Last updated on 31 July 2026

About me

I am a postdoc researcher at the Operation Research team of Laboratoire de recherche en informatique de Sorbonne Université (LIP6). I have a PhD in computer science (Docteur en Informatique). My main research interest lies in the theoretical analysis of randomized algorithms for black-box optimization. My work focuses in particular on evolutionary and recombination-based methods, with the goal of understanding their performance and deriving principled design guidelines. I have contributed to several core topics, including optimization under noise, diversity optimization, parameter control, and population dynamics. Currently, I am also involved in research on benchmarking and evaluation methodologies for stochastic optimization, on recombination-based optimization, and also on online algorithm selection. I am always happy to discuss anything related to the research, and the best ways to contact me are my email (below) and telegram (link on the left sidebar).

Current working address:

Case 169
4 Place Jussieu
75252
Paris, France
email: antipovden [at] yandex [dot] ru

Education

2017—2020:

PhD, Mathematics and Informatics, École Politechnique, Palaiseau, France.

2016—2020:

PhD, Mathematics and Informatics, ITMO University, St. Petersburg, Russia.

2014—2016:

M.Sc, Applied Mathematics and Informatics, ITMO University, St. Petersburg, Russia.

2010—2014:

B.Sc, Applied Mathematics and Informatics, ITMO University, St. Petersburg, Russia.

2007—2010:

Physics and Mathematics Lyceum 239, St. Petersburg, Russia.

I was involved into a double PhD program between École Politechnique and ITMO University and was supervised by Benjamin Doerr and Maxim Buzdalov. The topic of my thesis was "Methods for Tight Analysis of Population-based Evolutionary Algorithms" and the manuscript can be found here.

Employment history

2014—now:

Postdoc researcher, Sorbonne University.

2022—2024:

Postdoc researcher, the University of Adelaide.

2014—2022:

Programmer, researcher, ITMO University.

2013—2014:

Software engineer, OpenWay Group.

Participation in research projects

2024—now:

Participation in ERC project dynaBBO : Dynamic Selection and Configuration of Black-box Optimization Algorithms, Sorbonne University.

2022—2024:

Participation in project Evolutionary Diversity Optimisation, the University of Adelaide.

2021—2022:

Member of research center in the field of artificial intelligence Strong artificial intelligence in industry, ITMO University.

2020—2022:

Participation in project Theoretical Foundation of Dynamic Parameter Selection for Randomized Optimization Heuristics conducted in International research center Computer Technologies, ITMO University.

2018—2019:

Participation in project Intelligent technologies in the digital healthcare conducted in International research center Computer Technologies, ITMO University.

2018—2022:

Participation in project Methods, models and technologies of artificial intelligence in bioinformatics, social media, cyberphysical, biometric and speech systems conducted in International research center Computer Technologies, ITMO University.

2017—2018:

Participation in project Automated analysis of the space of chemical transformations for predictive modeling of catalytic processes conducted in International research center Computer Technologies, ITMO University.

2017—2020:

Participation in project Methods of the design of the effective evolutionary algorithms conducted in International research center Computer Technologies, ITMO University.

2016—2017:

Participation in project Increasing efficiency of the evolutionary algorithms with dynamically chosen auxilary optimization objectives conducted in International research center Computer Technologies, ITMO University.

2014—2016:

Participation in project Bioinformatics, artificial intelligence, programming technologies, coding theory conducted in International research center Computer Technologies, ITMO University.

Teaching

2021—2022:

Teaching the Probability Theory hybrid (online/offline) course (ITMO University, Faculty of Information Technologies and Programming, 2^nd year of bachelor studies).

2020:

Assistance in teaching the Mathematical Analysis online course (ITMO University, Faculty of Information Technologies and Programming, 1^st year of bachelor studies).

2019:

Assistance in teaching the Evolutionary Computation course (ITMO University, Faculty of Information Technologies and Programming, 4^th year of bachelor studies).

2014—2017:

Assistance in teaching the Mathematical Analysis course (ITMO University, Faculty of Information Technologies and Programming, Computer Technologies Department, 1^st and 2^nd years of bachelor studies).

Student supervisions

2018—2019:

Vitalii Karavaev, ITMO University.
Bachelor thesis: A Tight Runtime Analysis for the (1 + (λ, λ)) GA on the LeadingOnes Problem.

2020—2021:

Matvey Shnytkin, ITMO University.
Bachelor thesis: A Runtime Analysis for the (1 + (λ, λ)) GA on the Minimum Spanning Tree Problem.
Russian title: Анализ времени работы генетического алгоритма (1 + (λ, λ)) на задаче минимального остовного дерева.

2020—2023:

Simon Naumov, ITMO University.
Bachelor thesis: Runtime Analysis of Evolutionary Algorithms on Asymmetric Jump Functions.
Master thesis: Analysis of crossover-based evolutionary algorithms on rugged landscapes.
Russian title Анализ эволюционных алгоритмов с оператором скрещивания на ландшафтах с большим числом локальных оптимумов.

2021—2022:

Victoria Chernookaya, ITMO University.
Bachelor thesis :A Runtime Analysis for the (1 + (λ, λ)) GA on the Maximum Cut Problem.
Russian title: Анализ времени работы генетического алгоритма (1 + (λ, λ)) на задаче максимального разреза графа.

Co-supervision

2017:

Jiefeng Fang and Tangi Hetet, Master students, École Politechnique

2018:

Quentin Yang, Master student, École Politechnique

2022—now:

Saba Sadeghi Ahouei, PhD student, the University of Adelaide.

2023—2024:

Ishara Udayanthi Hewa Pathiranage, PhD student, the University of Adelaide.

