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Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling

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Genetic Programming (EuroGP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12101))

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Abstract

Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, the subtrees of the selected parents are randomly chosen in traditional GP for crossover and mutation, which may not be sufficiently effective, especially in a huge search space. This paper proposes new strategies to guide the subtree selection rather than picking them randomly. To be specific, the occurrences of features are used to measure the importance of each subtree of the selected parents. The probability to select a subtree is based on its importance and the type of genetic operators. This paper examines the proposed algorithm on six DFJSS scenarios. The results show that the proposed GP algorithm with the guided subtree selection for crossover can converge faster and achieve significantly better performance than its counterpart in half of the scenarios while no worse in all other scenarios without increasing the computational time.

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Author information

Authors and Affiliations

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

    Fangfang Zhang, Yi Mei & Mengjie Zhang

  2. Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, 3086, Australia

    Su Nguyen

Authors
  1. Fangfang Zhang
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  2. Yi Mei
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  3. Su Nguyen
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  4. Mengjie Zhang
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Corresponding author

Correspondence to Fangfang Zhang .

Editor information

Editors and Affiliations

  1. Queen's University, Kingston, ON, Canada

    Ting Hu

  2. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  3. University of Trieste, Trieste, Italy

    Eric Medvet

  4. Pablo de Olavide University, Seville, Spain

    Federico Divina

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Zhang, F., Mei, Y., Nguyen, S., Zhang, M. (2020). Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling. In: Hu, T., Lourenço, N., Medvet, E., Divina, F. (eds) Genetic Programming. EuroGP 2020. Lecture Notes in Computer Science(), vol 12101. Springer, Cham. https://doi.org/10.1007/978-3-030-44094-7_17

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  • DOI: https://doi.org/10.1007/978-3-030-44094-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44093-0

  • Online ISBN: 978-3-030-44094-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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