Background/ introduction: Support Vector Machine (SVM) is considered to be one of the most powerful learning algorithms and is used for a wide range of real world applications. The efficiency of SVM algorithm and its performance mainly depends on the kernel type and its parameters. Furthermore, the feature subset selection that is used to train the SVM model is another important factor that has a major influence on it classification accuracy. The feature subset selection is a very important step in machine learning, specially when dealing with high dimensional data sets. Most of the previous researches handled these important factors separately. Methods: In this paper, we propose a hybrid approach based on the Grasshopper Optimisation Algorithm (GOA), which is a recent algorithm inspired by the biological behaviour shown in swarms of grasshoppers. The goal of the proposed approach is to optimise the parameters of the SVM model, and locate the best features subset simultaneously. Re-() I. Aljarah • A

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