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.
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Journals
Year Citation 2024 Antipov, D., Buzdalov, M., & Doerr, B. (2024). Lazy Parameter Tuning and Control: Choosing All Parameters Randomly from a Power-Law Distribution. Algorithmica, 86(2), 442-484.
2022 Antipov, D., Buzdalov, M., & Doerr, B. (2022). Fast Mutation in Crossover-Based Algorithms. Algorithmica, 84(6), 1724-1761.
Scopus15 WoS62022 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.
Scopus15 WoS112022 Buzdalov, M., Doerr, B., Doerr, C., & Vinokurov, D. (2022). Fixed-Target Runtime Analysis. Algorithmica, 84(6), 1762-1793.
2021 Antipov, D., & Doerr, B. (2021). Precise Runtime Analysis for Plateau Functions. ACM Transactions on Evolutionary Learning and Optimization, 1(4), 1-28.
Scopus72021 Antipov, D., & Doerr, B. (2021). A Tight Runtime Analysis for the (μ+ λ) EA. Algorithmica, 83(4), 1054-1095.
Scopus10 WoS62019 Muravyov, S., Antipov, D., Buzdalova, A., & Filchenkov, A. (2019). Efficient computation of fitness function for evolutionary clustering. Mendel, 25(1), 87-94.
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Conference Papers
Year Citation 2023 Ivanova, A., Antipov, D., & Doerr, B. (2023). Larger Offspring Populations Help the (1 + (λ, λ)) Genetic Algorithm to Overcome the Noise. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO, 2023) (pp. 919-928). Online: Association for Computing Machinery.
2023 Antipov, D., Neumann, A., & Neumann, F. (2023). Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax. In Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms Vol. abs/2307.07248 (pp. 3-14). Potsdam, Germany: ACM.
2022 Neumann, A., Antipov, D., & Neumann, F. (2022). Coevolutionary Pareto diversity optimization.. In J. E. Fieldsend, & M. Wagner (Eds.), GECCO (pp. 832-839). ACM. 2022 Neumann, A., Antipov, D., & Neumann, F. (2022). Coevolutionary Pareto diversity optimization. In J. E. Fieldsend, & M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22) Vol. abs/2204.05457 (pp. 832-839). New York, NY: Association for Computing Machinery.
Scopus4 WoS32022 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.
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.
Scopus42021 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.
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 Vol. 86 (pp. 1115-1123). Lille France: ACM.
Scopus282020 Antipov, D., Buzdalov, M., & Doerr, B. (2020). First steps towards a runtime analysis when starting with a good solution. In Parallel Problem Solving from Nature - PPSN XVI Vol. 12270 (pp. 560-573). Leiden, The Netherlands: Springer International Publishing.
Scopus212020 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.
Scopus292020 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). New York, NY, USA: ACM.
Scopus252020 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). New York, NY, USA: ACM.
Scopus282019 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.
Scopus252019 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.
Scopus82019 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).
Scopus252018 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.
Scopus16 WoS102018 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.
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.
Scopus252017 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.
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.
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Preprint
Year Citation 2024 Antipov, D., Doerr, B., & Ivanova, A. (2024). Already Moderate Population Sizes Provably Yield Strong Robustness to
Noise.
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2023 Co-Supervisor Evolutionary Algorithms for Solving Chance Constrained Combinatorial Optimization Problems Doctor of Philosophy Doctorate Full Time Miss Saba Sadeghi Ahouei 2023 Co-Supervisor Artificial intelligence approaches for increasing the productivity in the mining Doctor of Philosophy Doctorate Full Time Mrs Ishara Udayanthi Hewa Pathiranage
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