遗传算法的一些应用

kmeans算法,许多同学做毕设需要用到-kmeans algorithm, many students needed to do to complete set up

hmm算法 包括前后向算法、viterbi算法、bw算法等另外包括内存申请等源代码-algorithm to include before and after algorithm, Viterbi algorithm, bw algorithm other memory applications including source code

遗传算法求解的程序，非常好用。适合建模问题的求解。-Genetic algorithm procedure is very easy to use. Suitable model for solving the problem.

Fuzzy Rule Base Implementation

Backpropagation Network Time-Series Forecasting 源码, 经典的BPN人工神经网络例子源码-Backpropagation Network Time-Series Forecasting source, the classic example of artificial neural network BPN source

基于SVM，分本分类，，是关于两类分类的比较好的工具，国外网站提供，效果很好的-based SVM, the sub-classification is on the classification of the two types of relatively good tool, overseas sites provide good results, the

这是我写毕业论文的源程序,用模拟退火算法求解VRPTW问题.在我机子上运行过了,结果很好.-This is the thesis I wrote the source code, using simulated annealing algorithm to solve VRPTW problems. In my machine run-off, resulting in very good.

9。《遗传算法原理及应用》，周明，孙树栋编着 国防工业出版社 1999年第一版 本书全面系统的介绍了遗传算法的基本原理、数学基础、各种主要实现技术，并给出了一些应用实例，同时还简单的介绍了进化计算的基本内容。本书具有系统性强、可读性强、可操作性强的特点。本书可供智能计算、自动控制、系统工程、优化计算、经济管理、运筹学、计算机等专业的研究人员、研究生、高年级大学生参考。 -9. "Genetic Algorithm Theory and Application", Zhou, Sun Shudong compile defense industry publisher in 1999 the first book version of the comprehensive and systematic introduction to the genetic algorithm to the basic principles mathematical basis, the major techniques, and gives some examples, also introduced a simple evolutionary computation of the basic elements. The book is systemic strong, readable, and it can be operational characteristics. The book smart, automatic control, systems engineering, optimization, economic management, and operations. computer and other professional researchers, graduate students and students of high-grade reference.

这是一个国外的Hopfield神经网络，有实例，可修改！-Hopfield Networks

7。《进化算法》，云庆夏编着 冶金工业出版社 2000年第一版 全书共分5章，分别为绪论、遗传算法、遗传规划、进化策略、进化规划。-7. "Evolutionary algorithm," Yun Qing Xia compile Metallurgical Industry Publishing House in 2000 the first edition of the book is divided into five chapters, each of the introduction, Genetic algorithms, genetic programming, evolutionary strategy, evolutionary programming.