Биоинформатика, машинное обучение, технологии программирования, теория кодирования, проактивные системы

  • Руководитель: Парфенов Владимир Глебович

Публикации

  1. 86. Chivilikhin D.S., Ulyantsev V.I., Shalyto A.A. Solving five instances of the artificial ant problem with ant colony optimization // IFAC Proceedings Volumes (IFAC-PapersOnline) - 2013, Vol. 9, No. 1, pp. 1043-1048 [SJR: 0.228]
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  2. 85. Zhabelova G., Yang C., Patil S., Pang C., Yan J., Shalyto A., Vyatkin V. Cyber-physical components for heterogeneous modelling, validation and implementation of smart grid intelligence // Proceedings of 2014 12th IEEE International Conference on Industrial Informatics (INDIN) - 2014, pp. 411-417
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  3. 84. Pang C., Patil S., Yang C., Vyatkin V., Shalyto A. A Portability Study of IEC 61499: Semantics and Tools // Proceedings of 2014 12th IEEE International Conference on Industrial Informatics (INDIN) - 2014, pp. 440-445
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  4. 83. Zadorozhnaya O., Kirsanov D., Buzhinsky I., Tsarev F., Abramova N., Bratov A., Munoz F.J., Ribo J., Bori J., Riva M.C., Legin A. Water pollution monitoring by an artificial sensory system performing in terms of Vibrio fischeri bacteria // Sensors and Actuators, B: Chemical - 2015, Vol. 207, No. Part B, pp. 1069-1075 [IF: 4.758, SJR: 1.305]
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  5. 82. Saenko I., Kotenko I. A Genetic Approach for Virtual Computer Network Design // Studies in Computational Intelligence - 2015, Vol. 570, pp. 95-105 [SJR: 0.187]
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  6. 81. Kotenko I., Шоров А. Simulation of bio-inspired security mechanisms against network infrastructure attacks // Studies in Computational Intelligence - 2015, Vol. 570, pp. 127-133 [SJR: 0.187]
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  7. 80. Buzdalov 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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  8. 79. Arkhipov V., Buzdalov M., Shalyto A. Worst-Case Execution Time Test Generation for Augmenting Path Maximum Flow Algorithms using Genetic Algorithms // Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 - 2013, Vol. 2, pp. 108-111
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  9. 78. Dubinkina V.B., Ischenko D.S., Ulyantsev V.I., Tyakht A.V., Alexeev D.G. Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis // BMC bioinformatics - 2016, Vol. 17, No. 1, pp. 38 [IF: 2.435, SJR: 1.722]
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  10. 77. Smetannikov I., Varlamov E., Filchenkov A. Swarm MeLiF: Feature selection with filter combination found via swarm intelligence // Advances in Intelligent Systems and Computing - 2016, Vol. 449, pp. 227-234
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  11. 76. Chivilikhin D. Experimental Study of Automated Parameter Tuning on the Example of irace and the Traveling Salesman Problem // GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference - 2016, pp. 45-46
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  12. 75. Campbell J., Alexandrov A., Kim J., Wala J., Berger A.H., Pedamallu C.S., Shukla S.A., Guo G., Brooks A.N., Murray B.A., Imielinski M., Hu X., Ling S., Akbani R., Rosenberg M., Cibulskis C., Ramachandran A., Collisson E.A., Kwiatkowski D.J., Lawrence M.S., Weinstein J.N., Verhaak R.G., Wu C.J., Hammerman P.S., Cherniack A.D., Getz G., Artyomov M.N., Schreiber R., Govindan R., Meyerson M. Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas // Nature Genetics - 2016, Vol. 48, No. 6, pp. 607–616 [IF: 31.616, SJR: 23.762]
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  13. 74. Eremeev A., Korneev G., Semenov A., Veijalainen J. The spanning tree based approach for solving the shortest path problem in social graphs // WEBIST 2016 - Proceedings of the 12th International Conference on Web Information Systems and Technologies - 2016, Vol. 1, pp. 42-53
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  14. 73. Zadorozhnaya O., Kirsanov D., Buzhinsky I., Tsarev F., Abramova N., Bratov A., Munoz F.J., Ribo J., Bori J., Riva M.C., Legin A. Water pollution monitoring by an artificial sensory system performing in terms of Vibrio fischeri bacteria // Sensors and Actuators, B: Chemical - 2015, Vol. 207, No. Part B, pp. 1069-1075 [IF: 4.758, SJR: 1.305]
