• DocumentCode
    1403922
  • Title

    Extremal optimization: heuristics via coevolutionary avalanches

  • Author

    Boettcher, Stefan

  • Author_Institution
    Dept. of Phys., Emory Univ., Atlanta, GA, USA
  • Volume
    2
  • Issue
    6
  • fYear
    2000
  • Firstpage
    75
  • Lastpage
    82
  • Abstract
    The extremal dynamics of the Bak-Sneppen model can be converted into an optimization algorithm called extremal optimization. Attractive features of the model include the following: it is straightforward to relate the sum of all fitnesses to the cost function of the system; in the self-organized critical state to which the system inevitably evolves, almost all species have a much better than random fitness; most species preserve a good fitness for long times unless they are connected to poorly adapted species, providing the system with a long memory; the system retains a potential for large, hill-climbing fluctuations at any stage; and the model accomplishes these features without any control parameters
  • Keywords
    evolutionary computation; optimisation; Bak-Sneppen model; coevolutionary avalanches; cost function; extremal dynamics; extremal optimization; heuristics; hill-climbing fluctuations; self-organized critical state; Circuit simulation; Computational modeling; Costs; Design optimization; Genetic algorithms; Intelligent networks; Process design; Simulated annealing; Space exploration; Stochastic processes;
  • fLanguage
    English
  • Journal_Title
    Computing in Science & Engineering
  • Publisher
    ieee
  • ISSN
    1521-9615
  • Type

    jour

  • DOI
    10.1109/5992.881710
  • Filename
    881710