Experimental Supplements to the Theoretical Analysis of EAs on Problems from Combinatorial Optimization. - Patrick Briest; Dimo Brockhoff; Bastian Degener; Matthias Englert; Christian Gunia; Oliver Heering; Thomas Jansen 001; Michael Leifhelm; Kai Plociennik; Heiko Röglin; Andrea Schweer; Dirk Sudholt; et al. (PPSN, 2004)

The Ising Model: Simple Evolutionary Algorithms as Adaptation Schemes. - Patrick Briest; Dimo Brockhoff; Bastian Degener; Matthias Englert; Christian Gunia; Oliver Heering; Thomas Jansen 001; Michael Leifhelm; Kai Plociennik; Heiko Röglin; Andrea Schweer; Dirk Sudholt; et al. (PPSN, 2004)

Design and analysis of an asymmetric mutation operator. - Thomas Jansen 001; Dirk Sudholt (Congress on Evolutionary Computation, 2005)

Crossover is provably essential for the ising model on trees. - Dirk Sudholt (GECCO, 2005)

On the analysis of the (1+1) memetic algorithm. - Dirk Sudholt (GECCO, 2006)

Local Search in Evolutionary Algorithms: The Impact of the Local Search Frequency. - Dirk Sudholt (ISAAC, 2006)

On the runtime analysis of the 1-ANT ACO algorithm. - Benjamin Doerr; Frank Neumann 001; Dirk Sudholt; Carsten Witt (GECCO, 2007)

Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions. - Frank Neumann 001; Dirk Sudholt; Carsten Witt (SLS, 2007)

Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search. - Frank Neumann 001; Dirk Sudholt; Carsten Witt (ANTS Conference, 2008)

Theoretical analysis of diversity mechanisms for global exploration. - Tobias Friedrich 001; Pietro S. Oliveto; Dirk Sudholt; Carsten Witt (GECCO, 2008)

Memetic algorithms with variable-depth search to overcome local optima. - Dirk Sudholt (GECCO, 2008)

Runtime analysis of binary PSO. - Dirk Sudholt; Carsten Witt (GECCO, 2008)

Self-stabilizing Cuts in Synchronous Networks. - Thomas Sauerwald; Dirk Sudholt (SIROCCO, 2008)

Computational complexity of evolutionary algorithms, hybridizations, and swarm intelligence. - Dirk Sudholt (2008)

Running Time Analysis of ACO Systems for Shortest Path Problems. - Christian Horoba; Dirk Sudholt (SLS, 2009)

Analysis of Diversity-Preserving Mechanisms for Global Exploration. - Tobias Friedrich 001; Pietro S. Oliveto; Dirk Sudholt; Carsten Witt (Evol. Comput., 2009)

Ingo Wegener. - Thomas Jansen 001; Melanie Schmidt 001; Dirk Sudholt; Carsten Witt; Christine Zarges (Evol. Comput., 2009)

Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. - Frank Neumann 001; Dirk Sudholt; Carsten Witt (Swarm Intell., 2009)

The impact of parametrization in memetic evolutionary algorithms. - Dirk Sudholt (Theor. Comput. Sci., 2009)

Computational Complexity of Ant Colony Optimization and Its Hybridization with Local Search. - Frank Neumann 001; Dirk Sudholt; Carsten Witt (Innovations in Swarm Intelligence, 2009)

Ant colony optimization for stochastic shortest path problems. - Christian Horoba; Dirk Sudholt (GECCO, 2010)

The benefit of migration in parallel evolutionary algorithms. - Jörg Lässig; Dirk Sudholt (GECCO, 2010)

A few ants are enough: ACO with iteration-best update. - Frank Neumann 001; Dirk Sudholt; Carsten Witt (GECCO, 2010)

Analysis of an Iterated Local Search Algorithm for Vertex Coloring. - Dirk Sudholt; Christine Zarges (ISAAC (1), 2010)

Optimizing Monotone Functions Can Be Difficult. - Benjamin Doerr; Thomas Jansen 001; Dirk Sudholt; Carola Winzen; Christine Zarges (PPSN (1), 2010)

Experimental Supplements to the Theoretical Analysis of Migration in the Island Model. - Jörg Lässig; Dirk Sudholt (PPSN (1), 2010)

General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms. - Jörg Lässig; Dirk Sudholt (PPSN (1), 2010)

