简单一元函数优化实例，利用遗传算法计算下面函数的最大值...

对于具有一定规模的TSP问题的求解，至今没有较完善的求解办法。本程序是用遗传算法来求解TSP问题。-for a certain size of the TSP solution and there has been no better way to solve. This procedure is used genetic algorithms to TSP.

svm_nu 分类源码 用于svm的分类编程同svm_c 相近-svm classification svm_nu source for the classification of programming with similar svm_c

带有QoS约束的组播路由问题是一个NP完全问题，遗传模拟退火算法是遗传算法和模拟退火算法的一种融合，可以为这类问题提供一个解决方案-With QoS constraint multicast routing problem is an NP-complete problem, genetic simulated annealing algorithm is a genetic algorithm and simulated annealing algorithm as a fusion, you can provide for such a solution

人工智能神经网络识别 人工智能神经网络识别-Artificial neural network artificial neural network to identify

数据挖掘算法(Apriori)--JAVA实现-(Apriori)--JAVA

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

关于遗传算法的matlab 程序代码，或许对有些这方面的同仁有用-on Genetic Algorithms Matlab code, perhaps some of us in this regard useful

一个用C编写的SOM（自组织特征映射）神经网络源代码。-a C prepared by the SOM (self-organizing feature map) neural network source code.

VC++ 遗传算法 VC++ 遗传算法 VC++ 遗传算法-VC++ Genetic Algorithm

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