• DocumentCode
    2060730
  • Title

    GENIE: A Genetic Algorithm Model Based Integrated Simulation Framework for Design of Embedded Systems

  • Author

    Northern, James, III ; Ribeiro, Miguel

  • Author_Institution
    Prairie View A&M Univ., Prairie View
  • fYear
    2007
  • fDate
    20-22 April 2007
  • Firstpage
    223
  • Lastpage
    227
  • Abstract
    In this paper, GENIE, a model based adaptable framework that facilitates rapid, performance evaluation of embedded systems, by seamlessly integrating a genetic algorithm to fine tune design parameters. Genetic evolvable network for intelligent embedded systems, (GENIE) provides a formal paradigm for specification of structural and behavioral aspects of embedded systems, an integrated model-based approach, and unified software environment for system design and simulation. The simple genetic algorithm is an optimization method based on the evolutionary process. This method is capable of handling large search spaces. This paper provides an overview of GENIE, discusses the model integrated computing philosophy, and illustrates the high-level modeling concepts being developed in the GENIE project for embedded systems design and evaluation.
  • Keywords
    embedded systems; genetic algorithms; simulation; software engineering; GENIE; embedded systems; formal paradigm; genetic algorithm; genetic evolvable network; integrated simulation; optimization; Algorithm design and analysis; Computational modeling; Embedded software; Embedded system; Genetic algorithms; Intelligent networks; Intelligent structures; Intelligent systems; Optimization methods; Software systems; Computer architecture; embedded systems; genetic algoritms; optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Region 5 Technical Conference, 2007 IEEE
  • Conference_Location
    Fayetteville, AR
  • Print_ISBN
    978-1-4244-1280-8
  • Electronic_ISBN
    978-1-4244-1280-8
  • Type

    conf

  • DOI
    10.1109/TPSD.2007.4380385
  • Filename
    4380385