A hybrid feature selection method for biomarker identification

Identifying disease-specific biomarkers from gene microarray data is crucial for disease diagnosis. Genetic algorithms can effectively select important features through efficient search strategies. However, traditional genetic algorithms face challenges such as retaining too many features, slow convergence, and instability due to the high dimensionality and redundancy of the data, as well as multiple random factors in the algorithm. This paper proposes a hybrid algorithm combining Maximum Relevance Minimum Redundancy and a Population Clustering Binary Genetic Algorithm. The proposed method selects the most important biomarkers through an initialization strategy based on Opposition-Based Learning, clustering, and dynamic mutation. Experimental results demonstrate that the proposed method achieves higher classification accuracy with fewer features across six high-dimensional microarray datasets.