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AddressFrank NeumannOptimisation and Logistics School of Computer Science Ingkarni Wardli, Office 4.55 The University of Adelaide Adelaide, SA 5005, Australia Phone: +61 8 8313 4477 email: frank.neumann@adelaide.edu.au Researcher Profile Group leader of Optimisation and Logistics |
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F. Neumann, C. Witt (2010): Bioinspired Computation in Combinatorial Optimization -- Algorithms and Their Computational Complexity. Natural Computing Series, Springer, ISBN 978-3-642-16543-6. Original publication at Springer (including online access) Book homepage (including free author-created final version) |
F. Neumann, C. Witt (2010):
Bioinspired Computation in Combinatorial
Optimization -- Algorithms and Their Computational Complexity.
Natural Computing Series, Springer, ISBN 978-3-642-16543-6.
Original publication at Springer (including online access),
Book homepage (including free author-created final version)
T. Friedrich, F. Neumann, A. M. Sutton (2016): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2016, ACM Press, 1174 pages.
T. Friedrich, F. Neumann, A. M. Sutton (2016): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2016, Companion, ACM Press, 1482 pages.
F. Neumann, B. Doerr, P. K. Lehre, P. C. Haddow (2014):
Special Issue on "Theoretical Foundations of Evolutionary Computation".
IEEE Transactions on Evolutionary Computation, Volume 18, Issue 5.
F. Neumann, K. De Jong (2013): FOGA 2013: Proceedings of the twelfth workshop on Foundations of Genetic Algorithms XII, ACM Press, 190 pages.
P. K. Lehre, F. Neumann, J. E. Rowe, X. Yao (2012): Special issue on "Theoretical Foundations of Evolutionary Computation". Theoretical Computer Science, Volume 425.
T. Jansen, F. Neumann (2010):
Special Issue on "Theoretical Aspects of Evolutionary Multi-Objective Optimization".
Evolutionary Computation, Volume 18, Issue 3, MIT Press.
B. Doerr, F. Neumann, I. Wegener (2010):
Special Issue on Genetic and Evolutionary Computation.
Algorithmica, Volume 57, Issue 1,
Springer.
M. Keijzer, G. Antoniol, C. Bates Congdon, K. Deb, B. Doerr, N. Hansen, J. H. Holmes, G. S. Hornby, D. Howard, J. Kennedy, S. Kumar, F. G. Lobo, J. Francis Miller, J. Moore, F. Neumann, M. Pelikan, J. Pollack, K. Sastry, K. Stanley, A. Stoica, E. Talbi, I. Wegener (2008):
GECCO 2008: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, ACM Press, New York, 1786 pages.
D. Thierens, H.-G. Beyer, J. Bongard, J. Branke, J. A. Clark, D. Cliff, C. B. Congdon, K. Deb, B. Doerr,
T. Kovacs, S. Kumar, J. F. Miller, J. Moore, F. Neumann, M. Pelikan, R. Poli, K. Sastry,
K. O. Stanley, T. Stützle, R. A. Watson, I. Wegener (2007):
GECCO 2007: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation,
ACM Press, New York, 2269 pages.
F. Neumann, A. M. Sutton (2020):
Parameterized complexity analysis of randomized search heuristics.
In: Doerr, Neumann: Theory of Evolutionary Computation -- Recent Developments in Discrete Optimization, Springer, 213-248.
Available:
[CoRR abs/2001.05120, Springer version]
F. Neumann, M. Pourhassan, V. Roostapour (2020):
Analysis of evolutionary algorithms in dynamic and stochastic environments.
In: Doerr, Neumann: Theory of Evolutionary Computation -- Recent Developments in Discrete Optimization, Springer, 213-248.
Available:
[CoRR abs/1806.08547, Springer version]
S. Poursoltan, F. Neumann (2015):
Ruggedness quantifying for constrained continuous fitness landscapes.
In: R. Datta, K. Deb (Eds.): Evolutionary Constrained Optimization, Infosys Science Foundation Series, Springer, 29-50.
Paper
M. Wagner, J. Day, D. Jordan, T. Kroeger, F. Neumann (2013): Evolving pacing strategies for team pursuit track cycling. In: L. Di Gaspero, A. Schaerf, T. Stützle (Eds.): Advances in Metaheuristics, Operations Research/Computer Science Interfaces Series, Volume 53, 61-76.
L. J. Schooler, C. Burgess, R. L. Goldstone, W.-T. Fu, S. Gavrilets, D. Lazer, J. A. R. Marshall, F. Neumann, J. Wiener (2012):
Search environments, representations, and encoding.
In: P. M. Todd, T. T.. Hills, T. W. Robbins (Eds.): Cognitive Search: Evolution, Algorithms, and the Brain, MIT Press, 317-333.
Paper, Ernst Strüngmann Forum,
Book at MIT Press
J. A. R. Marshall, F. Neumann (2012):
Foundations of search: a perspective from computer science.
In: P. M. Todd, T. T.. Hills, T. W. Robbins (Eds.): Cognitive Search: Evolution, Algorithms, and the Brain, MIT Press, 257-268.
Paper, Ernst Strüngmann Forum,
Book at MIT Press
F. Neumann, U.-M. O’Reilly, M. Wagner (2011):
Computational complexity analysis of genetic programming - initial results and future directions.
In: R. Riolo, E. Vladislavleva, J. H. Moore (Eds.): Genetic Programming Theory and Practice IX, Springer, pages 113-128.
Final Version
Sabine Helwig, Frank Neumann, Rolf Wanka (2010): Particle swarm optimization with
velocity adaptation.
In: B. K. Panigrahi, Y. Shi, M.-H. Lim (Eds): Handbook of swarm
intelligence - concepts, principles and applications, Springer (to appear).
C. Horoba, F. Neumann (2010):
Approximating Pareto-optimal sets using diversity strategies in evolutionary multi-objective optimization.
In: C. A. Coello Coello, C. Dhaenens, L. Jourdan (Eds.): Advances in multi-objective nature inspired computing, Studies in Computational Intelligence (SCI) 272, Springer, pages 23-44.
Available:
Final Version (pdf)
F. Neumann, D. Sudholt, C. Witt (2009):
Computational complexity of ant colony optimization and its hybridization with local search.
In: L.C. Jain, S. Dehuri, CP Lim (Eds.): Swarm Intelligence for Knowledge-Based Systems, Studies in Computational Intelligence (SCI) 248, Springer, pages 91-120 .
Available:
[Final Version]
F. Neumann, I. Wegener (2007):
Can single-objective optimization profit from multiobjective optimization?
In: Knowles, Corne, and Deb (Eds.): Multiobjective Problem Solving from Nature -
From Concepts to Applications, Springer, pages 115-130.
Available:
[Final Version]
F. Neumann, D. Sudholt, C. Witt (2024):
The compact genetic algorithm struggles on Cliff functions.
Algorithmica.
[CoRR abs/2204.04904]
A. Neumann, F. Neumann (2024):
Optimizing monotone chance-constrained submodular functions using evolutionary multi-objective algorithms .
Evolutionary Computation.
[CoRR abs/2006.11444, Final version]
F. Neumann, Carsten Witt (2024):
Runtime analysis of single- and multi-objective evolutionary algorithms for chance constrained optimization problems with normally distributed random variables.
Evolutionary Computation.
[CoRR abs/2109.05799, Final version]
D. Goel, M. Ward, A. Neumann, F. Neumann, H. Nguyen, M. Guo (2024):
Hardening active directory graphs via evolutionary diversity optimization based policies.
ACM Transactions on Evolutionary Learning and Optimization.
[CoRR abs/2204.03397, Final version]
A. Nikfarjam, A. Neumann, F. Neumann (2024):
On the use of quality diversity algorithms for the traveling thief problem.
ACM Transactions on Evolutionary Learning and Optimization.
[CoRR abs/2112.08627, Final version]
Z. Ghasemi, F. Neumann, M. Zanin, J. Karageorgos, L. Chen (2024):
A comparative study of prediction methods for semi-autogenous grinding mill throughput.
Minerals Engineering.
[Paper]
D. Dumuid , T. Olds, M. Wake, C. L. Rasmussen, Ž. Pedišić, J. H. Hughes, D. JR. Foster, R. Walmsley, A. J. Atkin, L. Straker, F. Fraysse, R. T. Smith, F. Neumann, R. S. Kenett, P. Jarle Mork, D. Bennett, A. Doherty, T. Stanford (2022):
Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures.
PLOS ONE.
[Paper]
A. V. Do, M. Guo, A. Neumann, F. Neumann (2022):
Analysis of evolutionary diversity optimisation for permutation problems.
ACM Transactions on Evolutionary Learning and Optimization.
[CoRR abs/2102.11469]
V. Roostapour, A. Neumann, F. Neumann (2022):
Single- and multi-objective evolutionary algorithms for the knapsack problem with dynamically changing constraints.
Theoretical Computer Science.
[CoRR abs/2004.12574]
V. Roostapour, A. Neumann, F. Neumann, T. Friedrich (2022):
Pareto optimization for subset selection with dynamic cost constraints.
Artificial Intelligence.
[CoRR abs/1811.07806]
D. Weber, F. Neumann (2021):
Amplifying influence through coordinated behaviour in social networks.
Social Network Analysis and Mining.
[CoRR abs/2103.03409]
B. Doerr, F. Neumann (2021):
A survey on recent progress in the theory of evolutionary algorithms for discrete optimization.
ACM Transactions on Evolutionary Learning and Optimization.
