Choosing Best Fitness Function with Reinforcement Learning. - Arina Afanasyeva; Maxim Buzdalov 001 (ICMLA (2), 2011)

Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning. - Arina Buzdalova; Maxim Buzdalov 001 (ICMLA (1), 2012)

Adaptive Selection of Helper-Objectives with Reinforcement Learning. - Arina Buzdalova; Maxim Buzdalov 001 (ICMLA (2), 2012)

Adaptive selection of helper-objectives for test case generation. - Maxim Buzdalov 001; Arina Buzdalova (IEEE Congress on Evolutionary Computation, 2013)

Generation of tests for programming challenge tasks using multi-objective optimization. - Maxim Buzdalov 001; Arina Buzdalova; Irina Petrova 001 (GECCO (Companion), 2013)

A First Step towards the Runtime Analysis of Evolutionary Algorithm Adjusted with Reinforcement Learning. - Maxim Buzdalov 001; Arina Buzdalova; Anatoly Shalyto 001 (ICMLA (1), 2013)

Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem. - Irina Petrova 001; Arina Buzdalova; Maxim Buzdalov 001 (ICMLA (2), 2013)

Generation of Tests for Programming Challenge Tasks Using Helper-Objectives. - Arina Buzdalova; Maxim Buzdalov 001; Vladimir Parfenov (SSBSE, 2013)

Onemax helps optimizing XdivK: : theoretical runtime analysis for RLS and EA+RL. - Maxim Buzdalov 001; Arina Buzdalova (GECCO (Companion), 2014)

NSGA-II implementation details may influence quality of solutions for the job-shop scheduling problem. - Maxim Buzdalov 001; Irina Petrova 001; Arina Buzdalova (GECCO (Companion), 2014)

Selecting evolutionary operators using reinforcement learning: initial explorations. - Arina Buzdalova; Vladislav Kononov; Maxim Buzdalov 001 (GECCO (Companion), 2014)

A New Algorithm for Adaptive Online Selection of Auxiliary Objectives. - Arina Buzdalova; Maxim Buzdalov 001 (ICMLA, 2014)

Improved Selection of Auxiliary Objectives Using Reinforcement Learning in Non-stationary Environment. - Irina Petrova 001; Arina Buzdalova; Maxim Buzdalov 001 (ICMLA, 2014)

Analysis of Q-learning with random exploration for selection of auxiliary objectives in random local search. - Maxim Buzdalov 001; Arina Buzdalova (CEC, 2015)

Can OneMax help optimizing LeadingOnes using the EA+RL method? - Maxim Buzdalov 001; Arina Buzdalova (CEC, 2015)

Selection of Auxiliary Objectives with Multi-Objective Reinforcement Learning. - Arina Buzdalova; Anna Matveeva; Georgiy Korneev (GECCO (Companion), 2015)

Selection of Auxiliary Objectives in the Travelling Salesman Problem using Reinforcement Learning. - Irina Petrova 001; Arina Buzdalova (GECCO (Companion), 2015)

AquaBridge: A novel method for systematic search of structural water molecules within the protein active sites. - Arina Afanasyeva; Sergey Izmailov; Michel Grigoriev; Michael Petukhov (J. Comput. Chem., 2015)

Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local Search. - Nina Bulanova; Arina Buzdalova; Maxim Buzdalov 001 (GECCO (Companion), 2016)

Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range. - Arkady Rost; Irina Petrova 001; Arina Buzdalova (GECCO (Companion), 2016)

Runtime analysis of different Approaches to select conflicting auxiliary objectives in the generalized OneMax problem. - Arina Buzdalova; Irina Petrova 001; Maxim Buzdalov 001 (SSCI, 2016)

Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives. - Denis Antipov; Arina Buzdalova (CEC, 2017)

Selection of Auxiliary Objectives Using Landscape Features and Offline Learned Classifier. - Anton O. Bassin; Arina Buzdalova (EvoCOP, 2017)

Reinforcement learning based dynamic selection of auxiliary objectives with preservation of the best found solution. - Irina Petrova 001; Arina Buzdalova (GECCO (Companion), 2017)

Reinforcement Learning Based Dynamic Selection of Auxiliary Objectives with Preserving of the Best Found Solution. - Irina Petrova 001; Arina Buzdalova (CoRR, 2017)

Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learning. - Denis Antipov; Arina Buzdalova; Andrew Stankevich (GECCO (Companion), 2018)

Illustrating the trade-off between time, quality, and success probability in heuristic search: a discussion paper. - Ivan Ignashov; Arina Buzdalova; Maxim Buzdalov 001; Carola Doerr (GECCO (Companion), 2019)

Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates. - Anna Rodionova; Kirill Antonov; Arina Buzdalova; Carola Doerr (GECCO, 2019)

Fixed-target runtime analysis of the (1 + 1) EA with resampling. - Dmitry Vinokurov; Maxim Buzdalov 001; Arina Buzdalova; Benjamin Doerr; Carola Doerr (GECCO (Companion), 2019)

Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm. - Arina Buzdalova; Carola Doerr; Anna Rodionova (PPSN (2), 2020)

Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms. - Kirill Antonov; Maxim Buzdalov 001; Arina Buzdalova; Carola Doerr (CEC, 2021)

Fast Re-Optimization of LeadingOnes with Frequent Changes. - Nina Bulanova; Arina Buzdalova; Carola Doerr (CEC, 2022)

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information. - 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; et al. (J. Chem. Inf. Model., 2024)