Learning Heuristics With Different Representations for Stochastic Routing
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- @Article{Jia:ieeeTC,
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author = "Ya-Hui Jia and Yi Mei and Mengjie Zhang",
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journal = "IEEE Transactions on Cybernetics",
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title = "Learning Heuristics With Different Representations for
Stochastic Routing",
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year = "2023",
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volume = "53",
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number = "5",
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pages = "3205--3219",
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month = may,
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keywords = "genetic algorithms, genetic programming, artificial
neural network, ANN, evolutionary learning,
hyperheuristic, stochastic routing, uncertain
capacitated arc routing",
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ISSN = "2168-2275",
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DOI = "doi:10.1109/TCYB.2022.3169210",
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size = "15 pages",
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abstract = "Uncertainty is ubiquitous in real-world routing
applications. The automated design of the routing
policy by hyperheuristic methods is an effective
technique to handle the uncertainty and to achieve
online routing for dynamic or stochastic routing
problems. Currently, the tree representation routing
policy evolved by genetic programming is commonly
adopted because of the remarkable flexibility. However,
numeric representations have never been used.
Considering the practicability of the numeric
representations and the capability of the numeric
optimization methods, in this article, we investigate
two numeric representations on a representative
stochastic routing problem and uncertain capacitated
arc routing problem. Specifically, a linear
representation and an artificial neural-network (ANN)
representation are implemented and compared with the
tree representation to reveal the potential of the
numeric representations and the characteristics of
their optimization. Experimental results show that the
tree representation is the best choice, but on a
majority of the test instances, the numeric
representations, especially the ANN representation, can
provide competitive performance. Further analyses also
show that training a good ANN representation policy
requires more training data than the tree
representation. Finally, a guideline of representation
selection is given.",
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notes = "Also known as \cite{9771076}",
- }
Genetic Programming entries for
Ya-Hui Jia
Yi Mei
Mengjie Zhang
Citations