Feature selection (FS) is a real-world problem that can be solved using optimization techniques. These techniques proposed solutions to make a predictive model, which minimizes the classifier's prediction errors by selecting informative or important features by discarding redundant, noisy, and irrelevant attributes in the original dataset. A new hybrid feature selection method is proposed using the Sine Cosine Algorithm (SCA) and Genetic Algorithm (GA), called SCAGA. Typically, optimization methods have two main search strategies; exploration of the search space and exploitation to determine the optimal solution. The proposed SCAGA resulted in better performance when balancing between exploitation and exploration strategies of the search space. The proposed SCAGA has also been evaluated using the following evaluation criteria: classification accuracy, worst fitness, mean fitness, best fitness, the average number of features, and standard deviation. Moreover, the maximum accuracy of a classification and the minimal features were obtained in the results. The results were also compared with a basic Sine Cosine Algorithm (SCA) and other related approaches published in literature such as Ant Lion Optimization and Particle Swarm Optimization. The comparison showed that the obtained results from the SCAGA method were the best overall the tested datasets from the UCI machine learning repository.

功能选择（FS）是可以使用优化技术解决的实际问题。这些技术提出了创建预测模型的解决方案，该模型通过丢弃原始数据集中的冗余，嘈杂和无关属性来选择信息性或重要特征，从而将分类器的预测误差降至最低。提出了一种使用正弦余弦算法（SCA）和遗传算法（GA）的新混合特征选择方法，称为SCAGA。通常，优化方法有两种主要的搜索策略：探索搜索空间并利用其确定最佳解决方案。当在搜索空间的开发策略和探索策略之间取得平衡时，提出的SCAGA可以带来更好的性能。拟议的SCAGA也已使用以下评估标准进行了评估：分类准确度，最差适应度，平均适应度，最佳适应度，平均特征数和标准差。此外，在结果中获得了分类的最大准确性和最小特征。还将结果与基本的正弦余弦算法（SCA）以及其他相关方法（如蚁狮优化和粒子群优化）相比较。比较表明，从SCAGA方法获得的结果是UCI机器学习存储库中测试数据集的最佳整体。还将结果与基本的正弦余弦算法（SCA）以及其他相关方法（如蚁狮优化和粒子群优化）相比较。比较表明，从SCAGA方法获得的结果是UCI机器学习存储库中测试数据集的最佳整体。还将结果与基本的正弦余弦算法（SCA）以及其他相关方法（如蚁狮优化和粒子群优化）相比较。比较表明，从SCAGA方法获得的结果是UCI机器学习存储库中测试数据集的最佳整体。