Dirk Sudholt

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Carsten WittTechnical University of DenmarkVerified email at imm.dtu.dk
Pietro S. OlivetoProfessor of Computer Science, Chair in Algorithms, The University of SheffieldVerified 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 LehreProfessor at University of Birmingham and UKRI Turing AI Acceleration FellowVerified email at cs.bham.ac.uk
Duc-Cuong DangUniversity of Southampton, UKVerified email at soton.ac.uk
Thomas JansenAberystwyth UniversityVerified email at aber.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. KrejcaLIX, CNRS, Ecole Polytechnique, IPPVerified email at polytechnique.edu
Edgar Covantes OsunaProfessorVerified email at tec.mx
Barbora TrubenováETH ZurichVerified email at env.ethz.ch
Benjamin DoerrProfessor at Ecole Polytechnique, FranceVerified email at lix.polytechnique.fr
Jakob BossekPaderborn UniversityVerified email at uni-paderborn.de
Carola DoerrCNRS research director (DR) at Sorbonne Université, Paris, FranceVerified email at mpi-inf.mpg.de
Mario Alejandro Hevia FajardoResearch Fellow, University of BirminghamVerified email at bham.ac.uk
Dogan CorusKadir Has UniversityVerified email at khas.edu.tr
Golnaz BadkobehGoldsmiths, University of LondonVerified email at gold.ac.uk
Johannes LenglerETH ZürichVerified email at inf.ethz.ch

Dirk Sudholt

University of Passau, Germany

Verified email at uni-passau.de - Homepage

Theory of nature-inspired algorithms randomised algorithms natural computation evolutionary 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	159	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	119	2017
Analysis of diversity-preserving mechanisms for global exploration T Friedrich, PS Oliveto, D Sudholt, C Witt Evolutionary Computation 17 (4), 455-476, 2009	119	2009
Crossover is provably essential for the Ising model on trees D Sudholt Proceedings of the 7th annual conference on Genetic and evolutionary …, 2005	111	2005
The choice of the offspring population size in the (1, λ) evolutionary algorithm JE Rowe, D Sudholt Theoretical Computer Science 545, 20-38, 2014	106	2014
Parallel evolutionary algorithms D Sudholt Springer Handbook of Computational Intelligence, 929-959, 2015	91	2015
How crossover helps in pseudo-Boolean optimization T Kötzing, D Sudholt, M Theile Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011	88	2011
Running time analysis of ant colony optimization for shortest path problems D Sudholt, C Thyssen Journal of Discrete Algorithms 10, 165-180, 2012	85	2012
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus F Neumann, D Sudholt, C Witt Swarm Intelligence 3, 35-68, 2009	82	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	81	2011
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	78	2013
The impact of parametrization in memetic evolutionary algorithms D Sudholt Theoretical Computer Science 410 (26), 2511-2528, 2009	78	2009
Unbiased black-box complexity of parallel search G Badkobeh, PK Lehre, D Sudholt Parallel Problem Solving from Nature–PPSN XIII: 13th International …, 2014	76	2014
Escaping local optima with diversity mechanisms and crossover DC Dang, T Friedrich, T Kötzing, MS Krejca, PK Lehre, PS Oliveto, ... Proceedings of the Genetic and Evolutionary Computation Conference 2016, 645-652, 2016	69	2016
The benefits of population diversity in evolutionary algorithms: a survey of rigorous runtime analyses D Sudholt Theory of evolutionary computation: Recent developments in discrete …, 2020	65	2020
How crossover speeds up building block assembly in genetic algorithms D Sudholt Evolutionary computation 25 (2), 237-274, 2017	62	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	60	2013
Crossover speeds up building-block assembly D Sudholt Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	56	2012
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
A simple ant colony optimizer for stochastic shortest path problems D Sudholt, C Thyssen Algorithmica 64, 643-672, 2012	54	2012

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