[CoRR abs/2006.16709, Final version]
J. Bossek, F. Neumann, P. Peng, D. Sudholt (2021):
Time complexity analysis of randomized search heuristics for the dynamic graph coloring problem.
Algorithmica.
[CoRR abs/2105.12525]
D. Assenmacher, D. Weber, M. Preuss, A. C. Valdez, A. Bradshaw, B. Ross, S. Cresci, H. Trautmann, F. Neumann, C. Grimme (2021):
Benchmarking crisis in social media analytics: a solution for the data sharing problem.
Social Science Computer Review.
[Paper]
F. Shi, F. Neumann, J. Wang (2021):
Runtime performances of randomized search heuristics for the dynamic weighted vertex cover problem.
Algorithmica.
[CoRR abs/2001.08903]
F. Shi, F. Neumann, J. Wang (2021):
Time complexity analysis of evolutionary algorithms for 2-hop (1,2)-minimum spanning tree problem.
Theoretical Computer Science.
[CoRR abs/2110.04701]
W. Gao, S. Nallaperuma, F. Neumann (2021):
Feature-based Diversity Optimization for Problem Instance Classification.
Evolutionary Computation.
Available: CORR 1510.08568
A. Neumann, B. Alexander, F. Neumann (2020):
Evolutionary image transition and painting using random walks.
Evolutionary Computation (to appear).
[CoRR abs/2003.01517, Videos]
B. Ghasemishabankareh, X. Li , M. Ozlen, F. Neumann (2020):
A probabilistic tree-based representation for non-convex minimum cost flow problems with nonlinear non-convex cost functions.
Applied Soft Computing.
M. Pourhassan, V. Roostapour, F. Neumann, (2020):
Runtime analysis of RLS and (1+1) EA for the dynamic weighted vertex cover problem.
Theoretical Computer Science.
Available:
[CoRR abs/1903.02195]
E. C. Osuna, W. Gao, F. Neumann, D. Sudholt (2020):
Design and analysis of diversity-based parent
selection schemes for speeding up evolutionary multi-objective
optimisation.
Theoretical Computer Science.
Available:
[CoRR abs/1805.01221]
M. Pourhassan, F. Shi , F. Neumann (2019):
Parameterized analysis of multi-objective evolutionary algorithms and the weighted vertex cover problem.
Evolutionary Computation, Vol. 27, No. 4, 559–575
Paper
P. Kerschke, H. H. Hoos, F. Neumann, H. Trautmann (2019):
Automated algorithm selection: survey and perspectives.
Evolutionary Computation.
Available:
[CoRR abs/1811.11597]
A. Neumann, F. Neumann, T. Friedrich (2019): Quasi-random Agents for Image Transition and Animation.
Australian Journal of Intelligent Information Processing Systems, Volume 16, Number 1, 10-18.
Available:
[CoRR abs/1710.07421]
M. Pourhassan, F. Neumann (2019):
Theoretical analysis of local search and simple evolutionary algorithms for the generalized travelling salesperson problem.
Evolutionary Computation. Vol. 27, No. 3, 525–-558
Paper
F. Shi, M. Schirneck, T. Friedrich, T. Kötzing, F. Neumann (2019):
Reoptimization times of evolutionary algorithms on linear functions under dynamic uniform constraints.
Algorithmica, Volume 81, 828–857
[Paper]
T. Friedrich, T. Kötzing, J. A. G. Lagodzinski, F. Neumann, M. Schirneck (2018):
Analysis of the (1+1) EA on subclasses of linear functions under uniform and linear constraints.
Theoretical Computer Science.
Paper
S. Nallaperuma, F. Neumann, D. Sudholt (2017):
Expected fitness gains of randomized search heuristics for the traveling salesperson problem.
Evolutionary Computation, Volume 25, Issue 4.
Paper
S. Polyakovskiy, F. Neumann (2017):
The packing while traveling problem.
European Journal of Operational Research, Volume 258, Issue 2, 424-439.
Available:
[CoRR abs/1512.08831]
B. Doerr, F. Neumann, A. M. Sutton (2017):
Time complexity analysis of evolutionary algorithms
on random satisfiable k-CNF formulas
Algorithmica, Volume 78, Issue 2, 561–586.
Paper
M. R. Bonyadi, Z. Michalewicz, S. Nallaperuma, F. Neumann (2017):
Ahura: A heuristic-based racer for the open racing car simulator.
IEEE Transactions on Computational Intelligence and AI in Games (to appear).
S. Polyakovskiy, R. Berghammer, F. Neumann (2016):
Solving hard control problems in voting systems via integer programming.
European Journal of Operational Research, Volume 250, Issue 1, 204-213
Available:
[CoRR abs/1408.5987]
D. Corus, P. K. Lehre, F. Neumann, M. Pourhassan (2016):
A parameterised complexity analysis of bi-level optimisation with evolutionary algorithms.
Evolutionary Computation, Volume 14, Issue 1, 183–203.
Available:
[CoRR abs/1401.1905]
T. Friedrich, F. Neumann (2015):
Maximizing submodular functions under Matroid constraints by evolutionary algorithms.
Evolutionary Computation, Volume 23, Issue 4, 543-558.
Paper
S. Nallaperuma, M. Wagner, F. Neumann (2015):
Analyzing the effects of instance features and algorithm parameters for Max Min Ant System and the traveling salesperson problem
Frontiers in Robotics and AI, 2:18.
Available:
[Final Version]
M. Wagner, K. Bringmann, T. Friedrich, F. Neumann (2015):
Efficient optimization of many objectives by approximation-guided evolution.
European Journal of Operational Research, Volume 243, Issue 2, 465–479
Available:
[Final Version]
T. Friedrich, F. Neumann, C. Thyssen (2015):
Multiplicative approximations, optimal hypervolume distributions, and the choice of the reference point.
Evolutionary Computation, Volume 23, Issue 1, 131-159.
Paper
A. Q. Nguyen, A. M. Sutton, F. Neumann (2015):
Population size matters: rigorous runtime results for maximizing the hypervolume indicator.
Theoretical Computer Science, Volume 561, 24-36.
Paper
A. M. Sutton, F. Neumann, S. Nallaperuma (2014):
Parameterized runtime analyses of evolutionary
algorithms for the planar Euclidean traveling salesperson problem.
Evolutionary Computation, Volume 22, Issue 4, 595–628.
Paper
T. Kötzing, A. M. Sutton, F. Neumann, and U.-M. O'Reilly (2014):
The Max problem revisited: the importance of mutation in genetic programming.
Theoretical Computer Science, Volume 545, 94-107.
Paper
O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, F. Neumann (2013):
A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem.
Annals of Mathematics and Artificial Intelligence, Volume 69, Issue 2, 151-182.
Available:
[CoRR abs/1208.2318]
M. Wagner, J. Day, F. Neumann (2013):
A fast and effective local search algorithm for optimizing the placement of wind turbines.
Renewable Energy, Volume 51, 64–70.
Available:
[CoRR abs/1204.4560]
T. Friedrich, T. Kroeger, F. Neumann (2013):
Weighted preferences in evolutionary multi-objective optimization.
International Journal of Machine Learning and Cybernetics, Volume 4, Issue 2, 139-148.
K. Vladislavleva, T. Friedrich, F. Neumann, M. Wagner (2013):
Predicting the energy output of wind farms based on weather data: important variables and their correlation.
Renewable Energy, Volume 50, 236–243.
Available:
[CoRR abs/1109.1922]
S. Kratsch, F. Neumann (2013): Fixed-parameter evolutionary algorithms and the vertex
cover problem.
Algorithmica, Volume 65, Issue 4, 754-771.
Available:
[Final Version]
B. Doerr, D. Johannsen, T. Kötzing, F. Neumann, M. Theile (2013):
More effective crossover operators for the all-pairs shortest path problem.
Theoretical Computer Science. Volume 471, 12-26.
R. Berghammer, T. Friedrich, F. Neumann (2012):
Convergence of set-based multi-objective optimization, indicators, and deteriorative cycles.
Theoretical Computer Science, Volume 456, 2-17.
Available:
[Final Version]
T. Kötzing, F. Neumann, H. Röglin, C. Witt (2012):
Theoretical analysis of two ACO approaches for the traveling salesman problem.
Swarm Intelligence, Volume 6, Issue 1 pages 1-21.
B. Doerr, A. Eremeev, F. Neumann, M. Theile, C. Thyssen (2011):
Evolutionary algorithms and dynamic programming.
Theoretical Computer Science, Volume 412, Issue 43, pages 6020-6035.
B. Doerr, F. Neumann, D. Sudholt, C. Witt (2011):
Runtime analysis of the 1-ANT ant colony optimizer.
Theoretical Computer Science, Volume 412, Issue 17, pages 1629-1644.
T. Friedrich, C. Horoba, F. Neumann (2011):
Illustration of fairness in evolutionary multi-objective optimization.
Theoretical Computer Science, Volume 412, Issue 17, pages 1546-1556.
F. Neumann, J. Reichel, M. Skutella (2011):
Computing minimum cuts by randomized search heuristics.
Algorithmica, Volume 59, Issue 3, 323-342.
Available:
[Final Version]
T. Friedrich, J. He, N. Hebbinghaus, F. Neumann, C. Witt (2010):
Approximating covering problems by randomized search heuristics using multi-objective models.
Evolutionary Computation, Volume 18, Issue 4, pages 617-633.
F. Neumann, C. Witt (2010):
Ant colony optimization and the minimum spanning tree problem.
Theoretical Computer Science, Volume 411, Issue 25, pages 2406-2413.
Available:
[Final Version]
T. Friedrich, F. Neumann (2010):
When to use bit-wise neutrality.
