Tuning struggle strategy in genetic algorithms for Scheduling in Computational Grids

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

Research output: Chapter in Book/Report/Conference proceedingChapter

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 languageEnglish
Title of host publicationSEVENTH INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT APPLICATIONS, PROCEEDINGS
PublisherIEEE
Pages275-280
ISBN (Print)978-0-7695-3184-7
Publication statusPublished - 2008

Cite this

Xhafa, F., Duran, B., Abraham, A., & Dahal, K. P. (2008). Tuning struggle strategy in genetic algorithms for Scheduling in Computational Grids. In SEVENTH INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT APPLICATIONS, PROCEEDINGS (pp. 275-280). IEEE.
Xhafa, Fatos ; Duran, Bernat ; Abraham, Ajith ; Dahal, Keshav P. / Tuning struggle strategy in genetic algorithms for Scheduling in Computational Grids. SEVENTH INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT APPLICATIONS, PROCEEDINGS. IEEE, 2008. pp. 275-280
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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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Xhafa, F, Duran, B, Abraham, A & Dahal, KP 2008, Tuning struggle strategy in genetic algorithms for Scheduling in Computational Grids. in SEVENTH INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT APPLICATIONS, PROCEEDINGS. IEEE, pp. 275-280.

Tuning struggle strategy in genetic algorithms for Scheduling in Computational Grids. / Xhafa, Fatos; Duran, Bernat; Abraham, Ajith; Dahal, Keshav P.

SEVENTH INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT APPLICATIONS, PROCEEDINGS. IEEE, 2008. p. 275-280.

Research output: Chapter in Book/Report/Conference proceedingChapter

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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

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

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Xhafa F, Duran B, Abraham A, Dahal KP. Tuning struggle strategy in genetic algorithms for Scheduling in Computational Grids. In SEVENTH INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT APPLICATIONS, PROCEEDINGS. IEEE. 2008. p. 275-280