Abstract
Genetic programming has been explored in recent works to evolve hyper-heuristics for dynamic flexible job shop scheduling. To generate optimum rules, the algorithm searches a space of trees composed from a set of terminals and operators. Since the search space is exponentially proportional to the size of the terminal set, it is preferred to opt out any insignificant terminals. Feature selection techniques has been employed to reduce the terminal set size without discarding any important information and they have proven to be effective for enhancing search performance and efficiency for dynamic flexible job shop scheduling. In this paper, we extends our previous work by adding a modified version of the two-stage genetic programming algorithm and by comparing the different methods in a larger experimental setup. The results show that feature selection can generate better rules in most of the cases while also being more efficient to in a production environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Blackstone, J.H., Phillips, D.T., Hogg, G.L.: A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. Prod. Res. 27–45 (1982)
Brucker, P., Schlie, R.: Job-shop scheduling with multi-purpose machines. Computing 45, 369–375 (1990)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Jakobovic, D., Marasovic, K.: Evolving priority scheduling heuristics with genetic programming. Appl. Soft Comput. 12, 2781–2789 (2012)
Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press (1992)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491–502 (2005)
Mei, Y., Zhang, M., Nyugen, S.: Feature selection in evolving job shop dispatching rules with genetic programming. In: GECCO (2016)
Mei, Y., Nguyen, S., Xue, B., Zhang, M.: An efficient feature selection algorithm for evolving job shop scheduling rules with genetic programming. IEEE Trans. Emerg. Top. Comput. Intell. 1(5), 339–353 (2017). https://doi.org/10.1109/TETCI.2017.2743758
Nguyen, S., Zhang, M., Johnston, M., Tan, K.: A computational study of representations in genetic programming to evolve dispatching rules for the job shop scheduling problem. IEEE Trans. Evol. Comput. 17(5), 621–639 (2013)
Nguyen, S., Zhang, M., Johnston, M., Tan, K.: Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevolution genetic programming. IEEE Trans. Evol. Comput. 18, 193–208 (2014)
Yska, D., Mei, Y., Zhang, M.: Genetic programming hyper-heuristic with cooperative coevolution for dynamic flexible job shop scheduling. In: Proceedings of the European Conference on Genetic Programming, pp. 306–321. Springer (2018). https://doi.org/10.1007/978-3-319-77553-1_19
Zakaria., Y., BahaaElDin., A., Hadhoud., M.: Applying feature selection to rule evolution for dynamic flexible job shop scheduling. In: Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: ECTA (IJCCI 2019), pp. 139–146. INSTICC, SciTePress (2019). https://doi.org/10.5220/0007957801390146
Zhang, F., Mei, Y., Zhang, M.: Genetic programming with multi-tree representation for dynamic flexible job shop scheduling. In: Australasian Joint Conference on Artificial Intelligence, pp. 472–484. Springer (2018). https://doi.org/10.1007/978-3-030-03991-2_43
Zhang, F., Mei, Y., Zhang, M.: Can stochastic dispatching rules evolved by genetic programming hyper-heuristics help in dynamic flexible job shop scheduling? In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 41–48 (2019)
Zhang, F., Mei, Y., Zhang, M.: Evolving dispatching rules for multi-objective dynamic flexible job shop scheduling via genetic programming hyper-heuristics. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1366–1373 (2019)
Zhang, F., Mei, Y., Zhang, M.: A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop scheduling. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’19, pp. 347–355. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3321707.3321790
Zhou, Y., Yang, J., Zheng, L.: Hyper-heuristic coevolution of machine assignment and job sequencing rules for multi-objective dynamic flexible job shop scheduling. IEEE Access 7, 68–88 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zakaria, Y., Zakaria, Y., BahaaElDin, A., Hadhoud, M. (2021). Niching-Based Feature Selection with Multi-tree Genetic Programming for Dynamic Flexible Job Shop Scheduling. In: Merelo, J.J., Garibaldi, J., Linares-Barranco, A., Warwick, K., Madani, K. (eds) Computational Intelligence. IJCCI 2019. Studies in Computational Intelligence, vol 922. Springer, Cham. https://doi.org/10.1007/978-3-030-70594-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-70594-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70593-0
Online ISBN: 978-3-030-70594-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)