• DocumentCode
    2325193
  • Title

    Are deception and complexity conjugate variables in genetic learning?

  • Author

    Lopez, Luis R.

  • Author_Institution
    Comput. Resources Eng. Office, US Army Strategic Defense Command, Huntsville, AL, USA
  • fYear
    1994
  • fDate
    27-29 Jun 1994
  • Firstpage
    607
  • Abstract
    This work provides an analytic starting point to the question: How deceptive is a randomly selected problem? It is shown that the bounding complexity of a large trap function is inversely proportional to the probability of a genetic algorithm encountering a fully deceptive instance, independent of problem size for gene length greater than 10 4. This result brings up interesting insights about the relationship between deception and complexity
  • Keywords
    computational complexity; genetic algorithms; learning (artificial intelligence); bounding complexity; complexity; conjugate variables; deception; genetic algorithm; genetic learning; large trap function; Cost function; Equations; Frequency; Genetic algorithms; Orbital robotics; Piecewise linear techniques; Testing;
  • 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.1994.349990
  • Filename
    349990
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2325193