Frank Neumann
Frank Neumann
Optimisation and Logistics, School of Computer
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Cited by
Cited by
Bioinspired computation in combinatorial optimization: algorithms and their computational complexity
F Neumann, C Witt
Springer, 2010
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
F Neumann, I Wegener
Theoretical Computer Science 378 (1), 32-40, 2007
On the effects of adding objectives to plateau functions
D Brockhoff, T Friedrich, N Hebbinghaus, C Klein, F Neumann, E Zitzler
IEEE Transactions on Evolutionary Computation 13 (3), 591-603, 2009
Runtime analysis of a simple ant colony optimization algorithm
F Neumann, C Witt
Algorithmica 54 (2), 243-255, 2009
Minimum spanning trees made easier via multi-objective optimization
F Neumann, I Wegener
Natural Computing 5 (3), 305-319, 2006
Approximating covering problems by randomized search heuristics using multi-objective models
T Friedrich, J He, N Hebbinghaus, F Neumann, C Witt
Evolutionary Computation 18 (4), 617-633, 2010
Optimal fixed and adaptive mutation rates for the LeadingOnes problem
S Böttcher, B Doerr, F Neumann
International Conference on Parallel Problem Solving from Nature, 1-10, 2010
Ant colony optimization and the minimum spanning tree problem
F Neumann, C Witt
Theoretical Computer Science 411 (25), 2406-2413, 2010
Automated algorithm selection: Survey and perspectives
P Kerschke, HH Hoos, F Neumann, H Trautmann
Evolutionary computation 27 (1), 3-45, 2019
A comprehensive benchmark set and heuristics for the traveling thief problem
S Polyakovskiy, MR Bonyadi, M Wagner, Z Michalewicz, F Neumann
Proceedings of the 2014 Annual Conference on Genetic and Evolutionary …, 2014
Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem
F Neumann
European Journal of Operational Research 181 (3), 1620-1629, 2007
A fast and effective local search algorithm for optimizing the placement of wind turbines
M Wagner, J Day, F Neumann
Renewable energy 51, 64-70, 2013
Analyzing hypervolume indicator based algorithms
D Brockhoff, T Friedrich, F Neumann
International Conference on Parallel Problem Solving from Nature, 651-660, 2008
Approximation-guided evolutionary multi-objective optimization
K Bringmann, T Friedrich, F Neumann, M Wagner
Twenty-Second International Joint Conference on Artificial Intelligence, 2011
Precipitation variability in the winter rainfall zone of South Africa during the last 1400 yr linked to the austral westerlies
JC Stager, PA Mayewski, J White, BM Chase, FH Neumann, ...
Climate of the Past 8 (3), 877-887, 2012
Expected runtimes of evolutionary algorithms for the Eulerian cycle problem
F Neumann
Computers & Operations Research 35 (9), 2750-2759, 2008
Expected runtimes of evolutionary algorithms for the Eulerian cycle problem
F Neumann
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No …, 2004
A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem
O Mersmann, B Bischl, H Trautmann, M Wagner, J Bossek, F Neumann
Annals of Mathematics and Artificial Intelligence 69 (2), 151-182, 2013
Theoretical analysis of fitness-proportional selection: landscapes and efficiency
F Neumann, PS Oliveto, C Witt
Proceedings of the 11th Annual conference on Genetic and evolutionary …, 2009
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus
F Neumann, D Sudholt, C Witt
Swarm Intelligence 3 (1), 35-68, 2009
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