A Multi-Objective Genetic Programming Hyper-Heuristic Approach to Uncertain Capacitated Arc Routing Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Wang:2020:CEC,
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author = "Shaolin Wang and Yi Mei and Mengjie Zhang",
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title = "A Multi-Objective Genetic Programming Hyper-Heuristic
Approach to 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 id24334",
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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/CEC48606.2020.9185890",
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abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP)
is a very important problem which has many real world
applications. Genetic Programming Hyper-heuristic
(GPHH), which can automatically evolve effective
routing policies, is considered as a promising
technique that can handle UCARP effectively. However,
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 by reducing the size of
the GP-evolved routing policies since smaller routing
policies tend to be easier to understand. We propose a
new Multi-Objective GP (MOGP) to optimise the
performance (total cost) and size simultaneously. One
main challenge is that the size is much easier to be
optimised than the performance. Thus, the population
tends to be biased to the small but poor routing
policies and quickly lose the ability of exploration.
To address this issue, we propose a MOGP approach with
$\alpha$ dominance strategy ($\alpha$-MOGP) which can
balance the tradeoff between performance and individual
size. The experimental results showed that
$\alpha$-MOGP could obtain much smaller routing
policies than the state-of-the-art single-objective
GPHH, without deteriorating the performance. Compared
with traditional MOGP, $\alpha$-MOGP can obtain a much
better and more widespread Pareto front.",
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notes = "https://wcci2020.org/
Victoria University of Wellington, New Zealand.
Also known as \cite{9185890}",
- }
Genetic Programming entries for
Shaolin Wang
Yi Mei
Mengjie Zhang
Citations