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MicroRNAs (miRNAs) are small non-coding RNAs that have been shown to play important roles in gene regulation and various biological processes. The abnormal expression of some specific miRNAs often results in the development of cancer. In this article, we have utilized a multiobjective genetic algorithm-based feature selection algorithm wrapped with support vector machine (SVM) classifier for selecting promising miRNAs having differential expression in benign and malignant tissue samples. Subsequently, the non-dominated sets of promising miRNAs are aggregated into a single most promising miRNA subset. Finally, the Signal-to-Noise Ratio (SNR) statistic has been applied on the obtained miRNA subset for identifying potential miRNA markers that distinguish the two classes (benign and malignant) of tissue samples. The performance has been demonstrated on four real-life miRNA expression datasets for different SVM kernel functions and the identified miRNA markers are reported.