Awards

Best Paper Award at GECCO 2026 for paper by Opris A. and Antipov D. Parent Selection Mechanisms in Elitist Crossover-Based Algorithms.
Outstanding reviewer at GECCO 2025 (Theory track).
Honorable mention at SIGEVO Dissertation Award 2021.
Winner of the IDIA Best Thesis Award 2021 in the field of Computer Science at Institut Polytechnique de Paris.
Best Paper Award at GECCO 2020 for paper by Antipov D., Buzdalov M., Doerr B. Fast Mutation in Crossover-based Algorithms.
Winner of the prize for the Best scientific production in ICST (Information and Communication Sciences and Technologies) on the Plateau de Saclay in 2020.
Winner of SIGEVO student travel grant for FOGA 2019.
Winner of GECCO 2019 student travel grant (not accepted for technical reasons).
Winner of GECCO 2018 student travel grant.
Winner of the Grant for PhD Students of Universities Located in Saint Petersburg in 2018.
Winner of the Bourse Ostrogradsky from the French Embassy in Russia in 2017.
Best Talk Award at XLVI Scientific and Pedagogical Conference of ITMO University in 2017 for the talk on the runtime analysis of the EA+RL method optimizing jump functions.

Seminar Organization

2023:

Co-organizer of the ThRaSH seminar (Theory of Randomized Search Heuristics) together with Martin Krejca

2025—now:

Organizer of the RO team seminar at LIP6

Publications

2026

Conference papers

@inproceedings{AntipovD26,
    author     = {Denis Antipov and
                  Carola Doerr},
    OPTeditor  = {Leonardo Trujillo and
                  Ting Hu},
    title      = {When Switching Algorithms Helps: {A} Theoretical Study of Online Algorithm
                  Selection},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2026},
    pages      = {1304--1312},
    publisher  = {{ACM}},
    year       = {2026},
    OPTurl     = {https://doi.org/10.1145/3795095.3805156},
    OPTdoi     = {10.1145/3795095.3805156},
}

Denis Antipov and Carola Doerr. When Switching Algorithms Helps: A Theoretical Study of Online Algorithm Selection. In Genetic and Evolutionary Computation Conference, GECCO 2026, pp. 1304—1312. ACM, 2026.

@inproceedings{OprisA26,
    author     = {Andre Opris and
                  Denis Antipov},
    OPTeditor  = {Leonardo Trujillo and
                  Ting Hu},
    title      = {Parent Selection Mechanisms in Elitist Crossover-Based Algorithms},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2026},
    pages      = {1358--1366},
    publisher  = {{ACM}},
    year       = {2026},
    OPTurl     = {https://doi.org/10.1145/3795095.3805131},
    OPTdoi     = {10.1145/3795095.3805131},
}

Andre Opris and Denis Antipov. Parent Selection Mechanisms in Elitist Crossover-Based Algorithms. In Genetic and Evolutionary Computation Conference, GECCO 2026, pp. 1358—1366. ACM, 2026.

@inproceedings{AntipovNNS26hop,
    author     = {Denis Antipov and Aneta Neumann and Frank Neumann and Andrew M. Sutton},
    title      = {Hot off the Press: Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem},
    booktitle  = {Genetic and Evolutionary Computation Conference Companion, {GECCO} 2026},
    publisher  = {{ACM}},
    year       = {2026},
    note       = {To appear},
}

Denis Antipov and Aneta Neumann and Frank Neumann and Andrew M. Sutton. Hot off the Press: Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem. In Genetic and Evolutionary Computation Conference Companion, GECCO 2026. ACM, 2026.

@inproceedings{AntipovD26hop,
    author     = {Denis Antipov and
                  Benjamin Doerr},
    title      = {Hot off the Press: Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It},
    booktitle  = {Genetic and Evolutionary Computation Conference Companion, {GECCO} 2026},
    publisher  = {{ACM}},
    year       = {2026},
    note       = {To appear},
}

Denis Antipov and Benjamin Doerr. Hot off the Press: Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It. In Genetic and Evolutionary Computation Conference Companion, GECCO 2026. ACM, 2026.

@inproceedings{AntipovV26,
    author     = {Denis Antipov and
                  Diederick Vermetten},
    title      = {Theory-Guided Online Algorithm Selection:  Benchmarking Algorithm Switching for Pseudo-Boolean Optimization },
    booktitle  = {Parallel Problem Solving from Nature, {PPSN} 2026},
    year       = {2026},
    note       = {to appear},
}

Denis Antipov and Diederick Vermetten. Theory-Guided Online Algorithm Selection: Benchmarking Algorithm Switching for Pseudo-Boolean Optimization . In Parallel Problem Solving from Nature, PPSN 2026. 2026.

2025

Journal papers

@article{AntipovBD25,
    author   = {Denis Antipov and
                Maxim Buzdalov and
                Benjamin Doerr},
    title    = {First Steps Toward a Runtime Analysis When Starting With a Good Solution},
    journal  = {{ACM} Transactions on Evolutionary Learning and Optimization},
    volume   = {5},
    number   = {2},
    pages    = {14:1--14:41},
    year     = {2025},
    OPTurl   = {https://doi.org/10.1145/3675783},
    OPTdoi   = {10.1145/3675783},
}

Denis Antipov, Maxim Buzdalov, and Benjamin Doerr. First Steps Toward a Runtime Analysis When Starting With a Good Solution. ACM Transactions on Evolutionary Learning and Optimization, 5:14:1—14:41, 2025.

