lzb的程序示例,方便修改,适合初学者使用!

本算法为自适应遗传算法，采用基于排序的选择策略，编码方案为实数编码。能够解决很多优化问题！-The algorithm for the adaptive genetic algorithm, based on the sort of choice strategy, coding schemes for real-coded. Many optimization problems can be solved!

利用遗传算法进行文本聚类的特征选择.把一种特征组合看作一个染色体，对其进行二进制编码，引入文本集密度作为适应度函数进行特征个体适应度的评价. -The use of genetic algorithm feature selection in text clustering. To a combination of characteristics as a chromosome, its binary code, the introduction of text set density as a characteristic of fitness function for evaluation of individual fitness.

资源分配神经网络解决Mackey-Glass时间序列预测函数逼近问题-Neural network to solve the allocation of resources Mackey-Glass time series prediction function approximation problem

简单的遗传算法源代码，挺实用的，大家可以借鉴-Simple genetic algorithm source code, very useful, we can learn from

自主式决策树学习的程序源码，对研究机器学习的同行很有用。-Autonomous Decision Tree learning procedures source, the study machine learning peer useful.

扩展的Infomax算法，具有比较快的收敛速度！而且程序非常简洁,明了-expansion of Infomax algorithm, with a faster rate of convergence! The procedure is very simple, straightforward

基于Matlab的人工神经网络反向传播程序设计

用C#语言开发基于模拟退火算法解决的10个城市的TSP问题-The Solution of TSP(C#)

基本蚁群算法的源代码 经过一些改正 改正了原来的一些错误 可以运行 -The basic ant colony algorithm source code through a number of corrections to correct the original error can run some

Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42. 使用神经网络集成方法诊断糖尿病,肝炎,乳腺癌症的案例研究. -Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42.