Genetic Algorithm NSGA

goat遗传算法工具包，可参考使用，节省遗传算法开发时间-goat genetic algorithm tool kit, refer to the use of genetic algorithms to save development time

fortran版 神经网络遗传算法-fortran version of neural network genetic algorithm

二进制的基本遗传算法，在VC++环境下编程实现。-binary basic genetic algorithm, the VC environment programming.

用遗传算法求解背包问题是南京航空航天大学信息与计算科学专业编写的.本程序利用遗传算法来求解背包问题.采用二进制字符串编码,1表示选择物体,0则不选择. 背包问题描述:在M件物品取出若干件放在空间为W的背包里，每件物品的重量为W1，W・2……Wn，与之相对应的价值为P1,P2……Pn。求出获得最大价值的方案。注意：在本题中，所有的重量值均为整数。-genetic algorithm knapsack problem is the Nanjing University of Aeronautics and Astronautics, Information and Computing Science prepared. The procedure the use of genetic algorithms to solve knapsack problem. binary string encoding, an option objects, 0 no choice. Knapsack Problem Description : M items removed several pieces on the space-W backpack, Each of the weight W1, W 2 ... Wn, the corresponding value of P1, P2 ... Pn. Sought to gain maximum value of the program. NOTE : In this issue, all the weight values are integers.

模式识别k_means聚类算法。-kmeans clutering algorithm for Pattern Recognition

该程序实现了简单的模糊ARTMAP神经网络算法。SFAM是神经网络分类器，它是简单和快速的模糊ARTMAP版本。-The program implements a simple fuzzy ARTMAP neural network algorithm. SFAM is a neural network classifier, which is simple and fast version of fuzzy ARTMAP.

为了测试评估贝叶斯分类器的性能,用不同数据集进行对比实验是必不可少的. -In order to test the assessment of the performance of Bayesian classifier, and compare different experimental data sets is essential.

小波神经网络电力负荷预测,有较强的学习价值和应用价值-Wavelet neural network power load forecasting, there are strong learning value and applied value

演化计算是基于随即搜索的新算法；它的技术模型源于自然的演化。下面是一个例子，该函数是典型的多峰（震动剧烈）的函数。用的算法是郭涛算法。-evolutionary computation is based on the then new search algorithm; It stems from the technical model of the natural evolution. Below is an example of this function is a typical multi-feng (severe shock) function. The algorithm is Guo Tao algorithm.

使用Astar算法实现的八数码问题。C++编写，控制台应用程序，实现简单，代码轻便。-Astar algorithm using eight digital issues. C++ written in the console application, to achieve simple and lightweight code.