@article{AntipovNNS25,
    author   = {Denis Antipov and Aneta Neumann and Frank Neumann and Andrew M. Sutton},
    title    = {Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective ({L}eading{O}nes, {T}railing{Z}eros) Problem},
    journal  = {Evolutionary Computation},
    pages    = {1-23},
    year     = {2025},
    month    = {12},
    OPTurl   = {https://doi.org/10.1162/EVCO.a.376},
    OPTdoi   = {10.1162/EVCO.a.376},
}

Denis Antipov and Aneta Neumann and Frank Neumann and Andrew M. Sutton. Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem. Evolutionary Computation, 1-23, 2025.

Conference papers

@inproceedings{AhoueiANN25,
    author     = {Saba Sadeghi Ahouei and
                  Denis Antipov and
                  Aneta Neumann and
                  Frank Neumann},
    OPTeditor  = {Martin S. Krejca and
                  Markus Wagner},
    title      = {Feature-Based Evolutionary Diversity Optimization of Discriminating
                  Instances for Chance-Constrained Optimization Problems},
    booktitle  = {Evolutionary Computation in Combinatorial Optimization, {E}vo{COP} 2025},
    volume     = {15610},
    series     = {Lecture Notes in Computer Science},
    pages      = {184--199},
    publisher  = {Springer},
    year       = {2025},
    OPTurl     = {https://doi.org/10.1007/978-3-031-86849-8\_12},
    OPTdoi     = {10.1007/978-3-031-86849-8\_12},
}

Saba Sadeghi Ahouei, Denis Antipov, Aneta Neumann, and Frank Neumann. Feature-Based Evolutionary Diversity Optimization of Discriminating Instances for Chance-Constrained Optimization Problems. In Evolutionary Computation in Combinatorial Optimization, EvoCOP 2025, pp. 184—199. Springer, 2025.

@inproceedings{CoviniAD25,
    author     = {Gianluca Covini and
                  Denis Antipov and
                  Carola Doerr},
    title      = {Enhancing Parameter Control Policies with State Information},
    booktitle  = {Foundations of Genetic Algorithms, {FOGA} 2025},
    pages      = {37--48},
    publisher  = {{ACM}},
    year       = {2025},
    OPTurl     = {https://doi.org/10.1145/3729878.3746633},
    OPTdoi     = {10.1145/3729878.3746633},
}

Gianluca Covini, Denis Antipov, and Carola Doerr. Enhancing Parameter Control Policies with State Information. In Foundations of Genetic Algorithms, FOGA 2025, pp. 37—48. ACM, 2025.

@inproceedings{Antipov0D25,
    author     = {Denis Antipov and
                  Maxim Buzdalov and
                  Benjamin Doerr},
    OPTeditor  = {Bogdan Filipic},
    title      = {Hot off the Press: First Steps Towards a Runtime Analysis When Starting
                  With a Good Solution},
    booktitle  = {Genetic and Evolutionary Computation Conference Companion, {GECCO} 2025},
    pages      = {11--12},
    publisher  = {{ACM}},
    year       = {2025},
    OPTurl     = {https://doi.org/10.1145/3712255.3734241},
    OPTdoi     = {10.1145/3712255.3734241},
}

Denis Antipov, Maxim Buzdalov, and Benjamin Doerr. Hot off the Press: First Steps Towards a Runtime Analysis When Starting With a Good Solution. In Genetic and Evolutionary Computation Conference Companion, GECCO 2025, pp. 11—12. ACM, 2025.

@inproceedings{AntipovD25,
    author     = {Denis Antipov and
                  Benjamin Doerr},
    title      = {Evolutionary Algorithms Are Significantly More Robust to Noise When
                  They Ignore It},
    booktitle  = {International Joint Conference on Artificial Intelligence, {IJCAI} 2025},
    pages      = {8842--8849},
    publisher  = {ijcai.org},
    year       = {2025},
    OPTurl     = {https://doi.org/10.24963/ijcai.2025/983},
    OPTdoi     = {10.24963/IJCAI.2025/983},
}

Denis Antipov and Benjamin Doerr. Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It. In International Joint Conference on Artificial Intelligence, IJCAI 2025, pp. 8842—8849. ijcai.org, 2025.

2024

Journal papers

@article{AntipovBD24,
    author   = {Denis Antipov and
                Maxim Buzdalov and
                Benjamin Doerr},
    title    = {Lazy Parameter Tuning and Control: Choosing All Parameters Randomly
                from a Power-Law Distribution},
    journal  = {Algorithmica},
    volume   = {86},
    number   = {2},
    pages    = {442--484},
    year     = {2024},
    OPTurl   = {https://doi.org/10.1007/s00453-023-01098-z},
    OPTdoi   = {10.1007/S00453-023-01098-Z},
}

Denis Antipov, Maxim Buzdalov, and Benjamin Doerr. Lazy Parameter Tuning and Control: Choosing All Parameters Randomly from a Power-Law Distribution. Algorithmica, 86:442—484, 2024.

Conference papers

@inproceedings{AntipovDI24,
    author     = {Denis Antipov and
                  Benjamin Doerr and
                  Alexandra Ivanova},
    OPTeditor  = {Xiaodong Li and
                  Julia Handl},
    title      = {Already Moderate Population Sizes Provably Yield Strong Robustness
                  to Noise},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2024},
    publisher  = {{ACM}},
    year       = {2024},
    OPTurl     = {https://doi.org/10.1145/3638529.3654196},
    OPTdoi     = {10.1145/3638529.3654196},
}

Denis Antipov, Benjamin Doerr, and Alexandra Ivanova. Already Moderate Population Sizes Provably Yield Strong Robustness to Noise. In Genetic and Evolutionary Computation Conference, GECCO 2024. ACM, 2024.

