Distributed Approach for Implementing Genetic Algorithms

A. Srivastava, Anup Kumar, R. M. Pathak
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引用次数: 3

Abstract

Genetic Algorithms are search techniques for global optimization in a complex search space. One of the interesting features of a Genetic Algorithm is that they lend themselves very well for parallel and distributed processing. This feature of Genetic Algorithm is useful in improving its computation efficiency for complex optimization problems. In this paper, we have implemented Genetic Algorithm in a distributed environment such that its implementation problem independent. This key attribute of distributed implementation allows it to be used for different types of optimization problems. Fault tolerance and user transparency are two other important features of our distributed Genetic Algorithm implementation. The effectiveness and generality of Genetic Algorithms have been demonstrated by solving two problems of network topology design and file allocation.
实现遗传算法的分布式方法
遗传算法是一种在复杂搜索空间中进行全局优化的搜索技术。遗传算法的一个有趣的特性是它们非常适合并行和分布式处理。遗传算法的这一特性有助于提高其处理复杂优化问题的计算效率。在本文中,我们在分布式环境中实现了遗传算法,使其实现问题独立。分布式实现的这个关键属性允许它用于不同类型的优化问题。容错性和用户透明性是分布式遗传算法实现的另外两个重要特性。通过解决网络拓扑设计和文件分配两个问题,证明了遗传算法的有效性和通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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