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Journal of Medical Systems
Published in: Journal of Medical Systems 6/2016

01-06-2016 | Systems-Level Quality Improvement

A note on the weight of inverse complexity in improved hybrid genetic algorithm

Authors: Siyuan Lu, Shuihua Wang, Yudong Zhang

Published in: Journal of Medical Systems | Issue 6/2016

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Excerpt

The recent paper [1] is very welcomed. Ahmad et al. (2013) [1] developed a novel intelligent medical disease diagnosis system based on computer vision and machine learning. They firstly extracted features from the images. Then, they proposed the improved hybrid genetic algorithm (IHGA) for feature selection and optimization of the weights and biases of the multilayer perceptron network (MLP). Finally, the trained MLP was used to classify the input images as healthy or pathological. The experiment results obtained over three standard medical disease diagnosis datasets (diabetes, heart, and cancer) suggested that the proposed IHGA was superior to previous works. …
Literature
1.
Ahmad, F., Isa, N. A., Hussain, Z., et al., Intelligent medical disease diagnosis using improved hybrid genetic algorithm--multilayer perceptron network. J. Med. Syst. 37(2):9934, 2013.CrossRefPubMed
2.
Zhang, Y., Yang, X., Cattani, C., et al., Tea category identification using a novel fractional fourier entropy and jaya algorithm. Entropy 18(3):77, 2016.CrossRef
3.
Ji, G., Yang, J., Wu, J., et al., Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization. Entropy 17(8):5711–5728, 2015.CrossRef
4.
Zhang, Y., Wang, S., Dong, Z., et al., Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Prog. Electromagn. Res. 152:41–58, 2015.CrossRef
Metadata
Title
A note on the weight of inverse complexity in improved hybrid genetic algorithm
Authors
Siyuan Lu
Shuihua Wang
Yudong Zhang
Publication date
01-06-2016
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 6/2016
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-016-0512-7

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