@inproceedings{AntipovN024,
    author     = {Denis Antipov and
                  Aneta Neumann and
                  Frank Neumann},
    OPTeditor  = {Xiaodong Li and
                  Julia Handl},
    title      = {A Detailed Experimental Analysis of Evolutionary Diversity Optimization
                  for OneMinMax},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2024},
    publisher  = {{ACM}},
    year       = {2024},
    OPTurl     = {https://doi.org/10.1145/3638529.3654082},
    OPTdoi     = {10.1145/3638529.3654082},
}

Denis Antipov, Aneta Neumann, and Frank Neumann. A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMax. In Genetic and Evolutionary Computation Conference, GECCO 2024. ACM, 2024.

@inproceedings{Pathiranage0AN24,
    author     = {Ishara Hewa Pathiranage and
                  Frank Neumann and
                  Denis Antipov and
                  Aneta Neumann},
    OPTeditor  = {Xiaodong Li and
                  Julia Handl},
    title      = {Effective 2- and 3-Objective {MOEA/D} Approaches for the Chance Constrained
                  Knapsack Problem},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2024},
    publisher  = {{ACM}},
    year       = {2024},
    OPTurl     = {https://doi.org/10.1145/3638529.3654066},
    OPTdoi     = {10.1145/3638529.3654066},
}

Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, and Aneta Neumann. Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem. In Genetic and Evolutionary Computation Conference, GECCO 2024. ACM, 2024.

@inproceedings{Pathiranage0AN24a,
    author     = {Ishara Hewa Pathiranage and
                  Frank Neumann and
                  Denis Antipov and
                  Aneta Neumann},
    OPTeditor  = {Xiaodong Li and
                  Julia Handl},
    title      = {Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained
                  Knapsack Problem},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2024},
    publisher  = {{ACM}},
    year       = {2024},
    OPTurl     = {https://doi.org/10.1145/3638529.3654067},
    OPTdoi     = {10.1145/3638529.3654067},
}

Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, and Aneta Neumann. Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem. In Genetic and Evolutionary Computation Conference, GECCO 2024. ACM, 2024.

@inproceedings{AntipovNNS24,
    author     = {Denis Antipov and
                  Aneta Neumann and
                  Frank Neumann and
                  Andrew M. Sutton},
    OPTeditor  = {Michael Affenzeller and
                  Stephan M. Winkler and
                  Anna V. Kononova and
                  Heike Trautmann and
                  Tea Tusar and
                  Penousal Machado and
                  Thomas B{\"{a}}ck},
    title      = {Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective
                  Version of the (LeadingOnes, TrailingZeros) Problem},
    booktitle  = {Parallel Problem Solving from Nature, {PPSN} 2024, Part {III}},
    volume     = {15150},
    series     = {Lecture Notes in Computer Science},
    pages      = {19--35},
    publisher  = {Springer},
    year       = {2024},
    OPTurl     = {https://doi.org/10.1007/978-3-031-70071-2\_2},
    OPTdoi     = {10.1007/978-3-031-70071-2\_2},
}

Denis Antipov, Aneta Neumann, Frank Neumann, and Andrew M. Sutton. Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective Version of the (LeadingOnes, TrailingZeros) Problem. In Parallel Problem Solving from Nature, PPSN 2024, Part III, pp. 19—35. Springer, 2024.

@inproceedings{AntipovKR24,
    author     = {Denis Antipov and
                  Timo K{\"{o}}tzing and
                  Aishwarya Radhakrishnan},
    OPTeditor  = {Michael Affenzeller and
                  Stephan M. Winkler and
                  Anna V. Kononova and
                  Heike Trautmann and
                  Tea Tusar and
                  Penousal Machado and
                  Thomas B{\"{a}}ck},
    title      = {Greedy Versus Curious Parent Selection for Multi-objective Evolutionary
                  Algorithms},
    booktitle  = {Parallel Problem Solving from Nature, {PPSN} 2024, Part {III}},
    volume     = {15150},
    series     = {Lecture Notes in Computer Science},
    pages      = {86--101},
    publisher  = {Springer},
    year       = {2024},
    OPTurl     = {https://doi.org/10.1007/978-3-031-70071-2\_6},
    OPTdoi     = {10.1007/978-3-031-70071-2\_6},
}

Denis Antipov, Timo Kötzing, and Aishwarya Radhakrishnan. Greedy Versus Curious Parent Selection for Multi-objective Evolutionary Algorithms. In Parallel Problem Solving from Nature, PPSN 2024, Part III, pp. 86—101. Springer, 2024.

@inproceedings{AntipovNN24,
    author     = {Denis Antipov and
                  Aneta Neumann and
                  Frank Neumann},
    OPTeditor  = {Michael Affenzeller and
                  Stephan M. Winkler and
                  Anna V. Kononova and
                  Heike Trautmann and
                  Tea Tusar and
                  Penousal Machado and
                  Thomas B{\"{a}}ck},
    title      = {Local Optima in Diversity Optimization: Non-trivial Offspring Population
                  is Essential},
    booktitle  = {Parallel Problem Solving from Nature, {PPSN} 2024, Part {III}},
    volume     = {15150},
    series     = {Lecture Notes in Computer Science},
    pages      = {181--196},
    publisher  = {Springer},
    year       = {2024},
    OPTurl     = {https://doi.org/10.1007/978-3-031-70071-2\_12},
    OPTdoi     = {10.1007/978-3-031-70071-2\_12},
}

Denis Antipov, Aneta Neumann, and Frank Neumann. Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential. In Parallel Problem Solving from Nature, PPSN 2024, Part III, pp. 181—196. Springer, 2024.

