Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • A. James Clark School of Engineering
    • Institute for Systems Research Technical Reports
    • View Item
    •   DRUM
    • A. James Clark School of Engineering
    • Institute for Systems Research Technical Reports
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A Population-Based Search from Genetic Algorithms through Thermodynamic Operation

    Thumbnail
    View/Open
    TR_94-78.pdf (1.094Mb)
    No. of downloads: 225

    Date
    1994
    Author
    Sun, Ray-Long
    Dayhoff, Judith E.
    Weigand, William A.
    Metadata
    Show full item record
    Abstract
    The guided random search techniques, genetic algorithms and simulated annealing, are very promising strategies, and both techniques are analogs from physical and biological systems. Through genetic algorithms, the simulation of evolution for the purposes of parameter optimization has generally demonstrated itself to be a robust and rapid optimization technique. The simulated annealing algorithm often finds high quality candidate solutions. Limitations, however, occur in performance because optimization may take large numbers of iterations or final parameter values may be found that there are not at global minimum (or maximum) points. In this paper we propose a population-based search algorithm that combines the approaches from genetic algorithms and simulated annealing. The combined approach, called GASA, maintains a population of individuals over a period of generations. In the GASA technique, simulated annealing is used in choices regarding a subset of individuals to undergo crossover and mutation. We show that the GASA technique performs superior to a genetic algorithm on the Bohachevsky function, an objective function with m any local minima. The methodology and the test results on function optimization are given and compared with classical genetic algorithms.
    URI
    http://hdl.handle.net/1903/5548
    Collections
    • Institute for Systems Research Technical Reports

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility