- author = "Fangfang Zhang and Yi Mei and Mengjie Zhang",
- title = "An Investigation of Terminal Settings on Multitask Multi-Objective Dynamic Flexible Job Shop Scheduling with Genetic Programming",
- booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
- year = "2023",
- editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
- pages = "259--262",
- address = "Lisbon, Portugal",
- series = "GECCO '23",
- month = "15-19 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling, terminal sets, multitask multi-objective: Poster",
- isbn13 = "9798400701207",
-
DOI = "
doi:10.1145/3583133.3590546",
- size = "4 pages",
- abstract = "Multitask learning has attracted widespread attention to handle multiple tasks simultaneously. Multitask genetic programming has been successfully used to learn scheduling heuristics for multiple multi-objective dynamic flexible job shop scheduling tasks simultaneously. With genetic programming, the learned scheduling heuristics consist of terminals that are extracted from the features of specific tasks. However, how to set proper terminals with multiple tasks still needs to be investigated. This paper has investigated the effectiveness of three strategies for this purpose, i.e., intersection strategy to use the common terminals between tasks, separation strategy to apply different terminals for different tasks, and union strategy to use all the terminals needed for all tasks. The results show that the union strategy which gives tasks the terminals needed by all tasks performs the best. In addition, we find that the learned routing/sequencing rule by the developed algorithm with union strategy in one multitask scenario can share knowledge between each other. On the other hand and more importantly, the learned routing/sequencing rule can also be specific to their tasks with distinguished knowledge represented by genetic materials.",
-
notes = "
GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",
