A Two-Stage Genetic Programming Hyper-Heuristic for Uncertain Capacitated Arc Routing Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Wang:2019:SSCI,
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author = "Shaolin Wang and Yi Mei and John Park and
Mengjie Zhang",
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booktitle = "2019 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "A Two-Stage Genetic Programming Hyper-Heuristic for
Uncertain Capacitated Arc Routing Problem",
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year = "2019",
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pages = "1606--1613",
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month = dec,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI44817.2019.9002912",
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abstract = "Genetic Programming Hyper-heuristic (GPHH) has been
successfully applied to automatically evolve effective
routing policies to solve the complex Uncertain
Capacitated Arc Routing Problem (UCARP). However, GPHH
typically ignores the interpretability of the evolved
routing policies. As a result, GP-evolved routing
policies are often very complex and hard to be
understood and trusted by human users. In this paper,
we aim to improve the interpretability of the
GP-evolved routing policies. To this end, we propose a
new Multi-Objective GP (MOGP) to optimise the
performance and size simultaneously. A major issue here
is that the size is much easier to be optimised than
the performance, and the search tends to be biased to
the small but poor routing policies. To address this
issue, we propose a simple yet effective Two-Stage GPHH
(TS-GPHH). In the first stage, only the performance is
to be optimised. Then, in the second stage, both
objectives are considered (using our new MOGP). The
experimental results showed that TS-GPHH could obtain
much smaller and more interpretable routing policies
than the state-of-the-art single-objective GPHH,
without deteriorating the performance. Compared with
traditional MOGP, TS-GPHH can obtain a much better and
more widespread Pareto front.",
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notes = "Also known as \cite{9002912}",
- }
Genetic Programming entries for
Shaolin Wang
Yi Mei
John Park
Mengjie Zhang
Citations