Design And Analysis Of A Low Pass FIR Filter Using Genetic Algorithm
Using Mean Square Error Approach
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Genetic Algorithm (GA) is a computational evolutionary search technique which is based on Darwin's theory of natural selection i.e. "best fittest will survive". The advantage of Evolutionary algorithm is it searches a population of points in parallel, not a single point. GA has been used for the designing & analysis of Finite Impulse Response (FIR) Low Pass Filter (LPF) by optimizes the error function i.e. Mean Square Error (MSE). In this the coefficients of the filter are treated as chromosomes which are optimized by the Genetic Algorithm to obtain a filter that would satisfy prescribed speci...
Genetic Algorithm (GA) is a computational evolutionary search technique which is based on Darwin's theory of natural selection i.e. "best fittest will survive". The advantage of Evolutionary algorithm is it searches a population of points in parallel, not a single point. GA has been used for the designing & analysis of Finite Impulse Response (FIR) Low Pass Filter (LPF) by optimizes the error function i.e. Mean Square Error (MSE). In this the coefficients of the filter are treated as chromosomes which are optimized by the Genetic Algorithm to obtain a filter that would satisfy prescribed specifications and also reduces the multipliers and adders of the FIR filter making the system cost effective. After designing of FIR low pass digital filter, it is also compared with other designing methods viz. Equiripple, Least Square, Window techniques and Parks-McClellan Algorithm; to verify the improved accuracy in terms of transition width and reduced side lobes.
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