• DocumentCode
    3438483
  • Title

    Adaptive integration using evolutionary strategies

  • Author

    de Doncker, E. ; Gupta, Ajay ; Greenwood, Garrison

  • Author_Institution
    Dept. of Comput. Sci., Western Michigan Univ., Kalamazoo, MI, USA
  • fYear
    1996
  • fDate
    19-22 Dec 1996
  • Firstpage
    94
  • Lastpage
    99
  • Abstract
    Multivariate integration problems arising in the real world often lead to computationally intensive numerical solutions. If the singularities and/or peaks in the integrand are not known a priori, the use of adaptive methods is recommended. The efficiency of adaptive methods depends heavily on focusing on the sub-regions that contain singularities or peaks in the integrands. In this paper, we present techniques based on evolutionary strategies that can be used to identify such subregions. Adaptive integration algorithms and evolutionary strategies can be parallelized easily and hence combining the parallel implementations of these result in efficient parallel adaptive integration algorithms
  • Keywords
    genetic algorithms; integration; parallel algorithms; adaptive methods; evolutionary strategies; integration; multivariate integration; parallel adaptive integration algorithms; parallelized; Computer science; Error correction; Finite element methods; Integral equations; Workstations;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    High Performance Computing, 1996. Proceedings. 3rd International Conference on
  • Conference_Location
    Trivandrum
  • Print_ISBN
    0-8186-7557-8
  • Type

    conf

  • DOI
    10.1109/HIPC.1996.565803
  • Filename
    565803