关于鱼群算法的一个程序，希望对学习智能算法的朋友有所帮助。...

一个A*算法的的实现过程的演示，是个非常清晰的能理解到A是怎么实现的-A* algorithm is a realization of the demonstration process, it is a very clear to understand how the realization of A

迁移学习的经典文章，大多为国际顶级会议论文-Transfer of learning classic articles, most of the international top-level Conference Papers

一个机器学习算法软件包,包括神经网络,模糊逻辑,支持向量机,采用MATLAB平台实现,-a machine learning algorithm packages, including neural networks, fuzzy logic, support vector machine, MATLAB platform.

一个经典的C语言遗传算法程序包，可直接用来开发遗传算法的应用程序-a classic genetic algorithm C language package can be used directly for the development of the genetic algorithm application

这个程序说明了数据挖掘中聚类算法的简单应用-This procedure describes the data mining clustering algorithm simple application

这是书上的常用搜索算法，有8数码游戏，黑白棋，A star算法类的实现。-This is a book commonly used search algorithm, there are 8 digital game, Reversi, A star algorithm category realize.

神经网络C程序的第3章-neural network C procedures for the Chapter 3

vb写的改进的遗传算法程序。是一些通用代码，与具体问题对应的代码空出来，用的时候填进去。根据我的经验，要想用好遗传算法，代码必须自己写，因为问题不同，编码，杂交，变异算子可能都不一样。特别是杂交算子，需要根据问题调整，甚至创造出新的杂交方法。而且适应度计算的代码也需要自己写，特别是带约束的优化问题 -vb write improvement in the genetic algorithm. Some common code, with the specific issues the corresponding code space, and the time spent filling into account. According to my experience, in order to make good use of genetic algorithms, code must write their own, because different issues, coding, hybridization and mutation operator may not like. In particular hybrid operator, the need to adjust the problem, or even create a new hybrid method. But fitness calculation of the code required to write their own, especially with the constrained optimization problem

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

这个是神经网络中的HopField网络模拟，是利用外向和的双极性HopField网络，有很好的借鉴价值。-This is a neural network of Discharge network simulation is the use of the outward and bipolar Discharge network, a very good reference value.