An adaptive immune genetic algorithm for edge detection

Ying Li; Bendu Bai; Yanning Zhang

doi:10.1007/978-3-540-74205-0_61

An adaptive immune genetic algorithm for edge detection

Ying Li
, Bendu Bai
, Yanning Zhang

School of Computer Science

Northwestern Polytechnical University Xian

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

8 Scopus citations

Abstract

An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

Original language	English
Title of host publication	Advanced Intelligent Computing Theories and Applications
Subtitle of host publication	With Aspects of Artificial Intelligence - Third International Conference on Intelligent Computing, ICIC 2007, Proceedings
Publisher	Springer Verlag
Pages	565-571
Number of pages	7
ISBN (Print)	9783540742012
DOIs	https://doi.org/10.1007/978-3-540-74205-0_61
State	Published - 2007
Event	3rd International Conference on Intelligent Computing, ICIC 2007 - Qingdao, China Duration: 21 Aug 2007 → 24 Aug 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4682 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Intelligent Computing, ICIC 2007
Country/Territory	China
City	Qingdao
Period	21/08/07 → 24/08/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-540-74205-0_61

Cite this

Li, Y., Bai, B., & Zhang, Y. (2007). An adaptive immune genetic algorithm for edge detection. In Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - Third International Conference on Intelligent Computing, ICIC 2007, Proceedings (pp. 565-571). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4682 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-74205-0_61

Li, Ying ; Bai, Bendu ; Zhang, Yanning. / An adaptive immune genetic algorithm for edge detection. Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - Third International Conference on Intelligent Computing, ICIC 2007, Proceedings. Springer Verlag, 2007. pp. 565-571 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1828efaf76324410b0c3251426ee4ba3,

title = "An adaptive immune genetic algorithm for edge detection",

abstract = "An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.",

author = "Ying Li and Bendu Bai and Yanning Zhang",

year = "2007",

doi = "10.1007/978-3-540-74205-0\_61",

language = "英语",

isbn = "9783540742012",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "565--571",

booktitle = "Advanced Intelligent Computing Theories and Applications",

note = "3rd International Conference on Intelligent Computing, ICIC 2007 ; Conference date: 21-08-2007 Through 24-08-2007",

}

Li, Y, Bai, B & Zhang, Y 2007, An adaptive immune genetic algorithm for edge detection. in Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - Third International Conference on Intelligent Computing, ICIC 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4682 LNAI, Springer Verlag, pp. 565-571, 3rd International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, 21/08/07. https://doi.org/10.1007/978-3-540-74205-0_61

An adaptive immune genetic algorithm for edge detection. / Li, Ying; Bai, Bendu; Zhang, Yanning.
Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - Third International Conference on Intelligent Computing, ICIC 2007, Proceedings. Springer Verlag, 2007. p. 565-571 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4682 LNAI).

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

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AB - An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

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Li Y, Bai B, Zhang Y. An adaptive immune genetic algorithm for edge detection. In Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - Third International Conference on Intelligent Computing, ICIC 2007, Proceedings. Springer Verlag. 2007. p. 565-571. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-74205-0_61

An adaptive immune genetic algorithm for edge detection

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