Maxim Buzdalov

Maxim Buzdalov

email: mbuzdalov [at] gmail [dot] com

ITMO University
PhD
Researcher at Faculty of Information Technologies and Programming

Research field: Theory of Evolutionary Computation, Algorithms and Data Structures, Search-Based Software Engineering
Teaching: Computational Geometry (2012/2013), Compiler Theory (2012/2013), Genetic and Evolutionary Computation (2015/2016–now)
Google Scholar page, DBLP
Page generation time: 2022-03-17T23:53:34.040198

Publications

2022

Journal papers (English)

  1. Antipov D., Buzdalov M., Doerr B. Fast Mutation in Crossover-based Algorithms // Algorithmica. — 2022. — Just Accepted.
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2021

Journal papers (English)

  1. Fixed-Target Runtime Analysis / M. Buzdalov [et al.] // Algorithmica. — 2021. — Early Access.
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Conference papers (English)

  1. Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms / K. Antonov [et al.] // Proceedings of Congress on Evolutionary Computation. — 2021. — P. 878–885.
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  2. Buzdalov M., Doerr C. Optimal static mutation strength distributions for the (1 + λ) evolutionary algorithm on OneMax // Proceedings of Genetic and Evolutionary Computation Conference. — 2021. — P. 660–668.
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  3. Antipov D., Buzdalov M., Doerr B. Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution // Proceedings of Genetic and Evolutionary Computation Conference. — 2021. — P. 1115–1123.
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  4. Mishra S., Prakash V., Buzdalov M. Labeling-oriented non-dominated sorting is Θ(MN3) // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2021. — P. 189–190.
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2020

Conference papers (English)

  1. Buzdalov M., Doerr C. Optimal Mutation Rates for the (1 + λ) EA on OneMax // Parallel Problem Solving from Nature – PPSN XVI. — 2020. — P. 574–587. — (Lecture Notes in Computer Science ; 12270).
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  2. Antipov D., Buzdalov M., Doerr B. First Steps Towards a Runtime Analysis When Starting With a Good Solution // Parallel Problem Solving from Nature – PPSN XVI. — 2020. — P. 560–573. — (Lecture Notes in Computer Science ; 12270).
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  3. Mishra S., Buzdalov M. Filter Sort is Ω(N3) in the Worst Case // Parallel Problem Solving from Nature – PPSN XVI. — 2020. — P. 675–685. — (Lecture Notes in Computer Science ; 12270).
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  4. Antipov D., Buzdalov M., Doerr B. Fast Mutation in Crossover-based Algorithms // Proceedings of Genetic and Evolutionary Computation Conference. — 2020. — P. 1268–1276.
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  5. Mishra S., Buzdalov M. If Unsure, Shuffle: Deductive Sort is Θ(MN3) but O(MN2) in Expectation over Input Permutations // Proceedings of Genetic and Evolutionary Computation Conference. — 2020. — P. 516–523.
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  6. Fixed-Target Runtime Analysis / M. Buzdalov [et al.] // Proceedings of Genetic and Evolutionary Computation Conference. — 2020. — P. 1295–1303.
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  7. Bassin A., Buzdalov M. The (1+(λ,λ)) Genetic Algorithm for Permutations // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2020. — P. 1669–1677.
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  8. Mishra S., Buzdalov M., Senwar R. Time Complexity Analysis of the Dominance Degree Approach for Non-Dominated Sorting // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2020. — P. 169–170.
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  9. Optimizing Robotic Cheetah Leg Parameters Using Evolutionary Algorithms / M. Buzdalov [et al.] // Proceedings of International Conference on Bioinspired Optimization Methods and Their Applications. — 2020. — P. 214–227. — (Lecture Notes in Computer Science ; 12438).
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  10. Bassin A., Buzdalov M. An Experimental Study of Operator Choices in the (1 + (λ,λ)) Genetic Algorithm // Proceedings of the International Conference on Mathematical Optimization Theory and Operations Research. — 2020. — P. 320–335. — (Communications in Computer and Information Science ; 1275).
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Journal papers (Russian)

  1. Басин А. О., Буздалов М. В., Шалыто А. А. Правило “одной пятой” с возвратами для настройки размера популяции в генетическом алгоритме (1+(λ,λ)) // Моделирование и анализ информационных систем. — 2020. — Т. 27, № 4. — С. 488–508.
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  2. Буздалов М. В., Винокуров Д. В. Применение метода уровней приспособленности для анализа динамики работы эволюционных алгоритмов // Научно-технический вестник информационных технологий, механики и оптики. — 2020. — Т. 20, № 5. — С. 701–707.
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2019

