Abstract
This paper describes the application of a multi-population genetic algorithm to the selection of feature subsets for classification problems. The multi-population genetic algorithm based on the independent evolution of different subpopulations is to prevent premature convergence of each subpopulation by migration. Experimental results with UCI standard data sets show that multi-population genetic algorithm outperforms simple genetic algorithm.
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Zhu, H., Jiao, L., Pan, J. (2006). Multi-population Genetic Algorithm for Feature Selection. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_59
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DOI: https://doi.org/10.1007/11881223_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
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