A novel genetic algorithm with cell crossover for circuit design optimization

Zhiguo Bao, Takahiro Watanabe

研究成果: Conference contribution

19 引用 (Scopus)

抄録

Evolvable Hardware (EHW) is a new field about the use of Evolutionary Algorithms (EA) to synthesize a circuit. Genetic Algorithm (GA) is one of the typical EA. In traditional GA, the crossover is one-point crossover or two-point crossover. One-point crossover and two-point crossover change the genes of individuals too many in one time and they are not flexible, so it may lose some useful genes. In this paper, we propose the novel cell crossover. The cell crossover can change genes more flexibly and enhance more diversification to search spaces than one-point crossover and two-point crossover, so that we can find better solution. We propose optimal circuit design by using GA with cell crossover (GAcc), and with fitness function composed of circuit complexity, power and signal delay. Simulation results show GAcc is superior to traditional GA in point of the best elite fitness, the average value of fitness of correct circuits and the number of correct circuits. The best optimal circuit generated by GAcc is 27.9% better in evaluating value than that by GA with one-point crossover.

元の言語English
ホスト出版物のタイトルProceedings - IEEE International Symposium on Circuits and Systems
ページ2982-2985
ページ数4
DOI
出版物ステータスPublished - 2009
イベント2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei
継続期間: 2009 5 242009 5 27

Other

Other2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Taipei
期間09/5/2409/5/27

Fingerprint

Genetic algorithms
Networks (circuits)
Genes
Evolutionary algorithms
Design optimization
Hardware

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

これを引用

Bao, Z., & Watanabe, T. (2009). A novel genetic algorithm with cell crossover for circuit design optimization. : Proceedings - IEEE International Symposium on Circuits and Systems (pp. 2982-2985). [5118429] https://doi.org/10.1109/ISCAS.2009.5118429

A novel genetic algorithm with cell crossover for circuit design optimization. / Bao, Zhiguo; Watanabe, Takahiro.

Proceedings - IEEE International Symposium on Circuits and Systems. 2009. p. 2982-2985 5118429.

研究成果: Conference contribution

Bao, Z & Watanabe, T 2009, A novel genetic algorithm with cell crossover for circuit design optimization. : Proceedings - IEEE International Symposium on Circuits and Systems., 5118429, pp. 2982-2985, 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009, Taipei, 09/5/24. https://doi.org/10.1109/ISCAS.2009.5118429
Bao Z, Watanabe T. A novel genetic algorithm with cell crossover for circuit design optimization. : Proceedings - IEEE International Symposium on Circuits and Systems. 2009. p. 2982-2985. 5118429 https://doi.org/10.1109/ISCAS.2009.5118429
Bao, Zhiguo ; Watanabe, Takahiro. / A novel genetic algorithm with cell crossover for circuit design optimization. Proceedings - IEEE International Symposium on Circuits and Systems. 2009. pp. 2982-2985
@inproceedings{be44cc00776b44aa8ba450e649b315f1,
title = "A novel genetic algorithm with cell crossover for circuit design optimization",
abstract = "Evolvable Hardware (EHW) is a new field about the use of Evolutionary Algorithms (EA) to synthesize a circuit. Genetic Algorithm (GA) is one of the typical EA. In traditional GA, the crossover is one-point crossover or two-point crossover. One-point crossover and two-point crossover change the genes of individuals too many in one time and they are not flexible, so it may lose some useful genes. In this paper, we propose the novel cell crossover. The cell crossover can change genes more flexibly and enhance more diversification to search spaces than one-point crossover and two-point crossover, so that we can find better solution. We propose optimal circuit design by using GA with cell crossover (GAcc), and with fitness function composed of circuit complexity, power and signal delay. Simulation results show GAcc is superior to traditional GA in point of the best elite fitness, the average value of fitness of correct circuits and the number of correct circuits. The best optimal circuit generated by GAcc is 27.9{\%} better in evaluating value than that by GA with one-point crossover.",
author = "Zhiguo Bao and Takahiro Watanabe",
year = "2009",
doi = "10.1109/ISCAS.2009.5118429",
language = "English",
isbn = "9781424438280",
pages = "2982--2985",
booktitle = "Proceedings - IEEE International Symposium on Circuits and Systems",

}

TY - GEN

T1 - A novel genetic algorithm with cell crossover for circuit design optimization

AU - Bao, Zhiguo

AU - Watanabe, Takahiro

PY - 2009

Y1 - 2009

N2 - Evolvable Hardware (EHW) is a new field about the use of Evolutionary Algorithms (EA) to synthesize a circuit. Genetic Algorithm (GA) is one of the typical EA. In traditional GA, the crossover is one-point crossover or two-point crossover. One-point crossover and two-point crossover change the genes of individuals too many in one time and they are not flexible, so it may lose some useful genes. In this paper, we propose the novel cell crossover. The cell crossover can change genes more flexibly and enhance more diversification to search spaces than one-point crossover and two-point crossover, so that we can find better solution. We propose optimal circuit design by using GA with cell crossover (GAcc), and with fitness function composed of circuit complexity, power and signal delay. Simulation results show GAcc is superior to traditional GA in point of the best elite fitness, the average value of fitness of correct circuits and the number of correct circuits. The best optimal circuit generated by GAcc is 27.9% better in evaluating value than that by GA with one-point crossover.

AB - Evolvable Hardware (EHW) is a new field about the use of Evolutionary Algorithms (EA) to synthesize a circuit. Genetic Algorithm (GA) is one of the typical EA. In traditional GA, the crossover is one-point crossover or two-point crossover. One-point crossover and two-point crossover change the genes of individuals too many in one time and they are not flexible, so it may lose some useful genes. In this paper, we propose the novel cell crossover. The cell crossover can change genes more flexibly and enhance more diversification to search spaces than one-point crossover and two-point crossover, so that we can find better solution. We propose optimal circuit design by using GA with cell crossover (GAcc), and with fitness function composed of circuit complexity, power and signal delay. Simulation results show GAcc is superior to traditional GA in point of the best elite fitness, the average value of fitness of correct circuits and the number of correct circuits. The best optimal circuit generated by GAcc is 27.9% better in evaluating value than that by GA with one-point crossover.

UR - http://www.scopus.com/inward/record.url?scp=70350169319&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70350169319&partnerID=8YFLogxK

U2 - 10.1109/ISCAS.2009.5118429

DO - 10.1109/ISCAS.2009.5118429

M3 - Conference contribution

AN - SCOPUS:70350169319

SN - 9781424438280

SP - 2982

EP - 2985

BT - Proceedings - IEEE International Symposium on Circuits and Systems

ER -