Skip to main content
SpringerLink
Log in
Book cover

European Conference on Genetic Programming (Part of EvoStar)

EuroGP 2022: Genetic Programming pp 162–178Cite as

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

  • Conference paper
  • First Online:

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

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Al-Sahaf, H., et al.: A survey on evolutionary machine learning. J. R. Soc. N. Z. 49(2), 205–228 (2019)

    Article  Google Scholar 

  2. Ardeh, M.A., Mei, Y., Zhang, M.: A novel multi-task genetic programming approach to uncertain capacitated Arc routing problem. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 759–767 (2021)

    Google Scholar 

  3. Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans. Evol. Comput. 5(1), 17–26 (2001)

    Article  Google Scholar 

  4. Brameier, M., Banzhaf, W.: Linear Genetic Programming, vol. 53. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-31030-5

    Book  MATH  Google Scholar 

  5. Branke, J., Nguyen, S., Pickardt, C.W., Zhang, M.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput. 20(1), 110–124 (2016)

    Article  Google Scholar 

  6. Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.R.: A classification of hyper-heuristic approaches: revisited. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics. ISORMS, vol. 272, pp. 453–477. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91086-4_14

    Chapter  Google Scholar 

  7. Chandra, R., Ong, Y.S., Goh, C.K.: Co-evolutionary multi-task learning for dynamic time series prediction. Appl. Soft Comput. J. 70, 576–589 (2018)

    Article  Google Scholar 

  8. Dal Piccol Sotto, L.F., De Melo, V.V.: A probabilistic linear genetic programming with stochastic context-free grammar for solving symbolic regression problems. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1017–1024 (2017)

    Google Scholar 

  9. Downey, C., Zhang, M., Liu, J.: Parallel linear genetic programming for multi-class classification. Genet. Program Evolvable Mach. 13(3), 275–304 (2012). https://doi.org/10.1007/s10710-012-9162-9

    Article  Google Scholar 

  10. Gupta, A., Ong, Y.S., Feng, L.: Multifactorial evolution: toward evolutionary multitasking. IEEE Trans. Evol. Comput. 20(3), 343–357 (2016)

    Article  Google Scholar 

  11. Huang, S., Zhong, J., Yu, W.J.: Surrogate-assisted evolutionary framework with adaptive knowledge transfer for multi-task optimization. IEEE Trans. Emerg. Top. Comput. 9(4), 1930–1944 (2019)

    Article  Google Scholar 

  12. Kantschik, W., Banzhaf, W.: Linear-tree GP and its comparison with other GP structures. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 302–312. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45355-5_24

    Chapter  Google Scholar 

  13. Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87–112 (1994). https://doi.org/10.1007/BF00175355

    Article  Google Scholar 

  14. Nguyen, S., Mei, Y., Zhang, M.: Genetic programming for production scheduling: a survey with a unified framework. Complex Intell. Syst. 3(1), 41–66 (2017). https://doi.org/10.1007/s40747-017-0036-x

    Article  Google Scholar 

  15. Nguyen, S., Zhang, M., Tan, K.C.: Surrogate-assisted genetic programming with simplified models for automated design of dispatching rules. IEEE Trans. Cybern. 47(9), 2951–2965 (2017)

    Article  Google Scholar 

  16. Park, J., Mei, Y., Nguyen, S., Chen, G., Zhang, M.: Evolutionary multitask optimisation for dynamic job shop scheduling using niched genetic programming. In: Mitrovic, T., Xue, B., Li, X. (eds.) AI 2018. LNCS (LNAI), vol. 11320, pp. 739–751. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03991-2_66

    Chapter  Google Scholar 

  17. Provorovs, S., Borisov, A.: Use of linear genetic programming and artificial neural network methods to solve classification task. Sci. J. Riga Tech. Univ. Comput. Sci. 45(1), 133–139 (2012)

    Google Scholar 

  18. Sanchez, M., Cruz-Duarte, J.M., Ortiz-Bayliss, J.C., Ceballos, H., Terashima-Marin, H., Amaya, I.: A systematic review of hyper-heuristics on combinatorial optimization problems. IEEE Access 8, 128068–128095 (2020)

    Article  Google Scholar 

  19. Wilson, G., Banzhaf, W.: A comparison of cartesian genetic programming and linear genetic programming. In: O’Neill, M., et al. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 182–193. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78671-9_16

    Chapter  Google Scholar 

  20. Xu, Q., Wang, N., Wang, L., Li, W., Sun, Q.: Multi-task optimization and multi-task evolutionary computation in the past five years: a brief review. Mathematics 9(8), 1–44 (2021)

    Article  Google Scholar 

  21. Yi, J., Bai, J., He, H., Zhou, W., Yao, L.: A multifactorial evolutionary algorithm for multitasking under interval uncertainties. IEEE Trans. Evol. Comput. 24(5), 908–922 (2020)

    Article  Google Scholar 

  22. Zhang, F., Mei, Y., Nguyen, S., Tan, K.C., Zhang, M.: Multitask genetic programming-based generative hyperheuristics: a case study in dynamic scheduling. IEEE Trans. Cybern. 1–14 (2021). https://doi.org/10.1109/TCYB.2021.3065340

  23. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: A preliminary approach to evolutionary multitasking for dynamic flexible job shop scheduling via genetic programming. In: Proceedings of Genetic and Evolutionary Computation Conference Companion, pp. 107–108 (2020)

    Google Scholar 

  24. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Collaborative multifidelity-based surrogate models for genetic programming in dynamic flexible job shop scheduling. IEEE Trans. Cybern. 1–15 (2021). https://doi.org/10.1109/TCYB.2021.3050141

  25. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Correlation coefficient-based recombinative guidance for genetic programming hyperheuristics in dynamic flexible job shop scheduling. IEEE Trans. Evol. Comput. 25(3), 552–566 (2021)

    Article  Google Scholar 

  26. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Evolving scheduling heuristics via genetic programming with feature selection in dynamic flexible job-shop scheduling. IEEE Trans. Cybern. 51(4), 1797–1811 (2021)

    Article  Google Scholar 

  27. Zhang, F., Mei, Y., Nguyen, S., Zhang, M., Tan, K.C.: Surrogate-assisted evolutionary multitasking genetic programming for dynamic flexible job shop scheduling. IEEE Trans. Evol. Comput. 25(4), 651–665 (2021)

    Article  Google Scholar 

  28. Zhang, F., Nguyen, S., Mei, Y., Zhang, M.: Genetic Programming for Production Scheduling - An Evolutionary Learning Approach. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-4859-5

    Book  Google Scholar 

  29. Zhou, L., Feng, L., Zhong, J., Ong, Y.S., Zhu, Z., Sha, E.: Evolutionary multitasking in combinatorial search spaces: a case study in capacitated vehicle routing problem. In: IEEE Symposium Series on Computational Intelligence (2016)

    Google Scholar 

Download references

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fangfang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mengjie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02055-1

  • Online ISBN: 978-3-031-02056-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation