Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images

Qingli Li; Jingao Liu; Yiting Wang; Chunni Dai

doi:10.1109/BMEI.2010.5639650

Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images

Qingli Li^*
, Jingao Liu
, Yiting Wang
, Chunni Dai

^*Corresponding author for this work

School of Communication and Electronic Engineering

East China Normal University
Guangxi University for Nationalities

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

2 Scopus citations

Abstract

Hyperspectral data cube usually includes hundreds of high-correlated single band images. It is necessary to reduce the dimensionality of hyperspectral images to facilitate the studies and analysis. This paper presents an adaptive non-dominated sorting genetic algorithm to process the wavelength combination of molecular hyperspectral images. In this algorithm, dynamic reproduction probabilities are employed to regulate the selection pressure. To evaluate the performance of this new algorithm on the combination optimization, the simulation results are compared with those of a non-dominated sorting genetic algorithm without adaptation and of a single-objective genetic algorithm having the same adaptive mechanism. The comparison revealed its better performance in the wavelength selection of molecular hyperspectral data of rat retinal sections.

Original language	English
Title of host publication	Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010
Publisher	IEEE Computer Society
Pages	82-85
Number of pages	4
ISBN (Print)	9781424464968
DOIs	https://doi.org/10.1109/BMEI.2010.5639650
State	Published - 2010

Publication series

Name	Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010
Volume	1

Keywords

Genetic algorithm
Hyperspectral imaging
Non-dominated sorting
Wavelength selection

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10.1109/BMEI.2010.5639650

Cite this

Li, Q., Liu, J., Wang, Y., & Dai, C. (2010). Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images. In Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010 (pp. 82-85). Article 5639650 (Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/BMEI.2010.5639650

Li, Qingli ; Liu, Jingao ; Wang, Yiting et al. / Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images. Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010. IEEE Computer Society, 2010. pp. 82-85 (Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010).

@inproceedings{6ae685d6ff714bef97c4759dc290ef3a,

title = "Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images",

abstract = "Hyperspectral data cube usually includes hundreds of high-correlated single band images. It is necessary to reduce the dimensionality of hyperspectral images to facilitate the studies and analysis. This paper presents an adaptive non-dominated sorting genetic algorithm to process the wavelength combination of molecular hyperspectral images. In this algorithm, dynamic reproduction probabilities are employed to regulate the selection pressure. To evaluate the performance of this new algorithm on the combination optimization, the simulation results are compared with those of a non-dominated sorting genetic algorithm without adaptation and of a single-objective genetic algorithm having the same adaptive mechanism. The comparison revealed its better performance in the wavelength selection of molecular hyperspectral data of rat retinal sections.",

keywords = "Genetic algorithm, Hyperspectral imaging, Non-dominated sorting, Wavelength selection",

author = "Qingli Li and Jingao Liu and Yiting Wang and Chunni Dai",

year = "2010",

doi = "10.1109/BMEI.2010.5639650",

language = "英语",

isbn = "9781424464968",

series = "Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010",

publisher = "IEEE Computer Society",

pages = "82--85",

booktitle = "Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010",

address = "美国",

}

Li, Q, Liu, J, Wang, Y & Dai, C 2010, Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images. in Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010., 5639650, Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010, vol. 1, IEEE Computer Society, pp. 82-85. https://doi.org/10.1109/BMEI.2010.5639650

Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images. / Li, Qingli; Liu, Jingao; Wang, Yiting et al.
Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010. IEEE Computer Society, 2010. p. 82-85 5639650 (Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010; Vol. 1).

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

TY - GEN

T1 - Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images

AU - Li, Qingli

AU - Liu, Jingao

AU - Wang, Yiting

AU - Dai, Chunni

PY - 2010

Y1 - 2010

N2 - Hyperspectral data cube usually includes hundreds of high-correlated single band images. It is necessary to reduce the dimensionality of hyperspectral images to facilitate the studies and analysis. This paper presents an adaptive non-dominated sorting genetic algorithm to process the wavelength combination of molecular hyperspectral images. In this algorithm, dynamic reproduction probabilities are employed to regulate the selection pressure. To evaluate the performance of this new algorithm on the combination optimization, the simulation results are compared with those of a non-dominated sorting genetic algorithm without adaptation and of a single-objective genetic algorithm having the same adaptive mechanism. The comparison revealed its better performance in the wavelength selection of molecular hyperspectral data of rat retinal sections.

AB - Hyperspectral data cube usually includes hundreds of high-correlated single band images. It is necessary to reduce the dimensionality of hyperspectral images to facilitate the studies and analysis. This paper presents an adaptive non-dominated sorting genetic algorithm to process the wavelength combination of molecular hyperspectral images. In this algorithm, dynamic reproduction probabilities are employed to regulate the selection pressure. To evaluate the performance of this new algorithm on the combination optimization, the simulation results are compared with those of a non-dominated sorting genetic algorithm without adaptation and of a single-objective genetic algorithm having the same adaptive mechanism. The comparison revealed its better performance in the wavelength selection of molecular hyperspectral data of rat retinal sections.

KW - Genetic algorithm

KW - Hyperspectral imaging

KW - Non-dominated sorting

KW - Wavelength selection

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DO - 10.1109/BMEI.2010.5639650

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SN - 9781424464968

T3 - Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010

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BT - Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010

PB - IEEE Computer Society

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Li Q, Liu J, Wang Y, Dai C. Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images. In Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010. IEEE Computer Society. 2010. p. 82-85. 5639650. (Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010). doi: 10.1109/BMEI.2010.5639650

Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images

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