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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Author:wunan_3@126.com |
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Description: LinPacker是一个优化矩形布局的工具，是在具有遗传算法的半无限大小区完成这一操作的。对缩减工厂的库存是有用的。-LinPacker optimization is a rectangular layout of the tool, with a genetic algorithm in the semi-infinite size of the area to complete the operation. To reduce factory stocks will be useful. Platform: |
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Author:苏明春 |
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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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Author:q |
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Description: 这是一本较详细介绍遗传算法原理和应用的书籍，很多应用在本书中多有介绍-This is a more detailed genetic algorithm theory and application of books, many applications in this book presents more Platform: |
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Author:沈伟翔 |
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Description: 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-comparison with the genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications Platform: |
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Author:wxd |
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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: 传算法伪代码实例，遗传算法中最重要的过程就是选择和交叉。 -Examples of pseudo-code algorithm, genetic algorithm is the most important process of selection and crossover. Platform: |
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Author:何炎雯 |
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Description: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码 的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用 Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu, 目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的 文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。 -It is a very simple genetic algorithm source code, is Denis Cormier (North Carolina State University) developed, Sita S. Raghavan (University of North Carolina at Charlotte) amendment. Code to ensure as little as possible, in fact, do not have to troubleshooting. The application of a specific amendment to this code, the user simply changes the definition of constants and the definition of "evaluation function" can be. Note that the code is designed to seek maximum value, in which the objective function can only take positive and the function value and the individual is no difference between the fitness value. The system uses the rate selection, the essence of model, single-point crossover and uniform mutation. If you replace the uniform mutation with Gaussian mutation, may be more effective. Code without any graphic or even no screen output, mainly to ensure a high portability between platforms. Readers can ftp.uncc.edu, directory coe/evol file prog.c obtained. Required input file sho Platform: |
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Author:李礼 |
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Description: 遗传算法（Genetic Algorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。(The genetic algorithm (Genetic Algorithm) is a computational model of biological evolution process of natural selection and genetic mechanism of the simulation of Darwin's theory of biological evolution, is a kind of method to search the optimal solution through simulating the process of natural evolution.) Platform: |
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Author:alfreddiao
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