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Intelligent Particle Swarm Optimization Using Q-Learning
Alvares P.A.
genetic algorithm
Novel GA-Based DNN Architecture for Identifying the Failure Mode with High Accuracy and Analyzing Its Effects on the System
Rezaeian N., Gurina R., Saltykova O.A., Hezla L., Nohurov M., Reza Kashyzadeh K.
architecture for all of them. To overcome this problem, a Genetic Algorithm (GA) was employed to find the best
Binary variational genetic programming for the problem of synthesis of control system
Diveev A.I., Balandina G.I., Konstantinov S.V.
genetic programming at crossover, reduces the search area and speeds up search algorithm by using small
Combinations of the Greedy Heuristic Method for Clustering Problems and Local Search Algorithms
Kazakovtsev, Lev, Antamoshkin, Alexander
In this paper, we investigate application of various options of algorithms with greedy
Control synthesis for traffic simulation in the urban road network
Alnovani G.H.A., Diveev A.I., Pupkov K.A., Sofronova E.A.
optimization. To solve the problem the genetic algorithm is used. The example is given. © 2011 IFAC.
Some methods of solving the NP-difficult problem of optimal schedule for the university
Diveev A.I., Bobr O.V., Kazaryan D.E., Hussein O.
method, genetic algorithm. The advantages and disadvantages of each of them are highlighted
Генетический алгоритм для оптимизации проектирования синхронного двигателя с инкорпорированными магнитами
Ле, Нго Фыонг
Genetic algorithm in optimization design of interior permanent magnet synchronous motor
A GIS-based multi-objective evolutionary algorithm for landslide susceptibility mapping
Razavi-Termeh S.V., Hatamiafkoueieh J., Sadeghi-Niaraki A., Choi S.-M., Al-Kindi K.M.
modeling in Khalkhal town, Iran. Two algorithms, namely the non-dominated sorting genetic algorithm II
Synthesis of fractional-order elements using the RC-EDP approach
Koton J., Kubanek D., Ushakov P.A., Maksimov K.
-order one. For the EFI synthesis, a genetic algorithm based tool has been developed that based
Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints
Amiri N., Farrahi G.H., Kashyzadeh K.R., Chizari M.
on the UT results by utilizing Artificial Neural Network (ANN) simulation. Next, Genetic Algorithm (GA

Страница 12 из 1628

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