2023

Conference papers

@inproceedings{Antipov0N23,
    author     = {Denis Antipov and
                  Aneta Neumann and
                  Frank Neumann},
    title      = {Rigorous Runtime Analysis of Diversity Optimization with {GSEMO} on
                  OneMinMax},
    booktitle  = {Foundations of Genetic Algorithms, {FOGA} 2023},
    pages      = {3--14},
    publisher  = {{ACM}},
    year       = {2023},
    OPTurl     = {https://doi.org/10.1145/3594805.3607135},
    OPTdoi     = {10.1145/3594805.3607135},
}

Denis Antipov, Aneta Neumann, and Frank Neumann. Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax. In Foundations of Genetic Algorithms, FOGA 2023, pp. 3—14. ACM, 2023.

@inproceedings{IvanovaAD23,
    author     = {Alexandra Ivanova and
                  Denis Antipov and
                  Benjamin Doerr},
    OPTeditor  = {Sara Silva and
                  Lu{\'{\i}}s Paquete},
    title      = {Larger Offspring Populations Help the {(1} + ({\(\lambda\)}, {\(\lambda\)}lambda))
                  Genetic Algorithm to Overcome the Noise},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2023},
    pages      = {919--928},
    publisher  = {{ACM}},
    year       = {2023},
    OPTurl     = {https://doi.org/10.1145/3583131.3590514},
    OPTdoi     = {10.1145/3583131.3590514},
}

Alexandra Ivanova, Denis Antipov, and Benjamin Doerr. Larger Offspring Populations Help the (1 + (λ, λlambda)) Genetic Algorithm to Overcome the Noise. In Genetic and Evolutionary Computation Conference, GECCO 2023, pp. 919—928. ACM, 2023.

2022

Journal papers

@article{AntipovDK22,
    author   = {Denis Antipov and
                Benjamin Doerr and
                Vitalii Karavaev},
    title    = {A Rigorous Runtime Analysis of the {(1} + ({\(\lambda\)} , {\(\lambda\)}
                {))} {GA} on Jump Functions},
    journal  = {Algorithmica},
    volume   = {84},
    number   = {6},
    pages    = {1573--1602},
    year     = {2022},
    OPTurl   = {https://doi.org/10.1007/s00453-021-00907-7},
    OPTdoi   = {10.1007/S00453-021-00907-7},
}

Denis Antipov, Benjamin Doerr, and Vitalii Karavaev. A Rigorous Runtime Analysis of the (1 + (λ , λ )) GA on Jump Functions. Algorithmica, 84:1573—1602, 2022.

@article{AntipovBD22,
    author   = {Denis Antipov and
                Maxim Buzdalov and
                Benjamin Doerr},
    title    = {Fast Mutation in Crossover-Based Algorithms},
    journal  = {Algorithmica},
    volume   = {84},
    number   = {6},
    pages    = {1724--1761},
    year     = {2022},
    OPTurl   = {https://doi.org/10.1007/s00453-022-00957-5},
    OPTdoi   = {10.1007/S00453-022-00957-5},
}

Denis Antipov, Maxim Buzdalov, and Benjamin Doerr. Fast Mutation in Crossover-Based Algorithms. Algorithmica, 84:1724—1761, 2022.

Conference papers

@inproceedings{AntipovD22,
    author     = {Denis Antipov and
                  Benjamin Doerr},
    OPTeditor  = {Jonathan E. Fieldsend and
                  Markus Wagner},
    title      = {Precise runtime analysis for plateau functions: (hot-off-the-press
                  track at {GECCO} 2022)},
    booktitle  = {Genetic and Evolutionary Computation Conference Companion, {GECCO} 2022},
    pages      = {13--14},
    publisher  = {{ACM}},
    year       = {2022},
    OPTurl     = {https://doi.org/10.1145/3520304.3534062},
    OPTdoi     = {10.1145/3520304.3534062},
}

Denis Antipov and Benjamin Doerr. Precise runtime analysis for plateau functions: (hot-off-the-press track at GECCO 2022). In Genetic and Evolutionary Computation Conference Companion, GECCO 2022, pp. 13—14. ACM, 2022.

@inproceedings{NeumannA022,
    author     = {Aneta Neumann and
                  Denis Antipov and
                  Frank Neumann},
    OPTeditor  = {Jonathan E. Fieldsend and
                  Markus Wagner},
    title      = {Coevolutionary Pareto diversity optimization},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2022},
    pages      = {832--839},
    publisher  = {{ACM}},
    year       = {2022},
    OPTurl     = {https://doi.org/10.1145/3512290.3528755},
    OPTdoi     = {10.1145/3512290.3528755},
}

Aneta Neumann, Denis Antipov, and Frank Neumann. Coevolutionary Pareto diversity optimization. In Genetic and Evolutionary Computation Conference, GECCO 2022, pp. 832—839. ACM, 2022.

2021

Journal papers

@article{AntipovD21algorithmica,
    author   = {Denis Antipov and
                Benjamin Doerr},
    title    = {A Tight Runtime Analysis for the ({\(\mu\)} + {\(\lambda\)} {)} {EA}},
    journal  = {Algorithmica},
    volume   = {83},
    number   = {4},
    pages    = {1054--1095},
    year     = {2021},
    OPTurl   = {https://doi.org/10.1007/s00453-020-00731-5},
    OPTdoi   = {10.1007/S00453-020-00731-5},
}

Denis Antipov and Benjamin Doerr. A Tight Runtime Analysis for the (μ + λ ) EA. Algorithmica, 83:1054—1095, 2021.

