Tuning struggle strategy in genetic algorithms for Scheduling in Computational Grids

Fatos Xhafa; Bernat Duran; Ajith Abraham; Keshav P. Dahal

Tuning struggle strategy in genetic algorithms for Scheduling in Computational Grids

Fatos Xhafa, Bernat Duran, Ajith Abraham, Keshav P. Dahal

School of Computing, Engineering and Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

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

Original language	English
Title of host publication	SEVENTH INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT APPLICATIONS, PROCEEDINGS
Publisher	IEEE
Pages	275-280
ISBN (Print)	978-0-7695-3184-7
Publication status	Published - 2008

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title = "Tuning struggle strategy in genetic algorithms for Scheduling in Computational Grids",

abstract = "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",

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AU - Duran, Bernat

AU - Abraham, Ajith

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N2 - 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

AB - 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

M3 - Chapter

SN - 978-0-7695-3184-7

SP - 275

EP - 280

BT - SEVENTH INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT APPLICATIONS, PROCEEDINGS

PB - IEEE

ER -

Tuning struggle strategy in genetic algorithms for Scheduling in Computational Grids

Abstract

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