The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, um Algoritmo Genético (AG) orientado a hardware foi proposto com o objetivo de economizar recursos de hardware e reduzir o tempo de execução do GAP. Com base no modelo de estado estacionário entre o modelo de geração contínua, o AG proposto utilizou seleção de torneio modificada, bem como condição especial de sobrevivência, substituída sempre que a aptidão da prole for melhor que a do pai com pior aptidão. O algoritmo proposto apresenta mais de 30% na velocidade de convergência em relação ao algoritmo convencional. Finalmente, empregando o protocolo eficiente de paralelização de pipeline e handshaking no GAP proposto, acima de 30% da velocidade de computação pode ser alcançada em GA baseado em sobrevivência, que executa um milhão de cruzamentos por segundo (1 MHz), quando a velocidade do dispositivo e o tamanho do aplicação são levados em conta no protótipo. Seria usado para processamento de alta velocidade, como processador central de hardware evolutivo, controle de robô e muitos problemas de otimização.
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Jinjung KIM, Yunho CHOI, Chongho LEE, Duckjin CHUNG, "Implementation of a High-Performance Genetic Algorithm Processor for Hardware Optimization" in IEICE TRANSACTIONS on Electronics,
vol. E85-C, no. 1, pp. 195-203, January 2002, doi: .
Abstract: In this paper, a hardware-oriented Genetic Algorithm (GA) was proposed in order to save the hardware resources and to reduce the execution time of GAP. Based on steady-state model among continuous generation model, the proposed GA used modified tournament selection, as well as special survival condition, with replaced whenever the offspring's fitness is better than worse-fit parent's. The proposed algorithm shows more than 30% in convergence speed over the conventional algorithm. Finally, by employing the efficient pipeline parallelization and handshaking protocol in proposed GAP, above 30% of the computation speed-up can be achieved over survival-based GA which runs one million crossovers per second (1 MHz), when device speed and size of application are taken into account on prototype. It would be used for high speed processing such of central processor of evolvable hardware, robot control and many optimization problems.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/e85-c_1_195/_p
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@ARTICLE{e85-c_1_195,
author={Jinjung KIM, Yunho CHOI, Chongho LEE, Duckjin CHUNG, },
journal={IEICE TRANSACTIONS on Electronics},
title={Implementation of a High-Performance Genetic Algorithm Processor for Hardware Optimization},
year={2002},
volume={E85-C},
number={1},
pages={195-203},
abstract={In this paper, a hardware-oriented Genetic Algorithm (GA) was proposed in order to save the hardware resources and to reduce the execution time of GAP. Based on steady-state model among continuous generation model, the proposed GA used modified tournament selection, as well as special survival condition, with replaced whenever the offspring's fitness is better than worse-fit parent's. The proposed algorithm shows more than 30% in convergence speed over the conventional algorithm. Finally, by employing the efficient pipeline parallelization and handshaking protocol in proposed GAP, above 30% of the computation speed-up can be achieved over survival-based GA which runs one million crossovers per second (1 MHz), when device speed and size of application are taken into account on prototype. It would be used for high speed processing such of central processor of evolvable hardware, robot control and many optimization problems.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - Implementation of a High-Performance Genetic Algorithm Processor for Hardware Optimization
T2 - IEICE TRANSACTIONS on Electronics
SP - 195
EP - 203
AU - Jinjung KIM
AU - Yunho CHOI
AU - Chongho LEE
AU - Duckjin CHUNG
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Electronics
SN -
VL - E85-C
IS - 1
JA - IEICE TRANSACTIONS on Electronics
Y1 - January 2002
AB - In this paper, a hardware-oriented Genetic Algorithm (GA) was proposed in order to save the hardware resources and to reduce the execution time of GAP. Based on steady-state model among continuous generation model, the proposed GA used modified tournament selection, as well as special survival condition, with replaced whenever the offspring's fitness is better than worse-fit parent's. The proposed algorithm shows more than 30% in convergence speed over the conventional algorithm. Finally, by employing the efficient pipeline parallelization and handshaking protocol in proposed GAP, above 30% of the computation speed-up can be achieved over survival-based GA which runs one million crossovers per second (1 MHz), when device speed and size of application are taken into account on prototype. It would be used for high speed processing such of central processor of evolvable hardware, robot control and many optimization problems.
ER -