搜索资源列表
GASVM.用遗传算法进行特征选取和svm参数优化的程序
- 用遗传算法进行特征选取和svm参数优化的程序。遗传算法工具箱goat已在压缩包 需要安装libsvm就可以直接运行。数据集采用UCI中的german数据集，并完成归一化操作,Genetic algorithm with feature selection and parameter optimization svm procedures. Genetic Algorithm Toolbox in goat need to install libsvm package can be run dir
Thesis-On-Cognitive-Radio
- This thesis aims to clearly describe the cognitive radio and its components and operations. Moreover, it aims on describing the expected outcome from the most common techniques that are proposed for use in cognitive radios. In addition, it describes
psoprogress.rar
- ％程序名称：求解约束优化问题的改进粒子群优化算法 ％程序功能：求解带各种约束条件的优化问题 ％输入条件：各种初始条件，以及设定参数 ％输出数值：最优解位置以及函数极小值 , Program name: for solving constrained optimization problems to improve particle swarm optimization algorithm program features: solving with a variety of constr
FuzzyBPNN
- matlab格式源代码。功能：模糊BP神经网络集成解耦算法和应用于控制优化模型问题。-matlab source code format. Function: fuzzy BP neural network ensemble decoupling control algorithm and optimization model applied to the problem.
PSO
- 关于粒子群算法的各种应用及当前研究前沿热点。-With regard to various applications of particle swarm optimization algorithm and the frontier of current research hotspot.
Apriorisimplement
- 本代码实现了数据挖掘中的一个经典算法Aprioris 。在此算法中，通过对平凡集的优化使其更加的完备也更加的有效。-The code to achieve the data mining algorithm in a classical Aprioris. In this algorithm, through an extraordinary set of optimization to make it more complete and more effective.
example
- 本算法是一种标准遗传算法，应用于函数优化，性能较好。-The algorithm is a standard genetic algorithm, applied to function optimization, performance better.
spea
- spea源码，是目前非常流行的解决多目标优化问题的进化算法。-Spea source, is very popular to solve multi-objective evolutionary algorithm for optimization problems.
DifferentialEvolutionAPracticalApproachtoGlobalOpt
- 这是一本讲微分进化的书，进化算法是以遗传算法为代表的一类随机算法的总称，95年由Rainer Storn和Kenneth Prici提出微分进化方法，比传统进化算法更好更简单，2004年该方法的原创者出版了长达580页的微分进化：一种全局优化的实用方法，本书是英文版，似乎还没有中文版，希望对感兴趣的人有用-This is a book stresses differential evolution, evolutionary algorithm based on genetic algorith
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.
ant
- 蚁群算法 却是一种源于自然现象的算法，即与具体问题关系不大的优化算法，也就是它是一种用来在图中寻找优化路径的机率型技术。-Ant colony algorithm Ant colony algorithm is a natural phenomenon due to the algorithm, that is, has little to do with the specific problem of the optimization algorithm, that is, it is a d
ant.colony.optimization
- 运用C语言编写的蚁群算法，实现算例的优化-The use of C language ant colony algorithm, optimization examples to achieve
ACO.ZIP
- ACO algorythm. This software package provides an implementation of various Ant Colony Optimization (ACO) algorithms applied to the symmetric Traveling Salesman Problem (TSP). The ACO algorithms implemented are Ant System, Elitist Ant System, MAX-MIN
Genetic_Algorithm
- 利用matlab编写的一些简单函数优化的遗传算法程序～-Matlab prepared to use some simple function of the genetic algorithm optimization process ~
mopsoPDF
- 有关多目标粒子群算法的论文，想研究多目标的可以参考-Of the multi-objective particle swarm optimization of the papers, I would like to research objectives can refer to
optimization
- 最优化算法的总结，包括了主要的搜索算法，NP问题，遗传算法，神经网络，拉格朗日松弛，对计算机优化计算提供理论基础。-Summary of optimization algorithms, including the main search algorithm, NP problems, genetic algorithms, neural networks, Lagrangian relaxation, to provide a theoretical basis for computer op
sine
- 用遗传算法优化神经网络权值 最后实现逼近sin函数曲线-Neural network using genetic algorithm optimization to achieve the right of the value of the final function curve approximation sin
lssvmMATLAB
- 本程序使用支持向量机法和粒子群算法，实现对数据的分类-This program uses support vector machine method and particle swarm optimization, to achieve the classification of data
genetic-optimization-to-solve-TSP
- 人工智能遗传算法优化解决TSP问题，很好用的代码-Artificial intelligence, genetic algorithm optimization to solve the TSP problem, a good code
Function optimization algorithm
- 遗传算法提供了求解非线性规划的通用框架，它不依赖于问题的具体领域。遗传算法的优点是将问题参数编码成染色体后进行优化， 而不针对参数本身， 从而不受函数约束条件的限制； 搜索过程从问题解的一个集合开始， 而不是单个个体， 具有隐含并行搜索特性， 可大大减少陷入局部最小的可能性。而且优化计算时算法不依赖于梯度信息，且不要求目标函数连续及可导，使其适于求解传统搜索方法难以解决的大规模、非线性组合优化问题。(Genetic algorithm provides a general framework f