Multi-population genetic algorithm for feature selection

Authors:
Huming Zhu;Licheng Jiao;Jin Pan
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaan'xi, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaan'xi, China;Dept. of Computer and Information Eng, Xi'an communication institute, Xi'an, Shaan'xi, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

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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.