Dirk Sudholt

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Co-authors

Carsten WittTechnical University of DenmarkVerified email at imm.dtu.dk
Pietro S. OlivetoSenior Lecturer, EPSRC Early Career Fellow, The University of Sheffield, UKVerified email at sheffield.ac.uk
Frank NeumannOptimisation and Logistics, School of ComputerVerified email at adelaide.edu.au
Tobias FriedrichChair for Algorithm Engineering, Hasso Plattner Institute, Potsdam, GermanyVerified email at hpi.de
Per Kristian LehreUniversity of BirminghamVerified email at cs.bham.ac.uk
Thomas JansenAberystwyth UniversityVerified email at aber.ac.uk
Duc-Cuong DangUniversity of Southampton, UKVerified email at soton.ac.uk
Tiago PaixaoInstituto Gulbenkian de CiênciaVerified email at igc.gulbenkian.pt
Christine ZargesDepartment of Computer Science, Aberystwyth UniversityVerified email at aber.ac.uk
Andrew M. SuttonUniversity of Minnesota DuluthVerified email at umn.edu
Martin S. KrejcaHasso Plattner Institute, University of Potsdam, Potsdam, GermanyVerified email at hpi.de
Barbora TrubenováETH ZurichVerified email at env.ethz.ch
Edgar Covantes OsunaProfessorVerified email at tec.mx
Benjamin DoerrProfessor at Ecole Polytechnique, France; senior researcher at Max Planck Institute for InformaticsVerified email at mpi-inf.mpg.de
Carola DoerrCNRS researcher at Sorbonne UniversityVerified email at mpi-inf.mpg.de
Dogan CorusUniversity of SheffieldVerified email at sheffield.ac.uk
Golnaz BadkobehThe University of WarwickVerified email at warwick.ac.uk
Xin YaoSouthern University of Science and Technology (SUSTech) and University of BirminghamVerified email at cs.bham.ac.uk
Jonathan RoweSchool of Computer Science, University of Birmingham Verified email at cs.bham.ac.uk
Andrea MambriniOptidromeVerified email at optidrome.com

Dirk Sudholt

University of Passau, Germany

Verified email at uni-passau.de - Homepage

Theory of nature-inspired algorithms randomised algorithms natural computation


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
A new method for lower bounds on the running time of evolutionary algorithms D Sudholt IEEE Transactions on Evolutionary Computation 17 (3), 418-435, 2012	118	2012
Crossover is provably essential for the Ising model on trees D Sudholt Proceedings of the 7th annual conference on Genetic and evolutionary …, 2005	98	2005
Analysis of diversity-preserving mechanisms for global exploration T Friedrich, PS Oliveto, D Sudholt, C Witt Evolutionary Computation 17 (4), 455-476, 2009	95	2009
The impact of parametrization in memetic evolutionary algorithms D Sudholt Theoretical Computer Science 410 (26), 2511-2528, 2009	76	2009
The choice of the offspring population size in the (1, λ) evolutionary algorithm JE Rowe, D Sudholt Theoretical Computer Science 545, 20-38, 2014	75	2014
How crossover helps in pseudo-boolean optimization T Kötzing, D Sudholt, M Theile Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011	70	2011
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus F Neumann, D Sudholt, C Witt Swarm Intelligence 3 (1), 35-68, 2009	70	2009
Adaptive population models for offspring populations and parallel evolutionary algorithms J Lässig, D Sudholt Proceedings of the 11th workshop proceedings on Foundations of genetic …, 2011	63	2011
Running time analysis of ant colony optimization for shortest path problems D Sudholt, C Thyssen Journal of Discrete Algorithms 10, 165-180, 2012	60	2012
Mutation rate matters even when optimizing monotonic functions B Doerr, T Jansen, D Sudholt, C Winzen, C Zarges Evolutionary computation 21 (1), 1-27, 2013	57	2013
On the runtime analysis of the 1-ANT ACO algorithm B Doerr, F Neumann, D Sudholt, C Witt Proceedings of the 9th annual conference on Genetic and evolutionary …, 2007	55	2007
Unbiased black-box complexity of parallel search G Badkobeh, PK Lehre, D Sudholt International Conference on Parallel Problem Solving from Nature, 892-901, 2014	54	2014
Escaping local optima using crossover with emergent diversity DC Dang, T Friedrich, T Kötzing, MS Krejca, PK Lehre, PS Oliveto, ... IEEE Transactions on Evolutionary Computation 22 (3), 484-497, 2017	53	2017
Crossover speeds up building-block assembly D Sudholt Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	52	2012
Parallel evolutionary algorithms D Sudholt Springer Handbook of Computational Intelligence, 929-959, 2015	50	2015
The choice of the offspring population size in the (1, λ) EA JE Rowe, D Sudholt Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	48	2012
A simple ant colony optimizer for stochastic shortest path problems D Sudholt, C Thyssen Algorithmica 64 (4), 643-672, 2012	46	2012
Rigorous analyses for the combination of ant colony optimization and local search F Neumann, D Sudholt, C Witt International Conference on Ant Colony Optimization and Swarm Intelligence …, 2008	44	2008
How crossover speeds up building block assembly in genetic algorithms D Sudholt Evolutionary Computation 25 (2), 237-274, 2017	42	2017
Improved evolutionary algorithm design for the project scheduling problem based on runtime analysis LL Minku, D Sudholt, X Yao IEEE Transactions on Software Engineering 40 (1), 83-102, 2013	41	2013

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