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Title: A new genetic algorithm method based on statistical-based replacement for the training of multiplicative neuron model artificial neural networks
Authors: Erol, Egrioglu
Crina, Grosan
Eren, Bas
Issue Date: 2023
Publisher: Springer
Abstract: 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.
Description: CC BY
URI: https://link.springer.com/article/10.1007/s11227-022-04935-0
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7331
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