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

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Genetic Programming (EuroGP 2022)

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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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 

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Correspondence to Fangfang Zhang .

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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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