General Lower Bounds for the Running Time of Evolutionary Algorithms. - Dirk Sudholt (PPSN (1), 2010)

Analysis of an Asymmetric Mutation Operator. - Thomas Jansen 001; Dirk Sudholt (Evol. Comput., 2010)

A self-stabilizing algorithm for cut problems in synchronous networks. - Thomas Sauerwald; Dirk Sudholt (Theor. Comput. Sci., 2010)

Runtime analysis of a binary particle swarm optimizer. - Dirk Sudholt; Carsten Witt (Theor. Comput. Sci., 2010)

Memetic Evolutionary Algorithms. - Dirk Sudholt (Theory of Randomized Search Heuristics, 2011)

Simple max-min ant systems and the optimization of linear pseudo-boolean functions. - Timo Kötzing; Frank Neumann 001; Dirk Sudholt; Markus Wagner 007 (FOGA, 2011)

Adaptive population models for offspring populations and parallel evolutionary algorithms. - Jörg Lässig; Dirk Sudholt (FOGA, 2011)

Using markov-chain mixing time estimates for the analysis of ant colony optimization. - Dirk Sudholt (FOGA, 2011)

How crossover helps in pseudo-boolean optimization. - Timo Kötzing; Dirk Sudholt; Madeleine Theile (GECCO, 2011)

On the effectiveness of crossover for migration in parallel evolutionary algorithms. - Frank Neumann 001; Pietro S. Oliveto; Günter Rudolph; Dirk Sudholt (GECCO, 2011)

Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization - (Extended Abstract). - Jörg Lässig; Dirk Sudholt (ISAAC, 2011)

Hybridizing Evolutionary Algorithms with Variable-Depth Search to Overcome Local Optima. - Dirk Sudholt (Algorithmica, 2011)

Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization - Jörg Lässig; Dirk Sudholt (CoRR, 2011)

Memetic Algorithms: Parametrization and Balancing Local and Global Search - Dirk Sudholt (CoRR, 2011)

Runtime analysis of the 1-ANT ant colony optimizer. - Benjamin Doerr; Frank Neumann 001; Dirk Sudholt; Carsten Witt (Theor. Comput. Sci., 2011)

Evolutionary algorithms for the project scheduling problem: runtime analysis and improved design. - Leandro L. Minku; Dirk Sudholt; Xin Yao 001 (GECCO, 2012)

Runtime analysis of convex evolutionary search. - Alberto Moraglio; Dirk Sudholt (GECCO, 2012)

The choice of the offspring population size in the (1, λ) EA. - Jonathan E. Rowe; Dirk Sudholt (GECCO, 2012)

Crossover speeds up building-block assembly. - Dirk Sudholt (GECCO, 2012)

Homogeneous and Heterogeneous Island Models for the Set Cover Problem. - Andrea Mambrini; Dirk Sudholt; Xin Yao 001 (PPSN (1), 2012)

A Simple Ant Colony Optimizer for Stochastic Shortest Path Problems. - Dirk Sudholt; Christian Thyssen (Algorithmica, 2012)

General Upper Bounds on the Running Time of Parallel Evolutionary Algorithms - Jörg Lässig; Dirk Sudholt (CoRR, 2012)

Running time analysis of Ant Colony Optimization for shortest path problems. - Dirk Sudholt; Christian Thyssen (J. Discrete Algorithms, 2012)

Parametrization and Balancing Local and Global Search. - Dirk Sudholt (Handbook of Memetic Algorithms, 2012)

When do evolutionary algorithms optimize separable functions in parallel? - Benjamin Doerr; Dirk Sudholt; Carsten Witt (FOGA, 2013)

A theoretical runtime and empirical analysis of different alternating variable searches for search-based testing. - Joseph Kempka; Phil McMinn; Dirk Sudholt (GECCO, 2013)

Mutation Rate Matters Even When Optimizing Monotonic Functions. - Benjamin Doerr; Thomas Jansen 001; Dirk Sudholt; Carola Winzen; Christine Zarges (Evol. Comput., 2013)

Design and analysis of migration in parallel evolutionary algorithms. - Jörg Lässig; Dirk Sudholt (Soft Comput., 2013)

A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms. - Dirk Sudholt (IEEE Trans. Evol. Comput., 2013)

Design and analysis of adaptive migration intervals in parallel evolutionary algorithms. - Andrea Mambrini; Dirk Sudholt (GECCO, 2014)

