Dirk Sudholt

发表

General Upper Bounds on the Running Time of Parallel Evolutionary Algorithms

Jörg Lässig, Dirk Sudholt, 2012, ArXiv.

Toward a unifying framework for evolutionary processes

Andrew M. Sutton, N. Barton, T. Friedrich, 2015, Journal of theoretical biology.

Selection Limits to Adaptive Walks on Correlated Landscapes

Dirk Sudholt, Tiago Paixão, Barbora Trubenová, 2016, Genetics.

Parallel Evolutionary Algorithms Chapter in the Handbook of Computational Intelligence

W. Pedrycz, Dirk Sudholt, 2013 .

Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods Runtime Analysis of Binary Pso Runtime Analysis of Binary Pso *

C. Witt, Dirk Sudholt, 2022 .

Theory of Swarm Intelligence

Dirk Sudholt, Dirk Sudholt, 2016, GECCO.

GECCO Companion ’15: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference

Dirk Sudholt, Gisele L. Pappa, Youhei Akimoto, 2015 .

Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1+(λ, λ)) Genetic Algorithm

Mario Alejandro Hevia Fajardo, Dirk Sudholt, 2022, ACM Transactions on Evolutionary Learning and Optimization.

Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter

Mario Alejandro Hevia Fajardo, Dirk Sudholt, 2021, GECCO.

On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization

C. Witt, Dirk Sudholt, 2018, Algorithmica.

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism

Dirk Sudholt, Tiago Paixão, Barbora Trubenová, 2017, Algorithmica.

Hard problems are easier for success-based parameter control

Mario Alejandro Hevia Fajardo, Dirk Sudholt, 2022, GECCO.

Hybridizing Evolutionary Algorithms with Variable-Depth Search to Overcome Local Optima

Dirk Sudholt, Dirk Sudholt, 2011, Algorithmica.

Computational complexity of evolutionary algorithms, hybridizations, and swarm intelligence

Dirk Sudholt, Dirk Sudholt, 2008 .

Adaptive population models for offspring populations and parallel evolutionary algorithms

Dirk Sudholt, Jörg Lässig, Jörg Lässig, 2011, FOGA '11.

On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization

Dirk Sudholt, Carsten Witt, C. Witt, 2018, Algorithmica.

How Crossover Speeds up Building Block Assembly in Genetic Algorithms

Dirk Sudholt, Dirk Sudholt, 2014, Evolutionary Computation.

The Complex Parameter Landscape of the Compact Genetic Algorithm

Dirk Sudholt, Carsten Witt, Johannes Lengler, 2020, Algorithmica.

More effective randomized search heuristics for graph coloring through dynamic optimization

Dirk Sudholt, Jakob Bossek, Frank Neumann, 2020, GECCO.

On the impact of the cutoff time on the performance of algorithm configurators

Pietro Simone Oliveto, Dirk Sudholt, George T. Hall, 2019, GECCO.

Medium step sizes are harmful for the compact genetic algorithm

Dirk Sudholt, Carsten Witt, Johannes Lengler, 2018, GECCO.

On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements?

Pietro Simone Oliveto, Dirk Sudholt, Samadhi Nallaperuma, 2018, Algorithmica.

When is it beneficial to reject improvements?

Pietro Simone Oliveto, Dirk Sudholt, Samadhi Nallaperuma, 2017, GECCO.

Escaping Local Optima using Crossover with Emergent or Reinforced Diversity pdf

Duc-Cuong Dang, Per Kristian Lehre, Pietro Simone Oliveto, 2016, ArXiv.

When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys

Pietro Simone Oliveto, Dirk Sudholt, Tiago Paixão, 2016, GECCO.

Update Strength in EDAs and ACO: How to Avoid Genetic Drift

Dirk Sudholt, Carsten Witt, C. Witt, 2016, GECCO.

Self-stabilizing Cuts in Synchronous Networks

Thomas Sauerwald, Dirk Sudholt, Thomas Sauerwald, 2008, SIROCCO.

Analysis of the Clearing Diversity-Preserving Mechanism

Dirk Sudholt, Edgar Covantes Osuna, Dirk Sudholt, 2017, FOGA '17.

