| Why rumors spread so quickly in social networks B Doer, M Fouz, T Friedrich Communications of the ACM 55 (6), 70-75, 2012 | 518 | 2012 |
| Approximating the volume of unions and intersections of high-dimensional geometric objects K Bringmann, T Friedrich Computational Geometry 43 (6-7), 601-610, 2010 | 211 | 2010 |
| Approximating the least hypervolume contributor: NP-hard in general, but fast in practice K Bringmann, T Friedrich Theoretical Computer Science 425, 104-116, 2012 | 184 | 2012 |
| Approximating covering problems by randomized search heuristics using multi-objective models T Friedrich, N Hebbinghaus, F Neumann, J He, C Witt Proceedings of the 9th annual conference on Genetic and evolutionary …, 2007 | 184 | 2007 |
| Social networks spread rumors in sublogarithmic time B Doerr, M Fouz, T Friedrich Proceedings of the forty-third annual ACM symposium on Theory of computing …, 2011 | 183 | 2011 |
| Approximation quality of the hypervolume indicator K Bringmann, T Friedrich Artificial Intelligence 195, 265-290, 2013 | 173* | 2013 |
| 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 | 162 | 2017 |
| Analyzing hypervolume indicator based algorithms D Brockhoff, T Friedrich, F Neumann International conference on parallel problem solving from nature, 651-660, 2008 | 157 | 2008 |
| Do additional objectives make a problem harder? D Brockhoff, T Friedrich, N Hebbinghaus, C Klein, F Neumann, E Zitzler Proceedings of the 9th annual conference on Genetic and evolutionary …, 2007 | 137 | 2007 |
| Analysis of diversity-preserving mechanisms for global exploration T Friedrich, PS Oliveto, D Sudholt, C Witt Evolutionary Computation 17 (4), 455-476, 2009 | 132 | 2009 |
| Quasirandom rumor spreading B Doerr, T Friedrich, T Sauerwald ACM Transactions on Algorithms (TALG) 11 (2), 1-35, 2014 | 126* | 2014 |
| Predicting the energy output of wind farms based on weather data: Important variables and their correlation E Vladislavleva, T Friedrich, F Neumann, M Wagner Renewable Energy 50, 236-243, 2013 | 124 | 2013 |
| The compact genetic algorithm is efficient under extreme gaussian noise T Friedrich, T Kötzing, MS Krejca, AM Sutton IEEE Transactions on Evolutionary Computation 21 (3), 477-490, 2016 | 114 | 2016 |
| An efficient algorithm for computing hypervolume contributions K Bringmann, T Friedrich Evolutionary Computation 18 (3), 383-402, 2010 | 114 | 2010 |
| Approximation-guided evolutionary multi-objective optimization K Bringmann, T Friedrich, F Neumann, M Wagner IJCAI Proceedings-International Joint Conference on Artificial Intelligence …, 2011 | 112 | 2011 |
| 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 | 109 | 2009 |
| Efficient embedding of scale-free graphs in the hyperbolic plane T Bläsius, T Friedrich, A Krohmer, S Laue IEEE/ACM transactions on Networking 26 (2), 920-933, 2018 | 102 | 2018 |
| On the diameter of hyperbolic random graphs T Friedrich, A Krohmer SIAM Journal on Discrete Mathematics 32 (2), 1314-1334, 2018 | 102 | 2018 |
| Two-dimensional subset selection for hypervolume and epsilon-indicator K Bringmann, T Friedrich, P Klitzke Proceedings of the 2014 Annual Conference on Genetic and Evolutionary …, 2014 | 93 | 2014 |
| Pareto optimization for subset selection with dynamic cost constraints V Roostapour, A Neumann, F Neumann, T Friedrich Artificial Intelligence 302, 103597, 2022 | 92 | 2022 |
Frank NeumannOptimisation and Logistics, School of ComputerVerified email at adelaide.edu.au
