genetic algorithm to achieve (reproduced)

专家系统控制算法pid-The control arithmetic of Expert Systems

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

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

本电子书很有意义！！！神经网络入门教程-The e-book makes sense! ! ! Neural Network Tutorial

模式识别分类程序，贝叶斯，神经网络分类训练程序，很不错啊-the classification procedures, Bayesian neural network classifier training procedures, and it is very responds :

人工智能里的A-star算法,用于机器人的路径规划和寻优.-AI"s A-star algorithm for robot path planning and optimization.

遗传问题的MATLAB代码,用于解决TSP问题的程序-Genetic problems MATLAB code for the procedure to solve TSP problems

建立Fuzzy模型－序列预测matlab代码-Fuzzy model-building- sequence prediction matlab code

美国麻省理工学院出的遗传算法方面的好书.-Massachusetts Institute of Technology in the United States out of aspects of genetic algorithms books.

分布估计算法（EDA）讲义，29页PPT 1. 从GA到 EDA 2. 链接学习与模型 3. 连续EDA 4. EDA 与玻耳兹曼分布 5. 资源 -estimation of distribution algorithm（EDA）tutorial 1. from the GA to the EDA 2. Linking learning and models 3. Continuous EDA 4. EDA with Boltzmann distribution 5. other Resources