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
Link To Document :
بازگشت