application of nsga

遗传算法原程序，并行算法，原代码，随机搜索算法-GA original program, parallel algorithm, the original code, random search algorithm

求函数优化的伊藤算法源代码，下载后可以直接使用-Ito demand function optimization algorithm source code download can be used directly

该进化算法的实现基于VC++6.0的开发平台，主要是用算子改进进化策略算法。-The evolutionary algorithm based on VC++ 6.0 development platform, the main operator is used to improve the evolutionary strategy algorithm.

A星算法实现最短路径的选择,使用方便，功能完善，是学习人工智能作业中必须完成的一步。-Choose the shortest path by the method of AStar

K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, including clustering and clustering batch. K-means clustering of the time complexity of n* k* m, n samples, several types of k, m sample dimension. The time complexity is a very objective. Because if we use one billion times per second the computer using 50 samples of two exhaustive method to find the optimal set out again about 66.7 days, divided into three categories, offering 3,500 years. Local optimal algorithm against the shortcomings, I was prepared simulated annealing process improvements. Early Feng hope for everyone, listen to the master teachings.

这是用matlab编写的用遗传算法优化神经网络的程序-using Matlab is prepared by using genetic algorithm optimization procedures for neural networks

一个很好的bp神经网络预测程序实例，有较好的预测精确性-Bp a good example of neural network forecasting process, a better prediction accuracy

这是一个神经网络的开发工具-This is a neural network development tools

新手上路： 自己编写的BP神经网络程序，还不是成熟，希望能够加入社区和高手们学习学习-Getting Started: I have written procedures for the BP neural network, is not yet mature, I hope you can join the community and learning to learn

pso优化算法的vc++源程序。在vc.net2003下面通过。 建议：看代码之前，请先弄明白pso是怎么回事。然后请对应着来：程序中用Agent代表一只鸟，PSO代表鸟群。阅读源代码，不要顺着看，先看main(),然后按照出现的东西的顺序，一个一个得来。-PSO optimization algorithm vc++ source. In the following vc.net2003 through. Recommendations: look the code before you find out how the PSO story. Then corresponds to: Program with Agent on behalf of a bird, PSO representative of birds. Read the source code, do not look down, look at the main (), then follow the order of things emerging, one by one come.