• DocumentCode
    2325842
  • Title

    Stack-based genetic programming

  • Author

    Perkis, Timothy

  • Author_Institution
    Antelope Eng., Albany, CA, USA
  • fYear
    1994
  • fDate
    27-29 Jun 1994
  • Firstpage
    148
  • Abstract
    Some recent work in the field of genetic programming (GP) has been concerned with finding optimum representations for evolvable and efficient computer programs. This paper describes a new GP system in which target programs run on a stack-based virtual machine. The system is shown to have certain advantages in terms of efficiency and simplicity of implementation, and for certain problems, its effectiveness is shown to be comparable or superior to current methods
  • Keywords
    genetic algorithms; learning (artificial intelligence); optimisation; optimum representations; stack-based genetic programming; stack-based virtual machine; Assembly; Genetic engineering; Genetic programming; Protection; Shape; Tagging; Virtual machining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-1899-4
  • Type

    conf

  • DOI
    10.1109/ICEC.1994.350025
  • Filename
    350025
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2325842