- author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang and Kay Chen Tan",
- title = "Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2021",
- volume = "25",
- number = "4",
- pages = "651--665",
- month = aug,
- keywords = "genetic algorithms, genetic programming, Surrogate, Multitask Learning, Hyper-heuristics, Dynamic Flexible Job Shop Scheduling, DFJSS, JSS",
- ISSN = "1089-778X",
-
URL = "
https://ieeexplore.ieee.org/document/9377470",
-
DOI = "
doi:10.1109/TEVC.2021.3065707",
- size = "15 pages",
- abstract = "Dynamic flexible job shop scheduling is an important combinatorial optimisation problem with complex routing and sequencing decisions under dynamic environments. Genetic programming, as a hyper-heuristic approach, has been successfully applied to evolve scheduling heuristics for job shop scheduling. However, its training process is time-consuming, and it faces the retraining problem once the characteristics of job shop scenarios vary. It is known that multitask learning is a promising paradigm for solving multiple tasks simultaneously by sharing knowledge among the tasks. To improve the training efficiency and effectiveness, this paper proposes a novel surrogate-assisted evolutionary multitask algorithm via genetic programming to share useful knowledge between different scheduling tasks. Specifically, we employ the phenotypic characterisation for measuring the behaviours of scheduling rules and building a surrogate for each task accordingly. The built surrogates are used not only to improve the efficiency of solving each single task but also for knowledge transfer in multitask learning with a large number of promising individuals. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for all scenarios. In addition, the proposed algorithm manages to solve multiple tasks collaboratively in terms of the evolved scheduling heuristics for different tasks in a multitask scenario.",
-
notes = "also known as \cite{9377470}
\cite{zhangSurrogateMultitask}
Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand.",
