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Arina Buzdalova
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2020 – today
- 2024
- [j2]Wouter Heyndrickx
, Lewis H. Mervin
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) - 2022
- [c30]Nina Bulanova, Arina Buzdalova
Fast Re-Optimization of LeadingOnes with Frequent Changes. CEC 2022: 1-8 - [i6]Nina Bulanova, Arina Buzdalova
Fast Re-Optimization of LeadingOnes with Frequent Changes. CoRR abs/2209.04391 (2022) - 2021
- [c29]Kirill Antonov, Maxim Buzdalov
Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms. CEC 2021: 878-885 - [i5]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
- [c28]Arina Buzdalova
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm. PPSN (2) 2020: 485-499 - [i4]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)
2010 – 2019
- 2019
- [c27]Anna Rodionova, Kirill Antonov, Arina Buzdalova
Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates. GECCO 2019: 855-863 - [c26]Ivan Ignashov, Arina Buzdalova
Illustrating the trade-off between time, quality, and success probability in heuristic search: a discussion paper. GECCO (Companion) 2019: 1807-1812 - [c25]Dmitry Vinokurov
Fixed-target runtime analysis of the (1 + 1) EA with resampling. GECCO (Companion) 2019: 2068-2071 - [i3]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) - 2018
- [c24]Denis Antipov
Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learning. GECCO (Companion) 2018: 1886-1889 - 2017
- [c23]Denis Antipov
Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives. CEC 2017: 2169-2176 - [c22]Anton O. Bassin, Arina Buzdalova
Selection of Auxiliary Objectives Using Landscape Features and Offline Learned Classifier. EvoCOP 2017: 173-188 - [c21]Irina Petrova, Arina Buzdalova
Reinforcement learning based dynamic selection of auxiliary objectives with preservation of the best found solution. GECCO (Companion) 2017: 1435-1438 - [i2]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
- [c20]Nina Bulanova, Arina Buzdalova
Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local Search. GECCO (Companion) 2016: 5-6 - [c19]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]Arina Buzdalova
Runtime analysis of different Approaches to select conflicting auxiliary objectives in the generalized OneMax problem. SSCI 2016: 1-7 - [i1]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) - 2015
- [j1]Arina Afanasyeva
AquaBridge: A novel method for systematic search of structural water molecules within the protein active sites. J. Comput. Chem. 36(26): 1973-1977 (2015) - [c17]Maxim Buzdalov
Can OneMax help optimizing LeadingOnes using the EA+RL method? CEC 2015: 1762-1768 - [c16]Maxim Buzdalov
Analysis of Q-learning with random exploration for selection of auxiliary objectives in random local search. CEC 2015: 1776-1783 - [c15]Arina Buzdalova
Selection of Auxiliary Objectives with Multi-Objective Reinforcement Learning. GECCO (Companion) 2015: 1177-1180 - [c14]Irina Petrova, Arina Buzdalova
Selection of Auxiliary Objectives in the Travelling Salesman Problem using Reinforcement Learning. GECCO (Companion) 2015: 1455-1456 - 2014
- [c13]Maxim Buzdalov
Onemax helps optimizing XdivK: : theoretical runtime analysis for RLS and EA+RL. GECCO (Companion) 2014: 201-202 - [c12]Arina Buzdalova
Selecting evolutionary operators using reinforcement learning: initial explorations. GECCO (Companion) 2014: 1033-1036 - [c11]Maxim Buzdalov
NSGA-II implementation details may influence quality of solutions for the job-shop scheduling problem. GECCO (Companion) 2014: 1445-1446 - [c10]Irina Petrova
Improved Selection of Auxiliary Objectives Using Reinforcement Learning in Non-stationary Environment. ICMLA 2014: 580-583 - [c9]Arina Buzdalova
A New Algorithm for Adaptive Online Selection of Auxiliary Objectives. ICMLA 2014: 584-587 - 2013
- [c8]Maxim Buzdalov
Adaptive selection of helper-objectives for test case generation. IEEE Congress on Evolutionary Computation 2013: 2245-2250 - [c7]Maxim Buzdalov
Generation of tests for programming challenge tasks using multi-objective optimization. GECCO (Companion) 2013: 1655-1658 - [c6]Maxim Buzdalov
A First Step towards the Runtime Analysis of Evolutionary Algorithm Adjusted with Reinforcement Learning. ICMLA (1) 2013: 203-208 - [c5]Irina Petrova
Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem. ICMLA (2) 2013: 374-377 - [c4]Arina Buzdalova
Generation of Tests for Programming Challenge Tasks Using Helper-Objectives. SSBSE 2013: 300-305 - 2012
- [c3]Arina Buzdalova
Adaptive Selection of Helper-Objectives with Reinforcement Learning. ICMLA (2) 2012: 66-67 - [c2]Arina Buzdalova
Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning. ICMLA (1) 2012: 150-155 - 2011
- [c1]Arina Afanasyeva
Choosing Best Fitness Function with Reinforcement Learning. ICMLA (2) 2011: 354-357
Coauthor Index
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