遗传算法编程很有用的，可以用来借鉴参考，希望对大家有帮助！...

这是一个模式识别中的K均值算法的一个简单仿真分类实例，其中female.txt和male.txt是训练样本，test.txt是测试样本，分类效果非常好，对于模式学习的初学者将会有很大帮助。-This is a pattern recognition algorithm of the K-means classification of a simple simulation example is one of female.txt and male.txt training samples, test.txt is the measurement, classification effect is very good for beginners will be learning model has great help to me.

This demo nstrates the use of the reversible jump MCMC simulated annealing for neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL

libsvm在matlab中的应用。把libsvm嵌入matlab中调用。-libsvm in matlab application. Embedded matlab to call libsvm.

tsp问题用神经网络方法解决-tsp uses neural network solution

用蚁群算法求解tsp问题。。。matlab编写。附有城市坐标用于检验。-Tsp with ant colony algorithm for solving the problem. . . matlab prepared. Coordinate with the city for inspection.

使用模拟退火算法解决TSP问题，该程序中的模拟退火没有采用复杂的运算，实现了一个比较简单但运行高效的算法-using simulated annealing algorithm to solve TSP, the process of simulated annealing without the use of complex arithmetic, achieve a relatively simple but efficient operation of the algorithm

多层神经网络的可执行例子 http://www.codeproject.com/cpp/MLP.asp?df=100&forumid=148477&exp=0&select=1141594#xx1141594-multilayer neural network executable examples http :// www.codeproject.com/cpp/MLP.asp df = 1

该算法为模糊自适应整定的PID控制算法，该算法在matlab环境下开发，可以直接运行 -The algorithm for fuzzy adaptive tuning of the PID control algorithm in the matlab environment development, can be directly run

蚁群算法的C++实现,让读者更清晰的了解蚁群算法的完整思路-Ant colony algorithm C++ Realize, to give readers a clearer picture of the integrity of the idea of ant colony algorithm

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.