遗传算法求解旅行商问题的求解

Genetic Programming。GP算法在方程逼近求解上的应用。-Genetic Programming. GP approximation algorithm for solving equations in the application.

VC中利用人工智能解决八迷宫问题。这是一个很有趣的人工智能小程序，不妨试试看。-use artificial intelligence to solve eight problems maze. This is a very interesting small artificial intelligence procedures, it may try.

双向广搜求解八数码问题,双向广搜求解八数码问题-Collected eight two-way digital problem solving, two-way collected eight digital problem solving

apriori算法是数据挖掘的经典算法,它基于关联规则的思想.此为我的第3个收藏-algorithm data mining algorithm is the classic algorithm, which is based on the idea of association rules. This was my first three collections

Matlab implementation of ID3 and NaiveBayes classifier. It also includes example dataset as well.

lzb的程序示例,方便修改,适合初学者使用!-lzb sample procedures to facilitate the modification, suitable for beginners to use!

使用遗传算法、模拟退火算法求解设计结构矩阵，并进行对比分析-The use of genetic algorithms, simulated annealing algorithm design structure matrix, and a comparative analysis of

More AI...(GEP) Gene Expression Programming in C# and .NET 由Michael Gold用C#开发的GEP程序.-AI ... (GEP) Gene Expression Programming in C# and. By Michael Gold NET C# development of the GEP procedures.

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.

K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, including clustering and clustering batch. K-means clustering of the time complexity of n* k* m, n samples, several types of k, m sample dimension. The time complexity is a very objective. Because if we use one billion times per second the computer using 50 samples of two exhaustive method to find the optimal set out again about 66.7 days, divided into three categories, offering 3,500 years. Local optimal algorithm against the shortcomings, I was prepared simulated annealing process improvements. Early Feng hope for everyone, listen to the master teachings.