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  15. 72. Zadorozhnaya O., Kirsanov D., Buzhinsky I., Tsarev F., Abramova N., Bratov A., Munoz F.J., Ribo J., Bori J., Riva M.C., Legin A. Water pollution monitoring by an artificial sensory system performing in terms of Vibrio fischeri bacteria // Sensors and Actuators, B: Chemical - 2015, Vol. 207, No. Part B, pp. 1069-1075 [IF: 4.758, SJR: 1.305]
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  16. 71. Buzdalov M.V., Kever M.E., Doerr B. Upper and Lower Bounds on Unrestricted Black-Box Complexity of Jump(n,l) // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2015, Vol. 9026, pp. 209-221 [SJR: 0.252]
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  17. 70. Chivilikhin D.S., Ulyantsev V.I. Learning Finite-State Machines: Conserving Fitness Function Evaluations by Marking Used Transitions // Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 - 2013, Vol. 2, pp. 90-95
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  18. 69. Ulyantsev V., Zakirzyanov I., Shalyto A. BFS-Based Symmetry Breaking Predicates for DFA Identification // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2015, Vol. 8977, pp. 611-622 [SJR: 0.252]
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  19. 68. Kapun E., Tsarev F.N. De Bruijn Superwalk with Multiplicities Problem is NP-hard // BMC bioinformatics - 2013, Vol. 14, No. 5, pp. S7 [IF: 2.435, SJR: 1.722]
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  20. 67. Buzdalov 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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  21. 66. Petrova 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]
  22. 65. Aleksandrov A.V., Tsarev F.N., Kazakov S.V., Sergushichev A.A., Shalyto A.A. The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior // Journal of Computer and Systems Sciences International - 2013, Vol. 52, No. 3, pp. 410-425 The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior [IF: 0.503, SJR: 0.27]
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  23. 64. Buzdalov M., Yakupov I., Stankevich A. Fast Implementation of the Steady-State NSGA-II Algorithm for Two Dimensions Based on Incremental Non-Dominated Sorting // GECCO'15: Proceedings of the 2015 Genetic and Evolutionary Computation Conference - 2015, pp. 647-654
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  24. 63. Chivilikhin D., Ivanov I., Shalyto A. Inferring Temporal Properties of Finite-State Machine Models with Genetic Programming // GECCO'15: Proceedings of the 2015 Genetic and Evolutionary Computation Conference - 2015, pp. 1185-1188
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  25. 62. Nikiforov V., Baranov S.N. Multi-partite graphs and verification of software applications for real-time systems // Cybernetics And Information Technologies - 2016, Vol. 16, No. 2, pp. 85-96 [SJR: 0.17]
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  26. 61. Buzdalov M., Parfenov V. Various Degrees of Steadiness in NSGA-II and Their Influence on the Quality of Results // GECCO'15: Proceedings of the 2015 Genetic and Evolutionary Computation Conference - 2015, pp. 749-750
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  27. 60. Kazakov S., Shalyto A. Overlap graph simplification using edge reliability calculation // Proceedings of the European Conference on Data Mining 2014 and International Conferences on Intelligent Systems and Agents 2014 and Theory and Practice in Modern Computing 2014 - Part of the Multi Conference on Computer Science and Information Systems, MCCSIS 2014 - 2014, pp. 222-226
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  28. 59. Buzdalov 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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  29. 58. Mironovich V., Buzdalov M. Generation of tests against a greedy algorithm for the knapsack problem using an evolutionary algorithm // Mendel - 2014, pp. 77-82 [SJR: 0.225]
  30. 57. Baranov S.N., Nikiforov V. Density of multi-task real-time applications // Proceedings of the 17th Conference of Open Innovations Association FRUCT - 2015, pp. 9-15
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  31. 56. Jha A.K., Huang S.C., Sergushichev A.A., Lampropoulou V., Ivanova Y., Loginicheva E., Chmielewski K., Stewart K.M., Ashall J., Everts B., Pearce E.J., Driggers E.M., Artyomov M.N. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization // Immunity - 2015, Vol. 42, No. 3, pp. 419-430 [SJR: 16.215]
