Maxim Buzdalov

Maxim Buzdalov

email: buzdalov [at] rain [dot] ifmo [dot] ru

ITMO University
PhD
Associate Professor at Department of Computer Technologies

Research field: Theory of Evolutionary Computation, Algorithms and Data Structures, Search-Based Software Engineering, Computational Bioinformatics
Teaching: Computational Geometry (2012/2013, group 3528), Compiler Theory (2012/2013, group 3528), Genetic and Evolutionary Computation (2015/2016, group M423X)
Google Scholar page, DBLP

Publications

2016

Journal papers (English)

  1. Buzdalov M., Doerr B., Kever M. The Unrestricted Black-Box Complexity of Jump Functions // Evolutionary Computation. — 2016. — to appear.
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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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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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