A fixed budget analysis of randomized search heuristics for the traveling salesperson problem. - Samadhi Nallaperuma; Frank Neumann 001; Dirk Sudholt (GECCO, 2014)

On the runtime analysis of stochastic ageing mechanisms. - Pietro S. Oliveto; Dirk Sudholt (GECCO, 2014)

Unbiased Black-Box Complexity of Parallel Search. - Golnaz Badkobeh; Per Kristian Lehre; Dirk Sudholt (PPSN, 2014)

On the Runtime Analysis of Fitness Sharing Mechanisms. - Pietro S. Oliveto; Dirk Sudholt; Christine Zarges (PPSN, 2014)

General Upper Bounds on the Runtime of Parallel Evolutionary Algorithms. - Jörg Lässig; Dirk Sudholt (Evol. Comput., 2014)

Analysis of speedups in parallel evolutionary algorithms and (1+λ) EAs for combinatorial optimization. - Jörg Lässig; Dirk Sudholt (Theor. Comput. Sci., 2014)

The choice of the offspring population size in the (1, λ) evolutionary algorithm. - Jonathan E. Rowe; Dirk Sudholt (Theor. Comput. Sci., 2014)

Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis. - Leandro L. Minku; Dirk Sudholt; Xin Yao 001 (IEEE Trans. Software Eng., 2014)

Black-box Complexity of Parallel Search with Distributed Populations. - Golnaz Badkobeh; Per Kristian Lehre; Dirk Sudholt (FOGA, 2015)

On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations. - Dogan Corus; Jun He 004; Thomas Jansen 001; Pietro S. Oliveto; Dirk Sudholt; Christine Zarges (GECCO, 2015)

First Steps Towards a Runtime Comparison of Natural and Artificial Evolution. - Tiago Paixão; Jorge Pérez Heredia; Dirk Sudholt; Barbora Trubenová (GECCO, 2015)

Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms. - Andrea Mambrini; Dirk Sudholt (Evol. Comput., 2015)

Design and analysis of different alternating variable searches for search-based software testing. - Joseph Kempka; Phil McMinn; Dirk Sudholt (Theor. Comput. Sci., 2015)

Parallel Evolutionary Algorithms. - Dirk Sudholt (Handbook of Computational Intelligence, 2015)

Escaping Local Optima with Diversity Mechanisms and Crossover. - Duc-Cuong Dang; Tobias Friedrich 001; Timo Kötzing; Martin S. Krejca; Per Kristian Lehre; Pietro S. Oliveto; Dirk Sudholt; Andrew M. Sutton (GECCO, 2016)

Runtime Analysis for the Parameter-less Population Pyramid. - Brian W. Goldman; Dirk Sudholt (GECCO, 2016)

When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys. - Pietro S. Oliveto; Tiago Paixão; Jorge Pérez Heredia; Dirk Sudholt; Barbora Trubenová (GECCO, 2016)

Theory of Swarm Intelligence. - Dirk Sudholt (GECCO (Companion), 2016)

Update Strength in EDAs and ACO: How to Avoid Genetic Drift. - Dirk Sudholt; Carsten Witt (GECCO, 2016)

Emergence of Diversity and Its Benefits for Crossover in Genetic Algorithms. - Duc-Cuong Dang; Tobias Friedrich 001; Timo Kötzing; Martin S. Krejca; Per Kristian Lehre; Pietro S. Oliveto; Dirk Sudholt; Andrew M. Sutton (PPSN, 2016)

Tutorials at PPSN 2016. - Carola Doerr; Nicolas Bredèche; Enrique Alba 001; Thomas Bartz-Beielstein; Dimo Brockhoff; Benjamin Doerr; Gusz Eiben; Michael G. Epitropakis; Carlos M. Fonseca; Andreia P. Guerreiro; Evert Haasdijk; Jacqueline Heinerman; et al. (PPSN, 2016)

Escaping Local Optima using Crossover with Emergent or Reinforced Diversity. - Duc-Cuong Dang; Tobias Friedrich 001; Timo Kötzing; Martin S. Krejca; Per Kristian Lehre; Pietro S. Oliveto; Dirk Sudholt; Andrew M. Sutton (CoRR, 2016)

Analysis of the Clearing Diversity-Preserving Mechanism. - Edgar Covantes Osuna; Dirk Sudholt (FOGA, 2017)