Natural Computing, Volume 9, Issue 1, pages 283-294.
Available:
[Final Version]
T. Friedrich, N. Hebbinghaus, F. Neumann (2010):
Plateaus can be harder in multi-objective optimization.
Theoretical Computer Science, Volume 411, Issue 6, pages 854-864 .
Available:
[Final Version]
D. Brockhoff, T. Friedrich, N. Hebbinghaus, C. Klein, F. Neumann, E. Zitzler (2009):
On the effects of adding objectives to plateau functions.
IEEE Transactions on Evolutionary Computation, Volume 13 , Issue 3, pages 591-603.
Available:
[Final Version]
T. Friedrich, N. Hebbinghaus, F. Neumann (2009):
Comparison of simple diversity mechanisms on plateau functions.
Theoretical Computer Science, Volume 420, Issue 26, pages 2455-2462.
Available:
[Final Version]
F. Neumann, C. Witt (2009):
Runtime analysis of a simple ant colony optimization algorithm.
Algorithmica, Volume 54, Issue 2, pages 243-255.
Available:
[Final Version]
T. Friedrich, J. He, N. Hebbinghaus, F. Neumann, C. Witt (2009):
Analyses of simple hybrid algorithms for the vertex cover problem.
Evolutionary Computation, Volume 17, Issue 1, pages 3-19.
F. Neumann, D. Sudholt, C. Witt (2009):
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus.
Swarm Intelligence, Volume 3, Issue 1, pages 35-68.
Available:
[Final Version]
F. Neumann (2008):
Expected runtimes of evolutionary algorithms for the Eulerian cycle
problem.
Computers and Operations
Research, Volume 35, Issue 9, pages 2750-2759.
Part Special Issue: Bio-inspired Methods in Combinatorial Optimization.
Available:
[Final Version]
B. Doerr, N. Hebbinghaus, F. Neumann (2007):
Speeding up
evolutionary algorithms through unsymmetric mutation operators.
Evolutionary Computation, Volume 15, Issue 4, pages 401-410.
Available:
[Final Version]
F. Neumann, I. Wegener (2007):
Randomized local Search, evolutionary algorithms, and the minimum
spanning tree problem.
Theoretical Computer Science, Volume 378, Issue 1, pages 32-40.
Available:
[Final Version]
F. Neumann (2007):
Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem.
European Journal of
Operational Research, Volume 181, Issue 3, pages 1620-1629.
Available:
[Final Version]
F. Neumann, I. Wegener (2006):
Minimum spanning trees made easier via multi-objective optimization.
Natural Computing, Volume 5, Number 3, Springer Netherlands, pages 305-319.
Available:
[Final Version]
F. Neumann, F. Simon (2003):
Specific evolutionary algorithms for permutation problems.
In: WSEAS Transactions on Systems 2(4), pages 900-908, WSEAS Press.
D. Antipov, A. Neumann, F. Neumann, A. M. Sutton (2024): Runtime analysis of evolutionary diversity optimization on the multi-objective (LeadingOnes, TrailingZeros) problem.
In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
[CoRR abs/2404.11496]
X. Yan, A. Neumann, F. Neumann (2024): Sliding window bi-objective evolutionary algorithms for optimizing chance-constrained monotone submodular functions.
In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
A. V. Do, M. Guo, A. Neumann, F. Neumann (2024): Evolutionary multi-objective diversity optimization.
In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
[CoRR abs/2401.07454]
F. Neumann, C. Witt (2024): Sliding window 3-objective Pareto optimization for problems with chance constraints.
In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
[CoRR abs/2406.04899]
J. G. Harder, A. Neumann, F. Neumann (2024): Analysis of evolutionary diversity optimisation for the maximum matching problem.
In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
[CoRR abs/2404.11784]
K. Perera, F. Neumann, A. Neumann (2024): Multi-objective evolutionary approaches for the knapsack problem with stochastic profits.
In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
[CoRR abs/2303.01695]
F. Neumann, G. Rudolph (2024): Archive-based single-objective evolutionary algorithms for submodular optimization.
In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
[CoRR abs/2406.13414]
D. Antipov, A. Neumann, F. Neumann (2024): Local optima in diversity optimization: non-trivial offspring population is essential.
In: Parallel Problem Solving from Nature XVIII, PPSN 2024.
T. C. Pathirage Don, A. Neumann, F. Neumann:
The chance constrained travelling thief problem: problem formulations and algorithms.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
I. Hewa Pathiranage, F. Neumann, D. Antipov, A. Neumann:
Effective 2- and 3-objective MOEA/D approaches for the chance constrained knapsack problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
I. Hewa Pathiranage, F. Neumann, D. Antipov, A. Neumann:
Using 3-objective evolutionary algorithms for the dynamic chance constrained knapsack problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
F. Ye, F. Neumann, J. de Nobel, A. Neumann, T. Bäck:
What performance indicators to use for self-adaptation in multi-objective evolutionary algorithms.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
D. Antipov, A. Neumann, F. Neumann:
A detailed experimental analysis of evolutionary diversity optimization for OneMinMax.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
A. Nikfarjam, T. Stanford, A. Neumann, D. Dumuid, F. Neumann:
Quality diversity approaches for time-use optimisation to improve health outcomes.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press. (Best Paper Award in the track "Real World Applications")
[Final version (open access)]
M. Schmidbauer, A. Opris, J. Bossek, F. Neumann, D. Sudholt:
Guiding quality diversity on monotone submodular functions: customising the feature space by adding Boolean conjunctions.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
B. Doerr, J. Knowles, A. Neumann, F. Neumann:
A block-coordinate descent EMO algorithm: theoretical and empirical analysis.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
X. Yan, A. Neumann, F. Neumann:
Sampling-based Pareto optimization for chance-constrained monotone submodular problems.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
S. Sadeghi Ahouei, J. de Nobel, A. Neumann, T. Bäck, F. Neumann:
Evolving reliable differentiating constraints for the chance-constrained maximum coverage problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
S. Gounder, F. Neumann, A. Neumann:
Evolutionary diversity optimisation for sparse directed communication networks.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
A. Opris, D. Dang, F. Neumann, Sudholt:
Runtime analyses of NSGA-III on many-objective problems.
In: Genetic and Evolutionary Computation Conference, GECCO 2024, ACM Press.
[Final version (open access)]
Z. Ghasemi, M. Neshat, C. Aldrich, M. Zanin, F. Neumann, L. Chen:
Enhanced genetic programming models with multiple equations for accurate semi-autogenous grinding mill throughput prediction.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2024.
[CoRR abs/2401.05382]
M. Guo, J. Li, A. Neumann, F. Neumann, H. Nguyen (2024): Limited query graph connectivity test.
In: Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024.
[CoRR abs/2302.13036]
V. A. Do, A. Neumann, F. Neumann, A. M. Sutton (2023):
Rigorous runtime analysis of MOEA/D for solving multi-objective minimum weight base problems.
In: Thirty-seventh Conference on Neural Information Processing Systems, NeurIPS 2023.
[CoRR abs/2306.03409]
X. Yan, V. A. Do, F. Shi, X. Qin, F. Neumann: (2023):
Optimizing chance constrained submodular problems with variable uncertainties.
In: 26th European Conference on Artificial Intelligence, ECAI 2023.
[CoRR abs/2309.14359, Paper]
F. Neumann, C. Witt: (2023):
Fast Pareto optimization using sliding window selection.
In: 26th European Conference on Artificial Intelligence, ECAI 2023.
[CoRR abs/2305.07178, Paper]
G. Zoltai, X. Yie, F. Neumann: (2023):
A study of fitness gains in evolving finite state machines.
In: Australasian Joint Conference on Artificial Intelligence, AJCAI 2023.
[CoRR abs/2310.13203]
D. Antipov, A. Neumann, F. Neumann: (2023):
Rigorous runtime analysis of diversity optimization with GSEMO on OneMinMax..
In: Foundations of Genetic Algorithmcs XVII, FOGA 2023, ACM Press.
[CoRR abs/2307.07248]
J. Kearney, F. Neumann, A. M. Sutton: (2023):
Fixed-parameter tractability of the (1+1) evolutionary algorithm on random planted vertex covers.
In: Foundations of Genetic Algorithmcs XVII, FOGA 2023, ACM Press.
[Paper]
V. A. Do, M. Guo, A. Neumann, F. Neumann, (2023):
Diverse approximations for monotone submodular maximization problems with a matroid constraint.
In: International Joint Conference on Artificial Intelligence, IJCAI 2023.
[CoRR abs/2307.07567]
F. Neumann, C. Witt: (2023):
3-objective Pareto optimization for problems with chance constraints.
In: Genetic and Evolutionary Computation Conference, GECCO 2023, ACM Press.
[CoRR abs/2304.08774]
A. Nikfarjam, R. Rothenberger, F. Neumann, T. Friedrich: (2023):
Evolutionary diversity optimisation in constructing satisfying assignments.
In: Genetic and Evolutionary Computation Conference, GECCO 2023, ACM Press.
[CoRR abs/2305.11457]
S. Baguley, T. Friedrich, A. Neumann, F. Neumann, M. Pappik, Z. Zeif: (2023):
Fixed parameter multi-Objective evolutionary algorithms for the W-separator problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2023, ACM Press.
[CoRR abs/2303.11281]
T. Friedrich, T. Kötzing, A. Neumann, F. Neumann, A. Radhakrishnan: (2023):
Analysis of (1+1) EA on LeadingOnes with constraints.
In: Genetic and Evolutionary Computation Conference, GECCO 2023, ACM Press.
