A virus evolutionary genetic algorithm using local selection

Ping Fu; Jia Qing Qiao; Hong Tao Yin

doi:10.1109/ICICIC.2007.107

A virus evolutionary genetic algorithm using local selection

Ping Fu^*
, Jia Qing Qiao
, Hong Tao Yin

^*Corresponding author for this work

School of Electronics and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Virus evolutionary genetic algorithm (VEGA) is an improved genetic algorithm (GA) that can prevent premature convergence, which introduces an additional virus population and two infection operators to GA. In this paper, a VEGA using the local selection scheme is proposed. Local selection can effectively maintain the diversity of the host population in VEGA and then improved the algorithm's performance. Survivals of local selection are with high fitness, and this indirectly leads to the elimination of the virus individuals that contain ineffective schemata, which partly suppresses the large iteration time due to the transduction operator of VEGA. The experimental result shows the effectiveness of the proposed algorithm.

Original language	English
Title of host publication	Second International Conference on Innovative Computing, Information and Control, ICICIC 2007
DOIs	https://doi.org/10.1109/ICICIC.2007.107
State	Published - 2008
Event	2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007 - Kumamoto, Japan Duration: 5 Sep 2007 → 7 Sep 2007

Publication series

Name	Second International Conference on Innovative Computing, Information and Control, ICICIC 2007

Conference

Conference	2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007
Country/Territory	Japan
City	Kumamoto
Period	5/09/07 → 7/09/07

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10.1109/ICICIC.2007.107

Cite this

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title = "A virus evolutionary genetic algorithm using local selection",

abstract = "Virus evolutionary genetic algorithm (VEGA) is an improved genetic algorithm (GA) that can prevent premature convergence, which introduces an additional virus population and two infection operators to GA. In this paper, a VEGA using the local selection scheme is proposed. Local selection can effectively maintain the diversity of the host population in VEGA and then improved the algorithm's performance. Survivals of local selection are with high fitness, and this indirectly leads to the elimination of the virus individuals that contain ineffective schemata, which partly suppresses the large iteration time due to the transduction operator of VEGA. The experimental result shows the effectiveness of the proposed algorithm.",

author = "Ping Fu and Qiao, \{Jia Qing\} and Yin, \{Hong Tao\}",

year = "2008",

doi = "10.1109/ICICIC.2007.107",

language = "英语",

isbn = "0769528821",

series = "Second International Conference on Innovative Computing, Information and Control, ICICIC 2007",

booktitle = "Second International Conference on Innovative Computing, Information and Control, ICICIC 2007",

note = "2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007 ; Conference date: 05-09-2007 Through 07-09-2007",

}

Fu, P , Qiao, JQ & Yin, HT 2008, A virus evolutionary genetic algorithm using local selection. in Second International Conference on Innovative Computing, Information and Control, ICICIC 2007., 4428224, Second International Conference on Innovative Computing, Information and Control, ICICIC 2007, 2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007, Kumamoto, Japan, 5/09/07. https://doi.org/10.1109/ICICIC.2007.107

A virus evolutionary genetic algorithm using local selection. / Fu, Ping ; Qiao, Jia Qing ; Yin, Hong Tao.
Second International Conference on Innovative Computing, Information and Control, ICICIC 2007. 2008. 4428224 (Second International Conference on Innovative Computing, Information and Control, ICICIC 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Qiao, Jia Qing

AU - Yin, Hong Tao

PY - 2008

Y1 - 2008

N2 - Virus evolutionary genetic algorithm (VEGA) is an improved genetic algorithm (GA) that can prevent premature convergence, which introduces an additional virus population and two infection operators to GA. In this paper, a VEGA using the local selection scheme is proposed. Local selection can effectively maintain the diversity of the host population in VEGA and then improved the algorithm's performance. Survivals of local selection are with high fitness, and this indirectly leads to the elimination of the virus individuals that contain ineffective schemata, which partly suppresses the large iteration time due to the transduction operator of VEGA. The experimental result shows the effectiveness of the proposed algorithm.

AB - Virus evolutionary genetic algorithm (VEGA) is an improved genetic algorithm (GA) that can prevent premature convergence, which introduces an additional virus population and two infection operators to GA. In this paper, a VEGA using the local selection scheme is proposed. Local selection can effectively maintain the diversity of the host population in VEGA and then improved the algorithm's performance. Survivals of local selection are with high fitness, and this indirectly leads to the elimination of the virus individuals that contain ineffective schemata, which partly suppresses the large iteration time due to the transduction operator of VEGA. The experimental result shows the effectiveness of the proposed algorithm.

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DO - 10.1109/ICICIC.2007.107

M3 - 会议稿件

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SN - 0769528821

SN - 9780769528823

T3 - Second International Conference on Innovative Computing, Information and Control, ICICIC 2007

BT - Second International Conference on Innovative Computing, Information and Control, ICICIC 2007

T2 - 2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007

Y2 - 5 September 2007 through 7 September 2007

ER -

A virus evolutionary genetic algorithm using local selection

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