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

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.

Authors and Affiliations

1. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, 599-8531, Japan

   Mohd Saberi Mohamad, Sigeru Omatu & Michifumi Yoshioka

2. Department of Software Engineering, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Johore, Malaysia

   Safaai Deris & Muhammad Faiz Misman

Corresponding authors

Correspondence to Mohd Saberi Mohamad or Sigeru Omatu.

This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008