Ibrahim, Hadeel TariqMazher, Wamidh JalilUçan, Osman NuriBayat, Oğuz2021-05-152021-05-1520190941-06431433-3058https://doi.org/10.1007/s00521-018-3414-4https://hdl.handle.net/20.500.12939/308Al-Rayes, Hadeel/0000-0001-9749-4024; mazher, wamidh jalil/0000-0003-2092-3745Support vector machines (SVM) are one of the important techniques used to solve classifications problems efficiently. Setting support vector machine kernel factors affects the classification performance. Feature selection is a powerful technique to solve dimensionality problems. In this paper, we optimized SVM factors and chose features using a Grasshopper Optimization Algorithm (GOA). GOA is a new heuristic optimization algorithm inspired by grasshoppers searching for food. It approved its ability to solve real-world problems with anonymous search space. We applied the proposed GOA + SVM approach on biomedical data sets for Iraqi cancer patients in 2010-2012 and for University of California Irvine data sets.eninfo:eu-repo/semantics/closedAccessSupport Vector MachinesFeature SelectionGrasshopper OptimizerArticle10.1007/s00521-018-3414-43110596559742-s2.0-85043386208Q1WOS:000491131700019Q2