Two wrapper feature selection approaches using salp swarm algorithm are proposed.

Three binary versions of Grasshopper Optimization Algorithm (BGOA) are proposed • Wrapper-based feature selection techniques are proposed using the BGOA algorithms • The proposed algorithms are benchmarked on 18 standard UCI datasets • The results are compared with 10 algorithms • The results show the merits of the proposed algorithms and feature selection methods

Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, grasshopper optimization algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and

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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