Self-adjusting offspring population sizes outperform fixed parameters on the cliff function

Dirk Sudholt, Mario Alejandro Hevia Fajardo, Dirk Sudholt, 2021, FOGA.

Runtime analysis of binary PSO

Dirk Sudholt, Carsten Witt, C. Witt, 2008, GECCO '08.

Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods Theoretical Analysis of Diversity Mechanisms for Global Exploration Theoretical Analysis of Diversity Mechanisms for Global Exploration

Pietro Simone Oliveto, Dirk Sudholt, Tobias Friedrich, 2022 .

On the runtime analysis of the 1-ANT ACO algorithm

Dirk Sudholt, Frank Neumann, Benjamin Doerr, 2007, GECCO '07.

Causes and effects of fitness landscapes in unit test generation

Dirk Sudholt, Gordon Fraser, Nasser Albunian, 2020, GECCO.

Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis

Dirk Sudholt, Xin Yao, Leandro L. Minku, 2014, IEEE Transactions on Software Engineering.

Do additional target points speed up evolutionary algorithms?

Dirk Sudholt, Jakob Bossek, 2023, Theor. Comput. Sci..

The compact genetic algorithm struggles on Cliff functions

C. Witt, Dirk Sudholt, F. Neumann, 2022, GECCO.

Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm

C. Witt, Dirk Sudholt, P. S. Oliveto, 2021, Algorithmica.

Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem

Dirk Sudholt, Jakob Bossek, Frank Neumann, 2021, Algorithmica.

Empirical Analysis of Diversity-Preserving Mechanisms on Example Landscapes for Multimodal Optimisation

Dirk Sudholt, Edgar Covantes Osuna, Dirk Sudholt, 2018, PPSN.

Escaping Local Optima Using Crossover With Emergent Diversity

Per Kristian Lehre, Dirk Sudholt, Andrew M. Sutton, 2018, IEEE Transactions on Evolutionary Computation.

Runtime analysis of probabilistic crowding and restricted tournament selection for bimodal optimisation

Dirk Sudholt, Edgar Covantes Osuna, Dirk Sudholt, 2018, GECCO.

Speeding up evolutionary multi-objective optimisation through diversity-based parent selection

Dirk Sudholt, Wanru Gao, Frank Neumann, 2017, GECCO.

On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation

Thomas Jansen, Pietro Simone Oliveto, Dirk Sudholt, 2016, Algorithmica.

Escaping Local Optima with Diversity Mechanisms and Crossover

Duc-Cuong Dang, Per Kristian Lehre, Pietro Simone Oliveto, 2016, GECCO.

Towards a Runtime Comparison of Natural and Artificial Evolution

Dirk Sudholt, Tiago Paixão, Jorge Pérez Heredia, 2015, Algorithmica.

Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms

Dirk Sudholt, Andrea Mambrini, Dirk Sudholt, 2015, Evolutionary Computation.

On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations

Thomas Jansen, Pietro Simone Oliveto, Dirk Sudholt, 2015, GECCO.

Analysis of speedups in parallel evolutionary algorithms and (1+λ) EAs for combinatorial optimization

Dirk Sudholt, Jörg Lässig, Jörg Lässig, 2014, Theor. Comput. Sci..

Unbiased Black-Box Complexity of Parallel Search

Per Kristian Lehre, Dirk Sudholt, Golnaz Badkobeh, 2014, PPSN.

On the Runtime Analysis of Fitness Sharing Mechanisms

Pietro Simone Oliveto, Dirk Sudholt, Christine Zarges, 2014, PPSN.

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

Dirk Sudholt, Jonathan E. Rowe, J. Rowe, 2014, Theor. Comput. Sci..

On the runtime analysis of stochastic ageing mechanisms

Pietro Simone Oliveto, Dirk Sudholt, Dirk Sudholt, 2014, GECCO.

Design and analysis of migration in parallel evolutionary algorithms

Dirk Sudholt, Jörg Lässig, Jörg Lässig, 2013, Soft Comput..

When do evolutionary algorithms optimize separable functions in parallel?

Dirk Sudholt, Benjamin Doerr, Carsten Witt, 2013, FOGA XII '13.

