The package focuses on the genetic algorithm. In this paper, genetic algorithm i...

主要实现了烧结矿碱度的神经网络预测，训练精度高-The main achievement of the sinter basicity of the neural network prediction, training and high precision

matlab 模拟退火算法simulated anealing 的工具箱-Matlab simulated annealing simulated anealing the Toolbox

测试无约束优化定理求解最小值,包括测试数据,在VC下可以实现,已经调试成功.-test unconstrained optimization for minimum theorem, including test data, the VC can be achieved, debugging has been successful.

基本遗传算法,用来求解函数的最大值!在VC和TC下均可实现.-basic genetic algorithm used to solve the function of the maximum! And the VC can be achieved under TC.

这是一种利用神经网络来进行人脸识别的算法。-This is a neural network to the face recognition algorithm.

RBF实现神经网络预测程序，压缩包，不用解压缩密码！-Realize RBF neural network prediction program, compressed packet, do not extract the password!

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

人工智能课程中关于产生式系统部分上机实验的源代码，有很大参考价值。-No

Aprior算法实现程序-Aprior algorithm program! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

6。《非数值并行算法――遗传算法》，刘勇等着 科学出版社 1995年第一版 本书系统地叙述了非数值并行算法之一的模拟退火算法的基本原理以及最新进展，同时为了便于读者解决实际问题，书中对具体算法的步骤作了详细介绍。本书共分七章，第一章介绍算法的思想、特点。发展过程和前景。第二章介绍算法的基本理论。第三章讨论算法解连续优化问题。第四章利用算法设计和优化神经网络。第五章介绍在组合优化中的应用。第六章介绍应用遗传程序设计解决程序设计自动化问题。第七章对遗传算法和其它适应性算法进行比较。 本书可供高校有关专业的师生、科研人员、工程技术人员阅读参考。-6. "Non- numerical parallel algorithms-- Genetic Algorithm" Liu Yong waiting 520-531 1995 version of the first book to systematically describe the non- numerical parallel algorithms one count of simulated annealing Law and the basic tenets of the latest progress, in order to help readers solve practical problems, book of the specific algorithm steps in detail. The book is divided into seven chapters, the first chapter describes the idea algorithm, characteristics. The development process and prospects. The second chapter describes the basic algorithm theory. The third chapter discusses Algorithm for continuous optimization problems. Chapter IV using the algorithm design and optimization of neural netw