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ORCIDVladimir Mironovich
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- affiliation: ITMO University, Saint Petersburg, Russia
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2020 – today
- 2022
[c10]Maxim Pikalov
, Vladimir Mironovich:
Parameter Tuning for the (1 + (λ , λ )) Genetic Algorithm Using Landscape Analysis and Machine Learning. EvoApplications 2022: 704-720- [c9]Egor Kurbatov, Vladimir Mironovich:
Evaluation of inverse selection operators on maximum flow test generation problem. GECCO Companion 2022: 61-62 - [c8]Aleksandr Smirnov, Vladimir Mironovich:
Towards landscape-aware parameter tuning for the (1 + (λ, λ)) genetic algorithm for permutations. GECCO Companion 2022: 2326-2329 - 2021
- [c7]Maxim Pikalov, Vladimir Mironovich:
Automated parameter choice with exploratory landscape analysis and machine learning. GECCO Companion 2021: 1982-1985
2010 – 2019
- 2019
- [c6]Vladimir Mironovich, Maxim Buzdalov, Valeriy Vyatkin:
Permutation Encoding for Automatic Reconstruction of Connections in Closed-Loop Control System using Evolutionary Algorithm. ETFA 2019: 1265-1268 - 2018
- [c5]Vladimir Mironovich, Maxim Buzdalov, Valeriy Vyatkin:
Automatic Plant-Controller Input/Output Matching using Evolutionary Algorithms. ETFA 2018: 1043-1046 - [c4]Vladimir Mironovich, Maxim Buzdalov, Valeriy Vyatkin:
From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications. GECCO (Companion) 2018: 1902-1905 - 2017
- [c3]Vladimir Mironovich, Maxim Buzdalov:
Evaluation of heavy-tailed mutation operator on maximum flow test generation problem. GECCO (Companion) 2017: 1423-1426 - [c2]Vladimir Mironovich, Maxim Buzdalov, Valeriy Vyatkin:
Automatic generation of function block applications using evolutionary algorithms: Initial explorations. INDIN 2017: 700-705 - 2015
- [c1]Vladimir Mironovich, Maxim Buzdalov:
Hard Test Generation for Maximum Flow Algorithms with the Fast Crossover-Based Evolutionary Algorithm. GECCO (Companion) 2015: 1229-1232
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