2026 Dirk Sudholt: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	38	10414	10122	658	650	159	4368

Discipline name

D-Index

World Ranking

Current World Ranking

National Ranking

Current National Ranking

Publications

Citations

Computer Science

10414

10122

658

650

159

4368

Overview

Dirk Sudholt is affiliated with the University of Sheffield in the United Kingdom. Their research primarily focuses on the field of Computer Science, with a strong emphasis on Artificial Intelligence, Computational Theory and Mathematics, and related subfields.

The main topics of their work include:

Metaheuristic Optimization Algorithms Research
Evolutionary Algorithms and Applications
Advanced Multi-Objective Optimization Algorithms
Evolution and Genetic Dynamics
Machine Learning and Algorithms
Constraint Satisfaction and Optimization
Machine Learning and Data Classification

Sudholt has contributed to numerous recent academic publications. Some notable papers include:

"Memetic algorithms outperform evolutionary algorithms in multimodal optimisation" (2020), published in Artificial Intelligence
"A Proof That Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation" (2023), published in Proceedings of the AAAI Conference on Artificial Intelligence
"The Complex Parameter Landscape of the Compact Genetic Algorithm" (2020), published in Algorithmica
"Analysing the Robustness of NSGA-II under Noise" (2023), published in Proceedings of the Genetic and Evolutionary Computation Conference
"Self-adjusting population sizes for non-elitist evolutionary algorithms" (2021), published in Proceedings of the Genetic and Evolutionary Computation Conference

The frequent venues where Sudholt has published include:

Proceedings of the Genetic and Evolutionary Computation Conference
arXiv (Cornell University)
Algorithmica
Artificial Intelligence
Proceedings of the Genetic and Evolutionary Computation Conference Companion

They have collaborated extensively with several coauthors, including:

Andre Opris
Duc-Cuong Dang
Johannes Lengler
Frank Neumann
Mario Alejandro Hevia Fajardo

Best Publications

A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms

D. Sudholt

203
Citations
Escaping Local Optima Using Crossover With Emergent Diversity

Duc-Cuong Dang;Tobias Friedrich;Timo Kotzing;Martin S. Krejca

179
Citations
Analysis of diversity-preserving mechanisms for global exploration*

Tobias Friedrich;Pietro S. Oliveto;Dirk Sudholt;Carsten Witt

131
Citations
The choice of the offspring population size in the (1,λ) evolutionary algorithm

Jonathan E. Rowe;Dirk Sudholt

128
Citations
Crossover is provably essential for the Ising model on trees

Dirk Sudholt

127
Citations
How crossover helps in pseudo-boolean optimization

Timo Kötzing;Dirk Sudholt;Madeleine Theile

106
Citations
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus

Frank Neumann;Dirk Sudholt;Carsten Witt

91
Citations
Adaptive population models for offspring populations and parallel evolutionary algorithms

Jörg Lässig;Dirk Sudholt

89
Citations
Running time analysis of Ant Colony Optimization for shortest path problems

Dirk Sudholt;Christian Thyssen

88
Citations
Parallel Evolutionary Algorithms

Dirk Sudholt

88
Citations
Mutation rate matters even when optimizing monotonic functions

Benjamin Doerr;Thomas Jansen;Dirk Sudholt;Carola Winzen

88
Citations
The impact of parametrization in memetic evolutionary algorithms

Dirk Sudholt

87
Citations
Unbiased Black-Box Complexity of Parallel Search

Golnaz Badkobeh;Per Kristian Lehre;Dirk Sudholt

85
Citations
Escaping Local Optima with Diversity Mechanisms and Crossover

Duc-Cuong Dang;Tobias Friedrich;Timo Kötzing;Martin S. Krejca

83
Citations
How crossover speeds up building block assembly inźgeneticźalgorithms

Dirk Sudholt

78
Citations
Runtime analysis of a binary particle swarm optimizer

Dirk Sudholt;Carsten Witt

73
Citations
General upper bounds on the runtime of parallel evolutionary algorithms

Jörg Lässig;Dirk Sudholt

69
Citations
A Simple Ant Colony Optimizer for Stochastic Shortest Path Problems

Dirk Sudholt;Christian Thyssen

68
Citations
Crossover speeds up building-block assembly

Dirk Sudholt

67
Citations
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses

Dirk Sudholt

67
Citations
The choice of the offspring population size in the (1,λ) EA

Jonathan E. Rowe;Dirk Sudholt

51
Citations

Frequent Co-Authors

Carsten Witt Technical University of Denmark

Frank Neumann University of Adelaide

Per Kristian Lehre University of Birmingham

Thomas Jansen Aberystwyth University

Benjamin Doerr École Polytechnique

Tobias Friedrich Hasso Plattner Institute

Markus Wagner Monash University

Julian F. Miller University of York

Xin Yao Lingnan University

Mike Preuss Leiden University

External Links

Personal website of Dirk Sudholt Google Scholar page

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