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Genetic Algorithms for Task Scheduling Problem

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Foundations of Computational Intelligence Volume 3

Part of the book series: Studies in Computational Intelligence ((SCI,volume 203))

Abstract

The scheduling and mapping of the precedence-constrained task graph to the processors is considered one of the most crucial NP-complete problems in the parallel and distributed computing systems. Several genetic algorithms have been developed to solve this problem. The primary distinction among most of them is being the used chromosomal representation for a schedule. However, these existing algorithms are monolithic as they attempt to scan the entire solution space without consideration how to reduce the complexity of the optimization. In this chapter, two genetic algorithms have been developed and implemented. Our developed algorithms are genetic algorithms with some heuristic principles have been added to improve the performance. According to the first developed genetic algorithm, two fitness functions have been applied one after another. The first fitness function is concerned with minimizing the total execution time (schedule length) and the second one is concerned with the load balance satisfaction. The second developed genetic algorithm is based on task duplication technique to overcome the communication overhead. Our proposed algorithms have been implemented and evaluated using benchmarks. According to the evolution results, it found that our algorithms always outperform the traditional algorithms.

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Author information

Authors and Affiliations

  1. Computer Science Department, Faculty of Computer and Information, Cairo University,  

    Fatma A. Omara

  2. Mathematics Dept., Faculty of Science Banha University,  

    Mona M. Arafa

Authors
  1. Fatma A. Omara
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  2. Mona M. Arafa
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Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  2. College of Business Administration Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Université Paris XII Fac. Sciences, LERISS, 61 avenue du Général de Gaulle Building P2 - Room 350, 94010, Créteil, France

    Patrick Siarry

  4. Department of Computer Science, University of Pretoria, 0002, Pretoria, South Africa

    Andries Engelbrecht

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© 2009 Springer-Verlag Berlin Heidelberg

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Omara, F.A., Arafa, M.M. (2009). Genetic Algorithms for Task Scheduling Problem. In: Abraham, A., Hassanien, AE., Siarry, P., Engelbrecht, A. (eds) Foundations of Computational Intelligence Volume 3. Studies in Computational Intelligence, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01085-9_16

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  • DOI: https://doi.org/10.1007/978-3-642-01085-9_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01084-2

  • Online ISBN: 978-3-642-01085-9

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