haraldsson, Domagoj Jakobovic, Pascal Kerschke, Krzysztof Krawiec, Per Kristian Lehre, Xiaodong Li, Andrei Lissovoi, Pekka Malo, Luis Martí, Yi Mei, Juan Julián Merelo Guervós, Julian F. Miller, Alberto Moraglio, Antonio J. Nebro, Su Nguyen, Gabriela Ochoa, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Marc Schoenauer, Roman Senkerik, Ankur Sinha, Ofer M. Shir, Dirk Sudholt, L. Darrell Whitley, Mark Wineberg, John R. Woodward, Mengjie Zhang
Tutorials at PPSN 2018. PPSN (2) (2018)
Timo Kötzing, Dirk Sudholt
Preface to the Special Issue on Theory of Genetic and Evolutionary Computation. Algorithmica 80 (5) (2018)
Samadhi Nallaperuma, Pietro S. Oliveto, Jorge Pérez Heredia, Dirk Sudholt
When is it beneficial to reject improvements? GECCO (2017)
Dirk Sudholt
Theory of swarm intelligence: tutorial at GECCO 2017. GECCO (Companion) (2017)
Andrei Lissovoi, Dirk Sudholt, Markus Wagner, Christine Zarges
Theoretical results on bet-and-run as an initialisation strategy. GECCO (2017)
Samadhi Nallaperuma, Frank Neumann, Dirk Sudholt
Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem. Evol. Comput. 25 (4) (2017)
Dogan Corus, Jun He, Thomas Jansen, Pietro S. Oliveto, Dirk Sudholt, Christine Zarges
On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation. Algorithmica 78 (2) (2017)
Dirk Sudholt
How Crossover Speeds up Building Block Assembly in Genetic Algorithms. Evol. Comput. 25 (2) (2017)
Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt
Speeding up evolutionary multi-objective optimisation through diversity-based parent selection. GECCO (2017)
Alberto Moraglio, Dirk Sudholt
Principled Design and Runtime Analysis of Abstract Convex Evolutionary Search. Evol. Comput. 25 (2) (2017)
Edgar Covantes Osuna, Dirk Sudholt
Analysis of the Clearing Diversity-Preserving Mechanism. FOGA (2017)
Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Barbora Trubenová
Towards a Runtime Comparison of Natural and Artificial Evolution. Algorithmica 78 (2) (2017)
Dirk Sudholt, Carsten Witt
Update Strength in EDAs and ACO: How to Avoid Genetic Drift. CoRR (2016)
Carola Doerr, Nicolas Bredèche, Enrique Alba, Thomas Bartz-Beielstein, Dimo Brockhoff, Benjamin Doerr, Gusz Eiben, Michael G. Epitropakis, Carlos M. Fonseca, Andreia P. Guerreiro, Evert Haasdijk, Jacqueline Heinerman, Julien Hubert, Per Kristian Lehre, Luigi Malagò, Juan Julián Merelo Guervós, Julian Francis Miller, Boris Naujoks, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Patricia Ryser-Welch, Giovanni Squillero, Jörg Stork, Dirk Sudholt, Alberto Paolo Tonda, L. Darrell Whitley, Martin Zaefferer
Tutorials at PPSN 2016. PPSN (2016)
Dirk Sudholt
Theory of Swarm Intelligence. GECCO (Companion) (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Dirk Sudholt