a Tankedaizhan source code, suitable for beginners VC, and I hope to help you!

基于java实现的遗传算法 神经网络 实数编码 交叉 选择 变异-achieve genetic neural network algorithm is coded cross-choice variant

野人与传教士问题的求解程序，对N=3，K=2给出正确路径-Savage with the missionary problem solving procedures, N = 3, K = 2 are given the correct path

More AI...(GEP) Gene Expression Programming in C# and .NET 由Michael Gold用C#开发的GEP程序.-AI ... (GEP) Gene Expression Programming in C# and. By Michael Gold NET C# development of the GEP procedures.

模式识别中的寻路算法：A星算法的实现及简单应用-Pattern Recognition routing algorithm: A Star algorithm and a simple application

基本的BP神经网络框架，比较适用于初学者快速掌握神经网络的构成。-basic framework BP neural network, suitable for beginners to grasp the composition of neural networks.

遗传算法的文章- Heredity algorithm article

多目标进化算法源代码-more evolutionary algorithm source code

双向广度优先搜索算法法见8SM2.cpp文件，vc6.0环境运行实现-two-way breadth- first search algorithm see 8SM2.cpp France, achieving operating environment vc6.0

遗传算法的源程序例子，对于初学者有很大帮助，建议多-Genetic algorithm source code examples are very helpful for beginners, the proposed multi-

文中设计了一个3层径向基神经网络(RBFN)用于对企业的5项评价指标进行聚类分析，并与蚁群算法做了比较分析。RBFN由输入层 到隐含层采用传统的K一均值算法，隐含层到输出层通过“模2递减”学习速率的BP学习；蚁群算法根据信息素的分配能够自动调整收索 路径，从而达到数据自动聚类的目的。结果表明，与蚁群算法相比，改进RBFN具有快速收敛、自动识别奇异样本的优点，而蚁群算法 无须教师学习，并能够达到全局最优。-In this paper, we designed a 3-layer RBF neural network (RBFN) for the 5-to-business evaluation indicators cluster analysis and ant colony algorithm has done a comparative analysis. RBFN from input layer to hidden layer using the traditional K-means algorithm, hidden layer to output layer through the Mode 2 decreasing learning rate of BP learning ant colony algorithm based on pheromone can automatically adjust the allocation of land Faso path, thereby to achieve the purpose of automatic data clustering. The results showed that compared with the ant colony algorithm to improve the RBFN has a fast convergence, automatic identification of singular advantage of the sample, while the ant colony algorithm do not need teachers to learn and be able to reach the global optimum.