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

Dirk Sudholt, Dirk Sudholt, 2011, IEEE Transactions on Evolutionary Computation.

Homogeneous and Heterogeneous Island Models for the Set Cover Problem

Dirk Sudholt, Xin Yao, Andrea Mambrini, 2012, PPSN.

Crossover speeds up building-block assembly

Dirk Sudholt, Dirk Sudholt, 2012, GECCO '12.

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

Dirk Sudholt, Jonathan E. Rowe, J. Rowe, 2012, GECCO '12.

Running time analysis of Ant Colony Optimization for shortest path problems

Dirk Sudholt, Christian Thyssen, Dirk Sudholt, 2012, J. Discrete Algorithms.

A Simple Ant Colony Optimizer for Stochastic Shortest Path Problems

Dirk Sudholt, Christian Thyssen, Dirk Sudholt, 2012, Algorithmica.

How crossover helps in pseudo-boolean optimization

Dirk Sudholt, Timo Kötzing, Madeleine Theile, 2011, GECCO '11.

On the effectiveness of crossover for migration in parallel evolutionary algorithms

Pietro Simone Oliveto, Dirk Sudholt, Günter Rudolph, 2011, GECCO '11.

The benefit of migration in parallel evolutionary algorithms

Dirk Sudholt, Jörg Lässig, Jörg Lässig, 2010, GECCO '10.

Ant colony optimization for stochastic shortest path problems

Dirk Sudholt, Christian Horoba, Christian Horoba, 2010, GECCO '10.

A few ants are enough: ACO with iteration-best update

Dirk Sudholt, Frank Neumann, Carsten Witt, 2010, GECCO '10.

Analysis of Diversity-Preserving Mechanisms for Global Exploration

Pietro Simone Oliveto, Dirk Sudholt, Tobias Friedrich, 2009, Evolutionary Computation.

The impact of parametrization in memetic evolutionary algorithms

Dirk Sudholt, Dirk Sudholt, 2009, Theor. Comput. Sci..

Crossover is provably essential for the Ising model on trees

Dirk Sudholt, Dirk Sudholt, 2005, GECCO '05.

Mutation Rate Matters Even When Optimizing Monotonic Functions

Thomas Jansen, Dirk Sudholt, Christine Zarges, 2013, Evolutionary Computation.

A Proof that Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation

Dirk Sudholt, D. Dang, Andre Opris, 2023, AAAI.

General Lower Bounds for the Running Time of Evolutionary Algorithms

Dirk Sudholt, Dirk Sudholt, 2010, PPSN.

Do additional optima speed up evolutionary algorithms?

Dirk Sudholt, Jakob Bossek, Dirk Sudholt, 2021, FOGA.

Time complexity analysis of RLS and (1 + 1) EA for the edge coloring problem

Dirk Sudholt, Jakob Bossek, Dirk Sudholt, 2019, FOGA '19.

Runtime analysis of a binary particle swarm optimizer

Dirk Sudholt, Carsten Witt, C. Witt, 2010, Theor. Comput. Sci..

Analysis of different MMAS ACO algorithms on unimodal functions and plateaus

Dirk Sudholt, Carsten Witt, Frank Neumann, 2009, Swarm Intelligence.

Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation pdf

Dirk Sudholt, Wanru Gao, Frank Neumann, 2018, Theor. Comput. Sci..

Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima

Dirk Sudholt, J. Lengler, J. Jorritsma, 2023, ArXiv.

Parallel Black-Box Complexity With Tail Bounds

Per Kristian Lehre, Dirk Sudholt, Dirk Sudholt, 2019, IEEE Transactions on Evolutionary Computation.

General Upper Bounds on the Runtime of Parallel Evolutionary Algorithms*

Dirk Sudholt, Jörg Lässig, Jörg Lässig, 2014, Evolutionary Computation.

Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem

Dirk Sudholt, Frank Neumann, Samadhi Nallaperuma, 2016, Evolutionary Computation.

Analysing Equilibrium States for Population Diversity

Dirk Sudholt, J. Lengler, Andre Opris, 2023, GECCO.

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

Dirk Sudholt, Dirk Sudholt, 2018, Theory of Evolutionary Computation.

