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Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling

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journal contribution
posted on 04.05.2022, 09:44 by Fangfang ZhangFangfang Zhang, Yi MeiYi Mei, S Nguyen, KC Tan, Mengjie ZhangMengjie Zhang
Evolutionary multitask learning has achieved great success due to its ability to handle multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which aims at generating a heuristic for a class of problems rather than solving one specific problem. The existing multitask hyperheuristic studies only focus on heuristic selection, which is not applicable to heuristic generation. To fill the gap, we propose a novel multitask generative hyperheuristic approach based on genetic programming (GP) in this article. Specifically, we introduce the idea in evolutionary multitask learning to GP hyperheuristics with a suitable evolutionary framework and individual selection pressure. In addition, an origin-based offspring reservation strategy is developed to maintain the quality of individuals for each task. To verify the effectiveness of the proposed approach, comprehensive empirical studies have been conducted on the homogeneous and heterogeneous multitask dynamic flexible job shop scheduling. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for each task in all the examined scenarios. In addition, the evolved scheduling heuristics verify the mutual help among the tasks in a multitask scenario.

History

Preferred citation

Zhang, F., Mei, Y., Nguyen, S., Tan, K. C. & Zhang, M. (2021). Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling. IEEE Transactions on Cybernetics, PP(99), 1-14. https://doi.org/10.1109/TCYB.2021.3065340

Journal title

IEEE Transactions on Cybernetics

Volume

PP

Issue

99

Publication date

01/01/2021

Pagination

1-14

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

ISSN

2168-2267

eISSN

2168-2275

Language

en