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  32. 55. Buzdalov 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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  33. 54. Buzhinsky I.P., Ulyantsev V.I., Veijalainen J., Viatkin V.V. Evolutionary Approach to Coverage Testing of IEC 61499 Function Block Applications // Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015 - 2015, pp. 1213-1218
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  34. 53. Bulanova 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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  35. 52. Vasin A., Buzdalov M. A Faster Algorithm for the Binary Epsilon Indicator Based on Orthant Minimum Search // GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference - 2016, pp. 613-620
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  36. 51. Buzdalova 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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  37. 50. Antipov D.S., Buzdalov M.V., Doerr B. Runtime Analysis of (1+1) Evolutionary Algorithm Controlled with Q-learning using Greedy Exploration Strategy on OneMax+ZeroMax Problem // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2015, Vol. 9026, pp. 160-172 [SJR: 0.252]
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  38. 49. Zabashta A., Smetannikov I., Filchenkov A. Study on meta-learning approach application in rank aggregation algorithm selection // CEUR Workshop Proceedings - 2015, Vol. 1455, pp. 115-117
  39. 48. Buzdalova 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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  40. 47. Glotov A.S., Kazakov S., Zhukova E.A., Alexandrov A., Glotov O.S., Pakin V.S., Danilova M.M., Poliakova I.V., Niyazova S.S., Chakova N.N., Komissarova S.M., Kurnikova E.A., Sarana A.M., Sherbak S.G., Sergushichev A., Shalyto A., Baranov V.S. Targeted next-generation sequencing (NGS) of nine candidate genes with custom AmpliSeq in patients and a cardiomyopathy risk group // Clinica Chimica Acta - 2015, Vol. 446, pp. 132-140 [IF: 2.799, SJR: 1.04]
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  41. 46. Yakupov I., Buzdalov M. Incremental Non-Dominated Sorting with O(N) Insertion for the Two-Dimensional Case // IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings - 2015, pp. 1853-1860
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  42. 45. Petrova 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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  43. 44. Vincent E.E., Sergushichev A.A., Griss T., Gingras M., Samborska B., Ntimbane T., Coelho P.P., Blagih J., Raissi T.C., Choiniere L., Bridon G., Loginicheva E., Flynn B.R., Thomas E.C., Tavare J.M., Avizonis D.Z., Pause A.P., Elder D.J., Artyomov M.N., Jones R.G. Mitochondrial Phosphoenolpyruvate Carboxykinase Regulates Metabolic Adaptation and Enables Glucose-Independent Tumor Growth // Molecular Cell - 2015, Vol. 60, No. 2, pp. 195-207 [IF: 14.018, SJR: 13.658]
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  44. 43. Golubtsov N., Galper D., Filchenkov A. Active adaptation of expert-based suggestions in ladieswear recommender system LookBooksClub via reinforcement learning // Advances in Intelligent Systems and Computing - 2016, Vol. 449, pp. 61-69
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  45. 42. Stoyanov D.Y., Filchenkov A.A., Gedertsev A.S. A method of measuring quality of algorithm in stream structure from Motion applied to aerial photography // Proceedings of the 19th International Conference on Soft Computing and Measurements, SCM 2016 - 2016, pp. 35-38
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  46. 41. Shalamov V., Filchenkov A., Shalyto A. Genetic Search of Pickup and Delivery Problem Solutions for Self-Driving Taxi Routing // IFIP International Conference on Artificial Intelligence Applications and Innovations - 2016, Vol. 475, pp. 348-355 [SJR: 0.16]
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  47. 40. Loboda A.A., Artyomov M.N., Sergushichev A.A. Solving generalized maximum-weight connected subgraph problem for network enrichment analysis // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2016, Vol. 9838, pp. 210-221 [SJR: 0.252]
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  48. 39. Buzhinsky I., Pang C., Vyatkin V. Formal Modeling of Testing Software for Cyber-Physical Automation Systems // 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015, Proceedings - 2015, Vol. 3, pp. 301-306
