said that the use of space rcc8 model spatial reasoning, problem solving csp. cs...

BP小波神经网络快速学习算法研究and 小波神经网络程序-BP wavelet neural network learning algorithm for rapid research and wavelet neural network procedures

解TSP问题的遗传算法C语言程序-solution of TSP genetic algorithm C Programming Language

一个完整BP神经网络源代码，包括数据和说明文档,很有用的。-A complete BP neural network source code, including data and documentation, very useful.

遗传算法优化神经网络结构和链接权，采用浮点数编码-Genetic algorithm neural network structure and link the right to use floating-point encoding

谢大同郭涛算法(函数优化)源代码下载，和大家分享自己的东西-Xie Datong Guo Tao algorithm (function optimization) source code download, and share things on your own

日本V01机器人运动学算法，包括正反运动学源程序及头文件的定义-Japan V01 robot kinematics algorithm, including the pros and kinematic source and header files to the definition

移动机器人运动学mathematica程序-Mobile robot kinematics mathematica 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

基于遗传算法的人工生命模拟，C++源代码-Based on genetic algorithm artificial life simulation, C++ Source code

简单遗传算法(SGA) 主要算法模块有:选择 交叉 变异 (三个遗传操作) 和 群体更新 -simple genetic algorithm (SGA) algorithm for the main modules are : choice of crossover and mutation (3 Genetic Manipulation) and update groups