Conference papers (English)

  1. Pavlenko A., Buzdalov M., Ulyantsev V. Fitness Comparison by Statistical Testing in Construction of SAT-Based Guess-and-Determine Cryptographic Attacks // Proceedings of Genetic and Evolutionary Computation Conference. — 2019. — P. 312–320.
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  2. Bulanova N., Buzdalov M. Black-Box Complexity of the Binary Value Function // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2019. — P. 423–424.
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  3. Bulanova N., Buzdalov M. Limited Memory, Limited Arity Unbiased Black-Box Complexity: First Insights // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2019. — P. 2020–2023.
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  4. Buzdalov M. Towards Better Estimation of Statistical Significance When Comparing Evolutionary Algorithms // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2019. — P. 1782–1788.
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  5. Fixed-Target Runtime Analysis of the (1+1) EA with Resampling / D. Vinokurov [et al.] // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2019. — P. 2068–2071.
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  6. Buzdalov M. Generalized Incremental Orthant Search: Towards Efficient Steady-State Evolutionary Multiobjective Algorithms // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2019. — P. 1357–1365.
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  7. Bassin A., Buzdalov M. The 1/5-th Rule with Rollbacks: On Self-Adjustment of the Population Size in the (1 + (λ,λ)) GA // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2019. — P. 277–278.
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  8. Illustrating the Trade-Off between Time, Quality, and Success Probability in Heuristic Search / I. Ignashov [et al.] // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2019. — P. 1807–1812.
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  9. Mironovich V., Buzdalov M., Vyatkin V. Permutation Encoding for Automatic Reconstruction of Connections in Closed-Loop Control System using Evolutionary Algorithm // Proceedings of International Conference on Emerging Technologies and Factory Automation. — IEEE. 2019. — P. 1265–1268.
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  10. Buzdalov M. Make Evolutionary Multiobjective Algorithms Scale Better with Advanced Data Structures: Van Emde Boas Tree for Non-Dominated Sorting // Proceedings of International Conference on Evolutionary Multi-Criterion Optimization. — 2019. — P. 66–77. — (Lecture Notes in Computer Science ; 11411).
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2018

Conference papers (English)

  1. Markina M., Buzdalov M. Towards Large-Scale Multiobjective Optimisation with a Hybrid Algorithm for Non-Dominated Sorting // Parallel Problem Solving from Nature – PPSN XV. Vol. 1. — 2018. — P. 347–358. — (Lecture Notes in Computer Science ; 11101).
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  2. Mironovich V., Buzdalov M., Vyatkin V. Automatic Plant-Controller Input/Output Matching using Evolutionary Algorithms // Proceedings of 23rd IEEE International Conference on Emerging Technologies and Factory Automation. — 2018. — P. 1043–1046.
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  3. Buzdalov M. Generalized offline orthant search: One code for many problems in multiobjective optimization // Proceedings of Genetic and Evolutionary Computation Conference. — 2018. — P. 593–600.
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  4. Yakupov I., Buzdalov M. On asynchronous non-dominated sorting for steady-state multiobjective evolutionary algorithms // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2018. — P. 205–206.
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  5. Mironovich V., Buzdalov M., Vyatkin V. From fitness landscape analysis to designing evolutionary algorithms: The case study in automatic generation of function block applications // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2018. — P. 1902–1905.
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  6. Bulanova N., Buzdalov M. Better fixed-arity unbiased black-box algorithms // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2018. — P. 322–323.
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2017

Conference papers (English)

  1. Yakupov I., Buzdalov M. Improved Incremental Non-dominated Sorting for Steady-State Evolutionary Multiobjective Optimization // Proceedings of Genetic and Evolutionary Computation Conference. — 2017. — P. 649–656.
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  2. Buzdalov M., Doerr B. Runtime Analysis of the (1 + (λ,λ)) Genetic Algorithm on Random Satisfiable 3-CNF Formulas // Proceedings of Genetic and Evolutionary Computation Conference. — 2017. — P. 1343–1350.
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  3. Bulanova N., Buzdalov M. On Binary Unbiased Operators Returning Multiple Offspring // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2017. — P. 1395–1398.
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  4. Mironovich V., Buzdalov M. Evaluation of Heavy-tailed Mutation Operator on Maximum Flow Test Generation Problem // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2017. — P. 1423–1426.
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  5. Markina M., Buzdalov M. Hybridizing Non-dominated Sorting Algorithms: Divide-and-Conquer Meets Best Order Sort // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2017. — P. 153–154.
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  6. Mironovich V., Buzdalov M., Vyatkin V. Automatic Generation of Function Block Applications Using Evolutionary Algorithms: Initial Explorations // Proceedings of IEEE International Conference on Industrial Informatics (INDIN). — 2017. — P. 700–705.
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2016

