Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Engineering Applications of MATLAB 5.3 and SIMULINK 3
Engineering Applications of MATLAB 5.3 and SIMULINK 3
Digital Signal Processing: A Practical Approach
Digital Signal Processing: A Practical Approach
Digital Signal Processing Using MATLAB
Digital Signal Processing Using MATLAB
Fuzzy Recombination for the Breeder Genetic Algorithm
Proceedings of the 6th International Conference on Genetic Algorithms
EH '01 Proceedings of the The 3rd NASA/DoD Workshop on Evolvable Hardware
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
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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.