Arina Buzdalova

Arina Buzdalova



PhD, ITMO University
Researcher at Computer Technologies Laboratory

Research projects: Interests: evolutionary computation, runtime analysis, multi-objectivization, reinforcement learning, search-based software engineering
Program committees: GECCO 2014-2021; FOGA 2019, 2021; Evo* 2019; PPSN 2018, 2020; ESANN 2015
Teaching: Data Structures and Algorithms, groups M3305-M3308 (2013 - 2017)

Links: Google Scholar page, DBLP
Colleagues: Maxim Buzdalov, Vladimir Mironovich, Vladimir Ulyantsev, Daniil Chivilikhin

Publications

Conference Papers

2021

  1. Antonov K., Buzdalova A., Buzdalov M., Doerr C. Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms. CEC 2021, best paper award

2020

  1. Buzdalova A., Doerr C., Rodionova A. Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm. PPSN 2020.
  2. Antonov K., Buzdalova A., Doerr C. Mutation Rate Control in the (1+lambda) Evolutionary Algorithm with a Self-adjusting Lower Bound. MOTOR 2020.

2019

  1. Rodionova A., Antonov K., Buzdalova A., Doerr C. Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates. GECCO 2019.
  2. Vinokurov D., Buzdalov M., Buzdalova A., Doerr B., Doerr C. Fixed-Target Analysis of (1+1) EA with Resampling. GECCO 2019.
  3. Ignashov I., Buzdalova A., Buzdalov M., Doerr C. Illustrating the Trade-Off between Time, Quality, and Success Probability in Heuristic Search. GECCO 2019.
  4. Muravyov S., Antipov D., Buzdalova A., Filchenkov A. Efficient Computation of Fitness Function for Evolutionary Clustering. Mendel 2019.

2018

  1. Antipov D., Buzdalova A., Stankevich A. Runtime Analysis of a Population-based Evolutionary Algorithm with Auxiliary Objectives Selected by Reinforcement Learning. GECCO 2018.

2017

  1. Bassin A., Buzdalova A. Selection of Auxiliary Objectives Using Landscape Features and Offline Learned Classifier. EvoCOP 2017.
  2. Petrova I., Buzdalova A. Reinforcement Learning Based Dynamic Selection of Auxiliary Objectives with Preserving of the Best Found Solution. GECCO 2017.
  3. Antipov D., Buzdalova A. Runtime Analysis of Random Local Search on Jump Function with Reinforcement Based Selection of Auxiliary Objectives. CEC 2017.

2016

  1. Bulanova N., Buzdalova A., Buzdalov M. Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local Search. GECCO 2016.
  2. Rost A., Petrova I., Buzdalova A. Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range. GECCO 2016
  3. Petrova I., Buzdalova A., Korneev G. Runtime Analysis of Random Local Search with Reinforcement Based Selection of Non-Stationary Auxiliary Objectives: Initial Study. Mendel 2016.
  4. Bulanova N., Buzdalova A., Parfenov V. Comparative Study of Methods for Combining Artificial Immune Systems and Random Local Search. Mendel 2016.
  5. Buzdalova A., Petrova I., Buzdalov M. Runtime Analysis of Different Approaches to Select Conflicting Auxiliary Objectives in the Generalized OneMax Problem. SSCI 2016.

2015

  1. Buzdalova A., Matveeva A., Korneev G. Selection of Auxiliary Objectives with Multi-Objective Reinforcement Learning. GECCO 2015.
  2. Petrova I., Buzdalova A. Selection of Auxiliary Objectives in the Travelling Salesman Problem using Reinforcement Learning. GECCO 2015.
  3. Buzdalova A., Bulanova N. Selection of Auxiliary Objectives in Artificial Immune Systems: Initial Explorations. Mendel 2015.
  4. Buzdalov M., Buzdalova A. Analysis of Q-Learning with Random Exploration for the Selection of Auxiliary Objectives in Random Local Search. CEC 2015.
  5. Buzdalov M., Buzdalova A. Can OneMax Help Optimizing LeadingOnes using the EA+RL Method? CEC 2015.

2014

  1. Buzdalova A., Buzdalov M. A New Algorithm for Adaptive Online Selection of Auxiliary Objectives. ICMLA 2014.
  2. Petrova I., Buzdalova A., Buzdalov M. Improved Selection of Auxiliary Objectives using Reinforcement Learning in Non-Stationary Environment. ICMLA 2014.
  3. Buzdalova A., Kononov V., Buzdalov M. Selecting Evolutionary Operators using Reinforcement Learning: Initial Explorations. GECCO 2014.
  4. Buzdalov M., Petrova I., Buzdalova A. NSGA-II Implementation Details May Influence Quality of Solutions for the Job-Shop Scheduling Problem. GECCO 2014.
  5. Buzdalov M., Buzdalova A. OneMax Helps Optimizing XdivK: Theoretical Runtime Analysis for RMHC and EA+RL. GECCO 2014.
  6. Petrova I., Buzdalova A., Buzdalov M. Selection of Extra Objectives using Reinforcement Learning in Non-Stationary Environment: Initial Explorations. Mendel 2014.
  7. Kravtsov N., Buzdalov M., Buzdalova A. Worst-Case Execution Time Test Generation using Genetic Algorithms with Automated Construction and Online Selection of Objectives. Mendel 2014.

