Last updated on 04 April 2024

About me

I am a postdoc researcher at the Optimization and Logistics group of the School of Computer and Mathematical Sciences in the University of Adelaide. I have a PhD in computer science (Docteur en Informatique) and my main research interest is the theory of random search heuristics. In particular, I have worked on the runtime analysis of evolutionary algorithms in different settings, and now I am studying how the evolutionary diversity optimization reaches its goals on simple benchmark functions. 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:

Ingkarni Wardli, Office 4.49a
The University of Adelaide
Adelaide, SA 5005, Australia
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.

Participation in research projects

2022—now:

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—now:

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

Awards

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.

Publications

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

@article{PathiranageNAN24static,
    author     = {Ishara Hewa Pathiranage and
                  Frank Neumann and
                  Denis Antipov and
                  Aneta Neumann},
    title      = {Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2024},
    pages      = {to appear},
    publisher  = {{ACM}},
    year       = {2024},
}

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, to appear. ACM, 2024.

@article{PathiranageNAN24dynamic,
    author     = {Ishara Hewa Pathiranage and
                  Frank Neumann and
                  Denis Antipov and
                  Aneta Neumann},
    title      = {Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2024},
    pages      = {to appear},
    publisher  = {{ACM}},
    year       = {2024},
}

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, to appear. ACM, 2024.

@article{AntipovNN24,
    author     = {Denis Antipov and
                  Aneta Neumann and
                  Frank Neumann},
    title      = {A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMax},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2024},
    pages      = {to appear},
    publisher  = {{ACM}},
    year       = {2024},
}

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, to appear. ACM, 2024.

@article{AntipovDI24,
    author     = {Denis Antipov and
                  Benjamin Doerr and
                  Alexandra Ivanova},
    title      = {Already Moderate Population Sizes Provably Yield Strong Robustness to Noise},
    booktitle  = {Genetic and Evolutionary Computation Conference, {GECCO} 2024},
    pages      = {to appear},
    publisher  = {{ACM}},
    year       = {2024},
}

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, to appear. ACM, 2024.

2023

Conference papers

@article{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.

@article{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} + ({\(\lambda\)}, {\(\lambda\)}))
                  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, λ)) 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

@article{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.

@article{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

@article{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.

@article{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.

@article{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

@article{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.

@article{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.

@article{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.

@article{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

Conference papers

@article{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.

@article{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.

@article{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

@article{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.

@article{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.

@article{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

@article{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.

2015

Conference papers

@article{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.