Genetic Programming with Cluster Selection for Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Xu:2022:CEC,
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author = "Meng Xu and Yi Mei and Fangfang Zhang and
Mengjie Zhang",
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title = "Genetic Programming with Cluster Selection 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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editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
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address = "Padua, Italy",
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month = "18-23 " # jul,
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isbn13 = "978-1-6654-6708-7",
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abstract = "Dynamic flexible job shop scheduling is a challenging
combinatorial optimisation problem, that aims to
optimise machine resources for producing jobs to meet
some goals. There are two important kinds of decisions
that the scheduling process needs to make under dynamic
environments, i.e., the routing decision for machine
assignment and the sequencing decision for operation
ordering. Genetic programming hyper-heuristic has been
successfully applied for solving the dynamic flexible
job shop scheduling problem with the advantage of
automatically evolving good scheduling heuristics.
Parent selection is an important process for genetic
programming, intending to select good individuals as
parents to generate offspring for the next generation.
Traditional genetic programming methods select parents
for crossover based on only fitness (e.g., tournament
selection). a new parent selection (i.e., cluster
selection) method is proposed to select parents not
only with good fitness but also with different
behaviours. The proposed cluster selection is combined
with genetic programming hyper-heuristic to study
whether considering different behaviours in parent
selection will improve the effectiveness of the evolved
scheduling heuristics. The experimental results show
that increasing the number of unique behaviours in the
population cannot help evolve effective scheduling
heuristics. Further analysis shows that considering
behaviour to select parents does increase the number of
unique behaviours in the population. However, it gives
individuals with poor fitness more probability to be
selected to generate offspring. This might be the
reason why the proposed method cannot outperform the
baseline method.",
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keywords = "genetic algorithms, genetic programming, Sequential
analysis, Job shop scheduling, Processor scheduling,
Sociology, Dynamic scheduling, Routing, dynamic
flexible job shop scheduling, cluster selection,
diversity",
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DOI = "doi:10.1109/CEC55065.2022.9870431",
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notes = "Also known as \cite{9870431}",
- }
Genetic Programming entries for
Meng Xu
Yi Mei
Fangfang Zhang
Mengjie Zhang
Citations