As mentioned before, we start performing the experiments with traditional methods (gradient-based methods defined in the Neurolabs Library). Table 2 shows a comparison between the traditional methods defined in the Neurolabs Library and GA. The experiments presented in this table were performed using a water demand time series of the flow meter p10012. From Table 2, we observe that GA presents a better performance in the validation set than the other three methods.

Table 2

Comparison between traditional methods versus GA

MethodMSE trainingMSE validation
Gradient descent backpropagation 0.42883913 0.42747427 
BFGS 0.0000124 0.00150682 
Conjugate gradient algorithm 0.00159929 0.00292422 
GAs 0.001002 0.0010163 
MethodMSE trainingMSE validation
Gradient descent backpropagation 0.42883913 0.42747427 
BFGS 0.0000124 0.00150682 
Conjugate gradient algorithm 0.00159929 0.00292422 
GAs 0.001002 0.0010163 

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