Black-box Complexity of Parallel Search with Distributed Populations

Per Kristian Lehre, Dirk Sudholt, Golnaz Badkobeh, 2015, FOGA.

On the impact of the performance metric on efficient algorithm configuration

Dirk Sudholt, Pietro S. Oliveto, George T. Hall, 2021, Artif. Intell..

Runtime analysis of randomized search heuristics for dynamic graph coloring

Dirk Sudholt, Jakob Bossek, Frank Neumann, 2019, GECCO.

On the robustness of evolutionary algorithms to noise: refined results and an example where noise helps

Dirk Sudholt, Dirk Sudholt, 2018, GECCO.

Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation

Dirk Sudholt, Edgar Covantes Osuna, Dirk Sudholt, 2021, Evolutionary Computation.

Memetic algorithms outperform evolutionary algorithms in multimodal optimisation

Dirk Sudholt, Phan Trung Hai Nguyen, Dirk Sudholt, 2020, Artif. Intell..

Runtime Analysis of Crowding Mechanisms for Multimodal Optimization

Dirk Sudholt, Edgar Covantes Osuna, Dirk Sudholt, 2020, IEEE Transactions on Evolutionary Computation.

On the benefits and risks of using fitness sharing for multimodal optimisation

Dirk Sudholt, P. S. Oliveto, C. Zarges, 2019, Theor. Comput. Sci..

On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism

Dirk Sudholt, Edgar Covantes Osuna, Dirk Sudholt, 2018, Evolutionary Computation.

Theoretical results on bet-and-run as an initialisation strategy

Dirk Sudholt, Christine Zarges, Markus Wagner, 2017, GECCO.

Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial

Dirk Sudholt, 2020, Algorithmica.

Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial

Dirk Sudholt, Dirk Sudholt, 2018, Algorithmica.

Memetic algorithms beat evolutionary algorithms on the class of hurdle problems

Dirk Sudholt, Phan Trung Hai Nguyen, Dirk Sudholt, 2018, GECCO.

Analysis of an Asymmetric Mutation Operator

Thomas Jansen, Dirk Sudholt, T. Jansen, 2010, Evolutionary Computation.

Evolutionary algorithms for the project scheduling problem: runtime analysis and improved design

Dirk Sudholt, Xin Yao, Leandro L. Minku, 2012, GECCO '12.

A tight lower bound on the expected runtime of standard steady state genetic algorithms

Dirk Sudholt, Carsten Witt, Pietro S. Oliveto, 2020, GECCO.

Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions

Dirk Sudholt, Frank Neumann, Carsten Witt, 2007, SLS.

Design and analysis of different alternating variable searches for search-based software testing

Dirk Sudholt, Phil McMinn, Joseph Kempka, 2015, Theor. Comput. Sci..

Runtime Analysis of Quality Diversity Algorithms

Dirk Sudholt, Jakob Bossek, 2023, 2305.18966.

Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (λ, λ)) Genetic Algorithm

Mario Alejandro Hevia Fajardo, Dirk Sudholt, 2022, ACM Trans. Evol. Learn. Optim..

On the choice of the parameter control mechanism in the (1+(λ, λ)) genetic algorithm

Dirk Sudholt, Mario Alejandro Hevia Fajardo, Dirk Sudholt, 2020, GECCO.

Optimizing Monotone Functions Can Be Difficult pdf

Thomas Jansen, Dirk Sudholt, Christine Zarges, 2010, PPSN.

Measuring and Maintaining Population Diversity in Search-Based Unit Test Generation

Dirk Sudholt, Gordon Fraser, Nasser Albunian, 2020, SSBSE.

Fast Perturbative Algorithm Configurators

Dirk Sudholt, Pietro S. Oliveto, George T. Hall, 2020, PPSN.

Runtime analysis of binary PSO

C. Witt, Dirk Sudholt, 2008, Annual Conference on Genetic and Evolutionary Computation.

Design and analysis of migration in parallel evolutionary algorithms

Jörg Lässig, Dirk Sudholt, 2013, Soft Computing.

Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization - (Extended Abstract) pdf

Dirk Sudholt, Jörg Lässig, Jörg Lässig, 2011, ISAAC.