@article{AntipovD21telo,
    author   = {Denis Antipov and
                Benjamin Doerr},
    title    = {Precise Runtime Analysis for Plateau Functions},
    journal  = {{ACM} Transactions on Evolutionary Learning and Optimization},
    volume   = {1},
    number   = {4},
    pages    = {13:1--13:28},
    year     = {2021},
    OPTurl   = {https://doi.org/10.1145/3469800},
    OPTdoi   = {10.1145/3469800},
}

Denis Antipov and Benjamin Doerr. Precise Runtime Analysis for Plateau Functions. ACM Transactions on Evolutionary Learning and Optimization, 1:13:1—13:28, 2021.

Conference papers

@inproceedings{AntipovN21,
    author     = {Denis Antipov and
                  Semen Naumov},
    OPTeditor  = {Steffen Finck and
                  Michael Hellwig and
                  Pietro S. Oliveto},
    title      = {The effect of non-symmetric fitness: the analysis of crossover-based
                  algorithms on RealJump functions},
    booktitle  = {Foundations of Genetic Algorithms, {FOGA} 2021},
    pages      = {10:1--10:15},
    publisher  = {{ACM}},
    year       = {2021},
    OPTurl     = {https://doi.org/10.1145/3450218.3477311},
    OPTdoi     = {10.1145/3450218.3477311},
}

Denis Antipov and Semen Naumov. The effect of non-symmetric fitness: the analysis of crossover-based algorithms on RealJump functions. In Foundations of Genetic Algorithms, FOGA 2021, pp. 10:1—10:15. ACM, 2021.

@inproceedings{AntipovBD21,
    author     = {Denis Antipov and
                  Maxim Buzdalov and
                  Benjamin Doerr},
    OPTeditor  = {Francisco Chicano and
                  Krzysztof Krawiec},
    title      = {Lazy parameter tuning and control: choosing all parameters randomly
                  from a power-law distribution},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2021},
    pages      = {1115--1123},
    publisher  = {{ACM}},
    year       = {2021},
    OPTurl     = {https://doi.org/10.1145/3449639.3459377},
    OPTdoi     = {10.1145/3449639.3459377},
}

Denis Antipov, Maxim Buzdalov, and Benjamin Doerr. Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution. In Genetic and Evolutionary Computation Conference, GECCO 2021, pp. 1115—1123. ACM, 2021.

@inproceedings{ShnytkinA21,
    author     = {Matvey Shnytkin and
                  Denis Antipov},
    OPTeditor  = {Krzysztof Krawiec},
    title      = {The lower bounds on the runtime of the {(1} + ({\(\lambda\)}, {\(\lambda\)}))
                  {GA} on the minimum spanning tree problem},
    booktitle  = {Genetic and Evolutionary Computation Conference Companion, {GECCO} 2021},
    pages      = {1986--1989},
    publisher  = {{ACM}},
    year       = {2021},
    OPTurl     = {https://doi.org/10.1145/3449726.3463220},
    OPTdoi     = {10.1145/3449726.3463220},
}

Matvey Shnytkin and Denis Antipov. The lower bounds on the runtime of the (1 + (λ, λ)) GA on the minimum spanning tree problem. In Genetic and Evolutionary Computation Conference Companion, GECCO 2021, pp. 1986—1989. ACM, 2021.

2020

Conference papers

@inproceedings{AntipovDK20,
    author     = {Denis Antipov and
                  Benjamin Doerr and
                  Vitalii Karavaev},
    OPTeditor  = {Carlos Artemio Coello Coello},
    title      = {The {(1} + (\emph{{\(\lambda\)}, {\(\lambda\)}})) {GA} is even faster
                  on multimodal problems},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2020},
    pages      = {1259--1267},
    publisher  = {{ACM}},
    year       = {2020},
    OPTurl     = {https://doi.org/10.1145/3377930.3390148},
    OPTdoi     = {10.1145/3377930.3390148},
}

Denis Antipov, Benjamin Doerr, and Vitalii Karavaev. The (1 + (λ, λ)) GA is even faster on multimodal problems. In Genetic and Evolutionary Computation Conference, GECCO 2020, pp. 1259—1267. ACM, 2020.

@inproceedings{AntipovBD20gecco,
    author     = {Denis Antipov and
                  Maxim Buzdalov and
                  Benjamin Doerr},
    OPTeditor  = {Carlos Artemio Coello Coello},
    title      = {Fast mutation in crossover-based algorithms},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2020},
    pages      = {1268--1276},
    publisher  = {{ACM}},
    year       = {2020},
    OPTurl     = {https://doi.org/10.1145/3377930.3390172},
    OPTdoi     = {10.1145/3377930.3390172},
}

Denis Antipov, Maxim Buzdalov, and Benjamin Doerr. Fast mutation in crossover-based algorithms. In Genetic and Evolutionary Computation Conference, GECCO 2020, pp. 1268—1276. ACM, 2020.

@inproceedings{AntipovD20,
    author     = {Denis Antipov and
                  Benjamin Doerr},
    OPTeditor  = {Thomas B{\"{a}}ck and
                  Mike Preuss and
                  Andr{\'{e}} H. Deutz and
                  Hao Wang and
                  Carola Doerr and
                  Michael T. M. Emmerich and
                  Heike Trautmann},
    title      = {Runtime Analysis of a Heavy-Tailed (1+({\(\lambda\)} , {\(\lambda\)}
                  {))} Genetic Algorithm on Jump Functions},
    booktitle  = {Parallel Problem Solving from Nature, {PPSN} 2020, Part {II}},
    volume     = {12270},
    series     = {Lecture Notes in Computer Science},
    pages      = {545--559},
    publisher  = {Springer},
    year       = {2020},
    OPTurl     = {https://doi.org/10.1007/978-3-030-58115-2\_38},
    OPTdoi     = {10.1007/978-3-030-58115-2\_38},
}

Denis Antipov and Benjamin Doerr. Runtime Analysis of a Heavy-Tailed (1+(λ , λ )) Genetic Algorithm on Jump Functions. In Parallel Problem Solving from Nature, PPSN 2020, Part II, pp. 545—559. Springer, 2020.