Theoretical results on bet-and-run as an initialisation strategy. - Andrei Lissovoi; Dirk Sudholt; Markus Wagner 007; Christine Zarges (GECCO, 2017)

When is it beneficial to reject improvements? - Samadhi Nallaperuma; Pietro S. Oliveto; Jorge Pérez Heredia; Dirk Sudholt (GECCO, 2017)

Speeding up evolutionary multi-objective optimisation through diversity-based parent selection. - Edgar Covantes Osuna; Wanru Gao; Frank Neumann 001; Dirk Sudholt (GECCO, 2017)

Theory of swarm intelligence: tutorial at GECCO 2017. - Dirk Sudholt (GECCO (Companion), 2017)

On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation. - Dogan Corus; Jun He 004; Thomas Jansen 001; Pietro S. Oliveto; Dirk Sudholt; Christine Zarges (Algorithmica, 2017)

Towards a Runtime Comparison of Natural and Artificial Evolution. - Tiago Paixão; Jorge Pérez Heredia; Dirk Sudholt; Barbora Trubenová (Algorithmica, 2017)

Principled Design and Runtime Analysis of Abstract Convex Evolutionary Search. - Alberto Moraglio; Dirk Sudholt (Evol. Comput., 2017)

Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem. - Samadhi Nallaperuma; Frank Neumann 001; Dirk Sudholt (Evol. Comput., 2017)

How Crossover Speeds up Building Block Assembly in Genetic Algorithms. - Dirk Sudholt (Evol. Comput., 2017)

Medium step sizes are harmful for the compact genetic algorithm. - Johannes Lengler; Dirk Sudholt; Carsten Witt (GECCO, 2018)

Memetic algorithms beat evolutionary algorithms on the class of hurdle problems. - Phan Trung Hai Nguyen; Dirk Sudholt (GECCO, 2018)

Runtime analysis of probabilistic crowding and restricted tournament selection for bimodal optimisation. - Edgar Covantes Osuna; Dirk Sudholt (GECCO, 2018)

On the robustness of evolutionary algorithms to noise: refined results and an example where noise helps. - Dirk Sudholt (GECCO, 2018)

Empirical Analysis of Diversity-Preserving Mechanisms on Example Landscapes for Multimodal Optimisation. - Edgar Covantes Osuna; Dirk Sudholt (PPSN (2), 2018)

Tutorials at PPSN 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; et al. (PPSN (2), 2018)

Preface to the Special Issue on Theory of Genetic and Evolutionary Computation. - Timo Kötzing; Dirk Sudholt (Algorithmica, 2018)

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism. - Pietro S. Oliveto; Tiago Paixão; Jorge Pérez Heredia; Dirk Sudholt; Barbora Trubenová (Algorithmica, 2018)

The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses. - Dirk Sudholt (CoRR, 2018)

Escaping Local Optima Using Crossover With Emergent Diversity. - Duc-Cuong Dang; Tobias Friedrich 001; Timo Kötzing; Martin S. Krejca; Per Kristian Lehre; Pietro S. Oliveto; Dirk Sudholt; Andrew M. Sutton (IEEE Trans. Evol. Comput., 2018)

Time complexity analysis of RLS and (1 + 1) EA for the edge coloring problem. - Jakob Bossek; Dirk Sudholt (FOGA, 2019)

Runtime analysis of randomized search heuristics for dynamic graph coloring. - Jakob Bossek; Frank Neumann 001; Pan Peng 001; Dirk Sudholt (GECCO, 2019)

On the impact of the cutoff time on the performance of algorithm configurators. - George T. Hall; Pietro S. Oliveto; Dirk Sudholt (GECCO, 2019)

Preface to the Special Issue on Theory of Genetic and Evolutionary Computation. - Carola Doerr; Dirk Sudholt (Algorithmica, 2019)

On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements? - Samadhi Nallaperuma; Pietro S. Oliveto; Jorge Pérez Heredia; Dirk Sudholt (Algorithmica, 2019)

On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization. - Dirk Sudholt; Carsten Witt (Algorithmica, 2019)

On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism. - Edgar Covantes Osuna; Dirk Sudholt (Evol. Comput., 2019)

On the benefits and risks of using fitness sharing for multimodal optimisation. - Pietro S. Oliveto; Dirk Sudholt; Christine Zarges (Theor. Comput. Sci., 2019)