Journal papers (English)

  1. Buzdalov M., Doerr B., Kever M. The Unrestricted Black-Box Complexity of Jump Functions // Evolutionary Computation. — 2016. — Vol. 24, no. 4. — P. 719–744.
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Conference papers (English)

  1. Vasin A., Buzdalov M. A Faster Algorithm for the Binary Epsilon Indicator Based on Orthant Minimum Search // Proceedings of Genetic and Evolutionary Computation Conference. — 2016. — P. 613–620.
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  2. Buzdalov M. An Algorithm for Computing Lower Bounds for Unrestricted Black-Box Complexities // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2016. — P. 147–148.
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  3. Nigmatullin N., Buzdalov M., Stankevich A. Efficient Removal of Points with Smallest Crowding Distance in Two-dimensional Incremental Non-dominated Sorting // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2016. — P. 1121–1128.
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  4. Bulanova N., Buzdalova A., Buzdalov M. Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local Search // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2016. — P. 5–6.
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  5. Polevaya T., Buzdalov M. Preserving Diversity in Auxiliary Objectives Provably Speeds Up Crossing Plateaus // Proceedings of IEEE Symposium Series on Computational Intelligence. — 2016. — Article No.: 7850145.
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  6. Buzdalova A., Petrova I., Buzdalov M. Runtime Analysis of Different Approaches to Select Conflicting Auxiliary Objectives in the Generalized OneMax Problem // Proceedings of IEEE Symposium Series on Computational Intelligence. — 2016. — Article No.: 7850140.
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  7. Antipov D., Buzdalov M., Korneev G. First Steps in Runtime Analysis of Worst-Case Execution Time Test Generation for the Dijkstra Algorithm using an Evolutionary Algorithm // Proceedings of International Conference on Soft Computing MENDEL. — 2016. — P. 43–48.
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  8. Mironovich V., Buzdalov M., Parfenov V. Comparative Study of Representations in the Maximum Flow Test Generation Problem // Proceedings of International Conference on Soft Computing MENDEL. — 2016. — P. 67–72.
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2015

Conference papers (English)

  1. Antipov D., Buzdalov M., Doerr B. Runtime Analysis of (1+1) Evolutionary Algorithm Controlled with Q-learning using Greedy Exploration Strategy on OneMax+ZeroMax Problem // Evolutionary Computation in Combinatorial Optimization. — 2015. — P. 160–172. — (Lecture Notes in Computer Science ; 9026).
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  2. Buzdalov M., Kever M., Doerr B. Upper and Lower Bounds on Unrestricted Black-Box Complexity of Jumpn,ℓ // Evolutionary Computation in Combinatorial Optimization. — 2015. — P. 209–221. — (Lecture Notes in Computer Science ; 9026).
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  3. Buzdalov M., Yakupov I., Stankevich A. Fast Implementation of the Steady-State NSGA-II Algorithm for Two Dimensions Based on Incremental Non-Dominated Sorting // Proceedings of Genetic and Evolutionary Computation Conference. — 2015. — P. 647–654.
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  4. Mironovich V., Buzdalov M. Hard Test Generation for Maximum Flow Algorithms with the Fast Crossover-Based Evolutionary Algorithm // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2015. — P. 1229–1232.
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  5. Buzdalov M., Parfenov V. Various Degrees of Steadiness in NSGA-II and Their Influence on the Quality of Results // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2015. — P. 749–750.
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  6. Buzdalov M., Shalyto A. Hard Test Generation for Augmenting Path Maximum Flow Algorithms using Genetic Algorithms: Revisited // Proceedings of IEEE Congress on Evolutionary Computation. — 2015. — P. 2121–2128.
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  7. Yakupov I., Buzdalov M. Incremental Non-Dominated Sorting with O(N) Insertion for the Two-Dimensional Case // Proceedings of IEEE Congress on Evolutionary Computation. — 2015. — P. 1853–1860.
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  8. Buzdalov M., Buzdalova A. Analysis of Q-Learning with Random Exploration for Selection of Auxiliary Objectives in Random Local Search // Proceedings of IEEE Congress on Evolutionary Computation. — 2015. — P. 1776–1783.
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  9. Buzdalov M., Buzdalova A. Can OneMax Help Optimizing LeadingOnes using the EA+RL Method? // Proceedings of IEEE Congress on Evolutionary Computation. — 2015. — P. 1762–1768.
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  10. Arkhipov V., Buzdalov M. An Asynchronous Implementation of the Limited Memory CMA-ES // Proceedings of International Conference on Machine Learning and Applications. — 2015. — P. 707–712.
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  11. Arkhipov V., Buzdalov M. An Asynchronous Implementation of the Limited Memory CMA-ES: First Results // Proceedings of International Conference on Soft Computing MENDEL. — 2015. — P. 37–40.
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2014

