遗传算法的源程序例子，对于初学者有很大帮助，建议多

用VC编制的集成的野人和八数码演示程序。其中野人程序用动态的效果演示，并能设置进度。八数码程序能根据给出的源状态转换成目标状态。-VC establishment of integrated Savage and eight digital presentations. Savage procedures which use dynamic demonstration effect, and can be programmed to progress. Eight digital process according to the source is converted into a state target state.

中文分词

用遗传算法求解TSP（旅行商）问题，按照TSP1.m中的格式输入程序参数即可。-Genetic Algorithm with TSP (traveling salesman) problems, in accordance with the format in TSP1.m parameter entry process.

基本遗传算法的源代码，比较各代种群的进化。-The basic genetic algorithm source code, comparing the generation of species evolution.

apriori算法是数据挖掘的经典算法之1,其基于关联规则的思想.这是我的第2个收藏算法-algorithm is data mining algorithms of a classic algorithms, based on the idea of association rules. This is my first two collections Algorithm

一个更精度的平滑涵数, 可用于股票交易系统.用于Amibroker 平台-a more accurate smooth average function for amibroker trading system

自适应遗传算法 最基础的版本需要可以自己修改目标函数-Adaptive genetic algorithm version of the most basic needs can modify the objective function

落煤残存瓦斯量的确定是采掘工作面瓦斯涌出量预测的重要环节,它直接影响着采掘工作面瓦斯涌出量预测的精度,并与煤的变质程度、落煤粒度、原始瓦斯含量、暴露时间等影响因素呈非线性关系。人工神经网络具有表示任意非线性关系和学习的能力,是解决复杂非线性、不确定性和时变性问题的新思想和新方法。基于此,作者提出自适应神经网络的落煤残存瓦斯量预测模型,并结合不同矿井落煤残存瓦斯量的实际测定结果进行验证研究。结果表明,自适应调整权值的变步长BP神经网络模型预测精度高,收敛速度快 该预测模型的应用可为采掘工作面瓦斯涌出量的动态预测提供可靠的基础数据,为采掘工作面落煤残存瓦斯量的确定提出了一种全新的方法和思路。-charged residual coal gas is to determine the volume of mining gas emission rate forecast an important link, which directly affect mining gas emission rate forecast accuracy, and with coal metamorphism, loading coal particle size, the original gas content, exposure time and other factors nonlinear relationship. Artificial neural networks have expressed arbitrary nonlinear relationships and the ability to solve complex nonlinear, time-varying uncertainty and the new ideas and new approaches. Based on this, the author of adaptive neural network loading coal residual gas production forecast model, and a combination of different loading coal mine gas remnants of the actual test results of research

BP网络解决异或问题的实例，有初始值和最终结果的显示。本网络结构为三层BP网络，各层神经元个数可变，更改相关#define即可。（注释较详细，主函数中调用的函数和矩阵类都定义在文件function.h中）-BP network to solve XOR problem instance, there are initial values and final results are displayed. The BP network structure for the three-tier network of neurons in the number of floors variable, change the relevant# Define you. (Note more detailed, the main function calls the function and matrices are defined in the document in function.h)