Design of narrowband frequency sampling FIR filters using Z-window
Signal Processing - Special section: Distributed source coding
A New Self-adaptative Crossover Operator for Real-Coded Evolutionary Algorithms
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Digital IIR filter design using multi-objective optimization evolutionary algorithm
Applied Soft Computing
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
FIR frequency sampling filters design based on adaptive particle swarm optimization algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Two-stage ensemble memetic algorithm: Function optimization and digital IIR filter design
Information Sciences: an International Journal
Fixed-point digital IIR filter design using two-stage ensemble evolutionary algorithm
Applied Soft Computing
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A new method of designing recursive and nonrecursive frequency sampling filters is presented. We investigate the use of a hybrid real-coded genetic algorithm (GA) for optimising transition sample values to give the maximum stopband attenuation. A modification allows the coefficient wordlength to be optimized concurrently, thereby reducing the overall number of design steps and simplifying the design process. The technique is able to consistently optimize filters with up to six transition samples. Designing digital filters is a complex process involving optimization at several discrete design steps. The techniques presented could form the basis for integrating several of the optimizations. Investigations into increasing this integration by using a binary-coded GA to optimize nonlinear phase, quantized coefficient FIR filters are introduced, with an analysis of the difficulty of the problem from a GA perspective