Parallel optimal statistical design method based on genetic algorithm

K. Y. Wu; Y. Shen; R. M M Chen; A. Wu

doi:10.1109/ISCAS.1996.542022

Parallel optimal statistical design method based on genetic algorithm

K. Y. Wu, Y. Shen, R. M M Chen, A. Wu

City University of Hong Kong

Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal

Abstract

Genetic Algorithms (GA), together with a boundary sampling strategy has been identified as a novel approach for optimal statistical design to achieve better performance and higher yield at a minimum cost. Due to the reduced number of circuit simulations, the proposed combination can provide a satisfactory model representation at improved computation speed for the selection of the response surface model function. In this paper, a number of possible approaches for parallelizing the GA operations is identified, and studied. The parallel GA was implemented on a parallel machine constructed from a cluster of networked workstations.

Original language	English
Pages (from-to)	477-480
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	4
DOIs	https://doi.org/10.1109/ISCAS.1996.542022
Publication status	Published - 1996
Event	1996 IEEE International Symposium on Circuits and Systems (ISCAS 96) - Atlanta, United States Duration: 12 May 1996 → 15 May 1996

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title = "Parallel optimal statistical design method based on genetic algorithm",

abstract = "Genetic Algorithms (GA), together with a boundary sampling strategy has been identified as a novel approach for optimal statistical design to achieve better performance and higher yield at a minimum cost. Due to the reduced number of circuit simulations, the proposed combination can provide a satisfactory model representation at improved computation speed for the selection of the response surface model function. In this paper, a number of possible approaches for parallelizing the GA operations is identified, and studied. The parallel GA was implemented on a parallel machine constructed from a cluster of networked workstations.",

author = "Wu, {K. Y.} and Y. Shen and Chen, {R. M M} and A. Wu",

year = "1996",

doi = "10.1109/ISCAS.1996.542022",

language = "English",

volume = "4",

pages = "477--480",

journal = "Proceedings - IEEE International Symposium on Circuits and Systems",

issn = "0271-4310",

publisher = "IEEE",

note = "1996 IEEE International Symposium on Circuits and Systems (ISCAS 96) ; Conference date: 12-05-1996 Through 15-05-1996",

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TY - JOUR

T1 - Parallel optimal statistical design method based on genetic algorithm

AU - Wu, K. Y.

AU - Shen, Y.

AU - Chen, R. M M

AU - Wu, A.

PY - 1996

Y1 - 1996

N2 - Genetic Algorithms (GA), together with a boundary sampling strategy has been identified as a novel approach for optimal statistical design to achieve better performance and higher yield at a minimum cost. Due to the reduced number of circuit simulations, the proposed combination can provide a satisfactory model representation at improved computation speed for the selection of the response surface model function. In this paper, a number of possible approaches for parallelizing the GA operations is identified, and studied. The parallel GA was implemented on a parallel machine constructed from a cluster of networked workstations.

AB - Genetic Algorithms (GA), together with a boundary sampling strategy has been identified as a novel approach for optimal statistical design to achieve better performance and higher yield at a minimum cost. Due to the reduced number of circuit simulations, the proposed combination can provide a satisfactory model representation at improved computation speed for the selection of the response surface model function. In this paper, a number of possible approaches for parallelizing the GA operations is identified, and studied. The parallel GA was implemented on a parallel machine constructed from a cluster of networked workstations.

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0029723903&origin=recordpage

U2 - 10.1109/ISCAS.1996.542022

DO - 10.1109/ISCAS.1996.542022

M3 - RGC 22 - Publication in policy or professional journal

SN - 0271-4310

VL - 4

SP - 477

EP - 480

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

T2 - 1996 IEEE International Symposium on Circuits and Systems (ISCAS 96)

Y2 - 12 May 1996 through 15 May 1996

ER -

Parallel optimal statistical design method based on genetic algorithm

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