@inproceedings{AntipovBD20ppsn,
    author     = {Denis Antipov and
                  Maxim Buzdalov and
                  Benjamin Doerr},
    OPTeditor  = {Thomas B{\"{a}}ck and
                  Mike Preuss and
                  Andr{\'{e}} H. Deutz and
                  Hao Wang and
                  Carola Doerr and
                  Michael T. M. Emmerich and
                  Heike Trautmann},
    title      = {First Steps Towards a Runtime Analysis When Starting with a Good Solution},
    booktitle  = {Parallel Problem Solving from Nature, {PPSN} 2020, Part {II}},
    volume     = {12270},
    series     = {Lecture Notes in Computer Science},
    pages      = {560--573},
    publisher  = {Springer},
    year       = {2020},
    OPTurl     = {https://doi.org/10.1007/978-3-030-58115-2\_39},
    OPTdoi     = {10.1007/978-3-030-58115-2\_39},
}

Denis Antipov, Maxim Buzdalov, and Benjamin Doerr. First Steps Towards a Runtime Analysis When Starting with a Good Solution. In Parallel Problem Solving from Nature, PPSN 2020, Part II, pp. 560—573. Springer, 2020.

2019

Journal papers

@article{MuravyovABF19,
    author   = {Sergey Muravyov and Denis Antipov and Arina Buzdalova and Andrey Filchenkov},
    title    = {Efficient Computation of Fitness Function for Evolutionary Clustering},
    journal  = {MENDEL},
    volume   = {25},
    number   = {1},
    pages    = {87-94},
    year     = {2019},
    month    = {Jun.},
    OPTurl   = {https://mendel-journal.org/index.php/mendel/article/view/83},
    OPTdoi   = {10.13164/mendel.2019.1.087},
}

Sergey Muravyov and Denis Antipov and Arina Buzdalova and Andrey Filchenkov. Efficient Computation of Fitness Function for Evolutionary Clustering. MENDEL, 25:87-94, 2019.

Conference papers

@inproceedings{AntipovDK19,
    author     = {Denis Antipov and
                  Benjamin Doerr and
                  Vitalii Karavaev},
    OPTeditor  = {Tobias Friedrich and
                  Carola Doerr and
                  Dirk V. Arnold},
    title      = {A tight runtime analysis for the {(1} + ({\(\lambda\)}, {\(\lambda\)}))
                  {GA} on leadingones},
    booktitle  = {Foundations of Genetic Algorithms, {FOGA} 2019},
    pages      = {169--182},
    publisher  = {{ACM}},
    year       = {2019},
    OPTurl     = {https://doi.org/10.1145/3299904.3340317},
    OPTdoi     = {10.1145/3299904.3340317},
}

Denis Antipov, Benjamin Doerr, and Vitalii Karavaev. A tight runtime analysis for the (1 + (λ, λ)) GA on leadingones. In Foundations of Genetic Algorithms, FOGA 2019, pp. 169—182. ACM, 2019.

@inproceedings{AntipovDY19,
    author     = {Denis Antipov and
                  Benjamin Doerr and
                  Quentin Yang},
    OPTeditor  = {Anne Auger and
                  Thomas St{\"{u}}tzle},
    title      = {The efficiency threshold for the offspring population size of the
                  (\emph{{\(\mathrm{\mu}\)}, {\(\lambda\)}}) {EA}},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2019},
    pages      = {1461--1469},
    publisher  = {{ACM}},
    year       = {2019},
    OPTurl     = {https://doi.org/10.1145/3321707.3321838},
    OPTdoi     = {10.1145/3321707.3321838},
}

Denis Antipov, Benjamin Doerr, and Quentin Yang. The efficiency threshold for the offspring population size of the (μ, λ) EA. In Genetic and Evolutionary Computation Conference, GECCO 2019, pp. 1461—1469. ACM, 2019.

@inproceedings{KaravaevAD19,
    author     = {Vitalii Karavaev and
                  Denis Antipov and
                  Benjamin Doerr},
    OPTeditor  = {Manuel L{\'{o}}pez{-}Ib{\'{a}}{\~{n}}ez and
                  Anne Auger and
                  Thomas St{\"{u}}tzle},
    title      = {Theoretical and empirical study of the {(1} + ({\(\lambda\)}, {\(\lambda\)}))
                  {EA} on the leadingones problem},
    booktitle  = {Genetic and Evolutionary Computation Conference Companion, {GECCO} 2019},
    pages      = {2036--2039},
    publisher  = {{ACM}},
    year       = {2019},
    OPTurl     = {https://doi.org/10.1145/3319619.3326910},
    OPTdoi     = {10.1145/3319619.3326910},
}

Vitalii Karavaev, Denis Antipov, and Benjamin Doerr. Theoretical and empirical study of the (1 + (λ, λ)) EA on the leadingones problem. In Genetic and Evolutionary Computation Conference Companion, GECCO 2019, pp. 2036—2039. ACM, 2019.

