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gep编程基础2-27
- GEP（基因表达式程序设计）是一种新的演化算法，该文件是基本的GEP编程，采用功能的封装格式，测试通过，能够方便的使用。-GEP (Gene Expression Program Design) is a new evolutionary algorithm, which is the basic GEP programming, functional package format, through testing, to facilitate their use.
DS
- DS证据理论中的合成规则的算法实现；该算法对两个基本可信度分配(BPA)的合成规则进行实现，算法包含在DS.h文件中，main.c文件对其进行调用，并对简单示例进行测试。-DS evidence theory in the synthesis of the rules of algorithm the algorithm two basic probability assignment (BPA) to achieve the synthesis rules, the algorithm is
bp1
- 基本的BP算法，将训练样本和测试样本已载入，可根据需要更换上述样本-Basic BP algorithm, the training samples and test samples have been loaded, may need to be replaced in accordance with the above-mentioned samples
genetic
- 理解普通的遗传算法和佳点集遗传算法的基本思想和不同点,用遗传算法测试一标准函数。佳点集算法测试一标准函数,理解普通的遗传算法和佳点集遗传算法的基本思想和不同点-Understanding of the general genetic algorithm and good point set genetic algorithm' s basic idea and the different points, by using genetic algorithms to test a stand
yichuansuanfa
- 基本遗传算法程序，用于程序测试，软件开发等，对遗传算法初学者作用较大-The basic genetic algorithm for program testing, software development, the role of genetic algorithm beginners larger
The-SPSO-testingprocedure
- 基本的粒子群程序，测试四个标准测试函数，画出收敛曲线，验证算法的寻优性能-The basic particle swarm procedure, testing four standard test functions, draw the convergence curve, verify the performance of algorithm optimization
shaffer
- 在实现基本DNA进化算法的基础上，提出对于概率取值使用自适应策略，最后利用模拟退火算法良好的局部寻优功能和DNA进化算法结合，通过对测试函数的测试，证明改进后的算法性能很好-Achieve basic DNA evolutionary algorithm based on the use of adaptive strategy proposed for the probability value, the final use of the simulated annealing algorit
A-Modified-Artificial-Fish
- 本文采用了自适应调制参数的方式对算法进行了改进，并用6个测试函数，结果表明，改进后的算法效果优异-In this paper, an adaptive AFSA algorithm is presented by adjusting the parameter automatically in basic AFSA. Six benchmark functions are used to check the performance of the new method. It shows th
PSOt
- pso优化算法的基本应用，并包含了其中主要的几个测试函数，内容相当全面。-pso optimization of the basic application, and contains several tests in which the main function, the content is very comprehensive.
A_Star
- A星算法解决八数码问题，代码共有两种，基础版用于接收外部输入，解决输入状态到输出状态的变化，训练版为在已写好的数据集（362880个数据）下测试运行效果，代码用C语言书写，较为简洁-A star algorithm to solve the problem of the digital code there are two basic version for receiving an external input, output changes state to resolve the inpu
IDA_star
- 使用IDA星算法解决八数码问题，效果比A星算法更好，运行速度更快，代码更为简洁，代码用C++语言编写，共包含基础版和测试版两个文件夹，测试版为在所写数据集上测试效果（362880个数据），基础版为人为输入源状态与目标状态，进行转化-IDA Star algorithm using eight digital problem solving, better than the A Star algorithm, run faster, more concise code, the code used
SFLA_testfuntion
- 混合蛙跳算法的基本程序，测试用例，给出测试结果-SFLA of basic procedures, test cases, test results are given
AF_
- 人工鱼群算法，实现了基本的算法框架以及常用的测试函数。-Artificial fish algorithm, the realization of the basic algorithm framework, and the commonly used test function
CSO_Matlab
- CSO算法，用matlab编写，直观画图显示结果。带五个基本测试函数-CSO algorithm, using MATLAB to prepare, intuitive drawing display results. With five basic test functions
6DOF_robot-
- 这是一套比较详细的6关节机器人源码，可供初学者学习开发，基本功能已经有了，还需添加一些就可以测试使用。-This is a more detailed set of 6 joint robot source, for beginners to learn development, the basic functions have been, and need to add some can be tested.
Bee-colony-algorithm-source-code
- 基本的(Artificial bee colony algorithm ABC)人工蜂群算法源代码，本人亲自测试并通过的，使用C语言编写。-The basic (Artificial bee colony algorithm ABC) artificial bee colony algorithm source code, the use of C language.
MachineLearning_Steps.py
- python做机器学习的基本步骤，包括了数据收集，数据归一化，数据的特征值选择，最后使用几个算法进行测试(The basic steps for machine learning with Python)
elm_train_predict
- 基础分类和回归实验，点击解压，输入训练和测试数据(Basic classification and prediction experiment)
mopso c
- 此文件夹为基本粒子群算法的c代码实现，测试了文章MOPSO-CD中的问题(This folder for the basic particle swarm algorithm c code to test the article MOPSO-CD problems)
kmean
- 一个学习k均值聚类的实例，代码实现了其基本原理，简单易懂，带有测试，训练数据集，可直接上手操作(A learning k-means clustering example, the code to achieve its basic principles, easy to understand, with a test, training data set can be used directly)