[CoRR abs/2305.18267]
D. Goel, A. Neumann, F. Neumann, H. Nguyen, M. Guo: (2023):
Evolving reinforcement learning environment to minimize learner's achievable reward: an application on hardening cctive directory systems.
In: Genetic and Evolutionary Computation Conference, GECCO 2023, ACM Press.
[CoRR abs/2304.03998]
A. Neumann, S. Goulder, X. Yan, G. Sherman, B. Campbell, M. Guo, F. Neumann: (2023):
Diversity optimization for the detection and concealment of spatially defined communication networks.
In: Genetic and Evolutionary Computation Conference, GECCO 2023, ACM Press.
[Paper, Final version]
J. Bossek, A. Neumann, F. Neumann: (2023):
On the impact of basic mutation operators and populations within evolutionary algorithms for the dynamic weighted traveling salesperson problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2023, ACM Press.
[CoRR abs/2305.18955]
F. Neumann, A. Neumann, C. Qian, V. A. Do, J. de Nobel, D. Vermetten, S. Sadeghi Ahouei, F. Ye, H. Wang, T. Bäck : (2023):
Benchmarking algorithms for submodular optimization problems using IOHProfiler.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2023.
[CoRR abs/2302.01464]
M. Stimson, W. Reid, A. Neumann, S. Ratcliffe, F. Neumann: (2023):
Improving confidence in evolutionary mine scheduling via uncertainty discounting.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2023.
[CoRR abs/2305.17957]
M. Guo, M. Ward, A. Neumann, F. Neumann, H. Nguyen (2022):
Scalable edge blocking algorithms for defending active directory style attack graphs.
In: Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023.
[CoRR abs/2212.04326]
A. Neumann, X. Yie, F. Neumann (2022):
Evolutionary algorithms for limiting the effect of uncertainty for the knapsack problem with stochastic profits.
In: Parallel Problem Solving from Nature XVII, PPSN 2022.
[CoRR abs/2204.05597]
A. Nikfarjam, A. V. Do, F. Neumann (2022):
Analysis of quality diversity algorithms for the knapsack problem.
In: Parallel Problem Solving from Nature XVII, PPSN 2022.
[CoRR abs/2207.14037]
F. Shi, X. Yan, F. Neumann (2022):
Runtime analysis of simple evolutionary algorithms for the chance-constrained makespan scheduling problem.
In: Parallel Problem Solving from Nature XVII, PPSN 2022.
[CoRR abs/2212.11478, Paper]
A. Nikfarjam, A. Neumann, J. Bossek, F. Neumann (2022):
Co-evolutionary diversity optimisation for the traveling thief problem.
In: Parallel Problem Solving from Nature XVII, PPSN 2022.
[CoRR abs/2207.14036]
A. Nikfarjam, A. Moosavi, A. Neumann, F. Neumann (2022):
Computing high-quality solutions for the patient admission scheduling problem using evolutionary diversity optimisation.
In: Parallel Problem Solving from Nature XVII, PPSN 2022.
[CoRR abs/2207.14112]
Y. Xie, A. Neumann, T. Stanford, C. L. Rasmussen, D. Dumuid, F. Neumann (2022):
Evolutionary time use optimization for improving children's health outcomes.
In: Parallel Problem Solving from Nature XVII, PPSN 2022.
[CoRR abs/2206.11505]
T. Friedrich, T. Kötzing, F. Neumann, A. Radhakrishnan (2022):
Theoretical study of optimizing rugged landscapes with the cGA.
In: Parallel Problem Solving from Nature XVII, PPSN 2022.
[CoRR abs/2211.13801]
F. Neumann, C. Witt (2022):
Runtime analysis of the (1+1) EA on weighted sums of transformed linear functions.
In: Parallel Problem Solving from Nature XVII, PPSN 2022.
[CoRR abs/2208.05670]
F. Neumann, C. Witt (2022):
Runtime analysis of single- and multi-objective evolutionary algorithms for chance constrained optimization problems with normally distributed random variables.
In: International Joint Conference on Artificial Intelligence and European Conference on Artificial Intelligence, IJCAI-ECAI 2022.
[Paper, CoRR abs/2109.05799]
F. Neumann, D. Sudholt, C. Witt (2022):
The compact genetic algorithm struggles on Cliff functions.
In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
[CoRR abs/2204.04904]
A. Neumann, D. Antipov, F. Neumann (2022):
Coevolutionary Pareto diversity optimization.
In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
[CoRR abs/2204.05457]
D. Goel, M. H. Ward-Graham, A. Neumann, F. Neumann, H. Nguyen, M. Guo (2022):
Defending active directory by combining neural network based dynamic program and evolutionary diversity optimisation.
In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
[CoRR abs/2204.03397 ]
A. Nikfarjam, A. Neumann, F. Neumann (2022):
Evolutionary diversity optimisation for the traveling thief problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
[CoRR abs/2204.02709]
V. A. Do, M. Guo, A. Neumann, F. Neumann (2022):
Niching-based evolutionary diversity optimization for the traveling salesperson problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
[CoRR abs/2201.10316]
A. Nikfarjam, A. Neumann, F. Neumann (2022):
On the use of quality diversity algorithms for the traveling thief problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
(Nominated for Best Paper Award in the track "Evolutionary Combinatorial Optimization and Metaheuristics")
[CoRR abs/2112.08627]
J. Bossek, F. Neumann (2022):
Exploring the feature space of TSP instances using quality diversity.
In: Genetic and Evolutionary Computation Conference, GECCO 2022, ACM Press.
[CoRR abs/2202.02077]
H. Assimi, F. Neumann, M. Wagner, X. Li (2022):
Novelty-driven binary particle swarm optimisation for truss optimisation problems.
In: The 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2022.
[CoRR abs/2112.07875]
H. Assimi, B. Koch, C. Garcia, M. Wagner and F. Neumann (2022):
Run-of-mine stockyard recovery scheduling and optimisation for multiple reclaimers.
In: The 37th ACM/SIGAPP Symposium On Applied Computing, SAC 2022.
[CoRR abs/2112.12294]
M. Guo, J. Li, A. Neumann, F. Neumann, H. Nguyen (2022):
Practical fixed-parameter algorithms for defending active directory style attack graphs.
In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022.
[CoRR abs/2112.13175]
A. Nikfarjam, Bossek, A. Neumann, F. Neumann (2021):
Computing diverse sets of high quality TSP tours by EAX-based evolutionary diversity optimisation.
In: Foundations of Genetic Algorithmcs XVI, FOGA 2021, ACM Press.
[CoRR abs/2108.05005]
J. Bossek, A. Neumann, F. Neumann (2021):
Exact counting and sampling of optima for the knapsack problem.
In: Learning and Intelligent Optimization Conference, LION 2021.
[CoRR abs/2106.07412]
T. Friedrich, F. Neumann, R. Rothenberger, A. M. Sutton (2021):
Solving non-uniform planted and filtered random SAT formulas greedily.
In: International Conference on Theory and Applications of Satisfiability Testing, SAT 2021.
[Paper]
C. Bian, C. Qian, F. Neumann, Y. Yu (2021):
Fast Pareto optimization for subset selection with dynamic cost constraints.
In: International Joint Conference on Artificial Intelligence, IJCAI 2021.
[Paper]
W. Reid, A. Neumann, S. Ratcliffe, F. Neumann (2021):
Advanced mine optimisation under uncertainty using Evolution.
GECCO 2021 Workshop Industrial Applications of Metaheuristics
In: Genetic and Evolutionary Computation Conference, GECCO 2021, Companion, ACM Press.
[CoRR abs/2102.05235]
Y. Xie, A. Neumann, F. Neumann (2021):
Heuristic strategies for solving complex interacting large-scale stockpile blending problems.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2021.
[CoRR abs/2104.03440]
A. Neumann, J. Bossek, F. Neumann (2021):
Diversifying greedy sampling and evolutionary diversity optimisation for constrained monotone submodular functions.
In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
[CoRR abs/2010.11486, Paper]
A. Nikfarjam, Bossek, A. Neumann, F. Neumann (2021):
Entropy-based evolutionary diversity optimisation for the traveling salesperson problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
[CoRR abs/2104.13538]
Y. Xie, A. Neumann, F. Neumann, A. M. Sutton (2021):
Runtime analysis of RLS and the (1+1) EA for the chance-constrained knapsack problem with correlated uniform weights.
In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
[CoRR abs/2102.05778]
Y. Xie, A. Neumann, F. Neumann (2021):
Heuristic strategies for complex interacting stockpile blending problems with chance constraints.
In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
[CoRR abs/2102.05303]
J. Bossek, A. Neumann, F. Neumann (2021):
Breeding diverse packings for the knapsack problem by means of diversity-tailored evolutionary algorithms.
In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
[CoRR abs/2104.13133]
J. Bossek, F. Neumann (2021):
Evolutionary diversity optimization and the minimum spanning tree problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
[CoRR abs/2010.10913]
A. V. Do, M. Guo, A. Neumann, F. Neumann (2021):
Analysis of evolutionary diversity optimisation for permutation problems.
In: Genetic and Evolutionary Computation Conference, GECCO 2021, ACM Press.
(Nominated for Best Paper Award in the track "Genetic Algorithms")
[CoRR abs/2102.11469]
A. V. Do, F. Neumann (2021):
Pareto optimization for subset selection with dynamic partition matroid constraints.
In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021.
[CoRR abs/2012.08738]
H. Assimi, B. Koch, C. Garcia, M. Wagner and F. Neumann (2021):
Modelling and optimization of run-of-mine stockpile recovery.
