| 2026 | GECCO | When Switching Algorithms Helps: A Theoretical Study of Online Algorithm Selection. | Denis Antipov, Carola Doerr |
| 2026 | GECCO | Hot off the Press: Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It. | Denis Antipov, Benjamin Doerr |
| 2026 | GECCO | Hot off the Press: Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem. | Denis Antipov, Aneta Neumann, Frank Neumann, Andrew M. Sutton |
| 2026 | GECCO | Parent Selection Mechanisms in Elitist Crossover-Based Algorithms. | Andre Opris, Denis Antipov |
| 2025 | EvoCOP | Feature-Based Evolutionary Diversity Optimization of Discriminating Instances for Chance-Constrained Optimization Problems. | Saba Sadeghi Ahouei, Denis Antipov, Aneta Neumann, Frank Neumann |
| 2025 | FOGA | Enhancing Parameter Control Policies with State Information. | Gianluca Covini, Denis Antipov, Carola Doerr |
| 2025 | GECCO | Hot off the Press: First Steps Towards a Runtime Analysis When Starting With a Good Solution. | Denis Antipov, Maxim Buzdalov, Benjamin Doerr |
| 2025 | IJCAI | Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It. | Denis Antipov, Benjamin Doerr |
| 2024 | GECCO | Already Moderate Population Sizes Provably Yield Strong Robustness to Noise. | Denis Antipov, Benjamin Doerr, Alexandra Ivanova |
| 2024 | GECCO | A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMax. | Denis Antipov, Aneta Neumann, Frank Neumann |
| 2024 | GECCO | Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem. | Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, Aneta Neumann |
| 2024 | GECCO | Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem. | Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, Aneta Neumann |
| 2024 | PPSN | Greedy Versus Curious Parent Selection for Multi-objective Evolutionary Algorithms. | Denis Antipov, Timo Ktzing, Aishwarya Radhakrishnan |
| 2024 | PPSN | Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential. | Denis Antipov, Aneta Neumann, Frank Neumann |
| 2024 | PPSN | Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective Version of the (LeadingOnes, TrailingZeros) Problem. | Denis Antipov, Aneta Neumann, Frank Neumann, Andrew M. Sutton |
| 2023 | FOGA | Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax. | Denis Antipov, Aneta Neumann, Frank Neumann |
| 2023 | GECCO | Larger Offspring Populations Help the (1 + (λ, λlambda)) Genetic Algorithm to Overcome the Noise. | Alexandra Ivanova, Denis Antipov, Benjamin Doerr |
| 2022 | GECCO | Precise runtime analysis for plateau functions: (hot-off-the-press track at GECCO 2022). | Denis Antipov, Benjamin Doerr |
| 2022 | GECCO | Coevolutionary Pareto diversity optimization. | Aneta Neumann, Denis Antipov, Frank Neumann |
| 2021 | FOGA | The effect of non-symmetric fitness: the analysis of crossover-based algorithms on RealJump functions. | Denis Antipov, Semen Naumov |
| 2021 | GECCO | Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution. | Denis Antipov, Maxim Buzdalov, Benjamin Doerr |
| 2021 | GECCO | The lower bounds on the runtime of the (1 + (λ, λ)) GA on the minimum spanning tree problem. | Matvey Shnytkin, Denis Antipov |
| 2020 | GECCO | Fast mutation in crossover-based algorithms. | Denis Antipov, Maxim Buzdalov, Benjamin Doerr |
| 2020 | GECCO | The (1 + ( | Denis Antipov, Benjamin Doerr, Vitalii Karavaev |
| 2020 | PPSN | First Steps Towards a Runtime Analysis When Starting with a Good Solution. | Denis Antipov, Maxim Buzdalov, Benjamin Doerr |
| 2020 | PPSN | Runtime Analysis of a Heavy-Tailed (1+(λ , λ )) Genetic Algorithm on Jump Functions. | Denis Antipov, Benjamin Doerr |
| 2019 | FOGA | A tight runtime analysis for the (1 + (λ, λ)) GA on leadingones. | Denis Antipov, Benjamin Doerr, Vitalii Karavaev |
| 2019 | GECCO | The efficiency threshold for the offspring population size of the ( | Denis Antipov, Benjamin Doerr, Quentin Yang |
| 2019 | GECCO | Theoretical and empirical study of the (1 + (λ, λ)) EA on the leadingones problem. | Vitalii Karavaev, Denis Antipov, Benjamin Doerr |
| 2018 | GECCO | Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learning. | Denis Antipov, Arina Buzdalova, Andrew Stankevich |
| 2018 | GECCO | A tight runtime analysis for the (μ + λ) EA. | Denis Antipov, Benjamin Doerr, Jiefeng Fang, Tangi Hetet |
| 2018 | PPSN | Precise Runtime Analysis for Plateaus. | Denis Antipov, Benjamin Doerr |
| 2017 | CEC | Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives. | Denis Antipov, Arina Buzdalova |