Principles of Data Mining is a data mining inside another in this classic textbo...

一个基本遗传算法的C++语言实现，有三个文件构成。-A basic genetic algorithm C++ Language, there are three files.

本文在VC++环境下用演化算法实现了非线性参数回归，实验结果表明，用此方法能够很好的实现非线性参数回归。-paper in VC environment using an evolutionary algorithm to achieve the nonlinear regression, experimental results indicate that this method can achieve good nonlinear regression.

使用线性分类器进行分类，采用感知器算法中的“奖惩算法”，各提取3类中的前25个样本共75个作为学习样本。-The use of a linear classifier for classification, perceptron algorithm using the reward and punishment algorithm , the extraction of 3 categories of the top 25 a total of 75 samples as the learning samples.

这是用VC编程实现模糊控制来控制机器人的运动-The misty control imitate really

关于演化算法的小程序.以一个二元二次方程的求极值为例.对输入参数作了简化的固定处理.较好的说明了演化算法.可直接运行.-on evolutionary algorithm small procedures. A binary quadratic equation for the extreme example. To simplify the input parameters fixed address. A good note evolutionary algorithm. Direct operation.

用隶属函数型神经网与模糊控制融合的解耦程序-Membership functions with neural networks and fuzzy control integration of the decoupling procedure

TSP问题是组合优化中的经典问题。其解决方法有局部优化方法和一些启发式算法,局部搜索方法充分考虑问题 的邻域结构,遗传算法有很好的全局搜索能力,memetic算法把遗传算法和局部优化算法相结合,试验结果证明,能很好地解 决TSP问题。-TSP problem is a classic combinatorial optimization problem. Its solution has a number of local optimization methods and heuristic algorithms, local search methods take full account of the issue of neighborhood structure, genetic algorithm has good ability of global search, memetic algorithm for the genetic algorithm and local optimization algorithm combining test results proved that well positioned to solve the TSP problem.

this about the artificial neural networks which uses the back propogation method.-this is about the artificial neural networks which uses the back propogation method.

决策树分类中经典算法的ID3和Ｃ４．５代码公共包！-Classical decision tree classification algorithm ID3 and C4.5 code public packet!

微粒群算法与其它进化类算法相类似，也采用“群体”与“进化”的概念，同样也是依据个体（微粒）的适应值大小进行操作。所不同的是，微粒群算法不像其它进化算法那样对于个体使用进化算子，而是将每个个体看作是在n维搜索空间中的一个没有重量和体积的微粒，并在搜索空间中以一定的速度飞行。-Particle swarm optimization algorithm with other similar type of evolution, but also the use of " groups" and " evolution" concept is also based on the individual (particle) size of the operation of fitness. The difference is that unlike other particle swarm optimization evolutionary algorithm as the use of evolution for the individual operator, but as each individual in the n-dimensional search space of a weight and size of the particles, and in the search space the speed of a certain flight.