很好的遗传算法值得一看

基于MATLAB完成的神经网络源程序 大家-Based on the MATLAB neural network to complete the U.S. source

结合均匀设计表和小边经验公式产生初始种群，使TSP应用遗传算法能求出更优更快解，详尽说明见paper.doc。-uniform design combining a small side table and have empirical formula initial population, TSP application of genetic algorithms can be obtained faster and better solutions, detailed description of paper.doc.

基于信息熵的免疫算法是一种修正的免疫算法，方法并不复杂，对于特定问题效果较好-Entropy-based immune algorithm is a modified immune algorithm, methods are not complicated, for the specific problem better

使用禁忌搜索法约求解瑟夫环改进算法，程序简单易懂。-use tabu search about solving Cerf Central improved algorithm, procedures simple and understandable.

神经网络和遗传算法组合应用-neural networks and genetic algorithms combined application

禁忌搜索法对初始解、邻域个数及禁忌列表的大小等参数有比较严格的要求, 这些参数直接影响着算法的优化能力。文章提出了一种改进的禁忌搜索法, 它用有效空间来压缩搜索范围, 这样可以提高搜索效率和全局搜索能力。用短期和长期禁忌列表存储器来保证算法能搜索到全和分局空间的每一点, 并且不重复搜索。经过验算析, 证明它是一种较好的全局启发式搜索法.-Tabu search method, the initial solution, neighborhood and tabu list size of the number of parameters such as have more stringent requirements, these parameters directly affect the ability to optimize the algorithm. This paper presents an improved tabu search method, it is used effectively to compress the search space, so that can improve search efficiency and global search capabilities. Short-term and long-term memory tabu list to ensure the algorithm can search the whole and the sub-space, each point, and do not repeat the search. After checking analysis to prove that it is a good global heuristic search method.

gmeans-- Clustering with first variation and splitting 文本聚类算法Gmeans ,使用了3种相似度函数,cosine,euclidean ,KL.文本数据使用的是稀疏矩阵形式. -gmeans clustering with first variation and splitting Gmeans,a text clustering algorithm, uses 3 functions,cosine,euclidean and KL in similarity measuring.Text data are described by sparse matrix.

雅可比(Jacobi)迭代算法的C++实现，供研究设计使用-Jacobi (Jacobi) iteration algorithm for C++ implementation for research designed to use

神经网络模式识别及其实现，第八章。 内含：KMEANS 、KOHONEN 、LVQ 、SOFM 算法。-pattern recognition and neural network to achieve, Chapter VIII. Intron : KMEANS, KOHONEN, LVQ, SOFM algorithm.