Do sophisticated evolutionary algorithms perform better than simple ones? - Michael Foster 001; Matthew Hughes; George O. O'Brien; Pietro S. Oliveto; James Pyle; Dirk Sudholt; James Williams (GECCO, 2020)

Causes and effects of fitness landscapes in unit test generation. - Nasser M. Albunian; Gordon Fraser 001; Dirk Sudholt (GECCO, 2020)

More effective randomized search heuristics for graph coloring through dynamic optimization. - Jakob Bossek; Frank Neumann 001; Pan Peng 001; Dirk Sudholt (GECCO, 2020)

On the choice of the parameter control mechanism in the (1+(λ, λ)) genetic algorithm. - Mario Alejandro Hevia Fajardo; Dirk Sudholt (GECCO, 2020)

Analysis of the performance of algorithm configurators for search heuristics with global mutation operators. - George T. Hall; Pietro S. Oliveto; Dirk Sudholt (GECCO, 2020)

A tight lower bound on the expected runtime of standard steady state genetic algorithms. - Pietro S. Oliveto; Dirk Sudholt; Carsten Witt (GECCO, 2020)

Fast Perturbative Algorithm Configurators. - George T. Hall; Pietro S. Oliveto; Dirk Sudholt (PPSN (1), 2020)

Measuring and Maintaining Population Diversity in Search-Based Unit Test Generation. - Nasser M. Albunian; Gordon Fraser 001; Dirk Sudholt (SSBSE, 2020)

Memetic algorithms outperform evolutionary algorithms in multimodal optimisation. - Phan Trung Hai Nguyen; Dirk Sudholt (Artif. Intell., 2020)

Design and analysis of diversity-based parent selection schemes for speeding up evolutionary multi-objective optimisation. - Edgar Covantes Osuna; Wanru Gao; Frank Neumann 001; Dirk Sudholt (Theor. Comput. Sci., 2020)

Parallel Black-Box Complexity With Tail Bounds. - Per Kristian Lehre; Dirk Sudholt (IEEE Trans. Evol. Comput., 2020)

Runtime Analysis of Crowding Mechanisms for Multimodal Optimization. - Edgar Covantes Osuna; Dirk Sudholt (IEEE Trans. Evol. Comput., 2020)

The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses. - Dirk Sudholt (Theory of Evolutionary Computation, 2020)

Do additional optima speed up evolutionary algorithms? - Jakob Bossek; Dirk Sudholt (FOGA, 2021)

Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. - Jakob Bossek; Frank Neumann 001; Pan Peng 001; Dirk Sudholt (Algorithmica, 2021)

The Complex Parameter Landscape of the Compact Genetic Algorithm. - Johannes Lengler; Dirk Sudholt; Carsten Witt (Algorithmica, 2021)

Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial. - Dirk Sudholt (Algorithmica, 2021)

Hard problems are easier for success-based parameter control. - Mario Alejandro Hevia Fajardo; Dirk Sudholt (GECCO, 2022)

Evolutionary Algorithms for Cardinality-Constrained Ising Models. - Vijay Dhanjibhai Bhuva; Duc-Cuong Dang; Liam Huber; Dirk Sudholt (PPSN (2), 2022)

On the impact of the performance metric on efficient algorithm configuration. - George T. Hall; Pietro S. Oliveto; Dirk Sudholt (Artif. Intell., 2022)

Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm. - Pietro S. Oliveto; Dirk Sudholt; Carsten Witt (Algorithmica, 2022)

Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation. - Edgar Covantes Osuna; Dirk Sudholt (Evol. Comput., 2022)

Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (λ, λ)) Genetic Algorithm. - Mario Alejandro Hevia Fajardo; Dirk Sudholt (ACM Trans. Evol. Learn. Optim., 2022)

A Proof That Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation. - Duc-Cuong Dang; Andre Opris; Bahare Salehi; Dirk Sudholt (AAAI, 2023)

The Cost of Randomness in Evolutionary Algorithms: Crossover can Save Random Bits. - Carlo Kneissl; Dirk Sudholt (EvoCOP, 2023)

Analysing the Robustness of NSGA-II under Noise. - Duc-Cuong Dang; Andre Opris; Bahare Salehi; Dirk Sudholt (GECCO, 2023)

Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima. - Joost Jorritsma; Johannes Lengler; Dirk Sudholt (GECCO, 2023)

