Genetic Programming Hyper-Heuristics with Probabilistic Prototype Tree Knowledge Transfer for Uncertain Capacitated Arc Routing Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Ardeh:2020:CEC,
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author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang",
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title = "Genetic Programming Hyper-Heuristics with
Probabilistic Prototype Tree Knowledge Transfer for
Uncertain Capacitated Arc Routing Problems",
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booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC
2020",
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year = "2020",
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editor = "Yaochu Jin",
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pages = "paper id24067",
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address = "internet",
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month = "19-24 " # jul,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-7281-6929-3",
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DOI = "doi:10.1109/CEC48606.2020.9185714",
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abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP)
is an important combinatorial optimisation problem with
extensive real-world applications. Genetic Programming
(GP) has shown effectiveness in automatically evolving
routing policies to handle the uncertain environment in
UCARP. However, whenever a UCARP scenario changes, e.g.
when a new vehicle is bought, the previously trained
routing policy may no longer work effectively, and one
has to retrain a new policy. Retraining a new policy
from scratch can be time-consuming but the transfer of
knowledge gained from solving the previous similar
scenarios may help improve the efficiency of the
retraining process. In this paper, we propose a novel
transfer learning method by learning the probability
distribution of good solutions from source domains and
modeling it as a probabilistic prototype tree. We
demonstrate that this approach is capable of capturing
more information about the source domain compared to
transfer learning based on (sub-)tree transfers and
even create good trees that are not seen in source
domains. Our experimental results showed that our
method made the retraining process more efficient and
one can obtain an initial state for solving difficult
problems that is significantly better than existing
methods. The final performance of all algorithms, were
comparable, implying that there was no negative
transfer.",
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notes = "https://wcci2020.org/
Victoria University of Wellington, New Zealand.
Also known as \cite{9185714}",
- }
Genetic Programming entries for
Mazhar Ansari Ardeh
Yi Mei
Mengjie Zhang
Citations