Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Zhu:2023:GECCO,
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author = "Luyao Zhu and Fangfang Zhang and Xiaodong Zhu and
Ke Chen2 and Mengjie Zhang",
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title = "Sample-Aware Surrogate-Assisted Genetic Programming
for Scheduling Heuristics Learning in Dynamic Flexible
Job Shop Scheduling",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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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",
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pages = "384--392",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, automated
scheduling heuristics design, dynamic flexible job shop
scheduling, surrogate samples",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583131.3590440",
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size = "9 pages",
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abstract = "Genetic programming (GP) has been successfully
introduced to learn scheduling heuristics for dynamic
flexible job shop scheduling (DFJSS) automatically.
However, the evaluations of GP individuals are normally
time-consuming, especially with long DFJSS simulations.
Taking k-nearest neighbour with phenotypic
characterisations of GP individuals as a surrogate
approach, has been successfully used to preselect GP
offspring to the next generation for effectiveness
improvement. However, this approach is not
straightforward to improve the training efficiency,
which is normally the primary goal of surrogate. In
addition, there is no study on which GP individuals
(samples) are good for building surrogate models. To
this end, first, this paper proposes a
surrogate-assisted GP algorithm to reduce the training
time of learning scheduling heuristics for DFJSS.
Second, this paper further proposes an effective
sampling strategy for surrogate-assisted GP. The
results show that our proposed algorithm can achieve
comparable performance with only about a third of
training time of traditional GP. With the same training
time, the proposed algorithm can significantly improve
the quality of learned scheduling heuristics in all
examined scenarios. Furthermore, the evolved scheduling
heuristics by the proposed sample-aware
surrogate-assisted GP are more interpretable with
smaller rule sizes than traditional GP.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Luyao Zhu
Fangfang Zhang
Xiaodong Zhu
Ke Chen2
Mengjie Zhang
Citations