并行遗传算法的研究及应用进展

并行遗传算法（PGA）将并行计算机的高速并行性和遗传算法天然的并行性相结合,极大地促进了遗传算法的研究与应用。该文对近年来并行遗传算法的模型、性能分析、算法改进、实现平台进行了归纳和评述,并且对并行遗传算法今后的主要研究方向和发展前景进行了展望。     

并行遗传算法的研究及应用进展

并行遗传算法(PGA)将并行计算机的高速并行性和遗传算法天然的并行性相结合,极大地促进了遗传算法的研究与应用。该文对近年来并行遗传算法的模型、性能分析、算法改进、实现平台进行了归纳和评述,并且对并行遗传算法今后的主要研究方向和发展前景进行了展望。     

XML在并行骨架描述中的应用

在对并行骨架的思想、描述和应用进行深入研究的基础上,本文提出了一种基于XML的并行骨架描述方法,为基于骨架的并行编程系统中各种骨架描述提供了一种统一的处理模型,对提高并行骨架库的通用性、可扩展性和可移植性具有重要意义.     

并行遗传算法分析

在科学计算机领域,并行遗传算法开始受到关注。分析了遗传算法并行化的同和实现模型,讨论了遗传算法隐含的并行性,对于灵活应用并行遗传算法有指导意义。     

并行遗传算法的研究

在分析了解决组合优化问题所遇到的困难的基础上，引入遗传算法.进而提出基本遗传算法在解决数量级很大的组合优化问题上的缺陷，并详细介绍了遗传算法的三种并行模型，最后给出改进的并行遗传算法模型．     

基于并行结构骨架的程序设计环境研究与实现

并行结构骨架理论提供了一种描述并行程序设计模式的通用模型,对设计模式进行更高层次的抽象,能有效解决基于设计模式的并行程序设计方法的局限性问题,降低并行程序设计开发难度.基于并行结构骨架的并行程序设计环境--PASBPE在并行结构骨架理论的基础上,使用参数化设置快速生成用户所需并行程序框架,同时通过可视化的程序设计交互环境,简化并行程序的开发过程,提高开发效率.     

并行遗传算法的研究

在分析了解决组合优化问题所遇到的困难的基础上,引入遗传算法。进而提出基本遗传算法在解决数量级很大的组合优化问题上的缺陷,并详细介绍了遗传算法的三种并行模型。最后给出改进的并行遗传算法模型。     

并行遗传算法在非均衡负载节点并行机上的实现

基于平衡负载、减小通信开销的考虑,对于非均衡负载节点并行机提出了两种并行遗传算法一动态负载平衡的孤岛模型和主从模型,并与基本的孤岛模型做了比较。两种算法在实际使用中均取得了较好的效果。     

一种粗粒度并行遗传算法的设计与实现

遗传算法(Genetic Algorithms,GA)作为搜索最优解的方法,有很广泛的应用,但随着问题的规模扩大,复杂度增加,GA的求解速度大大降低。并行遗传算法(Paralle Genetic Algorithms,PGA)成为解决遗传算法速度瓶颈的有效方法。本文提出了并行遗传算法新的应用平台-Internet,讨论了并行遗传算法应用于Internet的具体实现中的关键问题,并给出单向环拓扑的具体实现和仿真验证。     

并行遗传算法研究进展

Parallel Genetic Algorithm is an important branch of Evolutionary Computing and more and more AI specialists pay attention to it because of its simplified topologies and robust search a-bility. In this paper, we discussed theoretic and application achievement of Parallel Genetic Algorithm ,analyzed their advantage and disadvantage ,and point out its future research direction.     

Development of algorithmic algebra tools to design parallel programs using heuristics

The paper proposes a new approach and a system to develop parallel algorithms based on the joint use of the algebraic-algorithmic methodology of specification and development of programs and non-algorithmic (heuristic) techniques for code generation. The algebraic part of the methodology provides the formalized process of parallel program design through high-level algebraic-algorithmic specifications and automating transformations up to program code in a standard programming language. The heuristic part of the system is the dynamic adjustment of program code to a target platform and its optimization using self-learning code generation and heuristic technologies.     

