Genetic Programming with Multi-case Fitness for Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Xu:2022:CEC2,
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author = "Meng Xu and Fangfang Zhang and Yi Mei and
Mengjie Zhang",
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title = "Genetic Programming with Multi-case Fitness for
Dynamic Flexible Job Shop Scheduling",
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booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2022",
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abstract = "Dynamic flexible job shop scheduling has attracted
widespread interest from scholars and industries due to
its practical value. Genetic programming
hyper-heuristic has achieved great success in
automatically evolving effective scheduling heuristics
to make real-time decisions (i.e., operation ordering
and machine assignment) for dynamic flexible job shop
scheduling. The design of the training set and fitness
evaluation play key roles in improving the
generalisation of the evolved scheduling heuristics.
The commonly used strategies for improving the
generalisation of learned scheduling heuristics include
using multiple instances for evaluation at each
generation or using a single instance but changing the
instance at each new generation of the training process
of genetic programming. However, using multiple
instances is time-consuming, while changing a single
instance at each new generation, potentially promising
individuals that happen to underperform in one
particular generation might be lost. To address this
issue, this paper develops a genetic programming method
with a multi-case fitness evaluation strategy, which is
named GPMF to evolve the scheduling heuristics with
better generalisation ability for the dynamic flexible
job shop scheduling problem. The proposed multi-case
fitness evaluation strategy divides one instance into
multiple cases and uses the average value of the
multi-case objectives as the fitness. Experimental
results show that the proposed GPMF algorithm is
significantly better than the baseline method in all
the tested scenarios.",
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keywords = "genetic algorithms, genetic programming, Training,
Industries, Job shop scheduling, Heuristic algorithms,
Evolutionary computation, Dynamic scheduling, dynamic
flexible job shop scheduling",
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DOI = "doi:10.1109/CEC55065.2022.9870340",
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month = jul,
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notes = "Also known as \cite{9870340}",
- }
Genetic Programming entries for
Meng Xu
Fangfang Zhang
Yi Mei
Mengjie Zhang
Citations