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Arina Afanasyeva

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Journal Articles

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2024
[j2]
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Wouter Heyndrickx, Lewis H. Mervin, Tobias Morawietz, Noé Sturm, Lukas Friedrich, Adam Zalewski, Anastasia Pentina, Lina Humbeck, Martijn Oldenhof, Ritsuya Niwayama, Peter Schmidtke, Nikolas Fechner, Jaak Simm, Adam Arany, Nicolas Drizard, Rama Jabal, Arina Afanasyeva, Regis Loeb, Shlok Verma, Simon Harnqvist, Matthew Holmes, Balazs Pejo, Maria Telenczuk, Nicholas Holway, Arne Dieckmann, Nicola Rieke, Friederike Zumsande, Djork-Arné Clevert, Michael Krug, Christopher N. Luscombe, Darren V. S. Green, Peter Ertl, Peter Antal, David Marcus, Nicolas Do Huu, Hideyoshi Fuji, Stephen D. Pickett, Gergely Ács, Eric Boniface, Bernd Beck, Yax Sun, Arnaud Gohier, Friedrich Rippmann, Ola Engkvist, Andreas H. Göller, Yves Moreau, Mathieu N. Galtier, Ansgar Schuffenhauer, Hugo Ceulemans:
MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information. J. Chem. Inf. Model. 64(7): 2331-2344 (2024)
2015
[j1]
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Arina Afanasyeva, Sergey Izmailov, Michel Grigoriev, Michael Petukhov:
AquaBridge: A novel method for systematic search of structural water molecules within the protein active sites. J. Comput. Chem. 36(26): 1973-1977 (2015)

