目标函数是De Jong函数，是一个连续、凸起的单峰函数

OPENCV系列的，TOOLBOX工具，应该挺有用的，大家自己看看吧！-OPENCV series, TOOLBOX tool, it should be quite useless, we all see!

用禁忌搜索解决TSP问题，程序中使用对路径长度禁忌的方法，运行效率比较高。-Tabu search solution TSP, procedures for the use of the taboo path length, operating more efficient.

离散数学中---------一阶谓词演算源代码-Discrete Mathematics- First order predicate calculus source code

这个程序是关于遗传算法c源程序，可以通过这个源程序明白什么是遗传算法-on genetic algorithm c source, the source can understand what genetic algorithms

粗糙集属性约简算法程序和相应的学术文章,以及一些参考资料和学习网站-Rough Set Attribute Reduction Algorithm procedures and the corresponding academic article, as well as some reference and learning site

GENNET是一个通过遗传算法优化权重的16个神经元的全连接神经网络。训练目的是使网络在一定假设的激励下，随时间产生同目标函数同样的响应，即:可以把此网络制作为一个任意波形的信号发生器。 程序分3个示例说明遗传算法进化过程中网络输出的变化趋势。可以看出网络输出（蓝色曲线）随着时间变化逐渐逼近目标函数（红色曲线）。此程序不仅可以学习遗传算法，也可以用于研究全连接网络的直观示例。-GENNET is a genetic algorithm to optimize the weights by the 16 neurons fully connected neural networks. Training purposes is to enable network inspired by certain assumptions, over time, produce the same response with the objective function, namely: the network can be produced as an arbitrary waveform signal generator. Procedure, three examples illustrate the evolution of genetic algorithms in the network output trends. We can see that the network output (blue curve) changes over time, gradually approaching the objective function (red curve). This program can not only learn from the genetic algorithm can also be used to study visual example of a fully connected network.

Evenly spaces the cities on the perimeter of a wheel and returns a vector of their coordinates 利用AI技术生成一个圆-Evenly spaces the cities on the perimeter of a wheel and returns a vector of their coordinates the use of AI technology to generate a circle

支持向量机(3)svm_light-Support Vector Machines(3):svm_light

专家系统－神经网络代码-expert system-neural network code

贝叶斯（Bayesion）机器学习相互信息。用于求取熵、联合熵等。改进后可用于随机反演-Bayesian (Bayesion) machine learning of mutual information. Used to obtain entropy, joint entropy and so on. Improved can be used for stochastic inversion