Vladimir Mironovich

I am a researcher in the International Laboratory "Computer Technologies", studying evolutionary computation.
My main research interests lie in the area of practical application of evolutionary algorithms in search-based software engineering, industrial automation and other fields. Additionally, I study methods for parameter tuning using machine learning and fitness landscape analysis, and try to investigate artificial intelligence in games.

Education

Publications

Kurbatov E., Mironovich V. Evaluation of Inverse Selection Operators on Maximum Flow Test Generation Problem // GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion - 2022, pp. (to appear)

Smirnov A., Mironovich V. Towards Landscape-aware Parameter Tuning for the (1+(λ,λ)) Genetic Algorithm for Permutations // GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion - 2022, (to appear)

Pikalov M., Mironovich V. Parameter Tuning for the (1 + (λ, λ)) Genetic Algorithm using Landscape Analysis and Machine Learning // Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224, pp. 704-720

Mironovich V., Buzdalov M., Vyatkin V. Evaluation of Permutation-based Mutation Operators on the Problem of Automatic Connection Matching in Closed-loop Control System // Studies in Fuzziness and Soft Computing - 2021, Vol. 403, pp. 41-51

Pikalov M., Mironovich V. Automated Parameter Choice with Exploratory Landscape Analysis and Machine Learning // GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion - 2021, pp. 1982-1985

Mironovich V. Generation of function block programs for industrial cyber-physical systems using evolutionary algorithms // PhD Thesis, ITMO University, date of defence: 23.12.2020

Mironovich V., Buzdalov M., Vyatkin V. Permutation Encoding for Automatic Reconstruction of Connections in Closed-Loop Control System using Evolutionary Algorithm // 24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019 - 2019, pp. 1265-1268

Mironovich V., Buzdalov M., Vyatkin V. From Fitness Landscape Analysis to Designing Evolutionary Algorithms: The Case Study in Automatic Generation of Function Block Applications // GECCO 2018 - Proceedings of the Genetic and Evolutionary Computation Conference - 2018, pp. 1902-1905

Mironovich V., Buzdalov M., Vyatkin V. Automatic Plant-Controller Input/Output Matching using Evolutionary Algorithms // Proceedings of the 23rd IEEE International Conference on Emerging Technologies and Factory Automation (EFTA) - 2018, pp. 1043-1046

Mironovich V., Buzdalov M. Evaluation of Heavy-tailed Mutation Operator on Maximum Flow Test Generation Problem // Proceedings of Genetic and Evolutionary Computation Companion – 2017, pp. 1423-1426

Mironovich V., Buzdalov M., Vyatkin V. Automatic Generation of Function Block Applications Using Evolutionary Algorithms: Initial Explorations // Proceedings of the 15th IEEE International Conference on Industrial Informatics – 2017, pp. 700-705

Mironovich V., Buzdalov M., Parfenov V. Comparative Study of Representations in the Maximum Flow Test Generation Problem // Proceedings of 22nd International Conference on Soft Computing MENDEL 2016 - 2016, pp. 67-72

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

Mironovich V, Buzdalov M. Generation of tests against a greedy algorithm for knapsack problem using an evolutionary algorithm // Proceedings of 20th International Conference on Soft Computing MENDEL 2014, pp. 77-82

Research projects

Teaching