基于文化差分进化算法的最小二乘支持向量机及QSAR建模

针对最小二乘支持向量机最佳算法参数难以确定的缺陷，提出了基于文化差分进化算法的最小二乘支持向量机(Cultural Differential evolution Algorithm Least Square Support Vector Machine，CDE-LSSVM)。该算法通过新型的文化差分进化算法优化确定最小二乘支持向量机核宽度参数和惩罚系数，建立具有良好预测性能的模型。同时，针对药物定量构效关系（Quantitative Structure-Activity Relationships,QSAR）模型具有高度非线性、变量之间存在相关性的特征，采用CDE-LSSVM建立HIV-1蛋白酶抑制剂的药物定量构效关系模型。模型具有很好的拟合精度与预测精度，且优于最小二乘支持向量机、BP神经网络和径向基神经网络。

Least square support vector machine based on cultural differential evolution algorithm and its application to QSAR modeling

In order to obtain the best parameters of least square support vector machine(LS-SVM), a novel least square support vector machine algorithm integrating with cultural differential evolution (CDE-LSSVM) is proposed. In CDE-LSSVM, CDE algorithm is used to optimize the parameters of kernel width and the factor of punishment so as to obtain the model with better forecasting performance. Further, by considering that quantitative structure activity relationships (QSAR) model is of high nonlinearity and has relativity between independent variables, CDE-LSSVM is applied to develop HIV-1 protease inhibitors QSAR model. In order to illustrate the performance of CDE-LSSVM model, LS-SVM, back-propagation neural networks and radial basis function neural network are employed respectively to develop the QSAR models. The simulation results show that CDE-LSSVM model is of better performance of fitting and forecasting.