| Multiplicative drift analysis B Doerr, D Johannsen, C Winzen Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010 | 298 | 2010 |
| From black-box complexity to designing new genetic algorithms B Doerr, C Doerr, F Ebel Theoretical Computer Science 567, 87-104, 2015 | 213 | 2015 |
| Benchmarking in optimization: Best practice and open issues T Bartz-Beielstein, C Doerr, D Berg, J Bossek, S Chandrasekaran, ... arXiv preprint arXiv:2007.03488, 2020 | 143 | 2020 |
| Optimal Static and Self-Adjusting Parameter Choices for the Genetic Algorithm B Doerr, C Doerr Algorithmica 80, 1658-1709, 2018 | 116 | 2018 |
| Theory of parameter control for discrete black-box optimization: Provable performance gains through dynamic parameter choices B Doerr, C Doerr Theory of Evolutionary Computation: Recent Developments in Discrete …, 2020 | 114 | 2020 |
| Optimal parameter choices via precise black-box analysis B Doerr, C Doerr, J Yang Proceedings of the Genetic and Evolutionary Computation Conference 2016 …, 2016 | 107 | 2016 |
| 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 | 94* | 2013 |
| Benchmarking discrete optimization heuristics with IOHprofiler C Doerr, F Ye, N Horesh, H Wang, OM Shir, T Bäck Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019 | 83 | 2019 |
| Optimal parameter choices through self-adjustment: Applying the 1/5-th rule in discrete settings B Doerr, C Doerr Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015 | 83 | 2015 |
| IOHprofiler: A benchmarking and profiling tool for iterative optimization heuristics C Doerr, H Wang, F Ye, S Van Rijn, T Bäck arXiv preprint arXiv:1810.05281, 2018 | 78 | 2018 |
| Exploratory landscape analysis is strongly sensitive to the sampling strategy Q Renau, C Doerr, J Dreo, B Doerr Parallel Problem Solving from Nature–PPSN XVI: 16th International Conference …, 2020 | 74 | 2020 |
| Finding optimal volume subintervals with k points and calculating the star discrepancy are NP-hard problems M Gnewuch, A Srivastav, C Winzen Journal of Complexity 25 (2), 115-127, 2009 | 73 | 2009 |
| Faster black-box algorithms through higher arity operators B Doerr, D Johannsen, T Kötzing, PK Lehre, M Wagner, C Winzen Proceedings of the 11th workshop proceedings on Foundations of genetic …, 2011 | 70 | 2011 |
| IOHanalyzer: Detailed performance analyses for iterative optimization heuristics H Wang, D Vermetten, F Ye, C Doerr, T Bäck ACM Transactions on Evolutionary Learning and Optimization 2 (1), 1-29, 2022 | 69 | 2022 |
| Calculation of discrepancy measures and applications C Doerr, M Gnewuch, M Wahlström A panorama of discrepancy theory, 621-678, 2014 | 68 | 2014 |
| Self-adjusting mutation rates with provably optimal success rules B Doerr, C Doerr, J Lengler Proceedings of the Genetic and Evolutionary Computation Conference, 1479-1487, 2019 | 62 | 2019 |
| Discrepancy-based evolutionary diversity optimization A Neumann, W Gao, C Doerr, F Neumann, M Wagner Proceedings of the Genetic and Evolutionary Computation Conference, 991-998, 2018 | 57 | 2018 |
| Black-box optimization revisited: Improving algorithm selection wizards through massive benchmarking L Meunier, H Rakotoarison, PK Wong, B Roziere, J Rapin, O Teytaud, ... IEEE Transactions on Evolutionary Computation 26 (3), 490-500, 2021 | 52 | 2021 |
| Lessons from the black-box: Fast crossover-based genetic algorithms B Doerr, C Doerr, F Ebel Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013 | 51 | 2013 |
| Landscape-aware fixed-budget performance regression and algorithm selection for modular CMA-ES variants A Jankovic, C Doerr Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 841-849, 2020 | 50 | 2020 |
Benjamin DoerrProfessor at Ecole Polytechnique, FranceVerified email at lix.polytechnique.fr