In: The 36th ACM/SIGAPP Symposium On Applied Computing, SAC 2021.
Paper
A. Neumann, F. Neumann (2020):
Human interactive EEG-based evolutionary image animation.
In: IEEE Symposium Series on
Computational Intelligence, IEEE SSCI 2020.
M. Hasani-Shoreh, R, Hermoza Aragones, F. Neumann (2020):
Using neural networks and diversifying differential evolution for dynamic optimisation.
In: IEEE Symposium Series on
Computational Intelligence, IEEE SSCI 2020.
[CoRR abs/2008.04002]
D. Weber, F. Neumann (2020):
Who's in the gang? revealing coordinating communities in social media. (short paper)
In: IEEE/ACM International Conference on Social Networks Analysis and Mining, ASONAM 2020
[CoRR abs/2010.08180]
R. Sachdeva, F. Neumann, M. Wagner (2020):
The dynamic travelling thief problem: benchmarks and performance of evolutionary algorithms.
In: International Conference on Neural Information Processing, ICONIP 2020.
[CoRR abs/2004.12045]
A. Neumann, F. Neumann (2020):
Optimising chance-constrained submodular functions using evolutionary multi-objective algorithms.
In: Parallel Problem Solving from Nature XVI, PPSN 2020.
[CoRR abs/2006.11444]
A. V. Do, F. Neumann (2020):
Maximizing submodular or monotone functions under partition matroid constraints by multi-objective evolutionary algorithms.
In: Parallel Problem Solving from Nature XVI, PPSN 2020.
[CoRR abs/2006.12773]
J. Bossek, A. Neumann, F. Neumann (2020):
Optimising tours for the weighted traveling salesperson problem and the traveling thief problem: A structural comparison of solutions.
In: Parallel Problem Solving from Nature XVI, PPSN 2020.
[CoRR abs/2006.03260]
J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann (2020):
Evolving sampling strategies for one-shot optimization tasks.
In: Parallel Problem Solving from Nature XVI, PPSN 2020.
[CoRR abs/1912.08956]
A. V. Do, J. Bossek, A. Neumann, F. Neumann (2020):
Evolving diverse sets of tours for the travelling salesperson problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2020, ACM Press.
[CoRR abs/2004.09188]
J. Bossek, F. Neumann, P. Peng, D. Sudholt (2020):
More effective randomized search heuristics for graph coloring through dynamic optimisation.
In: Genetic and Evolutionary Computation Conference, GECCO 2020, ACM Press.
[CoRR abs/2005.13825]
V.Roostapour, J. Bossek, F. Neumann (2020):
Runtime analysis of evolutionary algorithms with biased mutation for the multi-objective minimum spanning tree problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2020, ACM Press.
[CoRR abs/2004.10424]
Y. Xie, A. Neumann, F. Neumann (2020):
Specific single- and multi-objective evolutionary algorithms for the chance-constrained knapsack problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2020, ACM Press.
[CoRR abs/2004.03205]
J. Bossek, K. Casel, P. Kerschke, F. Neumann (2020):
The node weight dependent traveling salesperson problem: approximation algorithms and randomized search heuristics.
In: Genetic and Evolutionary Computation Conference, GECCO 2020, ACM Press.
[CoRR abs/2002.01070]
H. Assimi, O. Harper, Y. Xie, A. Neumann, F. Neumann (2020):
Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives.
In: 24th European Conference on Artificial Intelligence, ECAI 2020.
[CoRR abs/2002.06766]
M. Hasani-Shoreh, R. Hermoza Aragonés, F. Neumann (2020):
Neural networks in evolutionary dynamic constrained optimization: computational cost and benefits.
In: 24th European Conference on Artificial Intelligence, ECAI 2020.
[CoRR abs/2001.11588]
V. Doskoc, T. Friedrich, A. Göbel, A. Neumann, F. Neumann, F. Quinzan (2020):
Non-Monotone submodular maximization with multiple knapsacks in static and dynamic settings.
In: 24th European Conference on Artificial Intelligence, ECAI 2020.
Paper
[CoRR abs/1911.06791]
B. Doerr, C. Doerr, A. Neumann, F. Neumann, A. M. Sutton (2020):
Optimization of chance-constrained submodular functions.
In: Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020.
[CoRR abs/1911.11451]
M. Hasani-Shoreh, F. Neumann: (2019):
On the use of diversity mechanisms in dynamic constrained continuous
optimization.
In: International Conference on Neural Information Processing, ICONIP 2019.
[CoRR abs/1910.06062]
F. Neumann, A. Sutton: (2019):
Runtime analysis of evolutionary algorithms for the chance-constrained knapsack problem.
In: Foundations of Genetic Algorithms XV, FOGA 2019, ACM Press.
Paper
F. Shi, F. Neumann, J. Wang: (2019):
Runtime analysis of evolutionary algorithms for the depth restricted minimum spanning tree problem.
In: Foundations of Genetic Algorithms XV, FOGA 2019, ACM Press.
Paper
V. Roostapour, M. Pourhassan, F. Neumann: (2019):
Analysis of baseline evolutionary algorithms for the Packing While Travelling problem.
In: Foundations of Genetic Algorithms XV, FOGA 2019, ACM Press.
[CoRR abs/1902.04692]
J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann: (2019):
Evolving diverse TSP instances by means of novel and creative mutation operators.
In: Foundations of Genetic Algorithms XV, FOGA 2019, ACM Press.
Paper
F. Neumann, M. Pourhassan, C. Witt: (2019):
Improved runtime results for simple randomised search heuristics on linear functions with a uniform constraint.
In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
Paper
B. Doerr, C. Doerr, F. Neumann: (2019):
Fast re-optimization via structural diversity.
In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
Available:
[CoRR abs/1902.00304]
J. Bossek, F. Neumann, P. Peng, D. Sudholt: (2019):
Runtime analysis of randomized search heuristics for dynamic graph coloring.
In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
Available:
[Whiterose145579]
A. Neumann, W. Gao, M. Wagner, F. Neumann: (2019):
Evolutionary diversity optimization using multi-objective indicators.
In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
(Nominated for Best Paper Award in the track "Genetic Algorithms")
Available:
[CoRR abs/1811.06804, Paper]
J. Bossek, C. Grimme, F. Neumann: (2019):
On the benefits of biased edge-exchange mutation for the multi-criteria spanning tree problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
Paper
Y. Xie, O. Harper, H. Assimi, A. Neumann, F. Neumann: (2019):
Evolutionary algorithms for the chance-constrained knapsack problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2019, ACM Press.
Available:
[CoRR abs/1902.04767]
M. H. Shoreh, M.-Y. Ameca-Alducin, W. Blaikie, F. Neumann, M. Schoenauer (2019):
On the behaviour of differential evolution for problems with dynamic linear constraints.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2019.
Available:
[CoRR abs/1905.04099]
W. Gao, M. Pourhassan, V. Roostapour, F. Neumann (2019):
Runtime analysis of evolutionary multi-objective algorithms optimizing the degree and diameter of spanning trees.
In: 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019.
Paper
V. Roostapour, A. Neumann, F. Neumann, T. Friedrich (2019):
Pareto optimization for subset selection with dynamic cost constraints.
In: Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019.
Available:
[CoRR abs/1811.07806], AAAI version
T. Friedrich, A. Göbel, F. Neumann, F. Quinzan, R. Rothenberger (2019):
Greedy maximization of functions with bounded curvature under partition matroid constraints.
In: Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019.
Available:
[CoRR abs/1811.05351], AAAI version
F. Neumann, A. M. Sutton (2019):
Evolving solutions to community-structured satisfiability formulas.
In: Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019.
AAAI version
F. Neumann, S. Polyakovskiy, M. Skutella, L. Stougie, J. Wu (2018):
A fully polynomial time approximation scheme for packing while traveling.
In: 4th International Symposium on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2018.
Available:
[CoRR abs/1702.05217]
T.-J. Chin, Z. Cai, F. Neumann (2018):
Robust fitting in computer vision: easy or hard?.
In: European Conference on Computer Vision, ECCV 2018 (oral presentation).
Available:
[CoRR abs/1802.06464]
F. Neumann, A. M. Sutton (2018):
Runtime analysis of evolutionary algorithms for the knapsack problem with favorably correlated weights.
In: Parallel Problem Solving from Nature XV, PPSN 2018.
Paper
V. Roostapour, A. Neumann, F. Neumann (2018):
On the performance of baseline evolutionary algorithms on the dynamic knapsack problem.
In: Parallel Problem Solving from Nature XV, PPSN 2018.
Paper
B. Ghasemishabankareh, M. Ozlen, F. Neumann, X. Li (2018):
A probabilistic tree-based representation for non-convex minimum cost flow problems.
In: Parallel Problem Solving from Nature XV, PPSN 2018.
Paper
A. Neumann, W. Gao, C. Doerr, F. Neumann, M. Wagner (2018):
Discrepancy-based evolutionary diversity optimization.
In: Genetic and Evolutionary Computation Conference, GECCO 2018, ACM Press.
Available:
[CoRR abs/1802.05448]
F. Shi, F. Neumann, J. Wang (2018):
Runtime analysis of randomized search heuristics for the dynamic weighted vertex cover problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2018, ACM Press.
Paper
W. Gao, T. Friedrich, F. Neumann, C. Hercher (2018):
Randomized greedy algorithms for covering problems.
In: Genetic and Evolutionary Computation Conference, GECCO 2018, ACM Press.
Paper
J. Wu, S. Polyakovskiy, M. Wagner, F. Neumann (2018):
Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2018, ACM Press.
