Optimizing Low-pass Digital Filter Parameters Using Standard Genetic Algorithm
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To optimize low-pass digital filter parameters, we implemented a standard genetic algorithm framework utilizing the minimum mean square error (MSE) method as the fitness function. This approach generates near-optimal transfer function coefficients through iterative selection, crossover, and mutation operations. The algorithm initializes a population of potential coefficient sets, evaluates their performance using MSE criteria against desired frequency response, and evolves solutions over multiple generations. Key implementation aspects include chromosome encoding of filter coefficients, tournament selection mechanisms, and adaptive mutation rates. This parameter optimization significantly improves filter performance in terms of stopband attenuation and passband ripple, thereby enhancing signal processing quality and accuracy in practical applications.
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