Denis Antipov

440 total citations
23 papers, 198 citations indexed

About

Denis Antipov is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Industrial and Manufacturing Engineering. According to data from OpenAlex, Denis Antipov has authored 23 papers receiving a total of 198 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 17 papers in Computational Theory and Mathematics and 3 papers in Industrial and Manufacturing Engineering. Recurrent topics in Denis Antipov's work include Metaheuristic Optimization Algorithms Research (18 papers), Advanced Multi-Objective Optimization Algorithms (17 papers) and Evolutionary Algorithms and Applications (16 papers). Denis Antipov is often cited by papers focused on Metaheuristic Optimization Algorithms Research (18 papers), Advanced Multi-Objective Optimization Algorithms (17 papers) and Evolutionary Algorithms and Applications (16 papers). Denis Antipov collaborates with scholars based in Russia, France and Australia. Denis Antipov's co-authors include Benjamin Doerr, Maxim Buzdalov, Benjamin Doerr, Aneta Neumann, Frank Neumann and Andrey Filchenkov and has published in prestigious journals such as SHILAP Revista de lepidopterología, Algorithmica and HAL (Le Centre pour la Communication Scientifique Directe).

In The Last Decade

Denis Antipov

23 papers receiving 198 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Denis Antipov Russia 10 155 132 13 12 9 23 198
J.M. Davoren Australia 6 70 0.5× 111 0.8× 32 2.5× 9 0.8× 10 1.1× 15 150
Kyungmin Bae South Korea 8 63 0.4× 116 0.9× 11 0.8× 5 0.4× 36 4.0× 24 145
Thomas Peikenkamp Germany 7 59 0.4× 94 0.7× 10 0.8× 6 0.5× 9 1.0× 9 152
Marcus Größer Germany 6 78 0.5× 121 0.9× 8 0.6× 2 0.2× 17 1.9× 11 150
Richard Trefler Canada 7 69 0.4× 106 0.8× 5 0.4× 3 0.3× 23 2.6× 13 142
Benedikt Bollig France 5 68 0.4× 89 0.7× 6 0.5× 2 0.2× 19 2.1× 32 115
Roberto Cavada Italy 6 84 0.5× 147 1.1× 10 0.8× 14 1.2× 10 1.1× 10 170
Paula Severi United Kingdom 7 121 0.8× 85 0.6× 3 0.2× 3 0.3× 28 3.1× 23 141
Bertrand Jeannet France 8 74 0.5× 125 0.9× 7 0.5× 6 0.5× 19 2.1× 14 165
John Fearnley United Kingdom 9 63 0.4× 78 0.6× 5 0.4× 2 0.2× 29 3.2× 35 181

Countries citing papers authored by Denis Antipov

Since Specialization
Citations

This map shows the geographic impact of Denis Antipov's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Denis Antipov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Denis Antipov more than expected).

Fields of papers citing papers by Denis Antipov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Denis Antipov. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Denis Antipov. The network helps show where Denis Antipov may publish in the future.

Co-authorship network of co-authors of Denis Antipov

This figure shows the co-authorship network connecting the top 25 collaborators of Denis Antipov. A scholar is included among the top collaborators of Denis Antipov based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Denis Antipov. Denis Antipov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Antipov, Denis, et al.. (2024). Already Moderate Population Sizes Provably Yield Strong Robustness to Noise. Proceedings of the Genetic and Evolutionary Computation Conference. 1524–1532. 2 indexed citations
2.
Antipov, Denis, Aneta Neumann, & Frank Neumann. (2024). A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMax. Proceedings of the Genetic and Evolutionary Computation Conference. 467–475. 2 indexed citations
3.
Neumann, Frank, et al.. (2024). Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem. Proceedings of the Genetic and Evolutionary Computation Conference. 520–528. 7 indexed citations
4.
Neumann, Frank, et al.. (2024). Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem. Proceedings of the Genetic and Evolutionary Computation Conference. 187–195. 7 indexed citations
5.
Antipov, Denis, Maxim Buzdalov, & Benjamin Doerr. (2024). First Steps Toward a Runtime Analysis When Starting With a Good Solution. arXiv (Cornell University). 5(2). 1–41. 1 indexed citations
6.
Antipov, Denis, Maxim Buzdalov, & Benjamin Doerr. (2023). Lazy Parameter Tuning and Control: Choosing All Parameters Randomly from a Power-Law Distribution. Algorithmica. 86(2). 442–484. 6 indexed citations
7.
Antipov, Denis, Aneta Neumann, & Frank Neumann. (2023). Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax. 3–14. 2 indexed citations
8.
Antipov, Denis, et al.. (2022). A Rigorous Runtime Analysis of the $$(1 + (\lambda , \lambda ))$$ GA on Jump Functions. Algorithmica. 84(6). 1573–1602. 10 indexed citations
9.
Antipov, Denis, Maxim Buzdalov, & Benjamin Doerr. (2022). Fast Mutation in Crossover-Based Algorithms. Algorithmica. 84(6). 1724–1761. 19 indexed citations
10.
Neumann, Aneta, Denis Antipov, & Frank Neumann. (2022). Coevolutionary Pareto diversity optimization. Proceedings of the Genetic and Evolutionary Computation Conference. 832–839. 10 indexed citations
11.
Antipov, Denis & Benjamin Doerr. (2021). A Tight Runtime Analysis for the (µ + λ) EA. HAL (Le Centre pour la Communication Scientifique Directe). 11 indexed citations
12.
Antipov, Denis, Maxim Buzdalov, & Benjamin Doerr. (2021). Lazy parameter tuning and control. Proceedings of the Genetic and Evolutionary Computation Conference. 1115–1123. 25 indexed citations
13.
Antipov, Denis, et al.. (2021). The effect of non-symmetric fitness. 1–15. 4 indexed citations
14.
Antipov, Denis & Benjamin Doerr. (2020). Runtime Analysis of a Heavy-Tailed $(1+(λ,λ))$ Genetic Algorithm on Jump Functions. arXiv (Cornell University). 545–559. 10 indexed citations
15.
Antipov, Denis, et al.. (2020). The (1 + ( λ,λ )) GA is even faster on multimodal problems. SPIRE - Sciences Po Institutional REpository. 1259–1267. 18 indexed citations
16.
Antipov, Denis, et al.. (2019). Efficient Computation of Fitness Function for Evolutionary Clustering. SHILAP Revista de lepidopterología. 25(1). 87–94. 3 indexed citations
17.
Antipov, Denis, et al.. (2019). The efficiency threshold for the offspring population size of the ( µ, λ ) EA. Proceedings of the Genetic and Evolutionary Computation Conference. 1461–1469. 13 indexed citations
18.
Antipov, Denis, et al.. (2019). A tight runtime analysis for the (1 + (λ, λ)) GA on leadingones. SPIRE - Sciences Po Institutional REpository. 169–182. 16 indexed citations
19.
Antipov, Denis, et al.. (2018). A tight runtime analysis for the (μ + λ) EA. Proceedings of the Genetic and Evolutionary Computation Conference. 1459–1466. 14 indexed citations
20.
Antipov, Denis, et al.. (2018). Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learning. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1886–1889. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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