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  49. 38. Buzdalova 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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  50. 37. Chivilikhin D.S., Ulyantsev V.I. MuACOsm - A New Mutation-Based Ant Colony Optimization Algorithm for Learning Finite-State Machines // GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference - 2013, pp. 511-518 MuACOsm - A New Mutation-Based Ant Colony Optimization Algorithm for Learning Finite-State Machines
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  51. 36. Petrova 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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  52. 35. Rost 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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  53. 34. Zabashta A., Smetannikov I., Filchenkov A. Rank aggregation algorithm selection meets feature selection // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2016, Vol. 9729, pp. 740-755 [SJR: 0.252]
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  54. 33. [SJR: 0.252]
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  55. 32. Buzdalov 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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  56. 31. Kravtsov 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]
  57. 30. Buzdalov M., Shalyto A. A Provably Asymptotically Fast Version of the Generalized Jensen Algorithm for Non-Dominated Sorting // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2014, Vol. 8672, pp. 528-537 [SJR: 0.252]
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  58. 29. Mironovich V., Buzdalov M. Hard Test Generation for Maximum Flow Algorithms with the Fast Crossover-Based Evolutionary Algorithm // GECCO'15: Proceedings of the 2015 Genetic and Evolutionary Computation Conference - 2015, pp. 1229-1232
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  59. 28. [SJR: 0.422]
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  60. 27. Arkhipov V., Buzdalov M. An asynchronous implementation of the limited memory CMA-ES: First results // Mendel - 2015, pp. 43-46 [SJR: 0.225]
  61. 26. Chivilikhin D.S., Ulyantsev V.I., Shalyto A.A. Modified ant colony algorithm for constructing finite state machines from execution scenarios and temporal formulas // Automation and Remote Control - 2016, Vol. 77, No. 3, pp. 473-484 [IF: 0.265, SJR: 0.362]
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  62. 25. Sergushichev A.A., Loboda A.A., Jha A.K., Vincent E.E., Driggers E.M., Jones R.G., Pearce E.J., Artyomov M.N. GAM: a web-service for integrated transcriptional and metabolic network analysis // Nucleic Acids Research - 2016, Vol. 44, No. 1, pp. W194-W200 [IF: 9.202]
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  63. 24. Buzhinsky I., Vyatkin V. Plant Model Inference for Closed-Loop Verification of Control Systems: Initial Explorations // Proceedings of IEEE International Conference on Industrial Informatics (INDIN 2016) - 2016, pp. 736-739
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  64. 23. Konoplich G.V., Putin E.O., Filchenkov A.A. Application of deep learning to the problem of vehicle detection in UAV images // Proceedings of the 19th International Conference on Soft Computing and Measurements, SCM 2016 - 2016, pp. 4-6
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  65. 22. Buzdalov 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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  66. 21. Buzhinsky I.P., Ulyantsev V.I., Tsarev F.N., Shalyto A.A. Search-based construction of finite-state machines with real-valued actions: New representation model // GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference - 2013, pp. 199-200
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  67. 20. Buzhinsky I.P., Ulyantsev V.I., Shalyto A.A. Test-based induction of finite-state machines with continuous output actions // IFAC Proceedings Volumes (IFAC-PapersOnline) - 2013, Vol. 9, No. 1, pp. 1049-1054 [SJR: 0.228]
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  68. 19. Kapun E., Tsarev F.N. On NP-hardness of the paired de Bruijn sound cycle problem // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2013, Vol. 8126, No. LNBI, pp. 59-69 [SJR: 0.252]
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  69. 18. Furia C.A., Meyer B.E., Oriol M., Tikhomirov A.V., Yi W. The search for the laws of automatic random testing // Proceedings of the ACM Symposium on Applied Computing - 2013, pp. 1211-1216
