A Novel Evolutionary Multitasking Feature Selection Approach for Genomic Data Classification

Abstract

Microarray-generated genomic data has recently sparked a wave of bioinformatics and data mining research. However, such data presents significant challenges for further analysis due to its high dimensionality and small sample sizes. Feature selection is a standard approach to address this issue, as it can enhance classification performance while reducing dimensionality. This paper introduces an Improved Gray Wolf Optimization-based Evolutionary Multitasking (EMT-IGWO) feature selection approach tailored for high-dimensional classification. It adopts multi-population co-evolving searching modes that can be regarded as a typical feature selection task via a specific information-sharing mechanism. Within the proposed multitasking framework, both population diversity and global searching capabilities of EMT-IGWO are improved. Moreover, several enhancements are incorporated into the two searching modes to help stagnant individuals escape from local optima with higher probabilities. Computational results show that EMT-IGWO outperforms other compared algorithms in effectiveness and efficiency evaluated across eight public gene expression datasets.