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.
Comparison between traditional methods versus GA
Method . | MSE training . | MSE 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 |
Method . | MSE training . | MSE 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 |