Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Practical genetic algorithms
Fundamentals of Digital Signal Processing with Cdrom
Fundamentals of Digital Signal Processing with Cdrom
Digital Signal Processing: A Practical Approach
Digital Signal Processing: A Practical Approach
MWSCAS '98 Proceedings of the 1998 Midwest Symposium on Systems and Circuits
Genetic subgradient method for solving location-allocation problems
Applied Soft Computing
A GA-based UWB pulse waveform design method
Digital Signal Processing
FPGA-based system for frequency detection of the main periodic component in time series information
Digital Signal Processing
A hierarchical evolutionary algorithm for automatic medical image segmentation
Expert Systems with Applications: An International Journal
Feasibility of applying genetic algorithms in space-time block coded multiuser detection systems
Digital Signal Processing
A high-speed, programmable, CSD coefficient FIR filter
IEEE Transactions on Consumer Electronics
Digital filter synthesis based on an algorithm to generate all minimal signed digit representations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Genetic algorithm with a hybrid select mechanism for fractal image compression
Digital Signal Processing
Fixed-point digital IIR filter design using two-stage ensemble evolutionary algorithm
Applied Soft Computing
Natural Computing: an international journal
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In this paper, the genetic algorithm (GA) based on Canonic Signed Digit (CSD) code was used to find the optimum design of a finite impulse response digital filter (FIR). By using the characteristics of the CSD structure, the circuit was able to be simplified and also the calculation speed was raised to increase the hardware's efficiency. However, CSD structure cannot be guaranteed by a general GA after the evolution of chromosomes. Thus in this research an algorithm was proposed which the CSD structure can be maintained. A CSD coded GA was used to the evolution of chromosome to reduce the time wasted by trials and errors during the evolution and then to accelerate the training speed. In this paper, a new hybrid code for the filter coefficients was proposed to improve the precision of the coefficient of FIR. An example is shown in this paper to verify the efficiency of the proposed algorithm.