Available:
[CoRR abs/1802.02434]
A. Neumann, F. Neumann (2018):
On the use of colour-based segmentation in evolutionary image composition.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2018.
Paper
M. Y. Ameca-Alducin, M. Hasani-Shoreh, W. Blaikie, F. Neumann, E. Mezura Montes (2018):
A comparison of constraint handling techniques for dynamic constrained optimization problems.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2018.
Available:
[CoRR abs/1802.05825]
M. Y. Ameca-Alducin, M. Hasani-Shoreh, F. Neumann (2018):
On the use of repair methods in differential evolution for dynamic constrained optimization.
In: International Conference on the Applications of Evolutionary Computation, EVOAPPLICATIONS 2018.
Paper
M. Pourhassan, V. Roostapour, F. Neumann (2017):
Improved runtime analysis of RLS and (1+1) EA for the dynamic vertex cover problem.
In: IEEE Symposium Series on
Computational Intelligence, IEEE SSCI 2017.
Available extended version:
[CoRR abs/1903.02195, Paper]
F. Neumann (2017):
Parameterized Analysis of Bio-inspired Computing.
In: IEEE Symposium Series on
Computational Intelligence, IEEE SSCI 2017.
Paper
J. Wu, M. Wagner, S. Polyakovskiy, F. Neumann (2017):
Exact approaches for the travelling thief problem.
In: International Conference on Simulated Evolution and Learning, SEAL 2017.
Paper
A. Neumann, Z. L. Szpak, W. Chojnacki, F. Neumann (2017):
Evolutionary image composition using feature covariance matrices
In: Genetic and Evolutionary Computation Conference, GECCO 2017, ACM Press.
Available:
[CoRR abs/1703.03773]
F. Shi, M. Schirneck, T. Friedrich, T. Kötzing, F. Neumann (2017):
Reoptimization times of evolutionary algorithms on linear functions under dynamic uniform constraints
In: Genetic and Evolutionary Computation Conference, GECCO 2017, ACM Press.
Paper
E. C. Osuna, W. Gao, D. Sudholt F. Neumann (2017):
Speeding up evolutionary multi-objective optimisation through diversity-based parent selection
In: Genetic and Evolutionary Computation Conference, GECCO 2017, ACM Press.
Paper
A. Neumann, B. Alexander, F. Neumann (2017):
Evolutionary image transition using random walks.
In: International Conference on Computational Intelligence in Music, Sound, Art and Design, EVOMUSART 2017.
Paper
T. Friedrich, F. Neumann (2017):
What's hot in evolutionary Computation.
In: AAAI Conference on Artificial Intelligence, AAAI 2017, 5064-5066.
Paper
T. Friedrich, T. Kötzing, J. A. G. Lagodzinski, F. Neumann, M. Schirneck (2017):
Analysis of the (1+1) EA on subclasses of linear functions under uniform and linear constraints.
In: Foundations of Genetic Algorithms XIV, FOGA 2017, ACM Press.
Paper
M. Pourhassan, T. Friedrich, F. Neumann (2017):
On the use of the dual formulation for minimum vertex cover in evolutionary algorithms.
In: Foundations of Genetic Algorithms XIV, FOGA 2017, ACM Press.
Paper
A. Neumann, B. Alexander, F. Neumann (2016):
The evolutionary process of image transition in conjunction with box and strip mutation.
In: International Conference on Neural Information Processing, ICONIP 2016.
Available:
[CoRR abs/1608.01783]
M. Pourhassan, F. Shi , F. Neumann (2016):
Parameterized analysis of multi-objective evolutionary algorithms and the weighted vertex cover problem.
In: Parallel Problem Solving from Nature XIV, PPSN 2016.
Paper
W. Gao, T. Friedrich, F. Neumann (2016):
Fixed-parameter single objective search heuristics for minimum vertex cover.
In: Parallel Problem Solving from Nature XIV, PPSN 2016.
Paper
W. Gao, S. Nallaperuma, F. Neumann (2016):
Feature-based diversity optimization for problem instance classification.
In: Parallel Problem Solving from Nature XIV, PPSN 2016.
Available: CORR 1510.08568
J. Wu, S. Polyakovskiy, F. Neumann (2016):
On the impact of the renting rate for the unconstrained nonlinear knapsack problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2016, ACM Press.
(Nominated for Best Paper Award in the track "Evolutionary Combinatorial Optimization and Metaheuristics")
Paper
J. Wu, S. Shekh, N. Sergiienko, B. Cazzolato, B. Ding, F. Neumann (2016):
Fast and effective optimisation of arrays of submerged wave energy converters.
In: Genetic and Evolutionary Computation Conference, GECCO 2016, ACM Press.
Paper
B. Doerr, W. Gao, F. Neumann (2016):
Runtime analysis of evolutionary diversity maximization for OneMinMax.
In: Genetic and Evolutionary Computation Conference, GECCO 2016, ACM Press.
Paper
T. Friedrich, T. Kötzing, M. S. Krejca, S. Nallaperuma, F. Neumann, M. Schirneck (2016):
Fast building block assembly by majority vote crossover.
In: Genetic and Evolutionary Computation Conference, GECCO 2016, ACM Press.
Paper
S. Poursoltan, F. Neumann (2016):
Feature-based algorithm selection for constrained continuous optimisation.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2016.
Available:
[CoRR abs/1602.02862, Paper]
T.-J. Chin, Y. H. Kee, A. Eriksson, F. Neumann (2016):
Guaranteed outlier removal with mixed integer linear programs.
In: Computer Vision and Pattern Recognition, CVPR 2016.
S. Poursoltan, F. Neumann (2015):
A feature-based comparison of evolutionary computing techniques for constrained continuous optimisation.
In: International Conference on Neural Information Processing, ICONIP 2015.
Available:
[CoRR abs/1509.06842]
S. Poursoltan, F. Neumann (2015):
A feature-based analysis on the impact of set of constraints for ε-constrained differential evolution.
In: International Conference on Neural Information Processing, ICONIP 2015.
Available:
[CoRR abs/1506.06848]
F. Neumann, C. Witt (2015):
On the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling.
In: International Joint Conference on Artificial Intelligence, IJCAI 2015.
Available:
[CoRR abs/1504.06363]
M. Pourhassan, W. Gao, F. Neumann (2015):
Maintaining 2-approximations for the dynamic vertex cover Problem using evolutionary algorithms.
In: Genetic and Evolutionary Computation Conference, GECCO 2015, ACM Press.
Paper
W. Gao, M. Pourhassan, F. Neumann (2015):
Runtime analysis of evolutionary diversity optimization and the vertex cover problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2015, Companion, ACM Press.
Paper
M. Pourhassan, F. Neumann (2015):
On the impact of local search operators and variable neighbourhood search for the generalized travelling salesperson problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2015, ACM Press.
Paper
Doerr, F. Neumann, A. M. Sutton (2015):
Improved runtime bounds for the (1+1) EA on random 3-CNF formulas based on fitness-distance correlation.
In: Genetic and Evolutionary Computation Conference, GECCO 2015, ACM Press. (Best Paper Award in the track "Theory")
Paper
S. Polyakovskiy, F. Neumann (2015):
Packing while traveling: mixed integer programming for a class of nonlinear knapsack problems.
In: Twelfth International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) techniques in Constraint Programming, CPAIOR 2015.
Available:
[CoRR abs/1411.5768]
F. Neumann, A. Nguyen (2014): On the impact of utility functions in interactive evolutionary multi-objective optimization.
In: Simulated Evolution and Artificial Life, SEAL 2014.
Paper
A. Nguyen, Markus Wagner, and Frank Neumann (2014): Incorporating user preferences into Approximation-Guided Multi-Objective Evolution.
In: Simulated Evolution and Artificial Life, SEAL 2014.
Paper
T. Friedrich, F. Neumann (2014):
Maximizing submodular functions under matroid constraints by multi-objective evolutionary algorithms.
In: Parallel Problem Solving from Nature XIII, PPSN 2014.
(Nominated for Best Paper Award)
Paper
A. M. Sutton, F. Neumann (2014):
Average-case analysis of evolutionary algorithms on high-density satisfiable 3-CNF formulas.
In: Parallel Problem Solving from Nature XIII, PPSN 2014.
Paper
S. Nallaperuma, M. Wagner, F. Neumann (2014):
Parameter prediction based on features of evolved instances for ant colony optimization and the traveling salesperson problem.
In: Parallel Problem Solving from Nature XIII, PPSN 2014.
Paper
W. Gao, F. Neumann (2014):
Runtime analysis for maximizing population diversity in single-objective optimization.
In: Genetic and Evolutionary Computation Conference, GECCO 2014, ACM Press.
Paper
S. Nallaperuma, F. Neumann, D. Sudholt (2014):
A fixed budget analysis of randomized search heuristics for the traveling salesperson problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2014, ACM Press. (Nominated for Best Paper Award in the track "Genetic Algorithms")
Paper
S. Nallaperuma, F. Neumann, M. R. Bonyadi, Z. Michalewicz (2014):
EVOR : An online evolutionary algorithm for car racing games.
In: Genetic and Evolutionary Computation Conference, GECCO 2014, ACM Press.
Paper
S. Polyakovskiy, M. R. Bonyadi, M. Wagner, F. Neumann, Z. Michalewicz (2014):
A comprehensive benchmark set and heuristics for the travelling thief problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2014, ACM Press.
Paper
S. Poursoltan, F. Neumann (2014):
A feature-based analysis on the impact of linear constraints for e-constrained differential evolution.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2014, IEEE Press.
