Application of a breeder genetic algorithm for filter optimization

In this work we are optimizing an adaptive finite impulse response filter applied to the problem of system identification. We are proposing a breeder genetic algorithm (BGA) for performing the parametric search in highly multimoldal landscapes since in this kind of filters there exits epistiasis. The results of BGA were compared to the traditional genetic algorithm, and we found that the BGA was clearly superior (in accuracy) in most of the cases. We used the statistical least mean squared for validating the results. We suggest to hybridized both methods for real world applications.