Evolving signal processing algorithms by genetic programming

Ken C. Sharman, Anna I.Esparcia Alcazar, Y. Li

Research output: Contribution to journalConference articlepeer-review

30 Citations (Scopus)


We introduce a novel genetic programming (GP) technique to evolve both the structure and parameters of adaptive digital signal processing algorithms. This is accomplished by defining a set of node functions and terminals to implement the basic operations commonly used in a large class of DSP algorithms. In addition, we show how simulated annealing may be employed to assist the GP in optimizing the numerical parameters of expression trees. The concepts are illustrated by using GP to evolve high performance algorithms for detecting binary data sequences at the output of a noisy, non-linear communications channel.

Original languageEnglish
Pages (from-to)473-480
Number of pages8
JournalIEE Conference Publication
Issue number414
Publication statusPublished - 1 Jan 1995
EventProceedings of the 1st IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications GALESIA '95 - Sheffield, Engl
Duration: 12 Sep 199514 Sep 1995


  • genetic algorithms
  • digital signal processing
  • learning algorithms
  • communication channels (information theory)
  • mathematical operators
  • binary sequences


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