Parallel Genetic Algorithms for hypercube machines

被引：0

作者：

Baraglia, R ^{[1
]}

Perego, R ^{[1
]}

机构：

[1] CNR, Ist Cnuce, I-56100 Pisa, Italy

来源：

VECTOR AND PARALLEL PROCESSING - VECPAR'98 | 1999年 / 1573卷

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper are investigate the design of highly parallel Genetic Algorithms. The Traveling Salesman Problem is used as a case study to evaluate and compare different implementations. To fix the various parameters of Genetic Algorithms to the case study considered, the Holland sequential Genetic Algorithm, which adopts different population replacement methods and crossover operators, has been implemented and;tested. Both fine - grained and coarse - grained parallel GAs which adopt the selected genetic operators have been designed and implemented on a 128-mode nCUBE 2 multicomputer. The fine - grained algorithm uses an innovative map-ping strategy that makes the number of-solutions managed independent of the number of processing nodes used. Complete performance results showing the behaviour of Parallel Genetic Algorithms for different population sizes, number of processors used, migration strategies are reported.

引用

页码：691 / 703

页数：13

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