• DocumentCode
    3250682
  • Title

    Enzyme genetic programming

  • Author

    Lones, Michael A. ; Tyrrell, Andy M.

  • Author_Institution
    Dept. of Electron., York Univ., UK
  • Volume
    2
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1183
  • Abstract
    The work reported in the paper follows from the hypothesis that better performance in certain domains of artificial evolution can be achieved by adhering more closely to the features that make natural evolution effective within biological systems. An important issue in evolutionary computation is the choice of solution representation. Genetic programming, whilst borrowing from biology in the evolutionary axis of behaviour, remains firmly rooted in the artificial domain with its use of a parse tree representation. Following concerns that this approach does not encourage solution evolvability, the paper presents an alternative method modelled upon representations used by biology. Early results are encouraging, demonstrating that the method is competitive when applied to problems in the area of combinatorial circuit design
  • Keywords
    circuit CAD; combinational circuits; genetic algorithms; grammars; proteins; trees (mathematics); alternative method; artificial domain; artificial evolution; biological systems; biology; combinatorial circuit design; enzyme genetic programming; evolutionary axis of behaviour; evolutionary computation; natural evolution; parse tree representation; solution evolvability; solution representation; Artificial intelligence; Biochemistry; Biological system modeling; Biological systems; Circuit synthesis; Computational biology; Evolution (biology); Evolutionary computation; Genetic mutations; Genetic programming;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
  • Conference_Location
    Seoul
  • Print_ISBN
    0-7803-6657-3
  • Type

    conf

  • DOI
    10.1109/CEC.2001.934325
  • Filename
    934325