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.
Student supervision
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
.
2022—now:
Saba Sadeghi Ahouei, PhD student, the University of Adelaide.
2023—now:
Ishara Udayanthi Hewa Pathiranage, PhD student, the University of Adelaide.
Publications
2023
Conference papers
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.
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
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.
Denis Antipov, Maxim Buzdalov, and Benjamin Doerr. Fast Mutation in Crossover-Based Algorithms. Algorithmica, 84:1724—1761, 2022.
Conference papers
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.
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
Denis Antipov and Benjamin Doerr. A Tight Runtime Analysis for the (μ + λ ) EA. Algorithmica, 83:1054—1095, 2021.
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
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.
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.
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
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.
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.
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.
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
Sergey Muravyov and
Denis Antipov, Arina Buzdalova, and Andrey Filchenkov. Efficient Computation Of Fitness Function For Evolutionary Clustering. MENDEL, 25:87—94, 2019.
Conference papers
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.
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.
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
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.
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.
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
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
Denis Antipov, 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 22nd International Conference on Soft Computing MENDEL 2016, pp. 43—48. 2016.
2015
Conference papers
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.