一种运算效率较高的遗传算法

一个改进的聚类算法FCMA,可以实现数据的自适应有效聚类,对2维数据测试效果较好- An improvement gathers kind of algorithm FCMA, may auto-adapted realize the data effectively to gather the kind, is better to 2 dimensions according to the test effect

关联规则并行算法Count Distribution的C与MPI实现-Parallel Algorithm for Association Rules Count Distribution of C and MPI realize

关于粒子群优化算法的实现.使用java语言实现。-On the Particle Swarm Optimization Algorithm. Using java language.

图像检测模板匹配的Visual C++代码-Image Detection template matching Visual C code

3个径向基网络的matlab源程序[matlab],一维输入，一维输出，逼近效果很好！-three RBF network Matlab source [Matlab], one-dimensional input and one-dimensional output. Approximation good effect!

地球模拟器，最经典的人工生命模型,研究人工智能的必看。一般地说，生命都具有新陈代谢、复制和进化的能力。在自然界中，生物是由有限的食物供给和有限的生存空间约束的。在Tierra中，“生物”由一系列能够自我复制的机器代码或程序组成，它在计算机中的复制分别受到计算机的存储空间和CPU时间约束。能有效地占有内存空间和利用CPU时间的生物体，将具有更高的适应度，传递到下一代的机会就越大。 在Tierra中，计算机的RAM（随机访问存储器）中有一块专门的空间，这个空间中放置了一个“祖先有机体”，该祖先有机体根据它的汇编程序代码中的指令开始复制对它的生存是基本的代码。随着有机体的数目的增加，RAM中的空间减少了，因此有机体为了自己的生存空间开始竞争。-Earth Simulator, the most classic of artificial life model, the study of artificial intelligence Watchable. Generally speaking, life is metabolism, reproduction and the ability of evolution. In nature, biomass is limited by the supply of food and limited living space constraints. In Tierra, "biological" to a series of self-replicating machine code or procedure, in which computers were copied by the computer storage space and CPU time constraints. Effective occupy memory space and CPU time using the organisms, will have a higher degree of adaptability, conveying to the next generation the greater the chances. In Tierra, computer RAM (random access memory) is

一个运用改良的遗传算法来解决旅行商问题的程序-an improved use of genetic algorithms to solve the traveling salesman problem procedures

多重网格法在step形状区域上的实现,三种不同光滑步可以实现.-Multi-Grid Method in the step shape of the region on the realization of three different smoothing step can be achieved.

Using Simulated annealing to solve TSP

遗传算法示例源码，VC学习案例，对遗传算法感兴趣的朋友学习。-Genetic algorithm source code examples, VC case study on the genetic algorithm of interest to people in learning.