GeneticNonlinearMatlab
所属分类：人工智能/神经网络/深度学习
开发工具：matlab
文件大小：43KB
下载次数：84
上传日期：2012-11-01 23:36:37
上 传 者：
linxk
说明： 遗传算法虽然全局搜索能力较强，但是局部搜索能力较弱，一般只能搜索到函数优化问题的次优解，而不是最优解，特别是函数具有多个峰值时，遗传算法易陷入局部极小，不能找到真正的全局最优解。非线性规划因多采用梯度下降方法求解，而具有极强的局部搜索能力。因此，本源代码结合两种算法的优点，一方面采用遗传算法进行全局搜索，另一方面采用非线性规划进行局部搜索，以得到函数优化问题的全局最优解。实验证明，这种方法不仅能解决多峰函数寻优易陷入局部极小的问题，而且具有很高的迭代寻优效率，取得了满意的结果。
(Global search ability of genetic algorithms, local search capability is weak, generally only be able to search a sub-optimal solution to the function optimization problem, rather than the optimal solution, especially the function has multiple peaks, genetic algorithm is easy to fall into the local polar can not find the true global optimal solution. Solving nonlinear programming due to the use of the gradient descent method, and has strong local search ability. Source code algorithm combines two advantages, on the one hand, the use of genetic algorithms for global search, on the other hand by nonlinear programming local search to obtain the global optimum function optimization problems. Experiments show that this method can not only solve the multimodal function optimization easy to fall into local minima problems, and has a highly iterative optimization efficiency, and achieved satisfactory results.)
文件列表:
GeneticNonlinearMatlab\Ackley.m (222, 2012-10-14)
GeneticNonlinearMatlab\AckleyStandardGAMain02.m (3176, 2012-10-23)
GeneticNonlinearMatlab\AckleyStandardGAMain05.m (3382, 2012-10-21)
GeneticNonlinearMatlab\AckleyStandardGAMain10.m (3398, 2012-10-21)
GeneticNonlinearMatlab\AckleyStandardGANonMain02.m (3413, 2012-10-23)
GeneticNonlinearMatlab\AckleyStandardGANonMain05.m (3671, 2012-10-21)
GeneticNonlinearMatlab\AckleyStandardGANonMain10.m (3844, 2012-10-23)
GeneticNonlinearMatlab\Code.m (389, 2012-04-29)
GeneticNonlinearMatlab\Cross.m (1393, 2012-04-29)
GeneticNonlinearMatlab\FigureRead.m (1153, 2012-10-29)
GeneticNonlinearMatlab\Griewank.m (144, 2012-10-14)
GeneticNonlinearMatlab\Mutation.m (1315, 2012-04-29)
GeneticNonlinearMatlab\Nonlinear.m (2200, 2012-04-29)
GeneticNonlinearMatlab\Rastrigin.m (127, 2012-10-14)
GeneticNonlinearMatlab\Schaffer.m (140, 2012-10-14)
GeneticNonlinearMatlab\Select.m (850, 2012-04-29)
GeneticNonlinearMatlab\Test.m (269, 2012-04-29)
GeneticNonlinearMatlab\TimeAckleyStandardGAMain02.m (3255, 2012-10-24)
GeneticNonlinearMatlab\TimeAckleyStandardGAMain05.m (3253, 2012-10-25)
GeneticNonlinearMatlab\TimeAckleyStandardGAMain10.m (3256, 2012-10-25)
GeneticNonlinearMatlab\TimeAckleyStandardGANonMain02.m (3825, 2012-10-24)
GeneticNonlinearMatlab\TimeAckleyStandardGANonMain05.m (3910, 2012-10-24)
GeneticNonlinearMatlab\TimeAckleyStandardGANonMain10.m (3678, 2012-10-28)
GeneticNonlinearMatlab\TimeGriewankStandardGAMain02.m (3267, 2012-10-25)
GeneticNonlinearMatlab\TimeGriewankStandardGAMain05.m (3265, 2012-10-25)
GeneticNonlinearMatlab\TimeGriewankStandardGAMain10.m (3268, 2012-10-25)
GeneticNonlinearMatlab\TimeGriewankStandardGANonMain02.m (3803, 2012-10-25)
GeneticNonlinearMatlab\TimeGriewankStandardGANonMain05.m (3897, 2012-10-25)
GeneticNonlinearMatlab\TimeGriewankStandardGANonMain10.m (3684, 2012-10-28)
GeneticNonlinearMatlab\TimeRastriginStandardGAMain02.m (3273, 2012-10-25)
GeneticNonlinearMatlab\TimeRastriginStandardGAMain05.m (3271, 2012-10-27)
GeneticNonlinearMatlab\TimeRastriginStandardGAMain10.m (3275, 2012-10-27)
GeneticNonlinearMatlab\TimeRastriginStandardGANonMain02.m (3474, 2012-10-25)
GeneticNonlinearMatlab\TimeRastriginStandardGANonMain05.m (3569, 2012-10-27)
GeneticNonlinearMatlab\TimeRastriginStandardGANonMain10.m (3660, 2012-10-28)
GeneticNonlinearMatlab\TimeSchafferStandardGAMain02.m (3267, 2012-10-26)
GeneticNonlinearMatlab\TimeSchafferStandardGAMain05.m (3265, 2012-10-26)
GeneticNonlinearMatlab\TimeSchafferStandardGAMain10.m (3268, 2012-10-26)
GeneticNonlinearMatlab\TimeSchafferStandardGANonMain02.m (3480, 2012-10-26)
GeneticNonlinearMatlab\TimeSchafferStandardGANonMain05.m (3543, 2012-10-26)
... ...
近期下载者：
相关文件：
收藏者：