Abstract
The multi-population agent genetic algorithm (MPAGAFS) for feature selection is proposed. The double chain-like agent structure is introduced to enhance the diversity of population. The structure can help to construct multi-population agent GA, thereby realizing parallel searching for an optimal feature subset. The experimental results show that the MPAGAFS can not only be used for serial feature selection but also parallel feature selection with satisfying precision. © 2009 Springer Berlin Heidelberg.
Cite
CITATION STYLE
Li, Y., & Zeng, X. (2009). Feature selection method with multi-population agent genetic algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5507 LNCS, pp. 493–500). https://doi.org/10.1007/978-3-642-03040-6_60
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