c 神经网络遗传算法

遗传编程是一门新兴的进化计算技术且引起了学术界的非常高的重视。虽然本程序是本人编写，略显粗糙，但需要的人还是可以借鉴一下！-Genetic programming is an emerging technology of evolutionary computation and has aroused increasing attention, although this code is done by myself, it can serve as a reference for anyone needs it!

一个蚁群算法，大家很想要得东东，共享给大家了。-an ant colony algorithm, we want to fine Eastern, we had to share.

以一个简单的例子说明模拟退火算法的思想。 模拟退火法求函数f(x,y) = 5sin(xy) + x^2 + y^2的最小值，对理解模拟退火算法是一个很好的程序示例。-to a simple example illustrates the simulated annealing algorithm thinking. Simulated Annealing for the function f (x, y) = 5sin (xy) x ^ 2 y ^ 2 minimum, the right understanding of simulated annealing method is a good example of the procedure.

用模拟退火法高效的计算图论中哈密顿贿赂的最短路径-simulated annealing method of calculating efficient graph theory Hamiltonian bribery Shortest Path

HOPFIELD的一个经典用例，解决TSP问题，代码完整，是我自己编写的哦-HOPFILED cases with a classic, TSP solution, code integrity, I was prepared oh

用Fast BP实现复杂的函数逼近,对初学者有一定的帮助,是在BP的基础上增加了R.-Fast BP achieved with a complex function approximation, there is some help for beginners, is based on the BP increase in R.

神经网络的HP算法，这是学习神经网络的入门课程．-HP neural network algorithm, which is learning neural network-oriented courses.

一种新的ica算法，主要是应用与盲分离算法，大家可以-ica a new algorithm, and are primarily used to blind separation algorithm, we can s

此程序用于测试神经元的库函数 // 它展示了怎样用这个库所生成的多层BP网络来求解宇称问题-this procedure for testing neuronal function of the library// it tells how to use this library generated by the multi-storey BP network to solve problems Parity

针对现有遗传算法在多维非线性优选方面的不足，本文提出了一种基于小生境进化算法（NEA）的非线性优选模型，探讨了NEA算法的参数选择原则。通过大量仿真和比较，表明算法在复杂非线性优选中具有快速、高效、鲁棒性强的特点，并能在全局范围内有效搜索所有最优解。 -against existing genetic algorithms in three-dimensional nonlinear optimization for the shortage, the paper presents a niche evolutionary algorithm (NEA) nonlinear optimization model, the NEA on the parameters chosen algorithm principle. Through simulation and large, the algorithm shown in a complex nonlinear optimization is fast, efficient, robust features of the strong, and the global scope effective search all the optimal solution.