Browsing by Author Crina, Grosan
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In this study, we propose a new genetic algorithm that uses a statistical-based chromosome replacement strategy determined by the empirical distribution of the objective function values. The proposed genetic algorithm is further used in the training process of a multiplicative neuron model artificial neural network. The objective function value for the genetic algorithm is the root mean square error of the multiplicative neuron model artificial neural network prediction. This combination of methods is proposed for a particular type of problems, that is, time-series prediction. |
