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A solution of synthesized optimal control problem for interaction of robots by evolutionary computations
Diveev A., Sofronova E., Shmalko E.
system in the form of oriented calculating graph by evolutionary algorithm. For solution of the second
Approaches to numerical solution of optimal control problem using evolutionary computations
Diveev A., Sofronova E., Konstantinov S.
in the quality criterion. Implemented methods were evolutionary algorithms and a random parameter search
Evolutionary Algorithms for Optimal Control Problem of Mobile Robots Group Interaction
Konstantinov S., Diveev A.
Evolutionary Algorithms for Optimal Control Problem of Mobile Robots Group Interaction
Evolutionary Algorithms for Optimal Control Problem of Mobile Robots Group Interaction
Konstantinov S., Diveev A.
. In a computational experiment the performance of hybrid evolutionary algorithm for solving this task
Synthesis and Computer Study of Population Dynamics Controlled Models Using Methods of Numerical Optimization, Stochastization and Machine Learning
Demidova Anastasia V., Druzhinina Olga V., Masina Olga N., Petrov Alexey A.
and numerical study of the models is carried out. Using computational experiments, the results of an analytical
Evolutional methods for creating artificial intelligence of robotic technical systems
Konyrbaev N.B., Ibadulla S.I., Diveev A.I.
This paper considers evolutionary methods of symbolic regression for the creation of artificial
Comparative research of random search algorithms and evolutionary algorithms for the optimal control problem of the mobile robot
Konstantinov S.V., Diveev A.I., Balandina G.I., Baryshnikov A.A.
An experimental comparison of evolutionary algorithms and random search algorithms for the optimal
Modified SOMA for Optimal Control Problem
DIveev A., Sofronova E., Shmalko E.
evolutionary computation
Dynamic and Information Properties of Intelligent Control Systems
Pupkov K.A., Brovarskaya Y.K.
evolutionary computations
Comparative Analysis of Evolutionary Algorithms for the Problem of Parametric Optimization of PID Controllers
Konstantinov S.V., Baryshnikov A.A.
Six modern and promising evolutionary algorithms are described: genetic algorithm, differential

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