4. "Evolutionary process

只是Java版实现的蚁群算法与程序结果演示，是学习和理解蚁群算法的好内容。-Only a Java version of the ant colony algorithm to achieve the results and procedures for the demonstration of learning and understanding better the content of ant colony algorithm.

神经网络的HP算法，这是学习神经网络的入门课程．-HP neural network algorithm, which is learning neural network-oriented courses.

用粒子群优化算法计算给定图像的平滑度。

遗传算法的文章- Heredity algorithm article

This code in this directory implements the binary hopfield network.Source code may be found in HOPNET.CPP. A sample training file is H7x8N4.trn. Sample test pattern files are: H7x8D4.TST, H5x8D7.TST,H5x8D7.TST and H5x8D9.TST, Output of the program goes to both the screen and a file, ARCHIVE.LST.

用VC++编写的基本遗传算法的源程序，而且有中文解释，适于初学者。-GA

人体运动检测与运动跟踪的源代码，用到了最新的算法，对这方面研究的人员很有帮助-Human Motion Detection and Motion Tracking source code, use the latest algorithms, this study helpful staff

遗传算法应用-Application of genetic algorithms

基于C开发的三个隐层神经网络，输出权值、阈值文件，训练样本文件，提供如下函数：1）初始化权、阈值子程序；2）第m个学习样本输入子程序；3）第m个样本教师信号子程序；4）隐层各单元输入、输出值子程序；5）输出层各单元输入、输出值子程序；6）输出层至隐层的一般化误差子程序；7）隐层至输入层的一般化误差子程序；8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序；9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序；10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序；11）第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序；12）N个样本的全局误差计算子程序。-C development based on the three hidden layer neural network, the output weights, threshold documents, training sample documents, for the following functions : a) initialization, the threshold subroutine; 2) m learning samples imported subroutine; 3) m samples teachers signal Subroutine ; 4) hidden layer of the module input and output value subroutine; 5) the output layer of the module input and output value subroutine; 6) the output layer to the hidden layer subroutine error of generalization; 7) hidden layer to the input layer subroutine error of generalization; 8) the output layer to the third hidden layer Weight adjustment, the output layer threshold adjustment routines; 9) 3rd hidden layer t

本算法中采取了种群规模为100，同时采用轮盘赌来获取种群。开始使用随机的方法得到初始的种群-the algorithm adopted a population size of 100, using roulette to access populations. Using the stochastic method initial Stocks