基于GA的步态特征与SVM参数协同优化研究

为了辅助医生对帕金森病患者进行更好的诊断，采用遗传算法对患者步态特征和支持向量机参数进行协同优化。在特征选择部分将步态特征和支持向量机参数使用二进制编码的方式生成染色体，同时输入到遗传算法中。考虑到遗传算法的搜索性能和运行时间，针对算法中的种群大小和进化代数这两个参数进行了寻优，找出合适的种群大小和进化代数。实验结果显示平均准确率为85.11%，相对于其他特征选择算法，准确率最高高出14%。表明使用该方法后对帕金森病患者的步态特征进行了有效去除，有利于患者的诊断。

Research on Collaborative Optimization of Gait Characteristics and SVM Parameters Based on GA

To assist doctors in better diagnosis of patients with Parkinson’s disease, genetic algorithm is used to optimize the gait characteristics and support vector machine parameters. In the feature selection section, the gait features and support vector machine parameters are generated in a binary coded manner and input into the genetic algorithm. Taking into account the search performance and runtime of the genetic algorithm, the two parameters of the population size and number of evolutionary generation in the algorithm are optimized to find the appropriate population size and number of evolutionary generation. The experimental results show that the average accuracy rate is 85.11%, compared with other feature selection algorithms, the accuracy rate is up to 14% higher. It shows that the use of this method can effectively remove the gait characteristics of patients with Parkinson’s disease, which is beneficial to the diagnosis of patients.