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An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling

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Genetic Programming (EuroGP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13223))

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Abstract

Dynamic job shop scheduling has a wide range of applications in reality such as order picking in warehouse. Using genetic programming to design scheduling heuristics for dynamic job shop scheduling problems becomes increasingly common. In recent years, multitask genetic programming-based hyper-heuristic methods have been developed to solve similar dynamic scheduling problem scenarios simultaneously. However, all of the existing studies focus on the tree-based genetic programming. In this paper, we investigate the use of linear genetic programming, which has some advantages over tree-based genetic programming in designing multitask methods, such as building block reusing. Specifically, this paper makes a preliminary investigation on several issues of multitask linear genetic programming. The experiments show that the linear genetic programming within multitask frameworks have a significantly better performance than solving tasks separately, by sharing useful building blocks.

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Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, 6140, New Zealand

    Zhixing Huang, Fangfang Zhang, Yi Mei & Mengjie Zhang

Authors
  1. Zhixing Huang
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  2. Fangfang Zhang
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  3. Yi Mei
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  4. Mengjie Zhang
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Corresponding author

Correspondence to Fangfang Zhang .

Editor information

Editors and Affiliations

  1. University of Trieste, Trieste, Italy

    Eric Medvet

  2. Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Gisele Pappa

  3. Victoria University of Wellington, Wellington, New Zealand

    Bing Xue

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Huang, Z., Zhang, F., Mei, Y., Zhang, M. (2022). An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling. In: Medvet, E., Pappa, G., Xue, B. (eds) Genetic Programming. EuroGP 2022. Lecture Notes in Computer Science, vol 13223. Springer, Cham. https://doi.org/10.1007/978-3-031-02056-8_11

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  • DOI: https://doi.org/10.1007/978-3-031-02056-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02055-1

  • Online ISBN: 978-3-031-02056-8

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