One-Dimensional Digital Signal Processing
One-Dimensional Digital Signal Processing
Journal of Global Optimization
DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Self-adaptive multimethod search for global optimization in real-parameter spaces
IEEE Transactions on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Artificial immune algorithm for IIR filter design
Engineering Applications of Artificial Intelligence
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
A canonic-signed-digit coded genetic algorithm for designing finite impulse response digital filter
Digital Signal Processing
Information Sciences: an International Journal
Digital IIR filter design using multi-objective optimization evolutionary algorithm
Applied Soft Computing
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
A WISE method for designing IIR filters
IEEE Transactions on Signal Processing
Evolutionary Design of Digital Filters With Application to Subband Coding and Data Transmission
IEEE Transactions on Signal Processing
Design of Optimal Digital IIR Filters by Using an Improved Immune Algorithm
IEEE Transactions on Signal Processing
Automatic design of frequency sampling filters by hybrid geneticalgorithm techniques
IEEE Transactions on Signal Processing
Complexity reduction of digital filters using shift inclusive differential coefficients
IEEE Transactions on Signal Processing
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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The research on optimal design of infinite-impulse response (IIR) filter design based on various optimization techniques, including evolutionary algorithms (EAs), has gained much attention in recent years. Previously, the parameters of digital IIR filters are encoded with floating-point representations. It is known that a fixed-point representation can effectively save computational resources and is more convenient for direct realization on hardware. Inherently, compared with the floating-point representation, the fixed-point representation would make the search space miss much useful gradient information and therefore, surely rises new challenges for continuous EAs. In this paper, we first analyze the fitness landscape properties of optimal digital IIR filter design. Based on the fitness landscape investigation, a two-stage ensemble evolutionary algorithm (TEEA) is applied to digital IIR filter design with fixed-point representation. In order to fully evaluate the performance of TEEA, we experimentally compare it with five state-of-the-art EAs on four types of digital IIR filters with different settings. Based on the experimental results, we can conclude that TEEA has higher convergence speed, better exploration, and higher success rate. In order to benchmark TEEA further, we apply it to some more difficult problems with shorter word length or higher order. We can find that TEEA can provide satisfying performance on these hard tasks as well.