Dirk Sudholt

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Pietro S. OlivetoProfessor of Computer Science, Chair in Algorithms, The University of SheffieldEmail verificata su sheffield.ac.uk
Carsten WittTechnical University of DenmarkEmail verificata su imm.dtu.dk
Frank NeumannOptimisation and Logistics, School of ComputerEmail verificata su adelaide.edu.au
Duc-Cuong DangUniversity of Southampton, UKEmail verificata su soton.ac.uk
Tobias FriedrichChair for Algorithm Engineering, Hasso Plattner Institute, Potsdam, GermanyEmail verificata su hpi.de
Per Kristian LehreProfessor at University of Birmingham and UKRI Turing AI Acceleration FellowEmail verificata su bham.ac.uk
Thomas JansenAberystwyth UniversityEmail verificata su aber.ac.uk
Tiago PaixaoInstituto Gulbenkian de CiênciaEmail verificata su igc.gulbenkian.pt
Andrew M. SuttonUniversity of Minnesota DuluthEmail verificata su umn.edu
Christine ZargesDepartment of Computer Science, Aberystwyth UniversityEmail verificata su aber.ac.uk
Martin S. KrejcaLIX, CNRS, Ecole Polytechnique, IPPEmail verificata su polytechnique.edu
Andre OprisAssistant Professor, University of PassauEmail verificata su uni-passau.de
Edgar Covantes OsunaData ScientistEmail verificata su heineken.com
Barbora TrubenováETH ZurichEmail verificata su env.ethz.ch
Mario Alejandro Hevia FajardoResearch Fellow, University of BirminghamEmail verificata su bham.ac.uk
Jakob BossekPaderborn UniversityEmail verificata su uni-paderborn.de
Benjamin DoerrProfessor at Ecole Polytechnique, FranceEmail verificata su lix.polytechnique.fr
Johannes LenglerETH ZürichEmail verificata su inf.ethz.ch
Golnaz BadkobehCity, University of LondonEmail verificata su city.ac.uk
Carola DoerrCNRS research director (DR) at Sorbonne Université, Paris, FranceEmail verificata su mpi-inf.mpg.de

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Dirk Sudholt

University of Passau, Germany

Email verificata su uni-passau.de - Home page

Theory of nature-inspired algorithms randomised algorithms natural computation evolutionary computation


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	172	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	145	2017
Analysis of diversity-preserving mechanisms for global exploration T Friedrich, PS Oliveto, D Sudholt, C Witt Evolutionary Computation 17 (4), 455-476, 2009	129	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	115	2014
Crossover is provably essential for the Ising model on trees D Sudholt Proceedings of the 7th annual conference on Genetic and evolutionary …, 2005	114	2005
Parallel evolutionary algorithms D Sudholt Springer Handbook of Computational Intelligence, 929-959, 2015	104	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	98	2011
Running time analysis of ant colony optimization for shortest path problems D Sudholt, C Thyssen Journal of Discrete Algorithms 10, 165-180, 2012	94	2012
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	88	2020
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus F Neumann, D Sudholt, C Witt Swarm Intelligence 3, 35-68, 2009	85	2009
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	83	2013
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	82	2011
The impact of parametrization in memetic evolutionary algorithms D Sudholt Theoretical Computer Science 410 (26), 2511-2528, 2009	82	2009
Unbiased black-box complexity of parallel search G Badkobeh, PK Lehre, D Sudholt Parallel Problem Solving from Nature–PPSN XIII: 13th International …, 2014	79	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	76	2016
How crossover speeds up building block assembly in genetic algorithms D Sudholt Evolutionary computation 25 (2), 237-274, 2017	73	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	64	2013
A simple ant colony optimizer for stochastic shortest path problems D Sudholt, C Thyssen Algorithmica 64, 643-672, 2012	60	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	58	2007
Crossover speeds up building-block assembly D Sudholt Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	57	2012

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