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  70. 17. Buzhinsky I.P., Ulyantsev V.I., Chivilikhin D.S., Shalyto A.A. Inducing finite state machines from training samples using ant colony optimization // Journal of Computer and Systems Sciences International - 2014, Vol. 53, No. 2, pp. 256-266 [IF: 0.503, SJR: 0.27]
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  71. 16. Buzdalov M., Shalyto A. Worst-Case Execution Time Test Generation for Solutions of the Knapsack Problem Using a Genetic Algorithm // Communications in Computer and Information Science - 2014, Vol. 472, pp. 1-10 [SJR: 0.149]
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  72. 15. Lukin M., Buzdalov M., Shalyto A. Formal Verification of 800 Genetically Constructed Automata Programs: A Case Study // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2014, Vol. 8855, pp. 165-170 [SJR: 0.252]
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  73. 14. Buzdalov 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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  74. 13. Kotenko I.V., Saenko I.B. Improved genetic algorithms for solving the optimisation tasks for design of access control schemes in computer networks // International Journal of Bio-Inspired Computation - 2015, Vol. 7, No. 2, pp. 98-110 [IF: 1.681, SJR: 0.77]
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  75. 12. Buzdalov M., Shalyto A. Hard Test Generation for Augmenting Path Maximum Flow Algorithms using Genetic Algorithms: Revisited // IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings - 2015, pp. 2121-2128
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  76. 11. Shalamov V., Filchenkov A., Chivilikhin D. Small-Moves Based Mutation For Pick-Up And Delivery Problem // GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference - 2016, pp. 1027-1030
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  77. 10. Buzdalov M. An Algorithm for Computing Lower Bounds for Unrestricted Black-Box Complexities // GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference - 2016, pp. 147-148
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  78. 9. Nigmatullin N., Buzdalov M., Stankevich A. Efficient removal of points with smallest crowding distance in two-dimensional incremental non-dominated sorting // GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference - 2016, pp. 1121-1128
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  79. 8. Buzhinsky I., Chivilikhin D., Ulyantsev V., Tsarev F. Improving the Quality of Supervised Finite-State Machine Construction Using Real-Valued Variables // GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference - 2014, pp. 1037-1040
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  80. 7. Chivilikhin D., Ulyantsev V. Inferring Automata-Based Programs from Specification With Mutation-Based Ant Colony Optimization // GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference - 2014, pp. 67-68
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  81. 6. Ulyantsev V., Melnik M. Constructing Parsimonious Hybridization Networks from Multiple Phylogenetic Trees Using a SAT-Solver // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - 2015, Vol. 9199, pp. 141-153 [SJR: 0.252]
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  82. 5. Chivilikhin D., Ulyantsev V. Learning finite-state machines with classical and mutation-based ant colony optimization: Experimental evaluation // Proceedings of the 1st BRICS Countries Congress on Computational Intelligence, BRICS-CCI 2013 - 2013, pp. 528-533
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  83. 4. Aksenov V., Kokhas K. Domino Tilings and Determinants // Journal of Mathematical Sciences - 2014, Vol. 200, No. 6, pp. 647-653 [SJR: 0.272]
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  84. 3. Buzdalova A., Bulanova N. Selection of Auxiliary Objectives in Artificial Immune Systems: Initial Explorations // Mendel - 2015, pp. 47-52 [SJR: 0.225]
  85. 2. Aksenov V.E., Kokhas K.P. Chip Removal. Urban Renewal Revisited // Journal of Mathematical Sciences - 2015, Vol. 209, No. 6, pp. 809-825 [SJR: 0.272]
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  86. 1. Levenets D.G., Zotov M.A., Romanov A.V., Tulupyev A.L., Zolotin A.A., Filchenkov A. Decremental and incremental reshaping of algebraic Bayesian networks global structures // Advances in Intelligent Systems and Computing - 2016, Vol. 451, pp. 57-67
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