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Data used to train the artificial neural networks have been acquired by utilizing genetic algorithms.
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Many optimization approaches utilizing genetic algorithms are proposed.
An availability evaluation algorithm of the proposed system is suggested and the corresponding optimization problem is solved utilizing genetic algorithms.
Wong et al. (2010) utilized genetic algorithms to optimize a layout for precast facilities on a construction site.
In this paper, we present NeverStop, which utilizes genetic algorithms and fuzzy control methods in big data intelligent transportation systems.
This method utilizes genetic algorithms and supervised single-layer artificial neural network in which one individual chromosome of the genetic algorithm can be used to represent a small number of genes from the dataset.
In [27] Ge and Wei utilized genetic algorithm (GA) as an optimization technique utilized by the master node to schedule the waiting tasks to computing nodes.
The approach utilizes genetic algorithm in conjunction with reservoir simulation for TDRM workflow to find reasonable multiple history-matched models.
The first model utilizes Genetic Algorithms (GA) while the second model utilizes Approximate Dynamic Programming (ADP).
The optimization process utilizes hybrid genetic algorithms, i.e., a combination of binary and real coded genetic algorithms.
Then we utilized Genetic Function Approximation (GFA) algorithm in DS 2.5 to find the appropriate molecular descriptors [ 41, 55].
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