Improving the Binary Fish School Search Algorithm for feature selection

Improving the Binary Fish School Search Algorithm for feature selection
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改进二元鱼群搜索算法以进行特征选择

DOI：
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发表时间：
2016
期刊：
Latin American Conference on Computational Intelligence
影响因子：
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通讯作者：
C. J. A. B. Filho
C. J. A. B. Filho
中科院分区：
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文献类型：
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作者：
Raphael F. Carneiro;C. J. A. B. Filho

文献摘要

被引文献

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鱼群搜索（FSS）算法有一个非常有用的参与机制，以避免陷入局部极小的简单反应代理。2014年，提出了FSS的二进制版本，并将其应用于特征选择。在本文中，我们提出了一些改进的二进制鱼群搜索算法（BFSS）。我们表明，这些修改的BFSS优于原来的BFSS。IBFSS在特征选择方面也优于其他著名的群体智能算法。
The Fish School Search (FSS) algorithm has a very useful engaging mechanism to avoid the simple reactive agents of being trapped into local minima. In 2014, a binary version of the FSS was proposed and applied for feature selection. In this paper we propose some improvements in the Binary Fish School Search algorithm (BFSS). We show that the BFSS with these modifications outperformed the original BFSS. The IBFSS also outperformed other well-known swarm intelligence algorithms for feature selection purposes.