• DocumentCode
    2691576
  • Title

    Measuring complexity by measuring structure and organization

  • Author

    Hornby, Gregory S.

  • Author_Institution
    U.C. Santa Cruz, Moffett Field
  • fYear
    2007
  • fDate
    25-28 Sept. 2007
  • Firstpage
    2017
  • Lastpage
    2024
  • Abstract
    Necessary for furthering the development of more powerful evolutionary design systems, capable of scaling to evolving more sophisticated and complex artifacts, is the ability to meaningfully and objectively compare these systems by applying complexity measures to the artifacts they evolve. Previously we have proposed measures of modularity, reuse and hierarchy (MR&H), here we compare these measures to ones from the fields of complexity, systems engineering and computer programming. In addition, we propose several ways of combining the MR&H measures into a single measure of structure and organization. We compare all of these measures empirically as well as on three sample objects and find that the best measures of complexity are two of the proposed measures of structure and organization.
  • Keywords
    computational complexity; evolutionary computation; evolutionary computation; evolutionary design systems; measuring complexity; measuring organization; measuring structure; Biology computing; Cells (biology); Computational biology; Design engineering; Embryo; Programming; Scalability; Size measurement; Systems engineering and theory; Tree graphs;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4244-1339-3
  • Electronic_ISBN
    978-1-4244-1340-9
  • Type

    conf

  • DOI
    10.1109/CEC.2007.4424721
  • Filename
    4424721