Surrogate-Assisted Genetic Programming with Diverse Transfer for the Uncertain Capacitated Arc Routing Problem
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gp-bibliography.bib Revision:1.7964
- @InProceedings{Ardeh:2021:CEC,
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author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang",
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booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Surrogate-Assisted Genetic Programming with Diverse
Transfer for the Uncertain Capacitated Arc Routing
Problem",
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year = "2021",
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editor = "Yew-Soon Ong",
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pages = "628--635",
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address = "Krakow, Poland",
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month = "28 " # jun # "-1 " # jul,
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isbn13 = "978-1-7281-8393-0",
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abstract = "The Uncertain Capacited Arc Routing Problem (UCARP) is
an important routing problem that can model
uncertainties of real-world scenarios. Genetic
Programming (GP) is a powerful method for evolving
routing policies for vehicles to enable them make
real-time decisions and handle environmental
uncertainties. When facing various problem domains,
knowledge transfer can improve the effectiveness of the
GP training. Previous studies have demonstrated that
due to the existence of duplicated GP individuals in
the source domain, the existing transfer learning
methods do not perform satisfactorily for UCARP. To
address this issue, in this work, we propose a method
for detecting duplicates in the source domain and
initialising the GP population in the target domain
with phenotypically unique individuals. Additionally,
since the presence of duplicates can limit the number
of good GP individuals, we propose a surrogate-assisted
initialisation approach that is able to generate much
more diversely distributed initial individuals in the
target domain. Our experiments demonstrate that our
proposed transfer learning method can significantly
improve the effectiveness of GP for training new UCARP
routing policies. Compared with the state-of-the-art GP
with knowledge transfer, the proposed approach can
obtain significantly better solutions on a wide range
of UCRP instances, in terms of both initial and final
quality.",
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keywords = "genetic algorithms, genetic programming, Training,
Adaptation models, Uncertainty, Transfer learning,
Sociology, Routing",
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DOI = "doi:10.1109/CEC45853.2021.9504817",
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notes = "Also known as \cite{9504817}",
- }
Genetic Programming entries for
Mazhar Ansari Ardeh
Yi Mei
Mengjie Zhang
Citations