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Multi-population Genetic Algorithm for Feature Selection

  • Huming Zhu
  • Licheng Jiao
  • Jin Pan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)

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.

Keywords

Genetic Algorithm Feature Selection Feature Subset Premature Convergence Neighbor Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huming Zhu
    • 1
  • Licheng Jiao
    • 1
  • Jin Pan
    • 2
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’anChina
  2. 2.Dept. of Computer and Information EngXi’an communication instituteXi’anChina

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