Parallel Evolutionary Algorithms

Dirk Sudholt, Dirk Sudholt, 2015, Handbook of Computational Intelligence.

General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms

Dirk Sudholt, Jörg Lässig, Jörg Lässig, 2010, PPSN.

Experimental supplements to the theoretical analysis of migration in the Island model

Dirk Sudholt, Jörg Lässig, Jörg Lässig, 2010, PPSN 2010.

Principled Design and Runtime Analysis of Abstract Convex Evolutionary Search

Dirk Sudholt, Alberto Moraglio, Dirk Sudholt, 2017, Evolutionary Computation.

Design and analysis of adaptive migration intervals in parallel evolutionary algorithms

Dirk Sudholt, Andrea Mambrini, Dirk Sudholt, 2014, GECCO.

Runtime analysis of convex evolutionary search

Dirk Sudholt, Alberto Moraglio, Dirk Sudholt, 2012, GECCO '12.

Simple max-min ant systems and the optimization of linear pseudo-boolean functions pdf

Dirk Sudholt, Markus Wagner, Frank Neumann, 2010, FOGA '11.

A self-stabilizing algorithm for cut problems in synchronous networks

Thomas Sauerwald, Dirk Sudholt, Thomas Sauerwald, 2010, Theor. Comput. Sci..

Do sophisticated evolutionary algorithms perform better than simple ones?

Dirk Sudholt, George O'Brien, Pietro S. Oliveto, 2020, GECCO.

Emergence of Diversity and Its Benefits for Crossover in Genetic Algorithms

Duc-Cuong Dang, Per Kristian Lehre, Pietro Simone Oliveto, 2016, PPSN.

Theory and Practice of Population Diversity in Evolutionary Computation

Dirk Sudholt, Giovanni Squillero, 2023, GECCO Companion.

Theory and practice of population diversity in evolutionary computation

Dirk Sudholt, Giovanni Squillero, 2022, GECCO Companion.

Theory and practice of population diversity in evolutionary computation

Dirk Sudholt, Giovanni Squillero, Dirk Sudholt, 2020, GECCO Companion.

Memetic Algorithms: Parametrization and Balancing Local and Global Search pdf

Dirk Sudholt, Dirk Sudholt, 2011, ArXiv.

On the analysis of the (1+1) memetic algorithm

Dirk Sudholt, Dirk Sudholt, 2006, GECCO.

Analysis of an Iterated Local Search Algorithm for Vertex Coloring

Dirk Sudholt, Christine Zarges, Dirk Sudholt, 2010, ISAAC.

The Ising Model: Simple Evolutionary Algorithms as Adaptation Schemes

I. Wegener, T. Jansen, Patrick Briest, 2004 .

Analysing the Robustness of NSGA-II under Noise

Dirk Sudholt, D. Dang, Andre Opris, 2023, GECCO.

A theoretical runtime and empirical analysis of different alternating variable searches for search-based testing

Dirk Sudholt, Phil McMinn, Joseph Kempka, 2013, GECCO '13.

Theory of swarm intelligence

Dirk Sudholt, Dirk Sudholt, 2011, GECCO.

On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements?

Dirk Sudholt, Samadhi Nallaperuma, J. Heredia, 2018, Algorithmica.

Using markov-chain mixing time estimates for the analysis of ant colony optimization

Dirk Sudholt, Dirk Sudholt, 2011, FOGA '11.

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism

Pietro Simone Oliveto, Dirk Sudholt, Tiago Paixão, 2017, Algorithmica.

How Crossover Speeds up Building Block Assembly in Genetic Algorithms

Dirk Sudholt, 2014, Evolutionary Computation.

Parallel Evolutionary Algorithms

El-Ghazali Talbi, Dirk Sudholt, 2015, Handbook of Computational Intelligence.

University of Birmingham Escaping local optima using crossover with emergent diversity

Andrew M. Sutton, Martin S. Krejca, Timo Kötzing, 2017 .

Running Time Analysis of ACO Systems for Shortest Path Problems

Dirk Sudholt, Christian Horoba, Christian Horoba, 2009, SLS.