Theory and Practice of Population Diversity in Evolutionary Computation. - Dirk Sudholt; Giovanni Squillero (GECCO Companion, 2023)

Do additional target points speed up evolutionary algorithms? - Jakob Bossek; Dirk Sudholt (Theor. Comput. Sci., 2023)

Evolutionary Algorithms for One-Sided Bipartite Crossing Minimisation (Poster Abstract). - Jakob Baumann; Ignaz Rutter; Dirk Sudholt (GD, 2024)

Evolutionary Computation Meets Graph Drawing: Runtime Analysis for Crossing Minimisation on Layered Graph Drawings. - Jakob Baumann; Ignaz Rutter; Dirk Sudholt (GECCO, 2024)

Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis. - Duc-Cuong Dang; Andre Opris; Dirk Sudholt (GECCO, 2024)

Runtime Analyses of NSGA-III on Many-Objective Problems. - Andre Opris; Duc-Cuong Dang; Frank Neumann 001; Dirk Sudholt (GECCO, 2024)

Hot of the Press: Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation. - Andre Opris; Duc-Cuong Dang; Dirk Sudholt (GECCO Companion, 2024)

A Tight O(4k/pc) Runtime Bound for a (μ+1)GA on Jumpk for Realistic Crossover Probabilities. - Andre Opris; Johannes Lengler; Dirk Sudholt (GECCO, 2024)

Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean Conjunctions. - Marcus Schmidbauer; Andre Opris; Jakob Bossek; Frank Neumann 001; Dirk Sudholt (GECCO, 2024)

On the Equivalence Between Stochastic Tournament and Power-Law Ranking Selection and How to Implement Them Efficiently. - Duc-Cuong Dang; Andre Opris; Dirk Sudholt (PPSN (3), 2024)

Level-Based Theorems for Runtime Analysis of Multi-objective Evolutionary Algorithms. - Duc-Cuong Dang; Andre Opris; Dirk Sudholt (PPSN (3), 2024)

Crossover can guarantee exponential speed-ups in evolutionary multi-objective optimisation. - Duc-Cuong Dang; Andre Opris; Dirk Sudholt (Artif. Intell., 2024)

A Tight O(4k/pc) Runtime Bound for a (μ+1) GA on Jumpk for Realistic Crossover Probabilities. - Andre Opris; Johannes Lengler; Dirk Sudholt (CoRR, 2024)

Evolutionary Algorithms for One-Sided Bipartite Crossing Minimisation. - Jakob Baumann; Ignaz Rutter; Dirk Sudholt (CoRR, 2024)

Theoretical Analysis of Quality Diversity Algorithms for a Classical Path Planning Problem. - Duc-Cuong Dang; Aneta Neumann; Frank Neumann 001; Andre Opris; Dirk Sudholt (CoRR, 2024)

Self-adjusting Population Sizes for Non-elitist Evolutionary Algorithms: Why Success Rates Matter. - Mario Alejandro Hevia Fajardo; Dirk Sudholt (Algorithmica, 2024)

Self-adjusting offspring population sizes outperform fixed parameters on the cliff function. - Mario Alejandro Hevia Fajardo; Dirk Sudholt (Artif. Intell., 2024)

Analysing Equilibrium States for Population Diversity. - Johannes Lengler; Andre Opris; Dirk Sudholt (Algorithmica, 2024)

Runtime Analysis of Quality Diversity Algorithms. - Jakob Bossek; Dirk Sudholt (Algorithmica, 2024)

Analysing the Effectiveness of Mutation Operators for One-Sided Bipartite Crossing Minimisation. - Jakob Baumann; Ignaz Rutter; Dirk Sudholt (GECCO, 2025)

Why Dominance Is Not Enough: Lessons from Practical Evolutionary Multi-Objective Algorithms. - Duc-Cuong Dang; Andre Opris; Dirk Sudholt (GECCO, 2025)

A Royal Road Function for Permutation Spaces: an Example Where Order Crossover is Provably Essential. - Andre Opris; Sebastian Sonntag; Dirk Sudholt (GECCO, 2025)

Empirical Linkage Learning Provably Builds Truthful Models on Concatenated Traps and H-IFF. - Marcus Schmidbauer; Dirk Sudholt (GECCO, 2025)

The Compact Genetic Algorithm Struggles on Cliff Functions. - Frank Neumann 001; Dirk Sudholt; Carsten Witt (Algorithmica, 2025)