资源列表
7941925pos
- 粒子群的优化算法，不仅可以方便地解决无约束优化问题，也可以方便的解决有约束的非线性优化问题。-Particle Swarm Optimization algorithm, not only can easily solve the unconstrained optimization problem can also be convenient to solve constrained nonlinear optimization problem.
pso3
- 带交叉因子的改进粒子群优化算法，算法用于示解多维无约束优化问题，收敛性强。-With cross-factor improvement of particle swarm optimization algorithm for multi-dimensional solution that unconstrained optimization problem, convergence is strong.
cpso
- 粒子群优化算法容易理解，实现简单，优化速度快，收敛性强。常用于解决种类最优化问题。-Particle swarm optimization algorithm easy to understand, easy to achieve, and optimize the speed and strong convergence. Types commonly used in the optimization problem to solve.
IGA-gaotv5
- 免疫遗传算法IGA，可以求解多目标遗传优化、改进遗传优化-IGA
find
- 用于寻找网络的clique,4个节点以下很快-For the search network clique, 4 nodes following soon
eightcode
- 优化后A*算法解八数码难题，只需要在源文件中将初始化的八数码输入即可，具有很好的泛化性。-Optimized solution of A* algorithm eight digital problems and only required source files will be initialized in the eight digital input, has a good generalization of.
generic2
- 遗传算法求解极值的源程序，只需将要求函数在源文件中输入，即可，种子数100，变异概率和交叉概率可调-Extremes of genetic algorithm source code, simply will be asked to function in the source file type, you can, seed number of 100, mutation probability and crossover probability adjustable
tsp
- hopfield神经网络求解TSP问题，改程序设置了10个城市的随机位置，进而解决城市间最短路径问题。-hopfield neural network to solve TSP problem, the procedures set up to 10 cities random location, then the shortest path between cities to solve problem.
generic_tsp
- 用遗传算法求解TSP问题，种子数100，遗传概率和交叉概率可以在源程序中修改。-Genetic Algorithm with TSP problem, a few hundred seeds, genetic probability and crossover probability can modify the source program.
gaknn2008-12-08
- 本算法是实现基于KNN的基因遗传算法，是对KNN算法的改进，具有更好的分类效果。-gaKnn[Genetic Algorithm Optimized K Nearest Neighbor Classification framework] is a frameowork for KNN optimization with a genetic algorithm.
DPSO
- 综合的微粒群智能分类算法，考虑了不同的学习因子，可根据不同的数据特点进行选择。-Particle Swarm integrated intelligent classification algorithms, taking into account different learning factor, according to the characteristics of different data selection.
ch3
- 神经网络实用教程----第三章源码（很不错喔）-Neural Network Practical Guide---- Chapter III source [Oh well]