2018

Conference papers

@inproceedings{AntipovDFH18,
    author     = {Denis Antipov and
                  Benjamin Doerr and
                  Jiefeng Fang and
                  Tangi Hetet},
    OPTeditor  = {Hern{\'{a}}n E. Aguirre and
                  Keiki Takadama},
    title      = {A tight runtime analysis for the ({\(\mu\)} + {\(\lambda\)}) {EA}},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2018},
    pages      = {1459--1466},
    publisher  = {{ACM}},
    year       = {2018},
    OPTurl     = {https://doi.org/10.1145/3205455.3205627},
    OPTdoi     = {10.1145/3205455.3205627},
}

Denis Antipov, Benjamin Doerr, Jiefeng Fang, and Tangi Hetet. A tight runtime analysis for the (μ + λ) EA. In Genetic and Evolutionary Computation Conference, GECCO 2018, pp. 1459—1466. ACM, 2018.

@inproceedings{AntipovBS18,
    author     = {Denis Antipov and
                  Arina Buzdalova and
                  Andrew Stankevich},
    OPTeditor  = {Hern{\'{a}}n E. Aguirre and
                  Keiki Takadama},
    title      = {Runtime analysis of a population-based evolutionary algorithm with
                  auxiliary objectives selected by reinforcement learning},
    booktitle  = {Genetic and Evolutionary Computation Conference Companion, {GECCO} 2018},
    pages      = {1886--1889},
    publisher  = {{ACM}},
    year       = {2018},
    OPTurl     = {https://doi.org/10.1145/3205651.3208231},
    OPTdoi     = {10.1145/3205651.3208231},
}

Denis Antipov, Arina Buzdalova, and Andrew Stankevich. Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learning. In Genetic and Evolutionary Computation Conference Companion, GECCO 2018, pp. 1886—1889. ACM, 2018.

@inproceedings{AntipovD18,
    author     = {Denis Antipov and
                  Benjamin Doerr},
    OPTeditor  = {Anne Auger and
                  Carlos M. Fonseca and
                  Nuno Louren{\c{c}}o and
                  Penousal Machado and
                  Lu{\'{\i}}s Paquete and
                  L. Darrell Whitley},
    title      = {Precise Runtime Analysis for Plateaus},
    booktitle  = {Parallel Problem Solving from Nature, {PPSN} 2018, Part {II}},
    volume     = {11102},
    series     = {Lecture Notes in Computer Science},
    pages      = {117--128},
    publisher  = {Springer},
    year       = {2018},
    OPTurl     = {https://doi.org/10.1007/978-3-319-99259-4\_10},
    OPTdoi     = {10.1007/978-3-319-99259-4\_10},
}

Denis Antipov and Benjamin Doerr. Precise Runtime Analysis for Plateaus. In Parallel Problem Solving from Nature, PPSN 2018, Part II, pp. 117—128. Springer, 2018.

2017

Conference papers

@inproceedings{AntipovB17,
    author     = {Denis Antipov and
                  Arina Buzdalova},
    title      = {Runtime Analysis of Random Local Search on {JUMP} function with Reinforcement
                  Based Selection of Auxiliary Objectives},
    booktitle  = {Congress on Evolutionary Computation, {CEC} 2017},
    pages      = {2169--2176},
    publisher  = {{IEEE}},
    year       = {2017},
    OPTurl     = {https://doi.org/10.1109/CEC.2017.7969567},
    OPTdoi     = {10.1109/CEC.2017.7969567},
}

Denis Antipov and Arina Buzdalova. Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives. In Congress on Evolutionary Computation, CEC 2017, pp. 2169—2176. IEEE, 2017.

2016

Conference papers

@inproceedings{AntipovBK16,
    author     = {Denis Antipov and Maxim Buzdalov and Georgiy Korneev},
    OPTeditor  = {Matousek Radek},
    title      = {First steps in runtime analysis of worst-case execution time test generation for the Dijkstra algorithm using an evolutionary algorithm},
    booktitle  = {MENDEL 2016 - 22nd International Conference on Soft Computing},
    series     = {Mendel},
    pages      = {43--48},
    publisher  = {Brno University of Technology},
    year       = {2016},
    address    = {Czech Republic},
}

Denis Antipov and Maxim Buzdalov and Georgiy Korneev. First steps in runtime analysis of worst-case execution time test generation for the Dijkstra algorithm using an evolutionary algorithm. In MENDEL 2016 - 22nd International Conference on Soft Computing, pp. 43—48. Brno University of Technology, 2016.

2015

Conference papers

@inproceedings{AntipovBD15,
    author     = {Denis Antipov and
                  Maxim Buzdalov and
                  Benjamin Doerr},
    OPTeditor  = {Gabriela Ochoa and
                  Francisco Chicano},
    title      = {Runtime Analysis of {(1+1)} Evolutionary Algorithm Controlled with
                  Q-learning Using Greedy Exploration Strategy on OneMax+ZeroMax Problem},
    booktitle  = {Evolutionary Computation in Combinatorial Optimization, {E}vo{COP} 2015},
    volume     = {9026},
    series     = {Lecture Notes in Computer Science},
    pages      = {160--172},
    publisher  = {Springer},
    year       = {2015},
    OPTurl     = {https://doi.org/10.1007/978-3-319-16468-7\_14},
    OPTdoi     = {10.1007/978-3-319-16468-7\_14},
}

Denis Antipov, Maxim Buzdalov, and Benjamin Doerr. Runtime Analysis of (1+1) Evolutionary Algorithm Controlled with Q-learning Using Greedy Exploration Strategy on OneMax+ZeroMax Problem. In Evolutionary Computation in Combinatorial Optimization, EvoCOP 2015, pp. 160—172. Springer, 2015.