Generate a Single Heuristic for Multiple Dynamic Flexible Job Shop Scheduling Tasks by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{chen:2024:CEC2,
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author = "Jiayin Chen and Ya-Hui Jia and Ying Bi and
Wei-Neng Chen",
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title = "Generate a Single Heuristic for Multiple Dynamic
Flexible Job Shop Scheduling Tasks by Genetic
Programming",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Training,
Technological innovation, Job shop scheduling,
Processor scheduling, Heuristic algorithms, Dynamic
scheduling, hyperheuristic, dynamic job shop
scheduling, multitask optimization",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611762",
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abstract = "Genetic programming (GP) hyper-heuristic method has
been extensively studied to solve multiple dynamic job
shop scheduling tasks by generating an effective
heuristic for each task simultaneously. However, a
fundamental question has not been answered. Do we need
to customize a specific heuristic for each task? To
fill this research gap, we propose to generate a single
heuristic for handling multiple tasks. Without
designing complex evolution mechanisms, only during the
evaluation process of GP, the fitness of a heuristic is
evaluated by multiple tasks. Since there are multiple
tasks, a heuristic has multiple objective values. A
rank aggregation (RA) fitness evaluation strategy is
designed to convert multiple objective values of
multiple tasks into a fitness value for a single
heuristic. To validate the effectiveness of the
generated solution and the proposed RA strategy, we
design multitask scenarios that encompass tasks with
diverse objectives, use levels, and maximum operation
times. The results demonstrate that the performance of
the single heuristic generated in multitask scenarios
is comparable to solutions generated by GP using the
single-task learning paradigm, meaning that with an
appropriate training method, GP can generate a
heuristic with good generality.",
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notes = "also known as \cite{10611762}
WCCI 2024",
- }
Genetic Programming entries for
Jiayin Chen
Ya-Hui Jia
Ying Bi
Wei-Neng Chen
Citations