野人过河问题属于人工智能学科中的一个经典问题,问题描述如下: 有三个牧师(也有的翻译为传教士)和三个野人过河,只有一条能装下两个人的船,在河的任何一方或者船上,...

遗传算法，生物技术，进化技术，主用用来求函数优化问题-Genetic algorithms, bio-technology, evolution of technology, the main use for the seek function optimization problems

这是一个我自己编写的JAVA程序，可以运行的。思想是将KMEANS和神经网络结合起来实现模式的分类。-This is a Java program writen by meself which can be run.The chief concept is to combine KMEANS and NN to achieve classification of pattens.

这个程序是从广度优先搜索,深度优先搜索,A算法以及A算法等多方面算法来解决八数码问题-This program is breadth-first search, depth-first search, A algorithm and A algorithm and many other algorithms to solve the problem of digital 8

This a GA implementation using binary and real coded variables. Mixed variables can be used. Constraints can also be handled. All constraints must be greater-than-equal-to type (g >= 0) and normalized (see the sample problem in prob1 in objective()).-This a GA implementation using binary and real coded variables. Mixed variables can be used. Constraints can also be handled. All constraints must be greater-than-equal-to type (g gt;= 0) and normalized (see the sample problem in prob1 in objective()).

学习遗传算法的标准入门程序,这是我当初入门的时候用的。-Perfect code to learn genetic algorithm

麻省理工开发的免费遗传算法类库GAlib,很好用，我用它实现过一些具体问题的遗传算法。- Hemp province technical development free heredity algorithm class storehouse GAlib, is very easy to use, I use it to realize some concrete questions heredity algorithms.

与神经网络有相似的根源。但近年来在分类中得到了广泛的应用。这意味着，如果我对一些事物进行了分类（但你对我如何分类一无所知，或者说你不知道用于分类的规则），当一个新数据出现时，SVM可以预测它应该属于哪个集合。

libview的libsvm程序，它的应用比较方便。-libview the libsvm procedure, its application more convenient.

使用神经网络的K算法来对数据进行分类，有具体的实验要求和图-K using the neural network algorithm to classify the data, there is a specific test requirements and plans

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