Efficient GA approach combined with Taguchi method for mixed constrained circuit design

Yiwen Su, Zhiguo Bao, Fangfang Wang, Takahiro Watanabe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper proposes a new circuit design optimization method where Genetic Algorithm (GA) with parameterized uniform crossover (GApuc) is combined with Taguchi method. The purposed are (a) using Taguchi method to search for optimal fitness value and (b) evaluating the power and signal delay of logic blocks in circuit design to get an optimum circuit in complexity, power and signal delay. The present study enhances the previous results by providing a much more detailed examination of mixed constrained circuit design. Experimental results show that our proposed approach can produce a good circuit in both fitness function and CPU time.

Original languageEnglish
Title of host publicationProceedings - 2011 International Conference on Computational Science and Its Applications, ICCSA 2011
Pages290-293
Number of pages4
DOIs
Publication statusPublished - 2011
Event11th International Conference on Computational Science and Its Applications, ICCSA 2011 - Santander
Duration: 2011 Jun 202011 Jun 23

Other

Other11th International Conference on Computational Science and Its Applications, ICCSA 2011
CitySantander
Period11/6/2011/6/23

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Keywords

  • Circuit optimization
  • Design of experiments
  • Evolutionary Algorithm (EA)
  • Evolutionary circuit design
  • Genetic Algorithm (GA)
  • Orthogonal array
  • Response surface methodology
  • Taguchi methodology

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications

Cite this

Su, Y., Bao, Z., Wang, F., & Watanabe, T. (2011). Efficient GA approach combined with Taguchi method for mixed constrained circuit design. In Proceedings - 2011 International Conference on Computational Science and Its Applications, ICCSA 2011 (pp. 290-293). [5959590] https://doi.org/10.1109/ICCSA.2011.44