2013

  1. Buzdalov M., Buzdalova A., Shalyto A. A First Step towards the Runtime Analysis of Evolutionary Algorithm Adjusted with Reinforcement Learning. ICMLA 2013.
  2. Petrova I., Buzdalova A., Buzdalov M. Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem. ICMLA 2013.
  3. A. Buzdalova, M. Buzdalov, V. Parfenov. Generation of Tests for Programming Challenge Tasks using Helper-Objectives. SSBSE 2013.
  4. M. Buzdalov, A. Buzdalova, I. Petrova. Generation of Tests for Programming Challenge Tasks Using Multi-Objective Optimization. GECCO 2013.
  5. M. Buzdalov, A. Buzdalova. Adaptive Selection of Helper-Objectives for Test Case Generation. CEC 2013.

2012

  1. A. Buzdalova and M. Buzdalov. Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning. ICMLA 2012.
  2. A. Buzdalova and M. Buzdalov. Adaptive Selection of Helper-Objectives with Reinforcement Learning. ICMLA 2012.
  3. A. Afanasyeva and M. Buzdalov. Optimization with Auxiliary Criteria using Evolutionary Algorithms and Reinforcement Learning. MENDEL 2012.

2011

  1. A. Afanasyeva and M. Buzdalov. Choosing Best Fitness Function with Reinforcement Learning. ICMLA 2011.

Talks at Conferences, Workshops and Seminars

  1. A. Buzdalova, C. Doerr, A. Rodionova, K. Antonov. Comparing Self-Adjusting (1+lambda) EAs under Large Dimensions: A Case Study. ImAppNIO Workshop, February 19, 2019.
  2. A. Buzdalova, I. Petrova, M. Buzdalov. Is it Necessary to Perform Multi-objective Optimization when Doing Multi-objectivization? Dagstuhl Seminar 17191, 2017.
  3. A. Buzdalova, M. Buzdalov. Selection of Auxiliary Objectives with Reinforcement Learning: Overview of Theoretical Results. Dagstuhl Seminar 15211, 2015.
  4. A. Buzdalova, M. Buzdalov. A Method of Auxiliary Objectives Selection using Reinforcement Learning: An Overview. PPSN 2014 [web article] [presentation]
  5. M. Buzdalov, A. Buzdalova. A First Step Towards the Runtime Analysis of Evolutionary Algorithm Adjusted with Reinforcement Learning. ThRaSH 2013.

Publications in Russian

Журнальные статьи

  1. Н. С. Буланова, А. С. Буздалова, А. А. Шалыто. Метод адаптивного выбора операторов мутации искусственных иммунных систем и локального поиска. Научно-технический вестник информационных технологий, механики и оптики, 2017, No 6 (17).
  2. И. А. Петрова, А. С. Буздалова, А. А. Шалыто. Теоретический анализ метода выбора переключающихся вспомогательных критериев на задаче XdivK. Научно-технический вестник информационных технологий, механики и оптики, 2017, No 3 (17).
  3. И. А. Петрова, А. С. Буздалова, А. А. Шалыто. Метод динамического выбора вспомогательных критериев в многокритериальных эволюционных алгоритмах. Научно-технический вестник информационных технологий, механики и оптики, 2016, No 3 (16).
  4. А. С. Буздалова, М. В. Буздалов. Метод повышения эффективности эволюционных алгоритмов с помощью обучения с подкреплением. Научно-технический вестник информационных технологий, механики и оптики, 2012, No 5 (81).
  5. А.С. Афанасьева, М. В. Буздалов. Выбор функции приспособленности особей генетического алгоритма с помощью обучения с подкреплением. Научно-технический вестник СПбГУ ИТМО, 2012, No 1 (77).

Публикации в трудах конференций

  1. И. А. Петрова, А. С. Буздалова. Теоретический анализ метода выбора вспомогательных критериев на задачах XdivK и Generalized OneMax. СПИСОК 2017.
  2. А. С. Буздалова, И. А. Петрова, М. В. Буздалов. Анализ времени работы методов выбора вспомогательных критериев оптимизации на обобщенной задаче OneMax. СПИСОК 2016.
  3. М. В. Буздалов, А. С. Буздалова. Сравнительный анализ метода выбора вспомогательных критериев и метода спуска со случайными мутациями. СПИСОК 2014.
  4. И. А. Петрова, А. С. Буздалова, М. В. Буздалов. Повышение эффективности эволюционных алгоритмов при помощи обучения с подкреплением в нестационарной среде. СПИСОК 2014.
  5. А. С. Буздалова, М. В. Буздалов. Анализ метода EA+RL на примере задачи с одним вспомогательным критерием. ВКМУ 2014.
  6. А. С. Буздалова, М. В. Буздалов. Использование вспомогательных функций приспособленности для тестирования решений олимпиадных задач по программированию. СПИСОК 2013.
  7. М. В. Буздалов, А. С. Буздалова. Оценка времени работы эволюционного алгоритма RMHC под управлением алгоритма Q-Learning на задаче OneMax с мешающим критерием оптимизации. СПИСОК 2013.
  8. А. С. Буздалова, М. В. Буздалов. Применение обучения с подкреплением к генерации тестов для олимпиадных задач по программированию. ВКМУ 2013.
  9. А. С. Афанасьева. Выбор функции приспособленности особей эволюционного алгоритма с помощью обучения с подкреплением. СПИСОК 2012.
  10. А. С. Афанасьева. Выбор функций приспособленности особей генетического алгоритма с помощью обучения с подкреплением. ВКМУ 2012.

Кандидатская диссертация

Магистерская диссертация

Бакалаврская работа