Automated Heuristic Design Using Genetic Programming Hyper-heuristic for Uncertain Capacitated Arc Routing Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Liu:2017:GECCOb,
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author = "Yuxin Liu and Yi Mei and Mengjie Zhang and
Zili Zhang",
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title = "Automated Heuristic Design Using Genetic Programming
Hyper-heuristic for Uncertain Capacitated Arc Routing
Problem",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4920-8",
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address = "Berlin, Germany",
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pages = "290--297",
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size = "8 pages",
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URL = "http://doi.acm.org/10.1145/3071178.3071185",
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DOI = "doi:10.1145/3071178.3071185",
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acmid = "3071185",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming,
hyper-heuristic, uncertain capacitated arc routing
problem",
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month = "15-19 " # jul,
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abstract = "Uncertain Capacitated Arc Routing Problem (UCARP) is a
variant of the well-known CARP. It considers a variety
of stochastic factors to reflect the reality where the
exact information such as the actual task demand and
accessibilities of edges are unknown in advance.
Existing works focus on obtaining a robust solution
beforehand. However, it is also important to design
effective heuristics to adjust the solution in real
time. In this paper, we develop a new Genetic
Programming-based Hyper-Heuristic (GPHH) for automated
heuristic design for UCARP. A novel effective
meta-algorithm is designed carefully to address the
failures caused by the environment change. In addition,
it employs domain knowledge to filter some infeasible
candidate tasks for the heuristic function. The
experimental results show that the proposed GPHH
significantly outperforms the existing GPHH methods and
manually designed heuristics. Moreover, we find that
eliminating the infeasible and distant tasks in advance
can reduce much noise and improve the efficacy of the
evolved heuristics. In addition, it is found that
simply adding a slack factor to the expected task
demand may not improve the performance of the GPHH.",
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notes = "Also known as \cite{Liu:2017:AHD:3071178.3071185}
GECCO-2017 A Recombination of the 26th International
Conference on Genetic Algorithms (ICGA-2017) and the
22nd Annual Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Yuxin Liu
Yi Mei
Mengjie Zhang
Zili Zhang
Citations