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A Coevolutionary Approach to Cellular Automata-Based Task Scheduling

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Cellular Automata (ACRI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7495))

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Abstract

Cellular Automata (CA) have been proposed for task scheduling in multiprocessor architectures. CA-based models aim to be fast and decentralized schedulers. Previous models employ an off-line learning stage in which an evolutionary method is used to discover cellular automata rules able to solve an instance of a task scheduling. A central point of CA-based scheduling is the reuse of transition rules learned for one specific program graph in the schedule of new instances. However, our investigation about previous models showed that evolved rules do not actually have such generalization ability. A new approach is presented here named multigraph coevolutionary learning, in which a population of program graphs is evolved simultaneously with rules population leading to more generalized transition rules. Results obtained have shown the evolution of rules with better generalization abilitywhen they are compared with those obtained using previous approaches.

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Authors and Affiliations

  1. Faculdade de Ciencia da Computacao, Universidade Federal de Uberlandia (UFU), Av. Joao Naves de Avila, 2121- Campus Santa Monica, Bloco B, sala 1B60, CEP: 38400-902, Uberlandia, MG, Brazil

    Gina M. B. Oliveira & Paulo M. Vidica

Authors
  1. Gina M. B. Oliveira
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  2. Paulo M. Vidica
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Laboratory of Electronics, Democritus University of Thrace, GR 67100, Xanthi, Greece

    Georgios Ch. Sirakoulis

  2. CSAI - Complex Systems and Artificial Intelligence Research Center, University of Milano-Bicocca, 20134, Milano, Italy

    Stefania Bandini

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© 2012 Springer-Verlag Berlin Heidelberg

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Oliveira, G.M.B., Vidica, P.M. (2012). A Coevolutionary Approach to Cellular Automata-Based Task Scheduling. In: Sirakoulis, G.C., Bandini, S. (eds) Cellular Automata. ACRI 2012. Lecture Notes in Computer Science, vol 7495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33350-7_12

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  • DOI: https://doi.org/10.1007/978-3-642-33350-7_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33349-1

  • Online ISBN: 978-3-642-33350-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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