A novel genetic algorithm with different structure selection for circuit design optimization

Zhiguo Bao, Takahiro Watanabe

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

1 Citation (Scopus)

Abstract

Evolvable Hardware (EHW) is a new research field about the use of Genetic Algorithm (GA) to synthesize an optimal circuit. In traditional GA, the tournament selection for crossover and mutation is based on fitness of individuals. It can make convergence easily, but maybe lose some useful genes. In selection, besides fitness, we consider the different structure from individuals comparing to elite one. First, select some individuals with more different structures, then cross over and mutate these ones to generate new individuals. By this way, GA can increase diversification to searching spaces, so that it can find better solution. We propose optimal circuit design by using GA with different structure selection (GAdss) and with fitness function composed of circuit complexity, power and signal delay. Its effectiveness is shown by simulations. From the results, we can see that the best elite fitness, the average value of fitness of correct circuits and the number of correct circuits of GAdss are better than GA. The best case of optimal circuits generated by GAdss is 8.1% better in evaluating value than the circuit of GA.

Original languageEnglish
Title of host publicationProceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09
Pages218-222
Number of pages5
Publication statusPublished - 2009 Dec 1
Event14th International Symposium on Artificial Life and Robotics, AROB 14th'09 - Oita, Japan
Duration: 2008 Feb 52009 Feb 7

Publication series

NameProceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09

Conference

Conference14th International Symposium on Artificial Life and Robotics, AROB 14th'09
CountryJapan
CityOita
Period08/2/509/2/7

Keywords

  • Circuit optimization
  • EA
  • EHW
  • GA

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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