  In this paper we present a hybrid system composed by a neural network based
estimator system and genetic algorithms. It uses an unsupervised Hebbian
nonlinear neural algorithm to extract the principal components which, in turn,
are used by the MUSIC frequency estimator algorithm to extract the frequencies.
We generalize this method to avoid an interpolation preprocessing step and to
improve the performance by using a new stop criterion to avoid overfitting.
Furthermore, genetic algorithms are used to optimize the neural net weight
initialization. The experimental results are obtained comparing our methodology
with the others known in literature on a Cepheid star light curve.
