S.V. Konstantinov
Peoples' Friendship University of Russia, Russian Academy of Sciences
Papers
8
Total Citations
114
H-Index
4
About
S.V. Konstantinov is a computational intelligence researcher whose work sits at the intersection of evolutionary computation, optimal control theory, and robotics. Specializing in the application of metaheuristic and machine learning methods to complex control problems, Konstantinov has made significant contributions to understanding how algorithms like genetic algorithms, differential evolution, particle swarm optimization, and symbolic regression can be leveraged to solve challenging optimal control tasks for mobile and wheeled robots. His most influential work, a 2018 study on the practical convergence of evolutionary algorithms for wheeled robot control (55 citations), provided a rigorous comparative benchmark that has guided subsequent researchers in algorithm selection. His 2019 follow-up (27 citations) extended this analysis by contrasting evolutionary and random search strategies, offering valuable experimental insights into real-world applicability. A notable methodological innovation is his development of binary variational genetic programming for control system synthesis, which improves upon classical genetic programming by narrowing the search space and accelerating convergence. More recently, Konstantinov has explored symbolic regression as a novel framework for deriving analytical control laws and has embraced reinforcement learning for robotics applications. Collectively accumulating over 110 citations, his body of work represents a thorough and evolving research program advancing intelligent, computationally derived solutions for autonomous robot control.
Research Focus
Key Achievements
Top Papers
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- 5Solution of the optimal control problem by symbolic regression method3 citations · 2021
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- 8Reinforcement Learning for Solving Control Problems in Robotics2 citations · 2023