A multi-objective strategy in genetic algorithms for gene selection of gene expression data

Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Muhammad Faiz Misman, Michifumi Yoshioka

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm.

Original languageEnglish
Pages (from-to)410-413
Number of pages4
JournalArtificial Life and Robotics
Issue number2
Publication statusPublished - 2009
Externally publishedYes


  • Cancer classification
  • Gene expression data
  • Gene selection
  • Genetic algorithm
  • Multi-objective

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • Artificial Intelligence


Dive into the research topics of 'A multi-objective strategy in genetic algorithms for gene selection of gene expression data'. Together they form a unique fingerprint.

Cite this