Conference papers (English)

  1. Buzdalov M., Shalyto A. A Provably Asymptotically Fast Version of the Generalized Jensen Algorithm for Non-Dominated Sorting // Parallel Problem Solving from Nature – PPSN XIII. — Springer, 2014. — P. 528–537. — (Lecture Notes in Computer Science ; 8672).
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  2. Petrova I., Buzdalova A., Buzdalov M. Selection of Extra Objectives using Reinforcement Learning in Non-Stationary Environment: Initial Explorations // Proceedings of 20th International Conference on Soft Computing MENDEL 2014. — Czech Republic, 2014. — P. 58–63.
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  3. Worst-Case Execution Time Test Generation using Genetic Algorithms with Automated Construction and Online Selection of Objectives / N. Kravtsov [et al.] // Proceedings of 20th International Conference on Soft Computing MENDEL 2014. — Czech Republic, 2014. — P. 111–116.
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  4. Mironovich V., Buzdalov M. Generation of Tests against a Greedy Algorithm for the Knapsack Problem using an Evolutionary Algorithm // Proceedings of 20th International Conference on Soft Computing MENDEL 2014. — Czech Republic, 2014. — P. 77–82.
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  5. Buzdalov M., Shalyto A. Worst-Case Execution Time Test Generation for Solutions of the Knapsack Problem using a Genetic Algorithm // Proceedings of 9th International Conference on Bio-inspired Computing: Theories and Applications. — 2014. — P. 1–10. — (Communications in Computer and Information Science ; 472).
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  6. Buzdalova A., Kononov V., Buzdalov M. Selecting Evolutionary Operators using Reinforcement Learning: Initial Explorations // Proceedings of Genetic and Evolutionary Computation Conference (Companion). — 2014. — P. 1033–1036.
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  7. Buzdalov M., Petrova I., Buzdalova A. NSGA-II Implementation Details May Influence Quality of Solutions for the Job-Shop Scheduling Problem // Proceedings of Genetic and Evolutionary Computation Conference (Companion). — 2014. — P. 1445–1446.
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  8. Buzdalov M., Buzdalova A. OneMax Helps Optimizing XdivK: Theoretical Runtime Analysis for RLS and EA+RL // Proceedings of Genetic and Evolutionary Computation Conference Companion. — ACM, 2014. — P. 201–202.
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  9. Lukin M., Buzdalov M., Shalyto A. Formal Verification of 800 Genetically Constructed Automata Programs: A Case Study // Proceedings of Haifa Verification Conference. — 2014. — P. 165–170. — (Lecture Notes in Computer Science ; 8855).
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  10. Buzdalova A., Buzdalov M. A New Algorithm for Adaptive Online Selection of Auxiliary Objectives // Proceedings of International Conference on Machine Learning and Applications. — 2014. — P. 584–587.
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  11. Petrova I., Buzdalova A., Buzdalov M. Improved Selection of Auxiliary Objectives using Reinforcement Learning in Non-Stationary Environment // Proceedings of International Conference on Machine Learning and Applications. — 2014. — P. 580–583.
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  12. Buzdalov M., Knyazev S., Porozov Y. Protein Conformation Motion Modeling using sep-CMA-ES // Proceedings of International Conference on Machine Learning and Applications. — 2014. — P. 35–40.
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  13. Buzdalov M. A Switch-and-Restart Algorithm with Exponential Restart Strategy for Objective Selection and its Runtime Analysis // Proceedings of the International Conference on Machine Learning and Applications. — IEEE Computer Society, 2014. — P. 141–146.
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2013

