The proposed method, which utilizes genetic algorithm to learn and merge multiple FCM models that are computed from subsets of the original data, is shown to be faster than other genetic algorithm-based designs while resulting in the FCMs of comparable quality.
In the year of 2005, a method which utilized genetic algorithms combined with neural network components was developed to determine gene interactions in temporal gene expression data sets [ 13].
For example, Desai et al. 1997 utilized genetic algorithms (GA) in an ANN forecasting model for individual credit, and the results showed that the GA-based techniques performed somewhat better than the other methods.
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.
This paper utilizes genetic algorithms (GAs) to convert a continuous-time uncertain system with input time delay to an equivalent discrete-time interval model.
Data used to train the artificial neural networks have been acquired by utilizing genetic algorithms.
An availability evaluation algorithm of the proposed system is suggested and the corresponding optimization problem is solved utilizing genetic algorithms.
In this paper, we present NeverStop, which utilizes genetic algorithms and fuzzy control methods in big data intelligent transportation systems.
Many optimization approaches utilizing genetic algorithms are proposed.
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.