Conference and Workshop Papers

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2022
[c30]
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Nina Bulanova, Arina Buzdalova, Carola Doerr:
Fast Re-Optimization of LeadingOnes with Frequent Changes. CEC 2022: 1-8
2021
[c29]
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Kirill Antonov, Maxim Buzdalov, Arina Buzdalova, Carola Doerr:
Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms. CEC 2021: 878-885
2020
[c28]
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Arina Buzdalova, Carola Doerr, Anna Rodionova:
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm. PPSN (2) 2020: 485-499
2019
[c27]
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Anna Rodionova, Kirill Antonov, Arina Buzdalova, Carola Doerr:
Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates. GECCO 2019: 855-863
[c26]
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Ivan Ignashov, Arina Buzdalova, Maxim Buzdalov, Carola Doerr:
Illustrating the trade-off between time, quality, and success probability in heuristic search: a discussion paper. GECCO (Companion) 2019: 1807-1812
[c25]
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Dmitry Vinokurov, Maxim Buzdalov, Arina Buzdalova, Benjamin Doerr, Carola Doerr:
Fixed-target runtime analysis of the (1 + 1) EA with resampling. GECCO (Companion) 2019: 2068-2071
2018
[c24]
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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
2017
[c23]
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Denis Antipov, Arina Buzdalova:
Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives. CEC 2017: 2169-2176
[c22]
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  - conf/evoW/BassinB17
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Anton O. Bassin, Arina Buzdalova:
Selection of Auxiliary Objectives Using Landscape Features and Offline Learned Classifier. EvoCOP 2017: 173-188
[c21]
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Irina Petrova, Arina Buzdalova:
Reinforcement learning based dynamic selection of auxiliary objectives with preservation of the best found solution. GECCO (Companion) 2017: 1435-1438
2016
[c20]
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Nina Bulanova, Arina Buzdalova, Maxim Buzdalov:
Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local Search. GECCO (Companion) 2016: 5-6
[c19]
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Arkady Rost, Irina Petrova, Arina Buzdalova:
Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range. GECCO (Companion) 2016: 141-142
[c18]
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Arina Buzdalova, Irina Petrova, Maxim Buzdalov:
Runtime analysis of different Approaches to select conflicting auxiliary objectives in the generalized OneMax problem. SSCI 2016: 1-7
2015
[c17]
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Maxim Buzdalov, Arina Buzdalova:
Can OneMax help optimizing LeadingOnes using the EA+RL method? CEC 2015: 1762-1768
[c16]
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Maxim Buzdalov, Arina Buzdalova:
Analysis of Q-learning with random exploration for selection of auxiliary objectives in random local search. CEC 2015: 1776-1783
[c15]
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Arina Buzdalova, Anna Matveeva, Georgiy Korneev:
Selection of Auxiliary Objectives with Multi-Objective Reinforcement Learning. GECCO (Companion) 2015: 1177-1180
[c14]
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Irina Petrova, Arina Buzdalova:
Selection of Auxiliary Objectives in the Travelling Salesman Problem using Reinforcement Learning. GECCO (Companion) 2015: 1455-1456
2014
[c13]
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Maxim Buzdalov, Arina Buzdalova:
Onemax helps optimizing XdivK: : theoretical runtime analysis for RLS and EA+RL. GECCO (Companion) 2014: 201-202
[c12]
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Arina Buzdalova, Vladislav Kononov, Maxim Buzdalov:
Selecting evolutionary operators using reinforcement learning: initial explorations. GECCO (Companion) 2014: 1033-1036
[c11]
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Maxim Buzdalov, Irina Petrova, Arina Buzdalova:
NSGA-II implementation details may influence quality of solutions for the job-shop scheduling problem. GECCO (Companion) 2014: 1445-1446
[c10]
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Irina Petrova, Arina Buzdalova, Maxim Buzdalov:
Improved Selection of Auxiliary Objectives Using Reinforcement Learning in Non-stationary Environment. ICMLA 2014: 580-583
[c9]
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Arina Buzdalova, Maxim Buzdalov:
A New Algorithm for Adaptive Online Selection of Auxiliary Objectives. ICMLA 2014: 584-587
2013
[c8]
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Maxim Buzdalov, Arina Buzdalova:
Adaptive selection of helper-objectives for test case generation. IEEE Congress on Evolutionary Computation 2013: 2245-2250
[c7]
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Maxim Buzdalov, Arina Buzdalova, Irina Petrova:
Generation of tests for programming challenge tasks using multi-objective optimization. GECCO (Companion) 2013: 1655-1658
[c6]
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Maxim Buzdalov, Arina Buzdalova, Anatoly Shalyto:
A First Step towards the Runtime Analysis of Evolutionary Algorithm Adjusted with Reinforcement Learning. ICMLA (1) 2013: 203-208
[c5]
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Irina Petrova, Arina Buzdalova, Maxim Buzdalov:
Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem. ICMLA (2) 2013: 374-377
[c4]
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Arina Buzdalova, Maxim Buzdalov, Vladimir Parfenov:
Generation of Tests for Programming Challenge Tasks Using Helper-Objectives. SSBSE 2013: 300-305
2012
[c3]
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Arina Buzdalova, Maxim Buzdalov:
Adaptive Selection of Helper-Objectives with Reinforcement Learning. ICMLA (2) 2012: 66-67
[c2]
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Arina Buzdalova, Maxim Buzdalov:
Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning. ICMLA (1) 2012: 150-155
2011
[c1]
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Arina Afanasyeva, Maxim Buzdalov:
Choosing Best Fitness Function with Reinforcement Learning. ICMLA (2) 2011: 354-357

Informal and Other Publications

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2022
[i6]
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  - journals/corr/abs-2209-04391
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Nina Bulanova, Arina Buzdalova, Carola Doerr:
Fast Re-Optimization of LeadingOnes with Frequent Changes. CoRR abs/2209.04391 (2022)
2021
[i5]
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  - journals/corr/abs-2102-11461
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Kirill Antonov, Maxim Buzdalov, Arina Buzdalova, Carola Doerr:
Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms. CoRR abs/2102.11461 (2021)
2020
[i4]
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  - journals/corr/abs-2006-11026
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Arina Buzdalova, Carola Doerr, Anna Rodionova:
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm. CoRR abs/2006.11026 (2020)
2019
[i3]
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  - journals/corr/abs-1904-08032
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Anna Rodionova, Kirill Antonov, Arina Buzdalova, Carola Doerr:
Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates. CoRR abs/1904.08032 (2019)
2017
[i2]
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Irina Petrova, Arina Buzdalova:
Reinforcement Learning Based Dynamic Selection of Auxiliary Objectives with Preserving of the Best Found Solution. CoRR abs/1704.07187 (2017)
2016
[i1]
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Arkady Rost, Irina Petrova, Arina Buzdalova:
Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range. CoRR abs/1603.06788 (2016)

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