Conference papers (English)

  1. Buzdalov M., Buzdalova A. Adaptive Selection of Helper-Objectives for Test Case Generation // 2013 IEEE Congress on Evolutionary Computation. Vol. 1. — 2013. — P. 2245–2250.
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  2. Buzdalova A., Buzdalov M., Parfenov V. Generation of Tests for Programming Challenge Tasks Using Helper-Objectives // 5th International Symposium on Search-Based Software Engineering. — Springer, 2013. — P. 300–305. — (Lecture Notes in Computer Science ; 8084).
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  3. Buzdalov M., Tsarev F. An Evolutionary Approach to Hard Test Case Generation for Shortest Common Superstring Problem // Proceedings of BRICS Countries Congress on Computation Intelligence. — 2013. — P. 81–85.
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  4. Buzdalov M., Buzdalova A., Petrova I. Generation of Tests for Programming Challenge Tasks Using Multi-Objective Optimization // Proceedings of Genetic and Evolutionary Computation Conference Companion. — ACM, 2013. — P. 1655–1658.
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  5. Buzdalov M., Buzdalova A., Shalyto A. A First Step towards the Runtime Analysis of Evolutionary Algorithm Adjusted with Reinforcement Learning // Proceedings of the International Conference on Machine Learning and Applications. Vol. 1. — IEEE Computer Society, 2013. — P. 203–208.
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  6. Petrova I., Buzdalova A., Buzdalov M. Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem // Proceedings of the International Conference on Machine Learning and Applications. Vol. 2. — IEEE Computer Society, 2013. — P. 374–377.
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  7. Arkhipov V., Buzdalov M., Shalyto A. Worst-Case Execution Time Test Generation for Augmenting Path Maximum Flow Algorithms using Genetic Algorithms // Proceedings of the International Conference on Machine Learning and Applications. Vol. 2. — IEEE Computer Society, 2013. — P. 108–111.
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2012

Conference papers (English)

  1. Afanasyeva A., Buzdalov M. Optimization with Auxiliary Criteria using Evolutionary Algorithms and Reinforcement Learning // Proceedings of 18th International Conference on Soft Computing MENDEL 2012. — Brno, Czech Republic, 2012. — P. 58–63.
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  2. Buzdalov M., Sokolov A. Evolving EFSMs Solving a Path-Planning Problem by Genetic Programming // Proceedings of Genetic and Evolutionary Computation Conference Companion. — 2012. — P. 591–594.
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  3. Buzdalova A., Buzdalov M. Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning // Proceedings of the International Conference on Machine Learning and Applications. Vol. 1. — 2012. — P. 150–155.
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  4. Buzdalova A., Buzdalov M. Adaptive Selection of Helper-Objectives with Reinforcement Learning // Proceedings of the International Conference on Machine Learning and Applications. Vol. 2. — IEEE Computer Society, 2012. — P. 66–67.
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  5. Buzdalov M. Generation of Tests for Programming Challenge Tasks on Graph Theory using Evolution Strategy // Proceedings of the International Conference on Machine Learning and Applications. Vol. 2. — IEEE Computer Society, 2012. — P. 62–65.
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Journal papers (Russian)

  1. Буздалова 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.
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  2. Афанасьева 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.
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2011

Conference papers (English)

  1. Buzdalov M. Generation of Tests for Programming Challenge Tasks Using Evolution Algorithms // Proceedings of Genetic and Evolutionary Computation Conference Companion. — ACM, 2011. — P. 763–766.
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  2. Afanasyeva A., Buzdalov M. Choosing Best Fitness Function with Reinforcement Learning // Proceedings of the Tenth International Conference on Machine Learning and Applications. Vol. 2. — Honolulu, HI, USA : IEEE Computer Society, 2011. — P. 354–357.
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Journal papers (Russian)

  1. Буздалов М. В. Генерация тестов для олимпиадных задач по программированию с использованием генетических алгоритмов // Научно-технический вестник СПбГУ ИТМО. — 2011. — 2(72). — С. 72–77.
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  2. Буздалов М. В. Генерация тестов для олимпиадных задач по теории графов с использованием эволюционных стратегий // Научно-технический вестник СПбГУ ИТМО. — 2011. — 6(76). — С. 123–127.
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