基于PVM的最小权三角划分并行遗传算法研究

平面点集的三角划分在计算机图形学,三维可视化等领域具有广泛地应用,在许多应用中需要提供形状最优的三角划分。但该类问题推测属于NP完全问题。为了快速有效地求解最小权三角划分(MWT)问题,提出了一种基于PVM的并行遗传算法来近似获取全局最优解,并系统地测试算法中一些重要的并行控制参数,包括迁移代数和节点平均负载对算法性能的影响。实验结果表明,该方法简单,可靠,大大缩短了优化过程的时间,提高获取全局最优解的概率。     

基于伪并行遗传算法的聚类分析方法

针对传统的基于聚类准则的聚类算法初始化敏感和容易陷入局部极值的问题,设计了一种新的基于伪并行遗传算法的聚类方法.采用实数编码方式对每个样本所属的类别进行编码,通过空类的识别和修复来修正不舍法的染色体.在引入离散随机变异算子和优化方向变异算子的基础上,结合迁移策略和插入策略,达到兼顾局部收敛速度和全局收敛性能的目的.与K-均值算法对比仿真实验,表明了这种基于伪并行遗传算法的聚类新方法的可行性和有效性.     

A topology-preserving parallel 3D thinning algorithm for extracting the curve skeleton

We introduce a new topology-preserving 3D thinning procedure for deriving the curve voxel skeleton from 3D binary digital images. Based on a rigorously defined classification procedure, the algorithm consists of sequential thinning iterations each characterized by six parallel directional sub-iterations followed by a set of sequential sub-iterations. The algorithm is shown to produce concise and geometrically accurate 3D curve skeletons. The thinning algorithm is also insensitive to object rotation and only moderately sensitive to noise. Although this thinning procedure is valid for curve skeleton extraction of general elongated objects, in this paper, we specifically discuss its application to the orientation modeling of trabecular biological tissues.     

基于分布式粗粒度并行计算的遗传规划算法研究

针对经典遗传规划算法(CGP)存在容易早熟收敛、运行效率低的缺陷,提出一种将分布式计算与遗传规划算法结合的计算模型.该模型利用个体迁移策略实现对种群的优化,克服易早熟的缺陷.并且采用分布式计算能够有效地节省算法的运行时间.最后通过对语音数据预测误差的比较,验证了改进后算法的有效性.实验表明,基于分布式粗粒度并行计算的遗传规划算法(CGGP)计算性能优于经典遗传规划算法(CGP).     

基于并行混合遗传算法的直接盲信号检测

借助接收数据阵补投影算子对待测发送序列向量的零化作用,把信号盲检测问题转化为整数约束下的二次规划问题,提出了基于并行混合遗传算法的直接盲信号检测的方法.仿真结果表明,该算法只需较小的数据量就可以快速地盲恢复出发送信号,误码率较低,收敛速度快,性能稳定,优于现有的经典盲均衡算法.     

On the scalability of parallel genetic algorithms

This paper examines the scalability of several types of parallel genetic algorithms (GAs). The objective is to determine the optimal number of processors that can be used by each type to minimize the execution time. The first part of the paper considers algorithms with a single population. The investigation focuses on an implementation where the population is distributed to several processors, but the results are applicable to more common master-slave implementations, where the population is entirely stored in a master processor and multiple slaves are used to evaluate the fitness. The second part of the paper deals with parallel GAs with multiple populations. It first considers a bounding case where the connectivity, the migration rate, and the frequency of migrations are set to their maximal values. Then, arbitrary regular topologies with lower migration rates are considered and the frequency of migrations is set to its lowest value. The investigationis mainly theoretical, but experimental evidence with an additively-decomposable function is included to illustrate the accuracy of the theory. In all cases, the calculations show that the optimal number of processors that minimizes the execution time is directly proportional to the square root of the population size and the fitness evaluation time. Since these two factors usually increase as the domain becomes more difficult, the results of the paper suggest that parallel GAs can integrate large numbers of processors and significantly reduce the execution time of many practical applications.