Description: 4。《演化程序——遗传算法和数据编码的结合》，[英]米凯利维兹着 科学出版社 2000年第一版
本书分三个部分共16章分别介绍了:1.遗传算法的概念、数学原理及方法步骤 2.遗传算法和数据编码联系起来所构成的演化程序 3.演化程序面向一些实际问题的应用。
本书语言生动，结构合理，较少使用专业性术语和深涩词汇，适合面临优化问题的研究生、程序员、设计师、工程师及科研工作人员参考。-4. "Evolutionary process-- genetic coding algorithm and data integration," [E] Mikhail Liweici a 520-531 2000 version of the first book in three parts a total of 16 chapters were introduced : 1. Genetic Algorithm concept mathematical principles and methods of Step 2. genetic algorithms and data coding linked posed by the evolution of procedures 3. Evolution-oriented procedures some of the practical problems of application. The book vivid language, reasonable structure, and less use of professional terminology and vocabulary deep acerbic suitable for optimization problems facing graduate students, programmers, designers, engineers and research personnel. Platform: |
Size: 7555072 |
Author:孙东 |
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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: 本文深入研究了 BP 神经网络与遗传算法理论，BP 神经网络在应用过程中面临
着网络训练时间长、容易陷入局部极小值、隐层节点数不易确定等缺点，为了有效
地克服 BP 网的困难，将遗传算法与 BP 网络有机地融合，使它们之间的相互补充增
强彼此的能力，从而获得更有力的解决实际问题的能力。
-this in-depth study of artificial neural networks and genetic algorithms theory, BP neural network applications in the process of facing network training a long time and easily into the local minimum value, hidden nodes is difficult to determine such shortcomings, in order to effectively overcome the difficulties BP network, Genetic Algorithm and BP organic integration of the network, so that they complement each other between enhance mutual capability, thus more effective to solve practical problems. Platform: |
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Author:罗旺 |
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Description: 英国Sheffield大学开发的基于matlab的遗传算法工具箱GATBX，虽然没有GAOT用的多，但是也很实用-Developed at the University of Sheffield UK-based genetic algorithm matlab toolbox GATBX, although not GAOT used, but also very practical Platform: |
Size: 423936 |
Author:mylifeforwar |
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Description: 遗传算法函数介绍及实用代码，保证能运行，并加上了程序解释和运行结果-Genetic algorithms and practical code function introduced to ensure that will be able to run, with the procedures explained and the results Platform: |
Size: 210944 |
Author:ly |
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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: |
Size: 2048 |
Author:魏巍 |
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Description: 排课问题是一个有约束的、多目标的组合优化问题，并且已经被证明是一个NP完全问题。
遗传算法借鉴生物界自然选择和自然遗传机制，使用群体搜索技术，尤其是用于处理传统搜索方法难以解决的复杂的和非线性的问题。经过近40年的发展，遗传算法在理论研究和实际应用中取得了巨大的成功，本文将遗传算法用于排课问题的求解，首先讨论了排课问题中的影响因素、主要约束条件、求解目标和难点，并用数学模型完整地描述了排课问题。其次对多个模糊排课目标进行了定量分析，建立了排课优化目标空间。针对排课问题研究了染色体编码方式以及遗传算子的设计，提出了适应度函数的计算方法。最后对排课问题进行了实验。实验结果表明，其过程的目标值跟踪显示，算法稳健趋优，所得结果令人满意。-Course Scheduling problem is a constrained, multi-objective optimization problem, and has proven to be a NP complete problem.
Genetic algorithms reference biosphere and the natural genetic mechanism of natural selection, using the group search technology, particularly the traditional search methods for handling complex and difficult to solve nonlinear problems. After nearly 40 years of development, the genetic algorithm in the theoretical study and practical application was a great success, this paper genetic algorithm for solving the course timetabling problem, first discussed the impact of factors in the course arrangement, the main constraints, to solve goals and difficulties, and a complete mathematical model to describe the course arrangement. Arranging multiple fuzzy goals followed by a quantitative analysis, the optimal target Arranging space. Arranging for the Study of the chromosome coding and genetic operators design, proposed fitness function is calculated. Finally, the co Platform: |
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Author:张林杰 |
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Search - Practical Genetic Algorithms