Description: Chapter 1: Genetic Algorithms: An Overview
Chapter 2: Genetic Algorithms in Problem Solving
Chapter 3: Genetic Algorithms in Scientific Models
Chapter 4: Theoretical Foundations of Genetic Algorithms
Charpter5 WHEN SHOULD A GENETIC ALGORITHM BE USED?
Chapter 6: Conclusions and Future Directions Platform: |
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Description: MIT Press - An Introduction to Genetic Algorithms.pdf-MIT Press- An Introduction to Genetic Algorithms.pdf Platform: |
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Description: 遗传算法的一个例子：旅行商问题。C++源代码，适合初学者研究遗传算法与最优化等方面的知识。-a genetic algorithm examples : the traveling salesman problem. C source code, suitable for beginners and genetic algorithm optimization and other kinds of knowledge. Platform: |
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Author:hu |
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Description: 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 networks. The fifth chapter in combinatorial optimization applications. Chapter V Platform: |
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Author:孙东 |
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Description: An Introduction to Genetic Algorithms for Scientists and Engineers
By David Coley, World Scientific Press, 1999 (US$28 / £ 19.04)
书中的算法源程序-An Introduction to Genetic Algorithms for Scientists and Engineers By David Coley, World Scientific Press, 1999 (U.S. $ 28 / Platform: |
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Description: 本文的题目是基于分形和遗传算法的人脸识别方法，对有限人群提出一种采用分形特征和遗传聚类的识别方法: 将图像分成很多小区域, 分别计算各个区域的分形特征, 以充分利用图像二维信息 同一个模式有多个样本, 通过遗传算法进行聚类以得到最优解实现不变性识别. 最后采用ORL 人脸图像库的一组图像对比了新方法、本征脸法和自联想神经网络方法, 结果表明该方法的识别率, 与本征脸法相似, 比自联想神经网络高.-The title of this article is based on fractal and genetic algorithms for face recognition method, a crowd of limited use of fractal characteristics and the identification of genetic clustering methods: the image is divided into many small regions, each region were calculated fractal characteristics, to take full advantage of two-dimensional image information with a model for a number of samples, through the genetic clustering algorithm in order to obtain the optimal solution to achieve invariant recognition. Finally, using ORL face image database of a group of image contrast of the new methods, eigenface law and auto-associative neural network methods, results show that the method of recognition rate, with the eigenface method is similar to auto-associative neural network than high. Platform: |
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Author:阳关 |
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Description: 遗传算法求解01背包问题＋实验报告＋参考文献。如果你看了这个程序还是不能明白遗传算法的巧妙，那么还是不要再看这个问题了。-Genetic Algorithm for Knapsack Problem 01 experimental report references. If you read this procedure should not understand the genetic algorithm or clever, then it is better not to look at this issue. Platform: |
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Author:gouyabin |
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Description: 在matlab环境下用人工智能算法--遗传算法求函数极植的matlab程序-In the matlab environment using artificial intelligence algorithms- genetic algorithm for matlab function very replanting program Platform: |
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Description: 遗传算法得一种编译程序,不是很正统，但是很新颖，并且方法简单，很实用，是计算机运行的时间更加快
-Genetic algorithms have a compiler, is not very orthodox, but very innovative, and method is simple, very practical, it is time the computer is running more to speed up Platform: |
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Author:魏巍 |
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Description: 遗传算法车间调度,车间作业调度问题遗传算法
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输入参数列表
M 遗传进化迭代次数
N 种群规模(取偶数)
Pm 变异概率
T m×n的矩阵，存储m个工件n个工序的加工时间
P 1×n的向量，n个工序中，每一个工序所具有的机床数目
输出参数列表
Zp 最优的Makespan值
Y1p 最优方案中，各工件各工序的开始时刻，可根据它绘出甘特图
Y2p 最优方案中，各工件各工序的结束时刻，可根据它绘出甘特图
Y3p 最优方案中，各工件各工序使用的机器编号
Xp 最优决策变量的值，决策变量是一个实数编码的m×n矩阵
LC1 收敛曲线1，各代最优个体适应值的记录
LC2 收敛曲线2，各代群体平均适应值的记录
最后，程序还将绘出三副图片：两条收敛曲线图和甘特图（各工件的调度时序图）-Genetic algorithm scheduling, job shop scheduling problems with genetic algorithms -------------------------------------------------------------------------- genetic evolution of the input parameter list, the number of iterations M N population size (taken even) Pm mutation probability T m × n matrix, stored m one piece n a process of processing time 1 × n vector, n a process in which each machine processes the number of Zp with the best value Y1p Optimal Makespan programs, the start time of each process the workpiece can be drawn based on its optimal solution Gantt Y2p, each time the workpiece end of the process, according to its draw Gantt Y3p optimal solution, each piece of the processes using machine code Xp optimal decision variable, decision variable is a real m × n matrix encoded LC1 convergence curve 1, the generation of the best individual record of LC2 fitness convergence curve 2, the average fitness value on behalf of groups record Finally, the program will draw three pict Platform: |
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Author:王龙隐 |
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Search - algorithms genetic