很好的遗传算法值得一看

利用C＋＋语言开发的BP算法，并将其封装在类中，用户可以自己设定参数，实现对BP网络的训练。-use C++ language BP algorithm, which is encapsulated in class, users can set their own parameters to achieve BP network training.

PSO算法求解带约束优化问题，包含所有源程序和运算图片。写论文是编的。-PSO algorithm for solving constrained optimization problems, including all source code and the computing picture. Writing papers is made of.

高斯混合模型算法-Gaussian mixture model algorithm

神经网络模式识别及其实现，第八章。 内含：KMEANS 、KOHONEN 、LVQ 、SOFM 算法。-pattern recognition and neural network to achieve, Chapter VIII. Intron : KMEANS, KOHONEN, LVQ, SOFM algorithm.

背包问题算法解决-Knapsack Problem Solving

人工神经网络BP算法 1、动态改变学习速率 2、加入动量项 3、运用了Matcom4.5的矩阵运算库（可免费下载,头文件matlib.h)， 方便矩阵运算,当然，也可自己写矩阵类 4、可暂停运算 5、可将网络以文件的形式保存、恢复-Artificial neural network BP algorithm 1, the dynamic change learning rate 2, adding momentum of 3, using the matrix calculation Matcom4.5 library (can be downloaded free of charge, the first document matlib.h), facilitate the matrix calculation, of course, can also write their own matrix Class 4, may be suspended by the operator 5, may be the form of network to the preservation, restoration

对支持向量机进行训练的一种改进的遗传算法。-On support vector machine for training an improved genetic algorithm.

（其中已经包含5个源码）所附源程序C或C++代码文件及其可执行文件 Scs.cpp 基本分类算法源程序， 输入数据文件cfile.txt,efile.txt,gfile.txt,pfile.txt ,rfile.txt,tfile.txt Sga.c 基本遗传算法源程序, 输入数据文件input，输出文件output A_life.c 基于遗传算法的人工生命模拟源程序, 输入数据文件world GA_nn.c 基于遗传算法优化神经网络结构源程序,输入数据文件sample Patmat.c 基于遗传算法提取基元图形源程序-(which includes five OSS) attached to C or C source code files and executable files Scs.c pp basic classification algorithm source files, input data files cfile.txt, efile.txt. gfile.txt, pfile.txt, rfile.txt. tfile.txt Sga.c basic genetic algorithm source files, input data files input, output file output A_life.c based on genetic algorithm simulation source Artificial Life, import data files GA_nn.c world based on genetic algorithm optimization neural network structure source, import data files Patmat.c sample extraction based on genetic algorithm-based graphic source yuan

模拟退火算法 模拟退火算法(Simulated Annealing,简称SA算法)是模拟加热熔化的金属的退火过程，来寻找全局最优解的有效方法之一。 模拟退火的基本思想和步骤如下： 设S＝{s1,s2,…,sn}为所有可能的状态所构成的集合， f：S―R为非负代价函数，即优化问题抽象如下： 寻找s*∈S，使得f(s*)＝min f(si) 任意si∈S （1）给定一较高初始温度T，随机产生初始状态S （2）按一定方式，对当前状态作随机扰动，产生一个新的状态S’ S’＝S＋sign(η).δ 其中δ为给定的步长， η为[－1,1]的随机数-simulated annealing algorithm (Simulated Annealing, or SA algorithm) is a simulation of heating molten metal in the annealing process, to find the global optimum one of the effective ways. Simulated Annealing basic ideas and the steps are as follows : S = (s1, s2, ..., sn) for all possible state posed by the pool, f : S-R non-negative cost function, that is abstract optimization problems are as follows : Find S* s, making f (s*) = min f (si) arbitrary si S (1) to set a higher initial temperature T, randomly generated initial state S (2) of a certain form, the current state of random disturbance, have a new state S "S" = S+ sign (). delta where given for the step, [-1,1] Random Number

演化程序――遗传算法和数据编码的结合 具体介绍了遗传算法的应用-evolutionary process-- genetic algorithms and data encoding the specific combination of the application of genetic algorithms