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Synthesis of Self-Adaptive Supervisors of Multi-Task Real-Time Object-Oriented Systems Using Developmental Genetic Programming

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Recent Advances in Computational Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 610))

Abstract

This chapter presents a procedure for automatic creation of self-adaptive artificial supervisors of multi-task real-time object-oriented systems (MT RT OOS). The procedure is based on developmental genetic programming. Early UML diagrams describing a MT RT OOS are used as input data to the procedure. Next, an artificial supervisor which optimizes the system use is automatically generated. The supervisor is self-adaptive what means that it is capable of keeping optimality of the system in spite of disruptions that may occur dynamically in time of the system work. A representative example of creation of a supervisor of building a house illustrates the procedure. Efficiency of the procedure from the point of view of self-adaptivity of the supervisor is investigated.

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Notes

  1. 1.

    Numerical prefixes are introduced to identify the use cases and will be used later on.

  2. 2.

    Automatic generation of TG from UML diagrams is possible but will not be discussed in the paper.

  3. 3.

    Remaining 16 sequences are very similar.

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Authors and Affiliations

  1. Department of Computer Engineering, Cracow University of Technology, Cracow, Poland

    Krzysztof Sapiecha

  2. Department of Computer Science, Kielce University of Technology, Kielce, Poland

    Leszek Ciopiński & Stanisław Deniziak

Authors
  1. Krzysztof Sapiecha
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  2. Leszek Ciopiński
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  3. Stanisław Deniziak
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Corresponding author

Correspondence to Leszek Ciopiński .

Editor information

Editors and Affiliations

  1. Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

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Sapiecha, K., Ciopiński, L., Deniziak, S. (2016). Synthesis of Self-Adaptive Supervisors of Multi-Task Real-Time Object-Oriented Systems Using Developmental Genetic Programming. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-21133-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-21133-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21132-9

  • Online ISBN: 978-3-319-21133-6

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