改进遗传算法程序

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

计算机模式识别技术-the technology of the computer pattern recognation

使用蚁群算法解决旅行商问题，mfc应用程序，城市数目及坐标在eil51.tsp里-use ant algorithm to solve the traveling salesman problem, and mfc applications, The number of cities and coordinates the eil51.tsp Lane

改进遗传算法－郭涛算法做最优化问题很管用,算法的基本思想是 先任意产生n个随机数，然后从n个数里随机选择m个数，再有这m个 数合成一个新数，将这个新数同n个数中间适应值函数值的最差的比较， 如果好的话就取代最差的那个，如果它比最好的还要好的话，则把最好的 也取代。如果比最差的坏，则重新合成一个新数。依次循环下去。 程序的奇妙之处是GA_crossover()函数，产生的新数确实比较好，看看 那位大侠能改进一下，产生比这跟好的数。-improved genetic algorithm- Guo Tao done optimization algorithm is very effective, and the algorithm is the first basic idea arbitrary n generated random number and then the number li n m randomly selected number, and this number m of a new synthesis of the new middle with n number fitness function of the worst by comparison, if a good case to replace the worst, um, if it even than the best, then ruled the best have replaced. If worse than the worst, a de novo synthesis of new. Followed by the cycle continues. The magic of procedures is GA_crossover () function, the new really good to see heroes who can be improved upon, it generated more than a few with a good.

ｍａｔｌａｂ编写的计算排序问题或者求最优值问题，适用于数学建模以及工程计算-Matlab calculations prepared by the scheduling problem or seek optimal values, apply to mathematical modeling, and engineering computing

本程序实现了一个人工神经网络的算法，相关领域的人可以参考。-This procedure implements a neural network algorithm, in related fields who can refer to.

经典BP算法的源程序，对人工神经网络中的bp有一定的帮助作用-BP algorithm is the classic source of artificial neural networks have certainly bp helpful

神经网络源码可应用于遥感图像分类，包括bp、kohonen等方法-neural network source can be used in remote sensing image classification, including bp, Kohonen methods

采用“提前停止”方法提高 BP 网络的推广能力。对于和例 2相同的问题，在本例中我们将采用训练函数 traingdx -The use of

k-means(欧氏距离)聚类算法是最基本的聚类算法，是理解和应用聚类算法的基础，通过k-means(欧氏距离)聚类算法我们才可以初步了解数据挖掘的原理。-k-means (Euclidean distance) clustering algorithm is the most basic clustering algorithm, is understanding and the basis for the application of clustering algorithm, through the k-means (Euclidean distance) clustering algorithm we are able to a preliminary understanding of the principles of data mining .