adaptive-genetic-algorithm

代码说明：

  自适应GA
SVM 参数选择算法研究Param eter selection algorithm for support vector machines based on adaptive genetic algorithm 支持向量机是一种非常有前景的学习机器, 它的回归算法已经成功地用于解决非线性函数的逼近问题. 但 是, SVM 参数的选择大多数是凭经验选取, 这种方法依赖于使用者的水平, 这样不仅不能获得最佳的函数逼近效果, 而且采用人工的方法选择 SVM 参数比较浪费时间, 这在很大程度上限制了它的应用. 为了能够自动地获得最佳的 SVM 参数, 提出了基于自适应遗传算法的 SVM 参数选取方法. 该方法根据适应度值自动调整交叉概率和变异概率, 减少了遗传算法的收敛时间并且提高了遗传算法的精度, 从而确保了 SVM 参数选择的准确性. 将该方法应用于船用 锅炉汽包水位系统建模, 仿真结果表明由该方法所得的 SVM 具有较简单的结构和较好的泛化能力, 仿真精度高, 具 有一定的理论推广意义.( The support vector m achine ( SVM ) is a prom ising artificia l inte lligence technique, in w hich the regres
sion a lgorithm has a lready been used to so lve the nonlinear function approach successfully. Un fortunate ly, m ost us
ers se lect param eters for an SVM by ru le o f thumb, so they frequently fail to generate the optim al approach ing e ffect for the function. Th is has restricted effective use o f SVM to a great degree. In order to get optim a l param eters auto
m atically, a new approach based on an adaptive genetic a lgorithm ( AGA ) is presented, w hich autom atica lly ad
justs the param eters for SVM. This m ethod se lects crossover probability and mutation probab ility accord ing to the fitness va lues of the object function, therefo re reduces the convergence tim e and im proves the prec ision o fGA, in
suring the accuracy of param eter se lection. Th is m ethod w as applied to m odeling of w ater level system o f a sh ip b)

文件列表：

自适应GA_SVM参数选择算法研究.pdf,1387308,2011-11-07