Paper
M. Wagner, F. Neumann (2014):
Single- and multi-objective genetic programming: new runtime results for SORTING.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2014, IEEE Press.
S. Nallaperuma, A. M. Sutton, F. Neumann (2013):
Parameterized complexity analysis and more effective construction methods for ACO algorithms and the Euclidean traveling salesperson problem.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2013, IEEE Press.
S. Nallaperuma, A. M. Sutton, F. Neumann (2013):
Fixed-parameter evolutionary algorithms for the Euclidean traveling salesperson problem.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2013, IEEE Press.
R. Tran, J. Wu, C. Denison, T. Ackling, M. Wagner, and Frank Neumann (2013):
Fast and effective multi-objective optimisation of wind turbine placement.
In: Genetic and Evolutionary Computation Conference, GECCO 2013, ACM Press.
D. Corus, P. K. Lehre, F. Neumann (2013):
The generalized minimum spanning tree problem: a parameterized complexity analysis of bi-level optimisation.
In: Genetic and Evolutionary Computation Conference, GECCO 2013, ACM Press. (Best Paper Award in the track "Evolutionary Combinatorial Optimization and Metaheuristics")
A. Nguyen, A. M. Sutton, F. Neumann (2013):
Population size matters: rigorous runtime results for maximizing the hypervolume indicator.
In: Genetic and Evolutionary Computation Conference, GECCO 2013, ACM Press.
M. Wagner, F. Neumann (2013):
A fast approximation-guided evolutionary multi-objective algorithm.
In: Genetic and Evolutionary Computation Conference, GECCO 2013, ACM Press.
S. Nallaperuma, M. Wagner, F. Neumann (2013):
Ant colony optimisation and the traveling salesperson problem -- hardness, features and parameter settings (extended abstract).
In: Genetic and Evolutionary Computation Conference, GECCO 2013, Companion Material Proceedings, ACM Press.
Samadhi Nallaperuma, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf Mersmann and Heike Trautmann (2013):
A feature-based comparison of local search and the Christofides algorithm for the travelling salesperson problem.
In: Foundations of Genetic Algorithms XII, FOGA 2013, ACM Press, 147-160.
A. Sutton, F. Neumann (2012):
A parameterized runtime analysis of simple evolutionary algorithms for makespan scheduling.
In: Parallel Problem Solving from Nature XII, PPSN 2012.
T. Urli, M. Wagner, F. Neumann (2012):
Experimental supplements to the computational complexity analysis of genetic programming for problems modelling isolated program semantics.
In: Parallel Problem Solving from Nature XII, PPSN 2012.
M. Wagner, F. Neumann (2012):
Parsimony pressure versus multi-objective optimization for variable length representations.
In: Parallel Problem Solving from Nature XII, PPSN 2012.
A. M. Sutton, F. Neumann (2012):
A parameterized runtime analysis of evolutionary algorithms for the euclidean traveling salesperson problem.
In: Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2012 (to appear).
Available:
[CoRR abs/1207.0578]
F. Neumann (2012):
Computational complexity analysis of multi-objective genetic programming.
In: Genetic and Evolutionary Computation Conference, GECCO 2012.
Available:
[CoRR abs/1203.4881]
T. Kötzing, A. M. Sutton, F. Neumann, U.-M. O'Reilly (2012):
The Max Problem revisited: the importance of mutation in genetic programming.
In: Genetic and Evolutionary Computation Conference, GECCO 2012.
A. M. Sutton, J. Day, F. Neumann (2012):
A parameterized runtime analysis of evolutionary algorithms for MAX-2-SAT.
In: Genetic and Evolutionary Computation Conference, GECCO 2012.
J. Yuen, S. Gao, M. Wagner, F. Neumann (2012):
An adaptive data structure for evolutionary multi-objective algorithms with unbounded archives.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2012.
K. Veeramachaneni, M. Wagner, U.-M. O'Reilly, F. Neumann (2012):
Optimizing energy output and layout costs for large wind farms using particle swarm optimization.
In: IEEE Congress on Evolutionary Computation, IEEE CEC 2012.
O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, F. Neumann (2012):
Local search and the traveling salesman problem: a feature-based characterization of problem hardness.
In: Learning and Intelligent OptimizatioN Conference 6, LION 2012.
T. Friedrich, T. Kroeger, F. Neumann (2011):
Weighted preferences in evolutionary multi-objective optimization
In: The 24th Australasian Joint Conference on Artificial Intelligence, AI 2011.
M. Wagner, J. Day, D. Jordan, T. Kroeger, F. Neumann (2011):
Evolving pacing strategies for team pursuit track cycling.
In: Metaheuristic International Conference, MIC 2011. (Best Paper Award)
Available:
[CoRR abs/1104.0775]
K. Bringmann, T. Friedrich, F. Neumann, M. Wagner (2011):
Approximation-guided evolutionary multi-objective optimization.
In: 22nd International Joint Conferences on Artificial Intelligence, IJCAI 2011.
T. Kötzing, F. Neumann, R. Spöhel (2011):
PAC learning and genetic programming.
In: Genetic and Evolutionary Computation Conference,
GECCO 2011.
F. Neumann, P. S. Oliveto, G. Rudolph, D. Sudholt (2011):
On the effectiveness of crossover for migration in parallel evolutionary algorithms.
In: Genetic and Evolutionary Computation Conference,
GECCO 2011.
M. Mainberger, S. Hoffmann, J. Weickert, C. H. Tang, D. Johannsen, F. Neumann, B. Doerr (2011):
Optimising spatial and tonal data for homogeneous diffusion inpainting.
In: Third International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011 (to appear).
M. Wagner, K. Veeramachaneni, F. Neumann, U.-M. O'Reilly (2011):
Optimizing the layout of 1000 wind turbines.
In: European Wind Energy Association Annual Event, EWEA 2011.
G. Durrett, F. Neumann, U.-M. O'Reilly (2011):
Computational complexity analysis of simple genetic programming on two
problems modeling isolated program semantics.
In: Foundations of Genetic Algorithms XI, FOGA 2011.
Available:
[CoRR abs/1007.4636]
T. Kötzing, F. Neumann, D. Sudholt, M. Wagner (2011):
Simple Max-Min ant systems and the optimization of linear pseudo-Boolean functions.
In: Foundations of Genetic Algorithms XI, FOGA 2011.
Available:
[CoRR abs/1007.4707]
A. Ghandar, Z. Michalewicz, F. Neumann (2010):
Evolving fuzzy rules: evaluation of a new approach.
In: Eighth International Conference on Simulated Evolution And Learning, SEAL 2010.
F. Neumann, M. Theile (2010):
How crossover speeds up evolutionary algorithms for the multi-criteria all-pairs-shortest-path problem.
In: Parallel Problem Solving from Nature XI, PPSN 2010.
S. Böttcher, B. Doerr, F. Neumann (2010):
Optimal fixed and adaptive mutation rates for the LeadingOnes problem.
In: Parallel Problem Solving from Nature XI, PPSN 2010.
B. Doerr, D. Johannsen, T. Kötzing, F. Neumann, M. Theile (2010):
More effective crossover operators for the all-pairs-shortest path problem.
In: Parallel Problem Solving from Nature XI, PPSN 2010.
S. Kratsch, P. K. Lehre, F. Neumann and P. S. Oliveto (2010):
Fixed parameter evolutionary algorithms and maximum leaf spanning trees: a matter of mutation.
In: Parallel Problem Solving from Nature XI, PPSN 2010.
T. Kötzing, F. Neumann, H. Röglin, C. Witt (2010):
Theoretical properties of two ACO
approaches for the traveling salesman problem.
In: Seventh International Conference on Ant Colony
Optimization and Swarm Intelligence, ANTS 2010, LNCS, Springer, 324-335. (Best Paper Award)
R. Berghammer, T. Friedrich, F. Neumann (2010):
Set-based multi-objective optimization, indicators, and deteriorative cycles.
In: Genetic and Evolutionary Computation Conference, GECCO 2010, ACM Press, 495-502.
F. Neumann, D. Sudholt, C. Witt (2010):
A few ants are enough: ACO with iteration-best update.
In: Genetic and Evolutionary Computation Conference, GECCO 2010, ACM Press, 63-70. (Nominated for Best Paper Award)
T. Kötzing, P. K. Lehre, P. S. Oliveto, F. Neumann (2010):
Ant colony optimization and the minimum cut problem.
In: Genetic and Evolutionary Computation Conference, GECCO
2010, ACM Press, 1393-1400.
S. Helwig, F. Neumann, R. Wanka (2009):
Particle swarm optimization with velocity adaptation.
In: International Conference on Adaptive and Intelligent Systems, ICAIS 2009, IEEE Press, 146-151. (Best Paper Award)
Paper (pdf)
S. Kratsch, F. Neumann (2009):
Fixed-parameter evolutionary algorithms and the vertex cover problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2009, ACM Press, 293-300. (Best Paper Award)
Paper (pdf)
T. Friedrich, C. Horoba, F. Neumann (2009):
Multiplicative approximations and the hypervolume indicator.
In: Genetic and Evolutionary Computation Conference, GECCO 2009, ACM Press 571--578. (Best Paper Award)
Paper (pdf)
B. Doerr, A. Eremeev, C. Horoba, F. Neumann, M. Theile (2009):
Evolutionary algorithms and dynamic programming.
In: Genetic and Evolutionary Computation Conference, GECCO 2009, ACM Press, 771-777.
Paper (pdf)
F. Neumann, P. S. Oliveto, C. Witt (2009):
Theoretical analysis of fitness-proportional selection: landscapes and efficiency.
