Coevolutionary Method for Gene Selection and Parameter Optimization in Microarray Data Analysis

  • Yingjie Hu
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5864)


This paper presents a coevolutionary algorithm based personalized modeling (cEAP) for gene selection and parameter optimization for microarray data analysis. The classification of different tumor types is a main application in microarray data analysis and of great importance in cancer diagnosis and drug discovery. However, the construction of an effective classifier involves gene selection and parameter optimization, which poses a big challenge to bioinformatics research. We have explored cEAP algorithm on four benchmark microarray datasets for gene selection and parameter optimization. The experimental results have shown that cEAP is an efficient method for co-evolving complex optimization problems in microarray data analysis.


Coevolutionary genetic algorithm optimization and gene selection 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yingjie Hu
    • 1
  • Nikola Kasabov
    • 1
  1. 1.The Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyNew Zealand

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