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ORCIDDenis Antipov
Person information
- affiliation: University of Adelaide, Australia
- affiliation: ITMO University, St. Petersburg, Russia
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Books and Theses
- 2020
[b1]Denis Antipov:
Methods for Tight Analysis of Population-based Evolutionary Algorithms. (Méthodes d'analyse précise des algorithmes évolutifs basés sur la population / Методы точного анализа популяционных эволюционных алгоритмов). ITMO University, Saint Petersburg, Russian Federation, 2020
Journal Articles
- 2024
- [j5]Denis Antipov
, Maxim Buzdalov, Benjamin Doerr:
Lazy Parameter Tuning and Control: Choosing All Parameters Randomly from a Power-Law Distribution. Algorithmica 86(2): 442-484 (2024) - 2022
- [j4]Denis Antipov, Benjamin Doerr, Vitalii Karavaev:
A Rigorous Runtime Analysis of the (1 + (λ , λ )) GA on Jump Functions. Algorithmica 84(6): 1573-1602 (2022) - [j3]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Fast Mutation in Crossover-Based Algorithms. Algorithmica 84(6): 1724-1761 (2022) - 2021
- [j2]Denis Antipov, Benjamin Doerr:
A Tight Runtime Analysis for the (μ + λ ) EA. Algorithmica 83(4): 1054-1095 (2021) - [j1]Denis Antipov, Benjamin Doerr:
Precise Runtime Analysis for Plateau Functions. ACM Trans. Evol. Learn. Optim. 1(4): 13:1-13:28 (2021)
Conference and Workshop Papers
- 2024
- [c26]Denis Antipov, Benjamin Doerr, Alexandra Ivanova:
Already Moderate Population Sizes Provably Yield Strong Robustness to Noise. GECCO 2024 - [c25]Denis Antipov, Aneta Neumann, Frank Neumann:
A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMax. GECCO 2024 - [c24]Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, Aneta Neumann:
Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem. GECCO 2024 - [c23]Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, Aneta Neumann:
Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem. GECCO 2024 - [c22]Denis Antipov, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective Version of the (LeadingOnes, TrailingZeros) Problem. PPSN (3) 2024: 19-35 - [c21]Denis Antipov, Timo Kötzing, Aishwarya Radhakrishnan:
Greedy Versus Curious Parent Selection for Multi-objective Evolutionary Algorithms. PPSN (3) 2024: 86-101 - [c20]Denis Antipov, Aneta Neumann, Frank Neumann:
Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential. PPSN (3) 2024: 181-196 - 2023
- [c19]Denis Antipov, Aneta Neumann, Frank Neumann:
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax. FOGA 2023: 3-14 - [c18]Alexandra Ivanova, Denis Antipov, Benjamin Doerr:
Larger Offspring Populations Help the (1 + (λ, λlambda)) Genetic Algorithm to Overcome the Noise. GECCO 2023: 919-928 - 2022
- [c17]Denis Antipov, Benjamin Doerr:
Precise runtime analysis for plateau functions: (hot-off-the-press track at GECCO 2022). GECCO Companion 2022: 13-14 - [c16]Aneta Neumann, Denis Antipov, Frank Neumann:
Coevolutionary Pareto diversity optimization. GECCO 2022: 832-839 - 2021
- [c15]Denis Antipov, Semen Naumov:
The effect of non-symmetric fitness: the analysis of crossover-based algorithms on RealJump functions. FOGA 2021: 10:1-10:15 - [c14]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution. GECCO 2021: 1115-1123 - [c13]Matvey Shnytkin, Denis Antipov:
The lower bounds on the runtime of the (1 + (λ, λ)) GA on the minimum spanning tree problem. GECCO Companion 2021: 1986-1989 - 2020
- [c12]Denis Antipov, Benjamin Doerr, Vitalii Karavaev:
The (1 + (λ, λ)) GA is even faster on multimodal problems. GECCO 2020: 1259-1267 - [c11]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Fast mutation in crossover-based algorithms. GECCO 2020: 1268-1276 - [c10]Denis Antipov, Benjamin Doerr:
Runtime Analysis of a Heavy-Tailed (1+(λ , λ )) Genetic Algorithm on Jump Functions. PPSN (2) 2020: 545-559 - [c9]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
First Steps Towards a Runtime Analysis When Starting with a Good Solution. PPSN (2) 2020: 560-573 - 2019
- [c8]Denis Antipov, Benjamin Doerr, Vitalii Karavaev:
A tight runtime analysis for the (1 + (λ, λ)) GA on leadingones. FOGA 2019: 169-182 - [c7]Denis Antipov, Benjamin Doerr, Quentin Yang:
The efficiency threshold for the offspring population size of the (µ, λ) EA. GECCO 2019: 1461-1469 - [c6]Vitalii Karavaev, Denis Antipov, Benjamin Doerr:
Theoretical and empirical study of the (1 + (λ, λ)) EA on the leadingones problem. GECCO (Companion) 2019: 2036-2039 - 2018
- [c5]Denis Antipov, Benjamin Doerr, Jiefeng Fang, Tangi Hetet:
A tight runtime analysis for the (μ + λ) EA. GECCO 2018: 1459-1466 - [c4]Denis Antipov, Arina Buzdalova, Andrew Stankevich:
Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learning. GECCO (Companion) 2018: 1886-1889 - [c3]Denis Antipov, Benjamin Doerr:
Precise Runtime Analysis for Plateaus. PPSN (2) 2018: 117-128 - 2017
- [c2]Denis Antipov, Arina Buzdalova:
Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives. CEC 2017: 2169-2176 - 2015
- [c1]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Runtime Analysis of (1+1) Evolutionary Algorithm Controlled with Q-learning Using Greedy Exploration Strategy on OneMax+ZeroMax Problem. EvoCOP 2015: 160-172
Informal and Other Publications
- 2024
- [i15]Denis Antipov, Benjamin Doerr, Alexandra Ivanova:
Already Moderate Population Sizes Provably Yield Strong Robustness to Noise. CoRR abs/2404.02090 (2024) - [i14]Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, Aneta Neumann:
Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem. CoRR abs/2404.06014 (2024) - [i13]Denis Antipov, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem. CoRR abs/2404.11496 (2024) - [i12]Denis Antipov, Aneta Neumann, Frank Neumann:
Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential. CoRR abs/2407.08963 (2024) - 2023
- [i11]Alexandra Ivanova, Denis Antipov, Benjamin Doerr:
Larger Offspring Populations Help the (1 + (λ, λ)) Genetic Algorithm to Overcome the Noise. CoRR abs/2305.04553 (2023) - [i10]Denis Antipov, Aneta Neumann, Frank Neumann:
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax. CoRR abs/2307.07248 (2023) - 2022
- [i9]Aneta Neumann, Denis Antipov, Frank Neumann:
Coevolutionary Pareto Diversity Optimization. CoRR abs/2204.05457 (2022) - 2021
- [i8]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law Distribution. CoRR abs/2104.06714 (2021) - 2020
- [i7]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Fast Mutation in Crossover-based Algorithms. CoRR abs/2004.06538 (2020) - [i6]Denis Antipov, Benjamin Doerr, Vitalii Karavaev:
The (1 + (λ, λ)) GA Is Even Faster on Multimodal Problems. CoRR abs/2004.06702 (2020) - [i5]Denis Antipov, Benjamin Doerr:
Runtime Analysis of a Heavy-Tailed (1+(λ, λ)) Genetic Algorithm on Jump Functions. CoRR abs/2006.03523 (2020) - [i4]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
First Steps Towards a Runtime Analysis When Starting With a Good Solution. CoRR abs/2006.12161 (2020) - 2019
- [i3]Denis Antipov, Benjamin Doerr, Quentin Yang:
The Efficiency Threshold for the Offspring Population Size of the (μ, λ) EA. CoRR abs/1904.06981 (2019) - 2018
- [i2]Denis Antipov, Benjamin Doerr:
Precise Runtime Analysis for Plateaus. CoRR abs/1806.01331 (2018) - [i1]Denis Antipov, Benjamin Doerr:
A Tight Runtime Analysis for the (μ+ λ) EA. CoRR abs/1812.11061 (2018)
Coauthor Index
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