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2008
IEEE

Tuning Struggle Strategy in Genetic Algorithms for Scheduling in Computational Grids

12 years 8 months ago
Tuning Struggle Strategy in Genetic Algorithms for Scheduling in Computational Grids
Job Scheduling on Computational Grids is gaining importance due to the need for efficient large-scale Grid-enabled applications. Among different optimization techniques addressed for the problem, Genetic Algorithm (GA) is a popular class of solution methods. As GAs are high level algorithms, specific algorithms can be designed by choosing the genetic operators as well as the evolutionary strategies. In this paper we focus on Struggle GAs and their tuning for the scheduling of independent jobs in computational grids. Our results showed that a careful hash implementation for computing the similarity of solutions was able to alleviate the computational burden of Struggle GA and perform better than standard similarity measures.
Fatos Xhafa, Bernat Duran, Ajith Abraham, Keshav P
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where CISIM
Authors Fatos Xhafa, Bernat Duran, Ajith Abraham, Keshav P. Dahal
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