Denis Antipov

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.


My research lies in the field of the theory of evolutionary computation. The majority of works are dedicated to the random dynamic parameters choices and the runtime analysis of the population-based evolutionary algorithms.

  • Journals

    Year Citation
    2022 Buzdalov, M., Doerr, B., Doerr, C., & Vinokurov, D. (2022). Fixed-Target Runtime Analysis. Algorithmica, 84(6), 1762-1793.
    DOI
    2022 Antipov, D., Buzdalov, M., & Doerr, B. (2022). Fast Mutation in Crossover-Based Algorithms. Algorithmica, 84(6), 1724-1761.
    DOI Scopus2 WoS1
    2022 Antipov, D., Doerr, B., & Karavaev, V. (2022). A Rigorous Runtime Analysis of the (1 + (λ, λ)) GA on Jump Functions. Algorithmica, 84(6), 1573-1602.
    DOI Scopus3
    2021 Antipov, D., & Doerr, B. (2021). Precise Runtime Analysis for Plateau Functions. ACM Transactions on Evolutionary Learning and Optimization, 1(4), 1-28.
    DOI
    2021 Antipov, D., & Doerr, B. (2021). A Tight Runtime Analysis for the (μ+ λ) EA. Algorithmica, 83(4), 1054-1095.
    DOI Scopus4 WoS3
    2019 Muravyov, S., Antipov, D., Buzdalova, A., & Filchenkov, A. (2019). Efficient computation of fitness function for evolutionary clustering. Mendel, 25(1), 87-94.
    DOI Scopus1
  • Conference Papers

    Year Citation
    2022 Neumann, A., Antipov, D., & Neumann, F. (2022). Coevolutionary Pareto diversity optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22) (pp. 832-839). New York, NY: Association for Computing Machinery.
    DOI
    2022 Antipov, D., & Doerr, B. (2022). Precise Runtime Analysis for Plateau Functions (Hot-off-the-Press Track at GECCO 2022). In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 13-14). ACM.
    DOI
    2021 Antipov, D., & Naumov, S. (2021). The effect of non-symmetric fitness: The analysis of crossover-based algorithms on RealJump functions. In FOGA 2021 - Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 15 pages). Virtual Event Austria: ACM.
    DOI Scopus1
    2021 Shnytkin, M., & Antipov, D. (2021). The lower bounds on the runtime of the (1 + (?, ?)) GA on the minimum spanning tree problem. In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 1986-1989). Lille France: ACM.
    DOI
    2021 Antipov, D., Buzdalov, M., & Doerr, B. (2021). Lazy parameter tuning and control: Choosing all parameters randomly from a power-law distribution. In GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference (pp. 1115-1123). Lille France: ACM.
    DOI Scopus15
    2020 Antipov, D., Buzdalov, M., & Doerr, B. (2020). First steps towards a runtime analysis when starting with a good solution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12270 LNCS (pp. 560-573). Springer International Publishing.
    DOI Scopus16
    2020 Antipov, D., & Doerr, B. (2020). Runtime analysis of a heavy-tailed $$(1+(\lambda,\lambda ))$$ Genetic Algorithm on Jump Functions. In Parallel Problem Solving from Nature – PPSN XVI Vol. 12270 LNCS (pp. 545-559). Switzerland: Springer International Publishing.
    DOI Scopus18
    2020 Antipov, D., Doerr, B., & Karavaev, V. (2020). The (1 + (λ,λ)) GA is even faster on multimodal problems. In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 1259-1267). ACM.
    DOI Scopus21
    2020 Antipov, D., Buzdalov, M., & Doerr, B. (2020). Fast mutation in crossover-based algorithms. In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 1268-1276). ACM.
    DOI Scopus23
    2019 Antipov, D., Doerr, B., & Karavaev, V. (2019). A tight runtime analysis for the (1 + (λ, λ)) GA on leading ones. In FOGA 2019 - Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 169-182). Potsdam, Germany: ACM Press.
    DOI Scopus16
    2019 Karavaev, V., Antipov, D., & Doerr, B. (2019). Theoretical and empirical study of the (1 + (?, ?)) Ea on the leadingones problem. In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 2036-2039). New York, NY, USA: ACM Digital Library.
    DOI Scopus6
    2019 Antipov, D., Doerr, B., & Yang, Q. (2019). The efficiency threshold for the offspring population size of the (µ, λ) EA. In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 1461-1469). New York, NY, USA: Association for Computing Machinery (ACM).
    DOI Scopus17
    2018 Antipov, D., & Doerr, B. (2018). Precise runtime analysis for plateaus. In Parallel Problem Solving from Nature – PPSN XV Vol. 11102 LNCS (pp. 117-128). Switzerland: Springer International Publishing.
    DOI Scopus15 WoS10
    2018 Antipov, D., Buzdalova, A., & Stankevich, A. (2018). Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learning. In GECCO 2018 Companion - Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1886-1889). Kyoto, Japan: ACM.
    DOI
    2018 Antipov, D., Fang, J., Doerr, B., & Hetet, T. (2018). A tight runtime analysis for the (+) EA. In H. Aguirre (Ed.), GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 1459-1466). Kyoto, japan: ACM.
    DOI Scopus22
    2017 Antipov, D., & Buzdalova, A. (2017). Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives. In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 2169-2176). Online: IEEE.
    DOI
    2016 Antipov, D., Buzdalov, M., & Korneev, G. (2016). First steps in runtime analysis of worst-case execution time test generation for the Dijkstra algorithm using an evolutionary algorithm. In Mendel (pp. 43-48).
    2015 Antipov, D., Buzdalov, M., & Doerr, B. (2015). Runtime analysis of (1 + 1) evolutionary algorithm controlled with Q-learning using greedy exploration strategy on OneMAX+ZEROMAX problem. In G. Ochoa, & F. Chicano (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9026 (pp. 160-172). Copenhagen, DENMARK: SPRINGER-VERLAG BERLIN.
    DOI Scopus1 WoS1

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