Digital IIR Filter Design with Fix-Point Representation Using Effective Evolutionary Local Search Enhanced Differential Evolution



Previously, the parameters of digital IIR filters were 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 floating-point representation, fixed-point representation may make the search space miss much useful gradient information and, therefore, raises new challenges. In this chapter, the universality of DE-based MA is improved by implementing more efficient evolutionary algorithms (EAs) as the local search techniques. The performance of the newly designed algorithm is experimentally verified in both function optimization tasks and digital IIR filter design problems.


Local Search Differential Evolution Global Search Memetic Algorithm Covariance Matrix Adaptation Evolution Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Alibaba GroupHangzhouChina
  2. 2.IBM Research-ChinaBeijingChina

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