Сотрудник подразделения

Публикации

  1. 31Bulanova N., Buzdalova A., Buzdalov M. Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local Search // GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference - 2016, pp. 5-6
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  2. 30Bulanova N., Buzdalova A., Parfenov V. Comparative Study of Methods for Combining Artificial Immune Systems and Random Local Search // Mendel - 2016, pp. 87-94 [SJR: 0.225]
  3. 29Buzdalova A., Petrova I., Buzdalov M. Runtime Analysis of Different Approaches to Select Conflicting Auxiliary Objectives in the Generalized OneMax Problem // IEEE Symposium Series on Computational Intelligence, SSCI 2016 - 2016, pp. 280-286
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  4. 28Petrova I., Buzdalova A., Korneev G. Runtime Analysis of Random Local Search with Reinforcement Based Selection of Non-Stationary Auxiliary Objectives: Initial Study // Mendel - 2016, pp. 95-102 [SJR: 0.225]
  5. 27Rost A., Petrova I., Buzdalova A. Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range // GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference - 2016, pp. 141-142
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  6. 26Буланова Н.С., Буздалова А.С., Буздалов М.В. Гибридизация искусственных иммунных систем и эволюционных алгоритмов // СПИСОК-2016 Материалы всероссийской научной конференции по проблемам информатики (СПб, 26-29 апреля 2016г.) - 2016. - С. 262-267
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  7. 25Петрова И.А., Буздалова А.С., Шалыто А.А. Метод динамического выбора вспомогательных критериев в многокритериальных эволюционных алгоритмах // Научно-технический вестник информационных технологий, механики и оптики - 2016. - Т. 16. - № 3(103). - С. 460-466 [IF: 0.28]
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  8. 24Рост А.Ю., Петрова И.А., Буздалова А.С. Адаптивная настройка параметров эволюционного алгоритма с динамическим разбиением диапазона с помощью обучения с подкреплением // СПИСОК-2016 Материалы всероссийской научной конференции по проблемам информатики (СПб, 26-29 апреля 2016г.) - 2016. - С. 268-274
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  9. 23Buzdalov M., Buzdalova A. Analysis of Q-Learning with Random Exploration for Selection of Auxiliary Objectives in Random Local Search // IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings - 2015, pp. 1776-1783
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  10. 22Buzdalov M., Buzdalova A. Can OneMax Help Optimizing LeadingOnes using the EA+RL Method? // IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings - 2015, pp. 1762-1768
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  11. 21Buzdalova A., Bulanova N. Selection of Auxiliary Objectives in Artificial Immune Systems: Initial Explorations // Mendel - 2015, pp. 47-52 [SJR: 0.225]
  12. 20Buzdalova A., Matveeva A., Korneev G. Selection of Auxiliary Objectives with Multi-Objective Reinforcement Learning // GECCO'15: Proceedings of the 2015 Genetic and Evolutionary Computation Conference - 2015, pp. 1177-1180
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  13. 19Petrova I., Buzdalova A. Selection of Auxiliary Objectives in the Travelling Salesman Problem using Reinforcement Learning // GECCO'15: Proceedings of the 2015 Genetic and Evolutionary Computation Conference - 2015, pp. 1455-1456
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  14. 18Buzdalov M., Buzdalova A. OneMax helps optimizing XdivK: Theoretical runtime analysis for RLS and EA+RL // GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference - 2014, pp. 201-202
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  15. 17Buzdalov M., Petrova I., Buzdalova A. NSGA-II Implementation Details May Influence Quality of Solutions for the Job-Shop Scheduling Problem // GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference - 2014, pp. 1445-1446
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  16. 16Buzdalova A., Buzdalov M. A New Algorithm for Adaptive Online Selection of Auxiliary Objectives // Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - 2014, pp. 584-587
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  17. 15Buzdalova A., Kononov V., Buzdalov M. Selecting Evolutionary Operators using Reinforcement Learning: Initial Explorations // GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference - 2014, pp. 1033-1036
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  18. 14Kravtsov N., Buzdalov M., Buzdalova A., Shalyto A. Worst-Case Execution Time Test Generation using Genetic Algorithms with Automated Construction and Online Selection of Objectives // Mendel - 2014, pp. 111-116 [SJR: 0.225]
  19. 13Petrova I., Buzdalova A., Buzdalov M. Improved Selection of Auxiliary Objectives using Reinforcement Learning in Non-Stationary Environment // Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - 2014, pp. 580-583
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  20. 12Petrova I., Buzdalova A., Buzdalov M. Selection of Extra Objectives using Reinforcement Learning in Non-Stationary Environment: Initial Explorations // Mendel - 2014, pp. 105-110 [SJR: 0.225]
  21. 11Buzdalov M., Buzdalova A. Adaptive selection of helper-objectives for test case generation // 2013 IEEE Congress on Evolutionary Computation, CEC 2013 - 2013, pp. 2245-2250
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  22. 10Buzdalov M., Buzdalova A., Shalyto A. A First Step towards the Runtime Analysis of Evolutionary Algorithm Adjusted with Reinforcement Learning // Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 - 2013, Vol. 1, pp. 203-208
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  23. 9Buzdalov M.V., Buzdalova A.S., Petrova I.A. Generation of tests for programming challenge tasks using multi-objective optimization // GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference - 2013, pp. 1655-1658
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  24. 8Buzdalova A.S., Buzdalov M.V., Parfenov V.G. Generation of tests for programming challenge tasks using helper-objectives // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2013, Vol. 8084, No. LNCS, pp. 300-305 [SJR: 0.252]
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  25. 7Petrova I., Buzdalova A., Buzdalov M. Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem // Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 - 2013, Vol. 2, pp. 374-377
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  26. 6Afanasyeva A., Buzdalov M. Optimization with Auxiliary Criteria using Evolutionary Algorithms and Reinforcement Learning // Mendel - 2012, pp. 58-63 [SJR: 0.225]
  27. 5Buzdalova A., Buzdalov M. Adaptive Selection of Helper-Objectives with Reinforcement Learning // Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 - 2012, Vol. 2, pp. 66-67
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  28. 4Buzdalova A., Buzdalov M. Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning // Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 - 2012, Vol. 1, pp. 150-155
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  29. 3Афанасьева А.С., Буздалов М.В. Выбор функции приспособленности особей генетического алгоритма с помощью обучения с подкреплением // Научно-технический вестник информационных технологий, механики и оптики - 2012. - № 1(77). - С. 77-81 [IF: 0.28]
  30. 2Буздалова А.С., Буздалов М.В. Метод повышения эффективности эволюционных алгоритмов с помощью обучения с подкреплением // Научно-технический вестник информационных технологий, механики и оптики - 2012. - № 5(81). - С. 115-119 [IF: 0.28]
  31. 1Afanasyeva A., Buzdalov M. Choosing Best Fitness Function with Reinforcement Learning // Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011 - 2011, Vol. 2, pp. 354-357
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