The simplest genetic algorithm source code, provided to facilitate the exchange.

人脸识别：利用奇异值分解和KL变换的投影，是很有价值的一篇文章-Face Recognition : The Singular Value Decomposition and KL transform projection, it is valuable to an article

用vc写的，同时有用bp算法和用rbf神经网络进行训练和识别的程序，可以看到两种不同方法得到的结果-vc used to write, while useful bp algorithm and the use of rbf neural network training and identification procedures, we can see the two different methods to obtain the result

模拟退火生成树，一种比较好分类方法。程序简单，易懂。-simulated annealing Spanning Tree, a better classification. Simple and straightforward.

带有QoS约束的组播路由问题是一个NP完全问题，遗传模拟退火算法是遗传算法和模拟退火算法的一种融合，可以为这类问题提供一个解决方案-With QoS constraint multicast routing problem is an NP-complete problem, genetic simulated annealing algorithm is a genetic algorithm and simulated annealing algorithm as a fusion, you can provide for such a solution

支持向量机多参数自动选择优化程序！机器学习，数据挖掘工具！matlab版！-Support vector machine multi-parameter optimization procedure automatically selects! Machine learning, data mining tool! matlab version!

无解压密码，可放心使用-without extracting passwords can be assured use

基于粒子滤波原理，选取纹理特征对目标进行描述，实现视频目标的跟踪。-Principle-based particle filter, select the texture features to describe on the target to achieve the objectives of tracking video.

使用Astar算法实现的八数码问题。C++编写，控制台应用程序，实现简单，代码轻便。-Astar algorithm using eight digital issues. C++ written in the console application, to achieve simple and lightweight code.

遗传算法进行优化神经网络结构-genetic algorithm optimization neural network architecture! !

提出了基于最小化估计的泛化误差界非优化估计方法。目前大多数的核参数选择方法都是通过极小化了来得到最优参数值，但是求解优化问题的计算代价相当的大，并且不能很好地体现数据的分布特征。本文采用非优化技术，通过极小化泛化误差来优化核及相关参数，由于直接计算最小半径和最大间隔，避免了对优化问题的直接求解，因此可以很好地降低计算代价。并且该方法直接从样本出发，可以很好地体现数据的分布特征，不管数据分布是否均匀都可以适用。给出了基于凸包估计的SVM核选择的模型及实现算法。 -Proposed based on minimizing the estimated generalization error bound of the non-optimal estimation method. Most of the nuclear parameter selection methods are to get the best by minimizing the parameter values, but the computational cost for solving optimization problems is quite large, and can not properly reflect the distribution characteristics of the data. In this paper, the non-optimization technology, by minimizing the generalization error to optimize the nuclear and related parameters, due to the direct calculation of the minimum radius and maximum interval, avoiding the direct solution of the optimization problem, it can very well reduce the computation cost. And that the method directly from the sample one can well reflect the distribution chara