Research of multi-population agent genetic algorithm for feature selection
Research of multi-population agent genetic algorithm for feature selection
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DOI：
10.1016/j.eswa.2009.03.032
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发表时间：
2009-11
期刊：
影响因子：
--
通讯作者：
Yongming Li;Sujuan Zhang;Xiaoping Zeng
中科院分区：
文献类型：
--
作者：
Yongming Li;Sujuan Zhang;Xiaoping Zeng
Search algorithm is an essential part of feature selection algorithm. In this paper, through constructing double chain-like agent structure and with improved genetic operators, the authors propose one novel agent genetic algorithm-multi-population agent genetic algorithm (MPAGAFS) for feature selection. The double chain-like agent structure is more like local environment in real world, the introduction of this structure is good to keep the diversity of population. Moreover, the structure can help to construct multi-population agent GA, thereby realizing parallel searching for optimal feature subset. In order to evaluate the performance of MPAGAFS, several groups of experiments are conducted. The experimental results show that the MPAGAFS cannot only be used for serial feature selection but also for parallel feature selection with satisfying precision and number of features.
