An adaptive genetic algorithm for determining the optimum filter coefficients in a recursive adaptive filter is presented. The algorithm is appropriate for use in problems where the function to be optimized is nonunimodal or nonquadratic, such as the mean squared error surface in a recursive adaptive filter. The mechanisms of the algorithm are inspired by adaptive processes observed in nature. After an initial set of possible filters is randomly selected, each filter is mapped to a binary string representation. Selected bit strings are then transformed using the operations of crossover and mutation to build new generations of filters. The probability of selecting a particular bit string to modify and/or replicate for the next generation is inversely proportional to its estimated mean squared error value. Hence, the process not only examines filter coefficient values, but also retains the advances made in previous generations.
NASA STI/Recon Technical Report N
- Pub Date:
- Adaptive Filters;
- Recursive Functions;
- Computerized Simulation;
- System Identification;
- Electronics and Electrical Engineering