| Fast mutation in crossover-based algorithms D Antipov, M Buzdalov, B Doerr Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 50 | 2020 |
| Runtime analysis of a heavy-tailed genetic algorithm on jump functions D Antipov, B Doerr International Conference on Parallel Problem Solving from Nature, 545-559, 2020 | 36 | 2020 |
| Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution D Antipov, M Buzdalov, B Doerr Proceedings of the Genetic and Evolutionary Computation Conference, 1115-1123, 2021 | 31 | 2021 |
| The (1 + (λ,λ)) GA is even faster on multimodal problems D Antipov, B Doerr, V Karavaev Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 29 | 2020 |
| A tight runtime analysis for the (1+(λ, λ)) GA on LeadingOnes D Antipov, B Doerr, V Karavaev Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic …, 2019 | 26 | 2019 |
| The efficiency threshold for the offspring population size of the (µ, λ) EA D Antipov, B Doerr, Q Yang Proceedings of the Genetic and Evolutionary Computation Conference, 1461-1469, 2019 | 24 | 2019 |
| A tight runtime analysis for the (μ+ λ) EA D Antipov, B Doerr, J Fang, T Hetet Proceedings of the Genetic and Evolutionary Computation Conference, 1459-1466, 2018 | 24 | 2018 |
| A Rigorous Runtime Analysis of the GA on Jump Functions D Antipov, B Doerr, V Karavaev Algorithmica 84 (6), 1573-1602, 2022 | 22 | 2022 |
| Runtime analysis for the (µ+ λ) EA optimizing OneMax D Antipov, B Doerr, J Fang, T Hetet Genetic and Evolutionary Computation Conference, GECCO, 1459-1466, 2018 | 22 | 2018 |
| First steps towards a runtime analysis when starting with a good solution D Antipov, M Buzdalov, B Doerr International Conference on Parallel Problem Solving from Nature, 560-573, 2020 | 20 | 2020 |
| Precise runtime analysis for plateaus D Antipov, B Doerr International Conference on Parallel Problem Solving from Nature, 117-128, 2018 | 16 | 2018 |
| Coevolutionary Pareto diversity optimization A Neumann, D Antipov, F Neumann Proceedings of the Genetic and Evolutionary Computation Conference, 832-839, 2022 | 14 | 2022 |
| Precise runtime analysis for plateau functions D Antipov, B Doerr ACM Transactions on Evolutionary Learning and Optimization 1 (4), 1-28, 2021 | 9 | 2021 |
| Using 3-objective evolutionary algorithms for the dynamic chance constrained knapsack problem IH Pathiranage, F Neumann, D Antipov, A Neumann arXiv preprint arXiv:2404.06014, 2024 | 8 | 2024 |
| Effective 2-and 3-objective MOEA/D approaches for the chance constrained knapsack problem IH Pathiranage, F Neumann, D Antipov, A Neumann Genetic and Evolutionary Computation Conference, GECCO, 2024 | 7 | 2024 |
| The effect of non-symmetric fitness: The analysis of crossover-based algorithms on RealJump functions D Antipov, S Naumov Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic …, 2021 | 5 | 2021 |
| A tight runtime analysis for the (𝜇+ 𝜆) EA D Antipov, B Doerr Algorithmica 83 (4), 1054-1095, 2021 | 5 | 2021 |
| Theoretical and empirical study of the (1+(λ, λ)) EA on the LeadingOnes problem V Karavaev, D Antipov, B Doerr Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019 | 5 | 2019 |
| Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax D Antipov, A Neumann, F Neumann Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic …, 2023 | 4 | 2023 |
| Efficient computation of fitness function for evolutionary clustering S Muravyov, D Antipov, A Buzdalova, A Filchenkov Mendel 25 (1), 87-94, 2019 | 3 | 2019 |