分布式存储架构并行遗传算法框架

2014 5th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems Pub Date : 2014-11-16 DOI:10.1109/ScalA.2014.13

Dobromir Georgiev, E. Atanassov, V. Alexandrov

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引用次数: 3

摘要

遗传算法是基于生物进化和遗传学原理的元启发式搜索方法。通过启发式搜索，他们能够在可接受的时间内找到好的解决方案。然而，随着适应度景观复杂性的增加和搜索空间的扩大，它们的运行时间迅速增加。为了利用现代计算平台的力量，使用遗传算法的并行实现是缓解这一问题的有力方法。本文给出了从MPI到MPI/OpenMP和MPI/ omps混合的几种并行实现。这些实现针对在紧密耦合的分布式内存系统上执行进行了优化。我们解决了运行分布式遗传算法时出现的问题，并提出了一种自适应迁移方案。并对它们的效率进行了比较。

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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A Framework for Parallel Genetic Algorithms for Distributed Memory Architectures

Genetic algorithms are metaheuristic search methods, based on the principles of biological evolution and genetics. Through a heuristic search they are able to find good solutions in acceptable time. However, with the increase of the complexity of the fitness landscape and the size of the search space their runtime increases rapidly. Using parallel implementations of genetic algorithms in order to harness the power of modern computational platforms, is a powerful approach to mitigating this issue. In this paper several parallel implementations ranging from MPI to hybrid MPI/OpenMP and MPI/OmpSs are made. These implementations are optimized for execution on tightly coupled distributed memory systems. We address issues that arise when running a distributed genetic algorithm and present an adaptive migration scheme. Comparison of their efficiency is also made.

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2014 5th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems

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