Hardware Framework for Accelerating the Execution Speed of a Genetic Algorithm

Barry SHACKLEFORD; Etsuko OKUSHI; Mitsuhiro YASUDA; Hisao KOIZUMI; Katsuhiko SEO; Takashi IWAMOTO

Hardware Framework for Accelerating the Execution Speed of a Genetic Algorithm

Barry SHACKLEFORD, Etsuko OKUSHI, Mitsuhiro YASUDA, Hisao KOIZUMI, Katsuhiko SEO, Takashi IWAMOTO

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Genetic algorithms were introduced by Holland in 1975 as a method of solving difficult optimization problems by means of simulated evolution. A major drawback of genetic algorithms is their slowness when emulated by software on conventional computers. Described is an adaptation of the original genetic algorithm that is advantageous to hardware implementation along with the architecture of a hardware framework that performs the functions of population storage, selection, crossover, mutation, fitness evaluation, and survival determination. Programming of the framework is illustrated with the set coverage problem that exhibits a 6,000 speed-up over software emulation on a 100 MHz workstation.

Publication: IEICE TRANSACTIONS on Electronics Vol.E80-C No.7 pp.962-969

Publication Date: 1997/07/25

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Type of Manuscript: Special Section PAPER (Special Issue on New Concept Device and Novel Architecture LSIs)

Category: Multi Processors

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Barry SHACKLEFORD, Etsuko OKUSHI, Mitsuhiro YASUDA, Hisao KOIZUMI, Katsuhiko SEO, Takashi IWAMOTO, "Hardware Framework for Accelerating the Execution Speed of a Genetic Algorithm" in IEICE TRANSACTIONS on Electronics, vol. E80-C, no. 7, pp. 962-969, July 1997, doi: .
Abstract: Genetic algorithms were introduced by Holland in 1975 as a method of solving difficult optimization problems by means of simulated evolution. A major drawback of genetic algorithms is their slowness when emulated by software on conventional computers. Described is an adaptation of the original genetic algorithm that is advantageous to hardware implementation along with the architecture of a hardware framework that performs the functions of population storage, selection, crossover, mutation, fitness evaluation, and survival determination. Programming of the framework is illustrated with the set coverage problem that exhibits a 6,000 speed-up over software emulation on a 100 MHz workstation.
URL: https://globals.ieice.org/en_transactions/electronics/10.1587/e80-c_7_962/_p

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@ARTICLE{e80-c_7_962,
author={Barry SHACKLEFORD, Etsuko OKUSHI, Mitsuhiro YASUDA, Hisao KOIZUMI, Katsuhiko SEO, Takashi IWAMOTO, },
journal={IEICE TRANSACTIONS on Electronics},
title={Hardware Framework for Accelerating the Execution Speed of a Genetic Algorithm},
year={1997},
volume={E80-C},
number={7},
pages={962-969},
abstract={Genetic algorithms were introduced by Holland in 1975 as a method of solving difficult optimization problems by means of simulated evolution. A major drawback of genetic algorithms is their slowness when emulated by software on conventional computers. Described is an adaptation of the original genetic algorithm that is advantageous to hardware implementation along with the architecture of a hardware framework that performs the functions of population storage, selection, crossover, mutation, fitness evaluation, and survival determination. Programming of the framework is illustrated with the set coverage problem that exhibits a 6,000 speed-up over software emulation on a 100 MHz workstation.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - Hardware Framework for Accelerating the Execution Speed of a Genetic Algorithm
T2 - IEICE TRANSACTIONS on Electronics
SP - 962
EP - 969
AU - Barry SHACKLEFORD
AU - Etsuko OKUSHI
AU - Mitsuhiro YASUDA
AU - Hisao KOIZUMI
AU - Katsuhiko SEO
AU - Takashi IWAMOTO
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Electronics
SN -
VL - E80-C
IS - 7
JA - IEICE TRANSACTIONS on Electronics
Y1 - July 1997
AB - Genetic algorithms were introduced by Holland in 1975 as a method of solving difficult optimization problems by means of simulated evolution. A major drawback of genetic algorithms is their slowness when emulated by software on conventional computers. Described is an adaptation of the original genetic algorithm that is advantageous to hardware implementation along with the architecture of a hardware framework that performs the functions of population storage, selection, crossover, mutation, fitness evaluation, and survival determination. Programming of the framework is illustrated with the set coverage problem that exhibits a 6,000 speed-up over software emulation on a 100 MHz workstation.
ER -