Computer Technology, Inf ormation Engineering |
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Learning effective connectivity network structure based on parallel searching of double firefly populations |
Zi-long JI( ),Jun-zhong JI*( ) |
Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing 100124, China |
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Abstract A new method to learn brain effective connectivity (EC) networks by parallel searching of double firefly populations in order to identify high-quality brain EC network from functional magnetic resonance imaging (fMRI) data. The double population optimization was used to learn brain EC networks. The initial population was divided into an elite population and a common population. Then the brain EC networks were gradually constructed through the directional movements of the elite population and the random movements of the common population. The population sizes of the elite population and the common population were dynamically adjusted in the process of network constructions, and the information exchange between the two populations was realized by using a population migration operation. An adaptive updating mechanism based on a diversity measure was used to dynamically update the two populations after a certain number of evolution iterations. The experimental results on many simulated datasets verify that the new algorithm has obvious advantages on the whole performance compared with other algorithms, and shows the potential practicability of the new algorithm on real datasets.
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Received: 22 January 2019
Published: 05 April 2020
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Corresponding Authors:
Jun-zhong JI
E-mail: 403796237@qq.com;jjz01@bjut.edu.cn
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基于双萤火虫种群并行搜索的脑效应连接网络学习方法
为了从功能磁共振成像(fMRI)数据中识别到高质量的脑效应连接网络,提出双萤火虫种群并行搜索的脑效应连接网络学习方法. 利用2个萤火虫种群寻优完成脑效应连接网络的学习,将萤火虫种群划分为精英种群和普通种群. 通过精英种群的定向移动和普通种群的随机移动,分别逐步构建脑效应连接网络;在网络构建过程中,利用种群迁移操作动态地调整精英种群和普通种群的种群规模,以实现两种群之间的信息交流. 每隔一定代数,使用基于多样性度量的种群自适应更新机制,动态地进行2个种群的更新. 在多组模拟数据上的实验结果表明,新算法与其他算法相比,在识别总体性能上具有明显优势. 在真实数据上的实验表明了算法的潜在实用性.
关键词:
脑网络,
脑效应连接网络,
贝叶斯网,
萤火虫算法,
双种群并行搜索
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