Reasonable and effective determination of uniaxial compressive strength (UCS) is critical for rock mass engineering stability research, design, and construction. To estimate the UCS of rock simply, conveniently, and accurately, a selective ensemble learning technology is introduced here based on modern artificial intelligence research, and a prediction method of the UCS of rock via genetic algorithm—selective ensemble learning (GA–SEL) is proposed. Based on a UCS data set, a batch of different base learners was firstly trained independently with the data sample and the algorithm parameter perturbation method. Then, the optimal base learner subset was searched using GA. Further, the GA–SEL model was constructed by fusing the base learners in that subset. According to the 161 data set collected, the prediction performance of the GA–SEL model was evaluated by four evaluation indices, then two empirical regression models and seven common machine learning models were compared with it. The results of the GA–SEL model agreed with the measured data very well, showing that the model had the best prediction and generalization ability, it was more stable and accurate than the empirical methods and common machine learning models. Because it only needs seven high-quality base learners, the GA–SEL model also has better operation efficiency compared to other ensemble learning models. Therefore, this method could be used as an effective method to predict the UCS of rock and serve for rock engineering problems.

合理有效地确定单轴抗压强度（UCS）对于岩体工程稳定性研究、设计和施工至关重要。为简单、方便、准确地估计岩石的 UCS，在现代人工智能研究的基础上，引入了一种选择性集成学习技术，一种基于遗传算法的岩石 UCS 预测方法——选择性集成学习（GA-SEL）建议的。基于一个UCS数据集，首先用数据样本和算法参数扰动方法独立训练一批不同的基学习器。然后，使用 GA 搜索最佳基学习器子集。此外，GA-SEL 模型是通过融合该子集中的基础学习器来构建的。根据收集到的161个数据集，GA-SEL模型的预测性能通过4个评价指标进行评价，然后与2个经验回归模型和7个常用机器学习模型进行比较。GA-SEL模型的结果与实测数据非常吻合，表明该模型具有最佳的预测和泛化能力，比经验方法和常见的机器学习模型更稳定、更准确。由于只需要 7 个高质量的基础学习器，GA-SEL 模型相比其他集成学习模型也具有更好的运行效率。因此，该方法可作为预测岩石UCS和解决岩石工程问题的有效方法。GA-SEL模型的结果与实测数据非常吻合，表明该模型具有最佳的预测和泛化能力，比经验方法和常见的机器学习模型更稳定、更准确。由于只需要 7 个高质量的基础学习器，GA-SEL 模型相比其他集成学习模型也具有更好的运行效率。因此，该方法可作为预测岩石UCS和解决岩石工程问题的有效方法。GA-SEL模型的结果与实测数据非常吻合，表明该模型具有最好的预测和泛化能力，比经验方法和常见的机器学习模型更稳定、更准确。由于只需要 7 个高质量的基础学习器，GA-SEL 模型相比其他集成学习模型也具有更好的运行效率。因此，该方法可作为预测岩石UCS和解决岩石工程问题的有效方法。与其他集成学习模型相比，GA-SEL 模型也具有更好的运行效率。因此，该方法可作为预测岩石UCS和解决岩石工程问题的有效方法。与其他集成学习模型相比，GA-SEL 模型也具有更好的运行效率。因此，该方法可作为预测岩石UCS和解决岩石工程问题的有效方法。