Description: 用分层实现遗传算法，高、低两个层次并行运行，自主选择种群规模、变异率及两层的交叉率，结果以文本方式输出。-layered with Genetic algorithms, low - and high-level parallel operations, choosing their own population size, mutation rate and the two-tier cross-rates, the results in text mode output. Platform: |
Size: 6730 |
Author:flying840310 |
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Description: 用分层实现遗传算法，高、低两个层次并行运行，自主选择种群规模、变异率及两层的交叉率，结果以文本方式输出。-layered with Genetic algorithms, low- and high-level parallel operations, choosing their own population size, mutation rate and the two-tier cross-rates, the results in text mode output. Platform: |
Size: 6144 |
Author:flying840310 |
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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: 遗传算法是一种借鉴生物界自然选择和进化机制发展起来的高度并行、随机、自适应搜索算法。由于其具有健壮性，特别适合于处理传统搜索算法解决不好的复杂的和非线性问题。以遗传算法为核心的进化算法已与模糊系统理论、人工神经网络等一起成为计算智能研究中的热点，受到许多学科的共同关注。 本书全面系统地介绍了遗传算法的基本理论，重点介绍了遗传算法的经典应用和国内外的新-Genetic Algorithm is a kind of drawing on biological mechanisms of natural selection and evolutionary development of highly parallel, randomized, adaptive search algorithm. Due to its robustness, particularly suited to deal with traditional search algorithms are not properly solved complex and nonlinear problems. To genetic algorithms as the core of the evolutionary algorithm with fuzzy system theory, artificial neural networks, along with computational intelligence research hotspot by many subjects of common concern. This book comprehensively and systematically introduce the genetic algorithm Platform: |
Size: 6230016 |
Author:涂满园 |
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Description: 本文详细介绍了并行遗传算法的定义，应用等等，不失为一篇好论文-This paper introduces in detail the definition of parallel genetic algorithms, applications, etc., may be a good thesis Platform: |
Size: 125952 |
Author:yuboyushui |
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Description: A Genetic Algorithm Approach to Scheduling
Communications for a Class of Parallel Space-Time Adaptive Processing Algorithms Platform: |
Size: 67584 |
Author:mohamed |
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Description: 遗传算法指南（A Genetic Algorithm Tutorial）：经典的遗传算法文献-This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. Platform: |
Size: 2241536 |
Author:columbia |
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Description: 实用GA算法进行，机器人运功规划，算法很经典，美国人写的-We present an ongoing research work on robot motion plan- ning using genetic algorithms. Our goal is to use this technique to build fast motion planners for robot with six or more degree of freedom. After a short review of the existing methods, we will introduce the genetic al- gorithms by showing how they can be used to solve the invers kinematic problem. In the second part of the paper, we show that the path planning problem can be expressed as an optimization problem and thus solved with a genetic algorithm. We illustrate the approach by building a path planner for a planar arm with two degree of freedom, then we demon- strate the validity of the method by planning paths for an holonomic mobile robot. Finally we describe an implementation of the selected ge- netic algorithm on a massively parallel machine and show that fast plan- ning response is made possible by using this appro Platform: |
Size: 408576 |
Author:xiaofang |
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Description: 为求解大规模TSP 问题, 提出了并行人工免疫系统的塔式主从模型(TMSM), 和基于TMSM 的并行免疫记忆克隆选择算法(PIMCSA) TMSM
是粗粒度的两层并行人工免疫模型, 其设计体现了分布式的免疫响应和免疫记忆机制. PIMCSA 用疫苗的迁移代替了抗体的迁移, 兼顾了种群多样性的保持和算法的收敛速度. 与其他算法相比, PIMCSA 在求解精度和运行时间上都更具优势, 而且问题规模越大优势越明显. TMSM 很好地体现了免疫系统的特性, PIMCSA 是适合求解大规模复杂优化问题
的并行人工免疫算法, 具有良好的可扩展性.-This paper presents a parallel model termed as towerlike master slave model (TMSM) for artificial immune systems. Based on TMSM, the parallel immune memory clonal selection algorithm ( PIMCSA) is also designed for dealing with large scale TSP problems. TMSM is a two level coarse grained parallel artificial immune model with distributed immune response and dis
tributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than antibodies as has been done in parallel genetic algorithms, it is a good balance between the diversity maintenance of populations and the convergent speed of the algorithm. PIMCSA shows superiority over other compared approaches both in solution quality and computation time, and the
lager the problem size the more outstanding the predominance will be. TMSM is a good simulation of biological immune system, and PIMCSA is a parallel artificial immune algorithm with good extensibility, which is capable of solving large scale and c Platform: |
Size: 435200 |
Author:崔冰 |
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Description: 基于动态岛屿群体模型的并行遗传算法,一种理想并行遗传算法模型,并行遗传算法与神经网络、模糊系统的结合,并行遗传算法在半导体器件综合中的应用-Parallel Genetic Algorithm for Dynamic island population model based on an ideal model of parallel genetic algorithms, parallel genetic algorithms and neural networks, fuzzy systems combine parallel genetic algorithm used in the semiconductor integrated device in Platform: |
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Author:Msunshine |
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Search - parallel Genetic Algorithms