MiRNA subset selection for microarray data classification using grey wolf optimizer and evolutionary population dynamics

Micro-ribonucleic acids (miRNAs) are tiny noncoding ribonucleic acid (RNA) molecules that involve various biological processes for cancer advancements. Classification of cancer is a crucial problem in microarray technology because of the huge number of miRNAs and a limited number of samples. Therefore, to improve the classification of cancer between benign and malignant samples, this paper presents a hybrid filter-wrapper method that uses the multi-filter ensemble (MFE) approach as a filter method used in selecting the top-ranked miRNAs. For the wrapper approach, three different algorithms based on simultaneous utilization of the grey wolf optimizer (GWO), evolutionary population dynamics (EPD), and two selection mechanisms, are presented to enhance the GWO algorithm performance. The first approach involves the application of the EPD mechanisms to the GWO to eliminate the worst solutions of GWO and repositions them toward alpha, beta, or delta wolves to improve exploitation. So also, the GWO needed to randomly reinitialize its poor solutions near to the search space, using the EPD for the enhancement of the exploration process. In the second and third approaches, the Roulette wheel selection (RWS) and tournament selection (TS) are applied to offer a chance for low fitness individuals to be selected during the search process that maintains the diversity of the selected solutions. The proposed GWO_EPD algorithms are applied for miRNAs selection, and obtained results demonstrate that the EPD can improve the search capability of GWO in terms of avoiding local optima, exploration, and convergence rate. Moreover, the results of GWO_EPD are compared with twelve miRNAs subset selection approaches using classification accuracy and the number of selected miRNAs.