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
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