In: Genetic and Evolutionary Computation Conference, GECCO 2009, ACM Press, 835-842.
Paper (pdf)
P. S. Oliveto, P. K. Lehre, F. Neumann (2009):
Theoretical analysis of rank-based mutation - combining exploration and exploitation.
In: IEEE Congress on Evolutionary Computation 2009, CEC 2009, IEEE Press, 1455-1462. (Nominated for Best Student Paper Award (P. S. Oliveto))
Paper (pdf)
C. Horoba, F. Neumann (2009):
Additive approximations of Pareto-optimal sets by evolutionary multi-objective algorithms.
In: Foundations of Genetic Algorithms 2009, FOGA 2009 (to appear).
Paper (pdf)
S. Baswana, S. Biswas, B. Doerr, T. Friedrich, P. P. Kurur, F. Neumann (2009):
Computing single source shortest paths using single-objective fitness functions.
In: Foundations of Genetic Algorithms 2009, FOGA 2009 (to appear).
Paper (pdf)
F. Neumann, J. Reichel (2008):
Approximating minimum multicuts by evolutionary multi-objective algorithms.
In: Parallel Problem Solving from Nature X, PPSN 2008, LNCS 5199, Springer, 72-81. (Best
Paper Award)
Paper (pdf)
D. Brockhoff, T. Friedrich, F. Neumann (2008):
Analyzing hypervolume indicator based algorithms.
In: Parallel Problem Solving from Nature X, PPSN 2008, LNCS 5199, Springer, 651-660.
Paper (pdf)
J. Kroeske, A. Ghandar, Z. Michalewicz, F. Neumann (2008):
Learning fuzzy rules with evolutionary algorithms - an analytic
approach.
In: Parallel Problem Solving from Nature X, PPSN 2008, LNCS 5199, Springer, 1051-1060.
Paper (pdf)
T. Friedrich, C. Horoba, F. Neumann (2008):
Runtime analyses for using fairness in evolutionary multi-objective optimization.
In: Parallel Problem Solving from Nature X, PPSN 2008, LNCS 5199, Springer, 671-680.
Paper (pdf)
F. Neumann, D. Sudholt, C. Witt (2008): Rigorous analyses for the combination of ant colony optimization and local search.
In: Sixth International Conference on Ant Colony Optimization and Swarm Intelligence, ANTS 2008, Springer, 132-143.
Paper (pdf)
E. Happ, D. Johannsen, C. Klein, F. Neumann (2008):
Rigorous analyses of fitness-proportional selection for optimizing linear functions.
In: Genetic and Evolutionary Computation Conference, GECCO 2008, ACM Press, 953-960. (Nominated for Best Paper Award)
Paper (pdf)
C. Horoba, F. Neumann (2008):
Benefits and drawbacks for the use of epsilon-dominance in evolutionary multi-objective optimization.
In: Genetic and Evolutionary Computation Conference, GECCO 2008, ACM Press, 641-680. (Nominated for Best Paper Award)
Paper (pdf)
F. Neumann, J. Reichel, M. Skutella (2008):
Computing minimum cuts by randomized search heuristics.
In: Genetic and Evolutionary Computation Conference, GECCO 2008, ACM Press, 779-786. (Nominated for Best Paper Award)
Paper (pdf)
F. Diedrich, F. Neumann (2008):
Using fast matrix multiplication in bio-inspired computation for complex optimization problems.
In: IEEE Congress on Evolutionary Computation 2008, CEC 2008,
IEEE Press, 3828-3833.
T. Friedrich, F. Neumann (2008):
When to use bit-wise neutrality.
In: IEEE Congress on Evolutionary Computation 2008, CEC 2008,
IEEE Press, 997-1003.
F. Diedrich, B. Kehden, F. Neumann (2008):
Multi-objective problems in terms of relational algebra.
In: 10th International Conference on Relational Methods in
Computer Science, RelMiCS 2008, LNCS 4988 Springer, 84-98.
F. Neumann, C. Witt (2008):
Ant colony optimization and the minimum spanning tree problem.
In: Learning and Intelligent OptimizatioN II, LION 2008, Springer, 153-166.
Electronic Colloquium on Computational Complexity (ECCC),
Report No. 143, 2006.
Available:
[ECCC
Report TR06-143]
B. Doerr, M. Gnewuch, N. Hebbinghaus, F. Neumann (2007):
A rigorous view on neutrality.
In: IEEE Congress on Evolutionary Computation 2007, CEC 2007,
IEEE press, 2591-2597.
T. Friedrich, J. He, N. Hebbinghaus, F. Neumann, C. Witt (2007):
On improving approximate solutions by evolutionary algorithms.
In: IEEE Congress on Evolutionary Computation 2007, CEC 2007,
IEEE press, 2614-2621.
T. Friedrich, N. Hebbinghaus, F. Neumann (2007):
Plateaus can be harder in multi-objective optimization.
In: IEEE Congress on Evolutionary Computation 2007, CEC 2007,
IEEE press, 2622-2629.
F. Neumann, D. Sudholt, C. Witt (2007):
Comparing variants of MMAS ACO algorithms on Pseudo-Boolean functions.
In: Engineering Stochastic Local Search Algorithms, SLS 2007, LNCS 4638, Springer, 61-75.
Available:
[Technical
Report CI 230/07]
T. Friedrich, N. Hebbinghaus, F. Neumann (2007):
Rigorous analyses of simple diversity mechanisms.
In: Genetic and Evolutionary Computation Conference, GECCO 2007, ACM Press, 1219-1225. (Nominated for Best Paper Award)
D. Brockhoff, T. Friedrich, N. Hebbinghaus, C. Klein, F. Neumann, E. Zitzler (2007):
Do additional objectives make a problem harder?
In: Genetic and Evolutionary Computation Conference, GECCO 2007, ACM Press, 765-772.
T. Friedrich, J. He, N. Hebbinghaus, F. Neumann, C. Witt (2007):
Approximating covering problems by randomized search heuristics using multi-objective models.
In: Genetic and Evolutionary Computation Conference, GECCO 2007, ACM Press, 797-804.
Electronic Colloquium on Computational Complexity (ECCC),
Report No. 27, 2007.
Available:
[ECCC
Report TR07-027]
B. Doerr, F. Neumann, D. Sudholt, C. Witt (2007):
On the runtime analysis of the 1-ANT ACO algorithm.
In: Genetic and Evolutionary Computation Conference, GECCO 2007, ACM Press, 33-40. (Best
Paper Award)
Available:
[Technical
Report CI 223/07]
F. Neumann, C. Witt (2006):
Runtime analysis of a simple Ant Colony Optimization algorithm.
In: 17th International Symposium on Algorithms and Computation, ISAAC 2006, LNCS 4288, Springer, 618-627
Electronic Colloquium on Computational Complexity (ECCC),
Report No. 84, 2006.
Available:
[ECCC
Report TR06-084]
B. Doerr, N. Hebbinghaus, F. Neumann (2006):
Speeding up evolutionary algorithms through restricted mutation operators.
In: Parallel Problem Solving from Nature IX, PPSN 2006, LNCS 4193, Springer, 978-987
Electronic Colloquium on Computational Complexity (ECCC),
Report No. 83, 2006.
Available: [ECCC
Report TR06-083]
B. Kehden, F. Neumann (2006):
A relation-algebraic view on evolutionary algorithms for some graph problems.
In: 6th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCop 2006, LNCS 3906, Springer, 147 - 158. (Best
Paper Award)
Available:
[Final Version]
F. Neumann, M. Laumanns (2006):
Speeding up approximation algorithms for NP-hard spanning
forest problems by multi-objective optimization.
In: 7th Latin American Theoretical Informatics, LATIN 2006, LNCS 3887, Springer, 745 - 756.
Electronic Colloquium on Computational Complexity (ECCC),
Report No. 29, 2005.
Available:
[Final Version, ECCC
Report TR05-029]
B. Kehden, F. Neumann, R. Berghammer (2006):
Relational implementation of simple parallel evolutionary algorithms.
In: 8th International Conference on Relational Methods in
Computer Science, RelMiCS 2005, LNCS 3929, Springer, 161 -
172.
Available:
[Final Version]
R. Berghammer, F. Neumann (2005):
RELVIEW - An OBDD-based Computer Algebra system for relations.
In: 8th International Workshop on Computer Algebra in
Scientific Computing, CASC 2005, LNCS 3718, Springer, 40 -51.
Available:
[Final Version]
F. Neumann, I. Wegener (2005):
Minimum spanning trees made easier via multi-objective optimization.
In: Genetic and Evolutionary Computation Conference,
GECCO 2005, ACM Press, 763 - 770.
(Best
Paper Award)
Available:
[Final
Version, Technical
Report CI 192/05 (SFB 531, University of Dortmund)]
F. Neumann (2004):
Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem.
In: Parallel Problem Solving from Nature VIII, PPSN 2004, LNCS 3242, Springer, 80 - 89.
Available:
[Final Version]
F. Neumann, I. Wegener (2004):
Randomized local search, evolutionary algorithms, and the minimum
spanning tree problem.
In: Genetic and Evolutionary Computation Conference, GECCO 2004,
LNCS 3102, Springer, 713 - 724.
Available:
[Final
version,
Technical Report CI 165/04 (SFB 531, University of Dortmund)]
F. Neumann (2004):
Expected runtimes of evolutionary algorithms for the Eulerian cycle
problem.
In: IEEE Congress on Evolutionary Computation 2004, CEC 2004,
volume 1, IEEE Press, 904 - 910.