Dirk Sudholt

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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
Edgar Covantes OsunaProfessorVerified email at tec.mx
Barbora TrubenováETH ZurichVerified email at env.ethz.ch
Benjamin DoerrProfessor at Ecole Polytechnique, France; senior researcher at Max Planck Institute for InformaticsVerified email at mpi-inf.mpg.de
Dogan CorusKadir Has UniversityVerified email at khas.edu.tr
Carola DoerrCNRS researcher at Sorbonne Université, Paris, FranceVerified email at mpi-inf.mpg.de
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


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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	119	2012
Analysis of diversity-preserving mechanisms for global exploration T Friedrich, PS Oliveto, D Sudholt, C Witt Evolutionary Computation 17 (4), 455-476, 2009	99	2009
Crossover is provably essential for the Ising model on trees D Sudholt Proceedings of the 7th annual conference on Genetic and evolutionary …, 2005	99	2005
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	74	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	71	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	71	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	64	2011
Running time analysis of ant colony optimization for shortest path problems D Sudholt, C Thyssen Journal of Discrete Algorithms 10, 165-180, 2012	61	2012
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	59	2017
Unbiased black-box complexity of parallel search G Badkobeh, PK Lehre, D Sudholt International Conference on Parallel Problem Solving from Nature, 892-901, 2014	57	2014
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	56	2007
Parallel evolutionary algorithms D Sudholt Springer Handbook of Computational Intelligence, 929-959, 2015	54	2015
Crossover speeds up building-block assembly D Sudholt Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	52	2012
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	47	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
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	43	2013
General lower bounds for the running time of evolutionary algorithms D Sudholt International Conference on Parallel Problem Solving from Nature, 124-133, 2010	43	2010

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